Sample records for additional important feature
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Permutation importance: a corrected feature importance measure.
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.
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Additivity of Feature-Based and Symmetry-Based Grouping Effects in Multiple Object Tracking
Wang, Chundi; Zhang, Xuemin; Li, Yongna; Lyu, Chuang
2016-01-01
Multiple object tracking (MOT) is an attentional process wherein people track several moving targets among several distractors. Symmetry, an important indicator of regularity, is a general spatial pattern observed in natural and artificial scenes. According to the “laws of perceptual organization” proposed by Gestalt psychologists, regularity is a principle of perceptual grouping, such as similarity and closure. A great deal of research reported that feature-based similarity grouping (e.g., grouping based on color, size, or shape) among targets in MOT tasks can improve tracking performance. However, no additive feature-based grouping effects have been reported where the tracking objects had two or more features. “Additive effect” refers to a greater grouping effect produced by grouping based on multiple cues instead of one cue. Can spatial symmetry produce a similar grouping effect similar to that of feature similarity in MOT tasks? Are the grouping effects based on symmetry and feature similarity additive? This study includes four experiments to address these questions. The results of Experiments 1 and 2 demonstrated the automatic symmetry-based grouping effects. More importantly, an additive grouping effect of symmetry and feature similarity was observed in Experiments 3 and 4. Our findings indicate that symmetry can produce an enhanced grouping effect in MOT and facilitate the grouping effect based on color or shape similarity. The “where” and “what” pathways might have played an important role in the additive grouping effect. PMID:27199875
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Is adermatoglyphia an additional feature of Kindler Syndrome?
Almeida, Hiram Larangeira de; Goetze, Fernanda Mendes; Fong, Kenneth; Lai-Cheong, Joey; McGrath, John
2015-01-01
A typical feature of Kindler Syndrome is skin fragility; this condition in currently classified as a form of epidermolysis bullosa. We describe a rarely reported feature of two cases, one sporadic and one familial; both patients noticed acquired adermatoglyphia. The loss of dermatoglyphics could be an additional feature of this syndrome.
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Is adermatoglyphia an additional feature of Kindler Syndrome?*
de Almeida Jr, Hiram Larangeira; Goetze, Fernanda Mendes; Fong, Kenneth; Lai-Cheong, Joey; McGrath, John
2015-01-01
A typical feature of Kindler Syndrome is skin fragility; this condition in currently classified as a form of epidermolysis bullosa. We describe a rarely reported feature of two cases, one sporadic and one familial; both patients noticed acquired adermatoglyphia. The loss of dermatoglyphics could be an additional feature of this syndrome. PMID:26375235
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ERIC Educational Resources Information Center
Mason-Baughman, Mary Beth; Wallace, Sarah E.
2014-01-01
Previous studies suggest that people with aphasia have incomplete lexical-semantic representations with decreased low-importance distinctive (LID) feature knowledge. In addition, decreased LID feature knowledge correlates with ability to discriminate among semantically related words. The current study seeks to replicate and extend previous…
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Importing perceived features into false memories.
Lyle, Keith B; Johnson, Marcia K
2006-02-01
False memories sometimes contain specific details, such as location or colour, about events that never occurred. Based on the source-monitoring framework, we investigated one process by which false memories acquire details: the reactivation and misattribution of feature information from memories of similar perceived events. In Experiments 1A and 1B, when imagined objects were falsely remembered as seen, participants often reported that the objects had appeared in locations where visually or conceptually similar objects, respectively, had actually appeared. Experiment 2 indicated that colour and shape features of seen objects were misattributed to false memories of imagined objects. Experiment 3 showed that perceived details were misattributed to false memories of objects that had not been explicitly imagined. False memories that imported perceived features, compared to those that presumably did not, were subjectively more like memories for perceived events. Thus, perception may be even more pernicious than imagination in contributing to false memories.
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NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.
Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan
2014-01-01
One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.
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NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms
Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan
2014-01-01
One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available
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The importance of internal facial features in learning new faces.
Longmore, Christopher A; Liu, Chang Hong; Young, Andrew W
2015-01-01
For familiar faces, the internal features (eyes, nose, and mouth) are known to be differentially salient for recognition compared to external features such as hairstyle. Two experiments are reported that investigate how this internal feature advantage accrues as a face becomes familiar. In Experiment 1, we tested the contribution of internal and external features to the ability to generalize from a single studied photograph to different views of the same face. A recognition advantage for the internal features over the external features was found after a change of viewpoint, whereas there was no internal feature advantage when the same image was used at study and test. In Experiment 2, we removed the most salient external feature (hairstyle) from studied photographs and looked at how this affected generalization to a novel viewpoint. Removing the hair from images of the face assisted generalization to novel viewpoints, and this was especially the case when photographs showing more than one viewpoint were studied. The results suggest that the internal features play an important role in the generalization between different images of an individual's face by enabling the viewer to detect the common identity-diagnostic elements across non-identical instances of the face.
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Feature based Weld-Deposition for Additive Manufacturing of Complex Shapes
NASA Astrophysics Data System (ADS)
Panchagnula, Jayaprakash Sharma; Simhambhatla, Suryakumar
2018-06-01
Fabricating functional metal parts using Additive Manufacturing (AM) is a leading trend. However, realizing overhanging features has been a challenge due to the lack of support mechanism for metals. Powder-bed fusion techniques like, Selective Laser Sintering (SLS) employ easily-breakable-scaffolds made of the same material to realize the overhangs. However, the same approach is not extendible to deposition processes like laser or arc based direct energy deposition processes. Although it is possible to realize small overhangs by exploiting the inherent overhanging capability of the process or by blinding some small features like holes, the same cannot be extended for more complex geometries. The current work presents a novel approach for realizing complex overhanging features without the need of support structures. This is possible by using higher order kinematics and suitably aligning the overhang with the deposition direction. Feature based non-uniform slicing and non-uniform area-filling are some vital concepts required in realizing the same and are briefly discussed here. This method can be used to fabricate and/or repair fully dense and functional components for various engineering applications. Although this approach has been implemented for weld-deposition based system, the same can be extended to any other direct energy deposition processes also.
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Costigan, Sarah A; Veitch, Jenny; Crawford, David; Carver, Alison; Timperio, Anna
2017-11-02
Parks in the US and Australia are generally underutilised, and park visitors typically engage in low levels of physical activity (PA). Better understanding park features that may encourage visitors to be active is important. This study examined the perceived importance of park features for encouraging park-based PA and examined differences by sex, age, parental-status and participation in PA. Cross-sectional surveys were completed by local residents ( n = 2775) living near two parks (2013/2015). Demographic variables, park visitation and leisure-time PA were self-reported, respondents rated the importance of 20 park features for encouraging park-based PA in the next fortnight. Chi-square tests of independence examined differences in importance of park features for PA among sub-groups of local residents (sex, age, parental-status, PA). Park features ranked most important for park-based PA were: well maintained (96.2%), feel safe (95.4%), relaxing atmosphere (91.2%), easy to get to (91.7%), and shady trees (90.3%). All subgroups ranked 'well maintained' as most important. Natural and built environment features of parks are important for promoting adults' park-based PA, and should be considered in park (re)design.
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Important features of Sustainable Aggregate Resource Management
Solar, Slavko V.; Shields, Deborah J.; Langer, William H.
2004-01-01
Every society, whether developed, developing or in a phase of renewal following governmental change, requires stable, adequate and secure supplies of natural resources. In the latter case, there could be significant need for construction materials for rebuilding infrastructure, industrial capacity, and housing. It is essential that these large-volume materials be provided in a rational manner that maximizes their societal contribution and minimizes environmental impacts. We describe an approach to resource management based on the principles of sustainable developed. Sustainable Aggregate Resource Management offers a way of addressing the conflicting needs and interests of environmental, economic, and social systems. Sustainability is an ethics based concept that utilizes science and democratic processes to reach acceptable agreements and tradeoffs among interests, while acknowledging the fundamental importance of the environment and social goods. We discuss the features of sustainable aggregate resource management.
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Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.
Wu, Ke; Edwards, Andrea; Fan, Wei; Gao, Jing; Zhang, Kun
2014-04-01
Data stream classification and imbalanced data learning are two important areas of data mining research. Each has been well studied to date with many interesting algorithms developed. However, only a few approaches reported in literature address the intersection of these two fields due to their complex interplay. In this work, we proposed an importance sampling driven, dynamic feature group weighting framework (DFGW-IS) for classifying data streams of imbalanced distribution. Two components are tightly incorporated into the proposed approach to address the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concepts are tackled by a weighted ensemble trained on a set of feature groups with each sub-classifier (i.e. a single classifier or an ensemble) weighed by its discriminative power and stable level. The un-even class distribution, on the other hand, is typically battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We derived the theoretical upper bound for the generalization error of the proposed algorithm. We also studied the empirical performance of our method on a set of benchmark synthetic and real world data, and significant improvement has been achieved over the competing algorithms in terms of standard evaluation metrics and parallel running time. Algorithm implementations and datasets are available upon request.
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NASA Astrophysics Data System (ADS)
Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan
2017-10-01
This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.
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Eiff, M Patrice; Green, Larry A; Jones, Geoff; Devlaeminck, Alex Verdieck; Waller, Elaine; Dexter, Eve; Marino, Miguel; Carney, Patricia A
2017-03-01
Little is known about how the patient-centered medical home (PCMH) is being implemented in residency practices. We describe both the trends in implementation of PCMH features and the influence that working with PCMH features has on resident attitudes toward their importance in 14 family medicine residencies associated with the P4 Project. We assessed 24 residency continuity clinics annually between 2007-2011 on presence or absence of PCMH features. Annual resident surveys (n=690) assessed perceptions of importance of PCMH features using a 4-point scale (not at all important to very important). We used generalized estimating equations logistic regression to assess trends and ordinal-response proportional odds regression models to determine if resident ratings of importance were associated with working with those features during training. Implementation of electronic health record (EHR) features increased significantly from 2007-2011, such as email communication with patients (33% to 67%), preventive services registries (23% to 64%), chronic disease registries (63% to 82%), and population-based quality assurance (46% to 79%). Team-based care was the only process of care feature to change significantly (54% to 93%). Residents with any exposure to EHR-based features had higher odds of rating the features more important compared to those with no exposure. We observed consistently lower odds of the resident rating process of care features as more important with any exposure compared to no exposure. Residencies engaged in educational transformation were more successful in implementing EHR-based PCMH features, and exposure during training appears to positively influence resident ratings of importance, while exposure to process of care features are slower to implement with less influence on importance ratings.
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Vrkljan, Brenda H; Anaby, Dana
2011-02-01
Certain vehicle features can help drivers avoid collisions and/or protect occupants in the event of a crash, and therefore, might play an important role when deciding which vehicle to purchase. The objective of this study was to examine the importance attributed to key vehicle features (including safety) that drivers consider when buying a car and its association with age and gender. A sample of 2,002 Canadian drivers aged 18 years and older completed a survey that asked them to rank the importance of eight vehicle features if they were to purchase a vehicle (storage, mileage, safety, price, comfort, performance, design, and reliability). ANOVA tests were performed to: (a) determine if there were differences in the level of importance between features and; (b) examine the effect of age and gender on the importance attributed to these features. Of the features examined, safety and reliability were the most highly rated in terms of importance, whereas design and performance had the lowest rating. Differences in safety and performance across age groups were dependent on gender. This effect was most evident in the youngest and oldest age groups. Safety and reliability were considered the most important features. Age and gender play a significant role in explaining the importance of certain features. Targeted efforts for translating safety-related information to the youngest and oldest consumers should be emphasized due to their high collision, injury, and fatality rates. Copyright © 2011 National Safety Council and Elsevier Ltd. All rights reserved.
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75 FR 66643 - Importation of Mexican Hass Avocados; Additional Shipping Options
Federal Register 2010, 2011, 2012, 2013, 2014
2010-10-29
...-0016] RIN 0579-AD15 Importation of Mexican Hass Avocados; Additional Shipping Options AGENCY: Animal... States by adding the option to ship avocados to the United States in bulk shipping bins when safeguarding... Mexico and inquiries from a U.S. maritime port. These actions will allow additional options for shipping...
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Kramer, Robin S S; Manesi, Zoi; Towler, Alice; Reynolds, Michael G; Burton, A Mike
2018-01-01
As faces become familiar, we come to rely more on their internal features for recognition and matching tasks. Here, we assess whether this same pattern is also observed for a card sorting task. Participants sorted photos showing either the full face, only the internal features, or only the external features into multiple piles, one pile per identity. In Experiments 1 and 2, we showed the standard advantage for familiar faces-sorting was more accurate and showed very few errors in comparison with unfamiliar faces. However, for both familiar and unfamiliar faces, sorting was less accurate for external features and equivalent for internal and full faces. In Experiment 3, we asked whether external features can ever be used to make an accurate sort. Using familiar faces and instructions on the number of identities present, we nevertheless found worse performance for the external in comparison with the internal features, suggesting that less identity information was available in the former. Taken together, we show that full faces and internal features are similarly informative with regard to identity. In comparison, external features contain less identity information and produce worse card sorting performance. This research extends current thinking on the shift in focus, both in attention and importance, toward the internal features and away from the external features as familiarity with a face increases.
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Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu
2006-11-27
Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.
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The Wavelet Element Method. Part 2; Realization and Additional Features in 2D and 3D
NASA Technical Reports Server (NTRS)
Canuto, Claudio; Tabacco, Anita; Urban, Karsten
1998-01-01
The Wavelet Element Method (WEM) provides a construction of multiresolution systems and biorthogonal wavelets on fairly general domains. These are split into subdomains that are mapped to a single reference hypercube. Tensor products of scaling functions and wavelets defined on the unit interval are used on the reference domain. By introducing appropriate matching conditions across the interelement boundaries, a globally continuous biorthogonal wavelet basis on the general domain is obtained. This construction does not uniquely define the basis functions but rather leaves some freedom for fulfilling additional features. In this paper we detail the general construction principle of the WEM to the 1D, 2D and 3D cases. We address additional features such as symmetry, vanishing moments and minimal support of the wavelet functions in each particular dimension. The construction is illustrated by using biorthogonal spline wavelets on the interval.
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Phonological Feature Re-Assembly and the Importance of Phonetic Cues
ERIC Educational Resources Information Center
Archibald, John
2009-01-01
It is argued that new phonological features can be acquired in second languages, but that both feature acquisition and feature re-assembly are affected by the robustness of phonetic cues in the input.
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27 CFR 479.83 - Transfer tax in addition to import duty.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Transfer tax in addition to import duty. 479.83 Section 479.83 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE...
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27 CFR 479.83 - Transfer tax in addition to import duty.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 27 Alcohol, Tobacco Products and Firearms 3 2014-04-01 2014-04-01 false Transfer tax in addition to import duty. 479.83 Section 479.83 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE...
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27 CFR 479.83 - Transfer tax in addition to import duty.
Code of Federal Regulations, 2013 CFR
2013-04-01
... 27 Alcohol, Tobacco Products and Firearms 3 2013-04-01 2013-04-01 false Transfer tax in addition to import duty. 479.83 Section 479.83 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE...
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27 CFR 479.83 - Transfer tax in addition to import duty.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 27 Alcohol, Tobacco Products and Firearms 3 2011-04-01 2010-04-01 true Transfer tax in addition to import duty. 479.83 Section 479.83 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES...
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27 CFR 479.83 - Transfer tax in addition to import duty.
Code of Federal Regulations, 2012 CFR
2012-04-01
... 27 Alcohol, Tobacco Products and Firearms 3 2012-04-01 2010-04-01 true Transfer tax in addition to import duty. 479.83 Section 479.83 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES...
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Rudichenko, V M
2012-01-01
In this article there were analyzed gender data about features of hyperuricaemia and gout: women are much older at the onset of gout arthritis (one of main reasons, probably, makes menopause by itself), have more associated comorbid deseases as hypertension and kidney failure and drinks less alcoholic beverages. It was noticed, that typical localisation of the lesion on the first toe is less often in women, and women are more inclined to use diuretics among medical drugs. Abovementioned clinical features are of some importance for the broad activity of general practitioners - family doctors. Gender features of polyarthicular gout are not uniformed. Scientific researches confirmed possibility of the genetic basis of the uric acid metabolism, which influences some fenotypical features of the organism. Several genes are known for their influence on serum uric acid: PDZK1, GCKR, SLC2A9, ABCG2, LRRC16A, SLC17A3, SLC16A9 and SLC22A12. However, conclusions of the research works confirm the necessity of scientific clarification of the importance of different factors of gender differences.
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NASA Astrophysics Data System (ADS)
Pace, M. L.; Guarnaccio, A.; Dolce, P.; Mollica, D.; Parisi, G. P.; Lettino, A.; Medici, L.; Summa, V.; Ciancio, R.; Santagata, A.
2017-10-01
's number, an increase of twinning due to the growth of grains and to the release of local stresses can be hypothesized following that an important role could be played by the presence of dislocations in cell boundaries as well as oxides nano-inclusion formed in-situ during the Additive Manufacturing process (Saeidi et al., 2015) [2]. From these outcomes it is going to be presented how the 3D components produced by Additive Manufacturing could change and improve their features for potential industrial applications during life cycles and enhance such a behavior by taking carefully into account the laser parameters and its scanning speed.
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Rough sets and Laplacian score based cost-sensitive feature selection.
Yu, Shenglong; Zhao, Hong
2018-01-01
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.
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[Imported malaria in adults. Clinical, epidemiological and analytical features].
Ramírez-Olivencia, G; Herrero, M D; Subirats, M; de Juanes, J R; Peña, J M; Puente, S
2012-01-01
Up to now, the epidemiological and clinical features of imported malaria in Spain have been described in small series from general hospitals. Almost all diagnosis had been made based on symptomatic patients. The aim of this study has been to determine the epidemiological, clinical and laboratorial characteristics of imported malaria in a Reference Unit for Tropical Diseases. We performed a cross-sectional, observational and retrospective study. The series consisted of patients diagnosed of malaria who had been attended at the Hospital Carlos III from January 1, 2002 to December 31, 2007. We identified 484 episodes of malaria, of which 398 cases were included in the analysis. Almost 50% of the patients were natives of endemic areas, while the rest were native-travelers or travelers. Most cases (88-98% according to the group) had not taken malaria chemoprophylaxis correctly when indicated. At the time of diagnosis, 30.4% of patients were asymptomatic and 28.1% of asymptomatic patients had anemia, 19.8% thrombocytopenia, 14% leukopenia, 5% hypocholesterolemia, 5% renal failure and 4.1% hypoglycemia. Low parasitemia was present in 97.5% of asymptomatic individuals compared to 80.5% of the symptomatic patients (P<0.001). Absence of chemoprophylaxis (or poor compliance) is the main reason for malaria in individuals traveling to endemic areas. Malaria must be ruled out in individuals coming from tropical countries with compatible symptoms, and it also should be suspected in certain groups of asymptomatic individuals with abnormal laboratorial parameters. Copyright © 2011 Elsevier España, S.L. All rights reserved.
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Rough sets and Laplacian score based cost-sensitive feature selection
Yu, Shenglong
2018-01-01
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884
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The Importance of Additive Reasoning in Children's Mathematical Achievement: A Longitudinal Study
ERIC Educational Resources Information Center
Ching, Boby Ho-Hong; Nunes, Terezinha
2017-01-01
This longitudinal study examines the relative importance of counting ability, additive reasoning, and working memory in children's mathematical achievement (calculation and story problem solving). In Hong Kong, 115 Chinese children aged 6 years old participated in 2 waves of assessments (T1 = first grade and T2 = second grade). Multiple regression…
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Feature Grouping and Selection Over an Undirected Graph.
Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping
2012-01-01
High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.
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Jaarsveld, Saskia; Lachmann, Thomas
2017-01-01
This paper discusses the importance of three features of psychometric tests for cognition research: construct definition, problem space, and knowledge domain. Definition of constructs, e.g., intelligence or creativity, forms the theoretical basis for test construction. Problem space, being well or ill-defined, is determined by the cognitive abilities considered to belong to the constructs, e.g., convergent thinking to intelligence, divergent thinking to creativity. Knowledge domain and the possibilities it offers cognition are reflected in test results. We argue that (a) comparing results of tests with different problem spaces is more informative when cognition operates in both tests on an identical knowledge domain, and (b) intertwining of abilities related to both constructs can only be expected in tests developed to instigate such a process. Test features should guarantee that abilities can contribute to self-generated and goal-directed processes bringing forth solutions that are both new and applicable. We propose and discuss a test example that was developed to address these issues. PMID:28220098
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Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.; ...
2017-05-12
Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.
Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less
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The relative importance of external and internal features of facial composites.
Frowd, Charlie; Bruce, Vicki; McIntyre, Alex; Hancock, Peter
2007-02-01
Three experiments are reported that compare the quality of external with internal regions within a set of facial composites using two matching-type tasks. Composites are constructed with the aim of triggering recognition from people familiar with the targets, and past research suggests internal face features dominate representations of familiar faces in memory. However the experiments reported here show that the internal regions of composites are very poorly matched against the faces they purport to represent, while external feature regions alone were matched almost as well as complete composites. In Experiments 1 and 2 the composites used were constructed by participant-witnesses who were unfamiliar with the targets and therefore were predicted to demonstrate a bias towards the external parts of a face. In Experiment 3 we compared witnesses who were familiar or unfamiliar with the target items, but for both groups the external features were much better reproduced in the composites, suggesting it is the process of composite construction itself which is responsible for the poverty of the internal features. Practical implications of these results are discussed.
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Bringloe, Trevor T; Cottenie, Karl; Martin, Gillian K; Adamowicz, Sarah J
2016-12-01
Additive diversity partitioning (α, β, and γ) is commonly used to study the distribution of species-level diversity across spatial scales. Here, we first investigate whether published studies of additive diversity partitioning show signs of difficulty attaining species-level resolution due to inherent limitations with morphological identifications. Second, we present a DNA barcoding approach to delineate specimens of stream caddisfly larvae (order Trichoptera) and consider the importance of taxonomic resolution on classical (additive) measures of beta (β) diversity. Caddisfly larvae were sampled using a hierarchical spatial design in two regions (subarctic Churchill, Manitoba, Canada; temperate Pennsylvania, USA) and then additively partitioned according to Barcode Index Numbers (molecular clusters that serve as a proxy for species), genus, and family levels; diversity components were expressed as proportional species turnover. We screened 114 articles of additive diversity partitioning and found that a third reported difficulties with achieving species-level identifications, with a clear taxonomic tendency towards challenges identifying invertebrate taxa. Regarding our own study, caddisfly BINs appeared to show greater subregional turnover (e.g., proportional additive β) compared to genus or family levels. Diversity component studies failing to achieve species resolution due to morphological identifications may therefore be underestimating diversity turnover at larger spatial scales.
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CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data.
Minhas, Fayyaz Ul Amir Afsar; Asif, Amina; Arif, Muhammad
2016-12-01
Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples. CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Prediction of lysine ubiquitylation with ensemble classifier and feature selection.
Zhao, Xiaowei; Li, Xiangtao; Ma, Zhiqiang; Yin, Minghao
2011-01-01
Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request.
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Important features of home-based support services for older Australians and their informal carers.
McCaffrey, Nikki; Gill, Liz; Kaambwa, Billingsley; Cameron, Ian D; Patterson, Jan; Crotty, Maria; Ratcliffe, Julie
2015-11-01
In Australia, newly initiated, publicly subsidised 'Home-Care Packages' designed to assist older people (≥ 65 years of age) living in their own home must now be offered on a 'consumer-directed care' (CDC) basis by service providers. However, CDC models have largely developed in the absence of evidence on users' views and preferences. The aim of this study was to determine what features (attributes) of consumer-directed, home-based support services are important to older people and their informal carers to inform the design of a discrete choice experiment (DCE). Semi-structured, face-to-face interviews were conducted in December 2012-November 2013 with 17 older people receiving home-based support services and 10 informal carers from 5 providers located in South Australia and New South Wales. Salient service characteristics important to participants were determined using thematic and constant comparative analysis and formulated into attributes and attribute levels for presentation within a DCE. Initially, eight broad themes were identified: information and knowledge, choice and control, self-managed continuum, effective co-ordination, effective communication, responsiveness and flexibility, continuity and planning. Attributes were formulated for the DCE by combining overlapping themes such as effective communication and co-ordination, and the self-managed continuum and planning into single attributes. Six salient service features that characterise consumer preferences for the provision of home-based support service models were identified: choice of provider, choice of support worker, flexibility in care activities provided, contact with the service co-ordinator, managing the budget and saving unspent funds. Best practice indicates that qualitative research with individuals who represent the population of interest should guide attribute selection for a DCE and this is the first study to employ such methods in aged care service provision. Further development of
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Peroxisome protein import: a complex journey.
Baker, Alison; Lanyon-Hogg, Thomas; Warriner, Stuart L
2016-06-15
The import of proteins into peroxisomes possesses many unusual features such as the ability to import folded proteins, and a surprising diversity of targeting signals with differing affinities that can be recognized by the same receptor. As understanding of the structure and function of many components of the protein import machinery has grown, an increasingly complex network of factors affecting each step of the import pathway has emerged. Structural studies have revealed the presence of additional interactions between cargo proteins and the PEX5 receptor that affect import potential, with a subtle network of cargo-induced conformational changes in PEX5 being involved in the import process. Biochemical studies have also indicated an interdependence of receptor-cargo import with release of unloaded receptor from the peroxisome. Here, we provide an update on recent literature concerning mechanisms of protein import into peroxisomes. © 2016 The Author(s).
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Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy
2010-04-01
A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.
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Chinese character recognition based on Gabor feature extraction and CNN
NASA Astrophysics Data System (ADS)
Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan
2018-03-01
As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.
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Perceived Importance of Wellness Features at a Cancer Center: Patient and Staff Perspectives.
Tinner, Michelle; Crovella, Paul; Rosenbaum, Paula F
2018-01-01
Determine the relative impact of 11 building wellness features on preference and on the ability to deliver/receive quality care for two groups: patients and caregivers. The impact of building features that promote wellness is of increasing interest to the building owners, designers, and occupants. This study performed a postoccupancy evaluation of two user groups at a healthcare facility with specific wellness features. Seventy-six staff and 62 patients of a cancer center were polled separately to determine their preferences in 11 categories. Results showed that all wellness features were viewed favorably by the two groups, with natural lighting, views of nature, and thermal comfort as top categories for both. The t-test comparisons were performed, and significant differences ( p < .05) between the two groups were found for three of the features (views of nature, art and murals, and indoor plants). Discussion of these differences and the interaction of competing design goals (thermal comfort, views of nature, natural light, and desire for privacy) are included. Designers and owners will want to consider the preferred use of roof gardens, art and murals, and indoor plants for patient spaces, where their relative value is greater. Access to private and quiet spaces is the top need for caregivers. Ease of movement, thermal comfort, and natural light were top needs for patients.
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Ma, Yuanyuan; Liang, Dongming; Liu, Jian; Wen, Jian-Guo; Servoll, Einar; Waaler, Gudmund; Sæter, Thorstein; Axcrona, Karol; Vlatkovic, Ljiljana; Axcrona, Ulrika; Paus, Elisabeth; Yang, Yue; Zhang, Zhiqian; Kvalheim, Gunnar; Nesland, Jahn M.; Suo, Zhenhe
2013-01-01
Androgen plays a vital role in prostate cancer development. However, it is not clear whether androgens influence stem-like properties of prostate cancer, a feature important for prostate cancer progression. In this study, we show that upon DHT treatment in vitro, prostate cancer cell lines LNCaP and PC-3 were revealed with higher clonogenic potential and higher expression levels of stemness related factors CD44, CD90, Oct3/4 and Nanog. Moreover, sex hormone binding globulin (SHBG) was also simultaneously upregulated in these cells. When the SHBG gene was blocked by SHBG siRNA knock-down, the induction of Oct3/4, Nanog, CD44 and CD90 by DHT was also correspondingly blocked in these cells. Immunohistochemical evaluation of clinical samples disclosed weakly positive, and areas negative for SHBG expression in the benign prostate tissues, while most of the prostate carcinomas were strongly positive for SHBG. In addition, higher levels of SHBG expression were significantly associated with higher Gleason score, more seminal vesicle invasions and lymph node metastases. Collectively, our results show a role of SHBG in upregulating stemness of prostate cancer cells upon DHT exposure in vitro, and SHBG expression in prostate cancer samples is significantly associated with poor clinicopathological features, indicating a role of SHBG in prostate cancer progression. PMID:23936228
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NASA Astrophysics Data System (ADS)
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
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Ensemble methods with simple features for document zone classification
NASA Astrophysics Data System (ADS)
Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing
2012-01-01
Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.
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Discriminative analysis of lip motion features for speaker identification and speech-reading.
Cetingül, H Ertan; Yemez, Yücel; Erzin, Engin; Tekalp, A Murat
2006-10-01
There have been several studies that jointly use audio, lip intensity, and lip geometry information for speaker identification and speech-reading applications. This paper proposes using explicit lip motion information, instead of or in addition to lip intensity and/or geometry information, for speaker identification and speech-reading within a unified feature selection and discrimination analysis framework, and addresses two important issues: 1) Is using explicit lip motion information useful, and, 2) if so, what are the best lip motion features for these two applications? The best lip motion features for speaker identification are considered to be those that result in the highest discrimination of individual speakers in a population, whereas for speech-reading, the best features are those providing the highest phoneme/word/phrase recognition rate. Several lip motion feature candidates have been considered including dense motion features within a bounding box about the lip, lip contour motion features, and combination of these with lip shape features. Furthermore, a novel two-stage, spatial, and temporal discrimination analysis is introduced to select the best lip motion features for speaker identification and speech-reading applications. Experimental results using an hidden-Markov-model-based recognition system indicate that using explicit lip motion information provides additional performance gains in both applications, and lip motion features prove more valuable in the case of speech-reading application.
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Spatial features register: toward standardization of spatial features
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.
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Classification of radiolarian images with hand-crafted and deep features
NASA Astrophysics Data System (ADS)
Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen
2017-12-01
Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.
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Frank, Laurence E; Heiser, Willem J
2008-05-01
A set of features is the basis for the network representation of proximity data achieved by feature network models (FNMs). Features are binary variables that characterize the objects in an experiment, with some measure of proximity as response variable. Sometimes features are provided by theory and play an important role in the construction of the experimental conditions. In some research settings, the features are not known a priori. This paper shows how to generate features in this situation and how to select an adequate subset of features that takes into account a good compromise between model fit and model complexity, using a new version of least angle regression that restricts coefficients to be non-negative, called the Positive Lasso. It will be shown that features can be generated efficiently with Gray codes that are naturally linked to the FNMs. The model selection strategy makes use of the fact that FNM can be considered as univariate multiple regression model. A simulation study shows that the proposed strategy leads to satisfactory results if the number of objects is less than or equal to 22. If the number of objects is larger than 22, the number of features selected by our method exceeds the true number of features in some conditions.
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PACS administrators' and radiologists' perspective on the importance of features for PACS selection.
Joshi, Vivek; Narra, Vamsi R; Joshi, Kailash; Lee, Kyootai; Melson, David
2014-08-01
Picture archiving and communication systems (PACS) play a critical role in radiology. This paper presents the criteria important to PACS administrators for selecting a PACS. A set of criteria are identified and organized into an integrative hierarchical framework. Survey responses from 48 administrators are used to identify the relative weights of these criteria through an analytical hierarchy process. The five main dimensions for PACS selection in order of importance are system continuity and functionality, system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the subdimensions, the highest weights were assessed for security, backup, and continuity; tools for continuous performance monitoring; support for multispecialty images; and voice recognition/transcription. PACS administrators' preferences were generally in line with that of previously reported results for radiologists. Both groups assigned the highest priority to ensuring business continuity and preventing loss of data through features such as security, backup, downtime prevention, and tools for continuous PACS performance monitoring. PACS administrators' next high priorities were support for multispecialty images, image retrieval speeds from short-term and long-term storage, real-time monitoring, and architectural issues of compatibility and integration with other products. Thus, next to ensuring business continuity, administrators' focus was on issues that impact their ability to deliver services and support. On the other hand, radiologists gave high priorities to voice recognition, transcription, and reporting; structured reporting; and convenience and responsiveness in manipulation of images. Thus, radiologists' focus appears to be on issues that may impact their productivity, effort, and accuracy.
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Deep-learning derived features for lung nodule classification with limited datasets
NASA Astrophysics Data System (ADS)
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.
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Using different classification models in wheat grading utilizing visual features
NASA Astrophysics Data System (ADS)
Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef
2018-04-01
Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively
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"Anti-Michael addition" of Grignard reagents to sulfonylacetylenes: synthesis of alkynes.
Esteban, Francisco; Boughani, Lazhar; García Ruano, José L; Fraile, Alberto; Alemán, José
2017-05-10
In this work, the addition of Grignard reagents to arylsulfonylacetylenes, which undergoes an "anti-Michael addition", resulting in their alkynylation under very mild conditions is described. The simplicity of the experimental procedure and the functional group tolerance are the main features of this methodology. This is an important advantage over the use of organolithium at -78 °C that we previously reported. Moreover, the synthesis of diynes and other examples showing functional group tolerance in this anti-Michael reaction is also presented.
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García, Danny E; Medina, Paulina A; Zúñiga, Valentina I
2017-11-01
Polyflavonoids from Pinus radiata (D. Don.) are an abundant natural oligomers highly desirable as renewable chemicals. However, structural modification of polyflavonoids is a viable strategy in order to use such polyphenols as macrobuilding-blocks for biomaterial design. Polyflavonoids were esterified with three five-member cyclic anhydrides (maleic, itaconic, and citraconic) at 20 °C during 24 h in order to diversify physicochemical-, and biological-properties for agricultural, and food-packaging applications. In addition, the influence of the chemical modification, as well as the chemical structure of the grafting on toxicological features was evaluated. Structural features of derivatives were analyzed by spectroscopy (FT-IR and 1 H-NMR), and the degree of substitution was calculated. Toxicological profile was assessed by using three target species in a wide range of concentration (0.01-100 mgL - 1 ). Effect of polyflavonoids on the growth rate (Selenastrum capricornutum), mortality (Daphnia magna), and germination and radicle length (Lactuca sativa) was determined. Chemical modification affects the toxicological profile on the derivatives in a high extent. Results described remarkable differences in function of the target specie. The bioassays indicate differences of the polyflavonoids toxicological profile associated to the chemical structure of the grafting. Results allowed conclude that polyflavonoids from pine bark show slight toxic properties. Copyright © 2017 Elsevier Ltd. All rights reserved.
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A Features Selection for Crops Classification
NASA Astrophysics Data System (ADS)
Liu, Yifan; Shao, Luyi; Yin, Qiang; Hong, Wen
2016-08-01
The components of the polarimetric target decomposition reflect the differences of target since they linked with the scattering properties of the target and can be imported into SVM as the classification features. The result of decomposition usually concentrate on part of the components. Selecting a combination of components can reduce the features that importing into the SVM. The features reduction can lead to less calculation and targeted classification of one target when we classify a multi-class area. In this research, we import different combinations of features into the SVM and find a better combination for classification with a data of AGRISAR.
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Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.
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.
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Realistic Free-Spins Features Increase Preference for Slot Machines.
Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J
2017-06-01
Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.
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Salgado, Teresa M; Fedrigon, Alexa; Riccio Omichinski, Donna; Meade, Michelle A
2018-01-01
for 60% (12/20) items in the medication list module, 100% (3/3) in the medication reminder module, 67% (2/3) in the medication administration record module, and 63% (10/16) in the additional features module. In addition to the medication list, medication reminder, and medication administration record features, experts selected the following top 3 most important additional features: automatic refills through pharmacies; ability to share medication information from the app with providers; and ability to share medication information from the app with family, friends, and caregivers. The top 3 least important features included a link to an official drug information source, privacy settings and password protection, and prescription refill reminders. Conclusions Although several mobile apps for medication management exist, few are specifically designed to support persons with developmental disabilities in the complex medication management process. Of the 42 different features assessed, 64% (27/42) achieved consensus for inclusion in a future medication management app. This study provides information on the features of a medication management app that are most important to persons with developmental disabilities, caregivers, and professionals. PMID:29792292
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How important is vehicle safety in the new vehicle purchase process?
Koppel, Sjaanie; Charlton, Judith; Fildes, Brian; Fitzharris, Michael
2008-05-01
Whilst there has been a significant increase in the amount of consumer interest in the safety performance of privately owned vehicles, the role that it plays in consumers' purchase decisions is poorly understood. The aims of the current study were to determine: how important vehicle safety is in the new vehicle purchase process; what importance consumers place on safety options/features relative to other convenience and comfort features, and how consumers conceptualise vehicle safety. In addition, the study aimed to investigate the key parameters associated with ranking 'vehicle safety' as the most important consideration in the new vehicle purchase. Participants recruited in Sweden and Spain completed a questionnaire about their new vehicle purchase. The findings from the questionnaire indicated that participants ranked safety-related factors (e.g., EuroNCAP (or other) safety ratings) as more important in the new vehicle purchase process than other vehicle factors (e.g., price, reliability etc.). Similarly, participants ranked safety-related features (e.g., advanced braking systems, front passenger airbags etc.) as more important than non-safety-related features (e.g., route navigation systems, air-conditioning etc.). Consistent with previous research, most participants equated vehicle safety with the presence of specific vehicle safety features or technologies rather than vehicle crash safety/test results or crashworthiness. The key parameters associated with ranking 'vehicle safety' as the most important consideration in the new vehicle purchase were: use of EuroNCAP, gender and education level, age, drivers' concern about crash involvement, first vehicle purchase, annual driving distance, person for whom the vehicle was purchased, and traffic infringement history. The findings from this study are important for policy makers, manufacturers and other stakeholders to assist in setting priorities with regard to the promotion and publicity of vehicle safety features
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Rentoumi, Vassiliki; Raoufian, Ladan; Ahmed, Samrah; de Jager, Celeste A; Garrard, Peter
2014-01-01
Mixed vascular and Alzheimer-type dementia and pure Alzheimer's disease are both associated with changes in spoken language. These changes have, however, seldom been subjected to systematic comparison. In the present study, we analyzed language samples obtained during the course of a longitudinal clinical study from patients in whom one or other pathology was verified at post mortem. The aims of the study were twofold: first, to confirm the presence of differences in language produced by members of the two groups using quantitative methods of evaluation; and secondly to ascertain the most informative sources of variation between the groups. We adopted a computational approach to evaluate digitized transcripts of connected speech along a range of language-related dimensions. We then used machine learning text classification to assign the samples to one of the two pathological groups on the basis of these features. The classifiers' accuracies were tested using simple lexical features, syntactic features, and more complex statistical and information theory characteristics. Maximum accuracy was achieved when word occurrences and frequencies alone were used. Features based on syntactic and lexical complexity yielded lower discrimination scores, but all combinations of features showed significantly better performance than a baseline condition in which every transcript was assigned randomly to one of the two classes. The classification results illustrate the word content specific differences in the spoken language of the two groups. In addition, those with mixed pathology were found to exhibit a marked reduction in lexical variation and complexity compared to their pure AD counterparts.
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Philpott, Stacy M; Pardee, Gabriella L; Gonthier, David J
2012-05-01
Interactions between predators and the degree of functional redundancy among multiple predator species may determine whether herbivores experience increased or decreased predation risk. Specialist parasites can modify predator behavior, yet rarely have cascading effects on multiple predator species and prey been evaluated. We examined influences of specialist phorid parasites (Pseudacteon spp.) on three predatory ant species and herbivores in a coffee agroecosystem. Specifically, we examined whether changes in ant richness affected fruit damage by the coffee berry borer (Hypothenemus hampei) and whether phorids altered multi-predator effects. Each ant species reduced borer damage, and without phorids, increasing predator richness did not further decrease borer damage. However, with phorids, activity of one ant species was reduced, indicating that the presence of multiple ant species was necessary to limit borer damage. In addition, phorid presence revealed synergistic effects of multiple ant species, not observed without the presence of this parasite. Thus, a trait-mediated cascade resulting from a parasite-induced predator behavioral change revealed the importance of functional redundancy, predator diversity, and food web complexity for control of this important pest.
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LBT Distributed Archive: Status and Features
NASA Astrophysics Data System (ADS)
Knapic, C.; Smareglia, R.; Thompson, D.; Grede, G.
2011-07-01
After the first release of the LBT Distributed Archive, this successful collaboration is continuing within the LBT corporation. The IA2 (Italian Center for Astronomical Archive) team had updated the LBT DA with new features in order to facilitate user data retrieval while abiding by VO standards. To facilitate the integration of data from any new instruments, we have migrated to a new database, developed new data distribution software, and enhanced features in the LBT User Interface. The DBMS engine has been changed to MySQL. Consequently, the data handling software now uses java thread technology to update and synchronize the main storage archives on Mt. Graham and in Tucson, as well as archives in Trieste and Heidelberg, with all metadata and proprietary data. The LBT UI has been updated with additional features allowing users to search by instrument and some of the more important characteristics of the images. Finally, instead of a simple cone search service over all LBT image data, new instrument specific SIAP and cone search services have been developed. They will be published in the IVOA framework later this fall.
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Neocentromeres and epigenetically inherited features of centromeres
Burrack, Laura S.; Berman, Judith
2012-01-01
Neocentromeres are ectopic sites where new functional kinetochores assemble and permit chromosome segregation. Neocentromeres usually form following genomic alterations that remove or disrupt centromere function. The ability to form neocentromeres is conserved in eukaryotes ranging from fungi to mammals. Neocentromeres that rescue chromosome fragments in cells with gross chromosomal rearrangements are found in several types of human cancers, and in patients with developmental disabilities. In this review, we discuss the importance of neocentromeres to human health and evaluate recently developed model systems to study neocentromere formation, maintenance, and function in chromosome segregation. Additionally, studies of neocentromeres provide insight into native centromeres; analysis of neocentromeres found in human clinical samples and induced in model organisms distinguishes features of centromeres that are dependent on centromere DNA from features that are epigenetically inherited together with the formation of a functional kinetochore. PMID:22723125
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NASA Astrophysics Data System (ADS)
Lee, Youngjoo; Kim, Namkug; Seo, Joon Beom; Lee, JuneGoo; Kang, Suk Ho
2007-03-01
In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p<0.01). Moreover, this enhancement was largely due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which are most hard to differentiate for radiologists. According to these experimental results, adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers.
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A closer look at prion strains: characterization and important implications.
Solforosi, Laura; Milani, Michela; Mancini, Nicasio; Clementi, Massimo; Burioni, Roberto
2013-01-01
Prions are infectious proteins that are responsible for transmissible spongiform encephalopathies (TSEs) and consist primarily of scrapie prion protein (PrP (Sc) ), a pathogenic isoform of the host-encoded cellular prion protein (PrP (C) ). The absence of nucleic acids as essential components of the infectious prions is the most striking feature associated to these diseases. Additionally, different prion strains have been isolated from animal diseases despite the lack of DNA or RNA molecules. Mounting evidence suggests that prion-strain-specific features segregate with different PrP (Sc) conformational and aggregation states. Strains are of practical relevance in prion diseases as they can drastically differ in many aspects, such as incubation period, PrP (Sc) biochemical profile (e.g., electrophoretic mobility and glycoform ratio) and distribution of brain lesions. Importantly, such different features are maintained after inoculation of a prion strain into genetically identical hosts and are relatively stable across serial passages. This review focuses on the characterization of prion strains and on the wide range of important implications that the study of prion strains involves.
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Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; ...
2017-02-20
The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. As a result, the proposed delineation of resilience can be important in understanding and communicating resilience concepts.
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Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; Palma-Oliveira, José M.; Woods, David D.; Linkov, Igor
2017-01-01
The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. The proposed delineation of resilience can be important in understanding and communicating resilience concepts.
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Novel Features for Brain-Computer Interfaces
Woon, W. L.; Cichocki, A.
2007-01-01
While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques. PMID:18364991
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Derrien, Benoît; Majeran, Wojciech; Effantin, Grégory; Ebenezer, Joseph; Friso, Giulia; van Wijk, Klaas J.; Steven, Alasdair C.; Maurizi, Michael R.; Vallon, Olivier
2012-01-01
The ClpP peptidase is a major constituent of the proteolytic machinery of bacteria and organelles. The chloroplast ClpP complex is unusual, in that it associates a large number of subunits, one of which (ClpP1) is encoded in the chloroplast, the others in the nucleus. The complexity of these large hetero-oligomeric complexes has been a major difficulty in their overproduction and biochemical characterization. In this paper, we describe the purification of native chloroplast ClpP complex from the green alga Chlamydomonas reinhardtii, using a strain that carries the Strep-tag II at the C-terminus of the ClpP1 subunit. Similar to land plants, the algal complex comprises active and inactive subunits (3 ClpP and 5 ClpR, respectively). Evidence is presented that a sub-complex can be produced by dissociation, comprising ClpP1 and ClpR1, 2, 3 and 4, similar to the ClpR-ring described in land plants. Our Chlamydomonas ClpP preparation also contains two ClpT subunits, ClpT3 and ClpT4, which like the land plant ClpT1 and ClpT2 show 2 Clp-N domains. ClpTs are believed to function in substrate binding and/or assembly of the two heptameric rings. Phylogenetic analysis indicates that ClpT subunits have appeared independently in Chlorophycean algae, in land plants and in dispersed cyanobacterial genomes. Negative staining electron microscopy shows that the Chlamydomonas complex retains the barrel-like shape of homo-oligomeric ClpPs, with 4 additional peripheral masses that we speculate represent either the additional IS1 domain of ClpP1 (a feature unique to algae) or ClpTs or extensions of ClpR subunits PMID:22772861
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75 FR 29680 - Importation of Mexican Hass Avocados; Additional Shipping Options
Federal Register 2010, 2011, 2012, 2013, 2014
2010-05-27
... Avocados; Additional Shipping Options AGENCY: Animal and Plant Health Inspection Service, USDA. ACTION... originating in Michoacan, Mexico, into the United States by adding the option to ship avocados to the United... additional options for shipping Hass avocados from Mexico to the United States and allow Mexican exporters to...
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Clinical features of the myasthenic syndrome arising from mutations in GMPPB.
Rodríguez Cruz, Pedro M; Belaya, Katsiaryna; Basiri, Keivan; Sedghi, Maryam; Farrugia, Maria Elena; Holton, Janice L; Liu, Wei Wei; Maxwell, Susan; Petty, Richard; Walls, Timothy J; Kennett, Robin; Pitt, Matthew; Sarkozy, Anna; Parton, Matt; Lochmüller, Hanns; Muntoni, Francesco; Palace, Jacqueline; Beeson, David
2016-08-01
Congenital myasthenic syndrome (CMS) due to mutations in GMPPB has recently been reported confirming the importance of glycosylation for the integrity of neuromuscular transmission. Review of case notes of patients with mutations in GMPPB to identify the associated clinical, neurophysiological, pathological and laboratory features. In addition, serum creatine kinase (CK) levels within the Oxford CMS cohort were retrospectively analysed to assess its usefulness in the differential diagnosis of this new entity. All patients had prominent limb-girdle weakness with minimal or absent craniobulbar manifestations. Presentation was delayed beyond infancy with proximal muscle weakness and most patients recall poor performance in sports during childhood. Neurophysiology showed abnormal neuromuscular transmission only in the affected muscles and myopathic changes. Muscle biopsy showed dystrophic features and reduced α-dystroglycan glycosylation. In addition, myopathic changes were present on muscle MRI. CK was significantly increased in serum compared to other CMS subtypes. Patients were responsive to pyridostigimine alone or combined with 3,4-diaminopyridine and/or salbutamol. Patients with GMPPB-CMS have phenotypic features aligned with CMS subtypes harbouring mutations within the early stages of the glycosylation pathway. Additional features shared with the dystroglycanopathies include myopathic features, raised CK levels and variable mild cognitive delay. This syndrome underlines that CMS can occur in the absence of classic myasthenic manifestations such as ptosis and ophthalmoplegia or facial weakness, and links myasthenic disorders with dystroglycanopathies. This report should facilitate the recognition of this disorder, which is likely to be underdiagnosed and can benefit from symptomatic treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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Recognizing human activities using appearance metric feature and kinematics feature
NASA Astrophysics Data System (ADS)
Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye
2017-05-01
The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.
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Microstructure and Mechanical Properties of Additively Manufactured Parts with Staircase Feature
NASA Astrophysics Data System (ADS)
Keya, Tahmina
This thesis focuses on a part with staircase feature that is made of Inconel 718 and fabricated by SLM process. The objective of the study was to observe build height effect on the microstructure and mechanical properties of the part. Due to the nature of SLM, there is possibility of different microstructure and mechanical properties in different locations depending on the design of the part. The objective was to compare microstructure and mechanical properties from different location and four comparison groups were considered: 1. Effect of thermal cycle; 2. External and internal surfaces; 3. Build height effect and 4. Bottom surfaces. To achieve the goals of this research, standard metallurgical procedure has been performed to prepare samples. Etching was done to reveal the microstructure of SLM processed Inconel 718 parts. Young's modulus and hardness were measured using nanoindentation technique. FEM analysis was performed to simulate nanoindentation. The conclusions drawn from this research are: 1. The microstructure of front and side surface of SLM processed Inconel 718 consists of arc shaped cut ends of melt pools with intermetallic phase at the border of the melt pool; 2. On top surface, melted tracks and scanning patterns can be observed and the average width of melted tracks is 100-150 microm; 3. The microstructure looks similar at different build height; 4. Microstructure on the top of a stair is more defined and organized than the internal surface; 5. The mechanical properties are highest at the bottom. OM images revealed slight difference in microstructure in terms of build height for this specific part, but mechanical properties seem to be vary noticeably. This is something to be kept in mind while designing or determining build orientation. External and internal surfaces of a stair at the same height showed difference in both microstructure and mechanical properties. To minimize that effect and to make it more uniform, gradual elevation can be
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Landscape Features Shape Genetic Structure in Threatened Northern Spotted Owls
Funk, W. Chris; Forsman, Eric D.; Mullins, Thomas D.; Haig, Susan M.
2008-01-01
Several recent studies have shown that landscape features can strongly affect spatial patterns of gene flow and genetic variation. Understanding landscape effects on genetic variation is important in conservation for defining management units and understanding movement patterns. The landscape may have little effect on gene flow, however, in highly mobile species such as birds. We tested for genetic breaks associated with landscape features in the northern spotted owl (Strix occidentalis caurina), a threatened subspecies associated with old forests in the U.S. Pacific Northwest and extreme southwestern Canada. We found little evidence for distinct genetic breaks in northern spotted owls using a large microsatellite dataset (352 individuals from across the subspecies' range genotyped at 10 loci). Nonetheless, dry low-elevation valleys and the Cascade and Olympic Mountains restrict gene flow, while the Oregon Coast Range facilitates it. The wide Columbia River is not a barrier to gene flow. In addition, inter-individual genetic distance and latitude were negatively related, likely reflecting northward colonization following Pleistocene glacial recession. Our study shows that landscape features may play an important role in shaping patterns of genetic variation in highly vagile taxa such as birds.
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Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre
2012-07-01
Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.
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Feature extraction and classification algorithms for high dimensional data
NASA Technical Reports Server (NTRS)
Lee, Chulhee; Landgrebe, David
1993-01-01
Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized
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Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture
NASA Astrophysics Data System (ADS)
Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans
2017-04-01
Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.
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Feature and Region Selection for Visual Learning.
Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando
2016-03-01
Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.
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Image ratio features for facial expression recognition application.
Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu
2010-06-01
Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.
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Kelly, Jonathan W; McNamara, Timothy P; Bodenheimer, Bobby; Carr, Thomas H; Rieser, John J
2009-02-01
Two experiments explored the role of environmental cues in maintaining spatial orientation (sense of self-location and direction) during locomotion. Of particular interest was the importance of geometric cues (provided by environmental surfaces) and featural cues (nongeometric properties provided by striped walls) in maintaining spatial orientation. Participants performed a spatial updating task within virtual environments containing geometric or featural cues that were ambiguous or unambiguous indicators of self-location and direction. Cue type (geometric or featural) did not affect performance, but the number and ambiguity of environmental cues did. Gender differences, interpreted as a proxy for individual differences in spatial ability and/or experience, highlight the interaction between cue quantity and ambiguity. When environmental cues were ambiguous, men stayed oriented with either one or two cues, whereas women stayed oriented only with two. When environmental cues were unambiguous, women stayed oriented with one cue.
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Feature-based attention elicits surround suppression in feature space.
Störmer, Viola S; Alvarez, George A
2014-09-08
It is known that focusing attention on a particular feature (e.g., the color red) facilitates the processing of all objects in the visual field containing that feature [1-7]. Here, we show that such feature-based attention not only facilitates processing but also actively inhibits processing of similar, but not identical, features globally across the visual field. We combined behavior and electrophysiological recordings of frequency-tagged potentials in human observers to measure this inhibitory surround in feature space. We found that sensory signals of an attended color (e.g., red) were enhanced, whereas sensory signals of colors similar to the target color (e.g., orange) were suppressed relative to colors more distinct from the target color (e.g., yellow). Importantly, this inhibitory effect spreads globally across the visual field, thus operating independently of location. These findings suggest that feature-based attention comprises an excitatory peak surrounded by a narrow inhibitory zone in color space to attenuate the most distracting and potentially confusable stimuli during visual perception. This selection profile is akin to what has been reported for location-based attention [8-10] and thus suggests that such center-surround mechanisms are an overarching principle of attention across different domains in the human brain. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Albury, Mary S; Elliott, Catherine; Moore, Anthony L
2010-12-01
The alternative oxidase (AOX) is a non-protonmotive ubiquinol oxidase that is found in all plants, some fungi, green algae, bacteria and pathogenic protozoa. The lack of AOX in the mammalian host renders this protein an important potential therapeutic target in the treatment of pathogenic protozoan infections. Bioinformatic searches revealed that, within a putative ubiquinol-binding crevice in AOX, Gln242, Asn247, Tyr253, Ser256, His261 and Arg262 were highly conserved. To confirm that these amino-acid residues are important for ubiquinol-binding and hence activity substitution mutations were generated and characterised. Assessment of AOX activity in isolated Schizosaccharomyces pombe mitochondria revealed that mutation of either Gln242, Ser256, His261 and Arg262 resulted in >90% inhibition of antimycin A-insensitive respiration suggesting that hydroxyl, guanidino, imidazole groups, polar and charged residues in addition to the size of the amino-acid chain are important for ubiquinone-binding. Substitution of Asn247 with glutamine or Tyr253 with phenylalanine had little effect upon the respiratory rate indicating that these residues are not critical for AOX activity. However replacement of Tyr253 by alanine resulted in a 72% loss of activity suggesting that the benzoquinone group and not hydroxyl group is important for quinol binding. These results provide important new insights into the ubiquinol-binding site of the alternative oxidase, the identity of which maybe important for future rational drug design. Copyright © 2010 Elsevier B.V. All rights reserved.
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Coding of visual object features and feature conjunctions in the human brain.
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.
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Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.
Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung
2018-02-01
Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.
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Flexible feature interface for multimedia sources
Coffland, Douglas R [Livermore, CA
2009-06-09
A flexible feature interface for multimedia sources system that includes a single interface for the addition of features and functions to multimedia sources and for accessing those features and functions from remote hosts. The interface utilizes the export statement: export "C" D11Export void FunctionName(int argc, char ** argv,char * result, SecureSession *ctrl) or the binary equivalent of the export statement.
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Infrared vehicle recognition using unsupervised feature learning based on K-feature
NASA Astrophysics Data System (ADS)
Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen
2018-02-01
Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.
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Numerous features have been included to facilitate the modeling process, from model setup and data input, presentation and analysis of results, to easy export of results to spreadsheet programs for additional analysis.
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Taylor, Kirsten I.; Devereux, Barry J.; Acres, Kadia; Randall, Billi; Tyler, Lorraine K.
2013-01-01
Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. PMID:22137770
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Etcoff, Nancy L; Stock, Shannon; Haley, Lauren E; Vickery, Sarah A; House, David M
2011-01-01
Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first
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Etcoff, Nancy L.; Stock, Shannon; Haley, Lauren E.; Vickery, Sarah A.; House, David M.
2011-01-01
Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first
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Gene/protein name recognition based on support vector machine using dictionary as features.
Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi
2005-01-01
Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.
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Armenti, Nicholas A; Babcock, Julia C
2018-04-01
Individuals with borderline personality features may be susceptible to react to situational stressors with negative and interpersonally maladaptive emotionality (e.g., anger) and aggression. The current study attempted to test two moderated mediation models to investigate dispositional risk factors associated with borderline personality features and intimate partner violence (IPV). Results from an experimental rejection induction paradigm were examined using moderated regression to observe contextual reactions to imagined romantic rejection from a current romantic partner among individuals with borderline personality features. An ethnically diverse sample of 218 undergraduates at a large public university in the southwestern United States was recruited. Participants responded to demographic questions and self-report measures, and engaged in an experimental rejection induction paradigm. Borderline personality features was positively associated with rejection sensitivity, physical assault, and psychological aggression. Contrary to initial hypotheses, rejection sensitivity did not serve as a mediator of the relations between borderline personality features and physical assault and psychological aggression. However, trait anger mediated the relation between borderline personality features and psychological aggression. As such, trait anger may be an important explanatory variable in the relation between borderline personality features and psychological aggression specifically. Results of the rejection induction paradigm indicated that, for individuals who were asked to imagine an ambiguous rejection, the relation between borderline personality features and state anger post-rejection was strengthened. For individuals who imagined a critical rejection, there was no significant relation between borderline personality features and state anger post-rejection. Findings suggest that trait anger may be an important dispositional factor in the link between borderline personality
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Tiled vector data model for the geographical features of symbolized maps.
Li, Lin; Hu, Wei; Zhu, Haihong; Li, You; Zhang, Hang
2017-01-01
Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and 'addition' (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.
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NASA Astrophysics Data System (ADS)
Bastos, Isadora T. S.; Costa, Fanny N.; Silva, Tiago F.; Barreiro, Eliezer J.; Lima, Lídia M.; Braz, Delson; Lombardo, Giuseppe M.; Punzo, Francesco; Ferreira, Fabio F.; Barroso, Regina C.
2017-10-01
LASSBio-1755 is a new cycloalkyl-N-acylhydrazone parent compound designed for the development of derivatives with antinociceptive and anti-inflammatory activities. Although single crystal X-ray diffraction has been considered as the golden standard in structure determination, we successfully used X-ray powder diffraction data in the structural determination of new synthesized compounds, in order to overcome the bottle-neck due to the difficulties experienced in harvesting good quality single crystals of the compounds. We therefore unequivocally assigned the relative configuration (E) to the imine double bond and a s-cis conformation of the amide function of the N-acylhydrazone compound. These features are confirmed by a computational analysis performed on the basis of molecular dynamics calculations, which are extended not only to the structural characteristics but also to the analysis of the anisotropic atomic displacement parameters, a further information - missed in a typical powder diffraction analysis. The so inferred data were used to perform additional cycles of refinement and eventually generate a new cif file with additional physical information. Furthermore, crystal morphology prediction was performed, which is in agreement with the experimental images acquired by scanning electron microscopy, thus providing useful information on possible alternative paths for better crystallization strategies.
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Amin Yavari, S; Chai, Y C; Böttger, A J; Wauthle, R; Schrooten, J; Weinans, H; Zadpoor, A A
2015-06-01
Anodizing could be used for bio-functionalization of the surfaces of titanium alloys. In this study, we use anodizing for creating nanotubes on the surface of porous titanium alloy bone substitutes manufactured using selective laser melting. Different sets of anodizing parameters (voltage: 10 or 20V anodizing time: 30min to 3h) are used for anodizing porous titanium structures that were later heat treated at 500°C. The nanotopographical features are examined using electron microscopy while the bioactivity of anodized surfaces is measured using immersion tests in the simulated body fluid (SBF). Moreover, the effects of anodizing and heat treatment on the performance of one representative anodized porous titanium structures are evaluated using in vitro cell culture assays using human periosteum-derived cells (hPDCs). It has been shown that while anodizing with different anodizing parameters results in very different nanotopographical features, i.e. nanotubes in the range of 20 to 55nm, anodized surfaces have limited apatite-forming ability regardless of the applied anodizing parameters. The results of in vitro cell culture show that both anodizing, and thus generation of regular nanotopographical feature, and heat treatment improve the cell culture response of porous titanium. In particular, cell proliferation measured using metabolic activity and DNA content was improved for anodized and heat treated as well as for anodized but not heat-treated specimens. Heat treatment additionally improved the cell attachment of porous titanium surfaces and upregulated expression of osteogenic markers. Anodized but not heat-treated specimens showed some limited signs of upregulated expression of osteogenic markers. In conclusion, while varying the anodizing parameters creates different nanotube structure, it does not improve apatite-forming ability of porous titanium. However, both anodizing and heat treatment at 500°C improve the cell culture response of porous titanium
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Breast masses in mammography classification with local contour features.
Li, Haixia; Meng, Xianjing; Wang, Tingwen; Tang, Yuchun; Yin, Yilong
2017-04-14
Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well. In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass. The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier. The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.
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Espinosa, Alejandro Martínez
2018-01-01
International evidence regarding the relationship between maternal employment and school-age children overweight and obesity shows divergent results. In Mexico, this relationship has not been confirmed by national data sets analysis. Consequently, the objective of this article was to evaluate the role of the mothers' participation in labor force related to excess body weight in Mexican school-age children (aged 5-11 years). A cross-sectional study was conducted on a sample of 17,418 individuals from the National Health and Nutrition Survey 2012, applying binomial logistic regression models. After controlling for individual, maternal and contextual features, the mothers' participation in labor force was associated with children body composition. However, when the household features (living arrangements, household ethnicity, size, food security and socioeconomic status) were incorporated, maternal employment was no longer statically significant. Household features are crucial factors for understanding the overweight and obesity prevalence levels in Mexican school-age children, despite the mother having a paid job. Copyright: © 2018 Permanyer.
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2001-10-25
neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial
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Li, Jing; Hong, Wenxue
2014-12-01
The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.
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Linguistic feature analysis for protein interaction extraction
2009-01-01
Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches. PMID:19909518
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Planillo, Aimara; Malo, Juan E
2018-01-01
Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations
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Textural features for radar image analysis
NASA Technical Reports Server (NTRS)
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.
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Sensor feature fusion for detecting buried objects
DOE Office of Scientific and Technical Information (OSTI.GOV)
Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.
1993-04-01
Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less
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Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection
NASA Astrophysics Data System (ADS)
Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.
2015-04-01
SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.
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Nauta, I H; Rietbergen, M M; van Bokhoven, A A J D; Bloemena, E; Witte, B I; Heideman, D A M; Baatenburg de Jong, R J; Brakenhoff, R H; Leemans, C R
2018-02-09
Oropharyngeal squamous cell carcinomas (OPSCCs) are traditionally caused by smoking and excessive alcohol consumption. However, in the last decades high-risk human papillomavirus (HR-HPV) infections play an increasingly important role in tumorigenesis. HPV-driven OPSCCs are known to have a more favorable prognosis, which has led to important and marked changes in the recently released TNM-8. In this edition, OPSCCs are divided based on p16-immunostaining, with p16-overexpression as surrogate marker for the presence of HPV. The aims of this study are to evaluate TNM-8 on a Dutch consecutive cohort of patients with p16-positive OPSCC and to determine the relevance of additional HPV DNA-testing. All OPSCC patients without distant metastases at diagnosis and treated with curative intent at VU University Medical Center (2000-2015) and Erasmus Medical Center (2000-2006) were included (N = 1,204). HPV-status was established by p16-immunostaining followed by HPV DNA-PCR on the p16-immunopositive cases. We compared TNM-7 and TNM-8 using the Harrell's C index. In total, 388 of 1,204 (32.2%) patients were p16-immunopositive. In these patients, TNM-8 had a markedly better predictive prognostic power than TNM-7 (Harrell's C index 0.63 versus 0.53). Of the 388 p16-positive OPSCCs, 48 tumors (12.4%) were HPV DNA-negative. This subgroup had distinct demographic, clinical and morphologic characteristics and showed a significantly worse five-year overall survival compared to the HPV DNA-positive tumors (P < 0.001). TNM-8 has a better predictive prognostic power than TNM-7 in patients with p16-positive OPSCC. However, within p16-positive OPSCCs there is an HPV DNA-negative subgroup with distinct features and a worse overall survival, indicating the importance to perform additional HPV DNA-testing when predicting prognosis and particularly for selecting patients for de-intensified treatment regimens. © The Author 2018. Published by Oxford University Press on behalf of the
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NASA Astrophysics Data System (ADS)
Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun
2012-10-01
Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.
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Lee, Shawn; Singla, Montish
2016-01-01
We present a case report of 46-year-old man with no medical history, who complained of extreme fatigue, near-syncope, and palpitations. He initially presented in complete heart block. A transvenous pacemaker was placed in the emergency department, and he was started empirically on Ceftriaxone for Lyme disease. He was admitted and over the course of the next few days, his rhythm regressed to Mobitz type I first-degree atrioventricular block and then to normal sinus rhythm. This case report highlights some important features regarding Lyme carditis, a rare presentation of early disseminated Lyme disease (seen in a few weeks to months after the initial tick bite). In 25%-30% of patients, the characteristic targetoid rash may not be seen, a likely culprit of the disease not being detected early and progressing to disseminated disease. The most common cardiac complaint of Lyme disease is palpitations, occurring in 6.6% of patients, which may not accurately reflect progression into disseminated Lyme disease because it is a nonspecific finding. Conduction abnormality, occurring in 1.8% of patients, is a more specific finding of Borrelia invading cardiac tissue. Finally, this case report highlights a recommendation that patients with confirmed Lyme disease or those presenting with cardiac abnormalities or symptoms who have an atypical profile for a cardiac event should be screened with a 12-lead electrocardiogram, Lyme serology, and be considered for antibiotic therapy with the possibility of temporary pacing.
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Self-Assembling Block Copolymer Resist Mixtures towards Lithographic Resists for Sub-10 nm Features
NASA Astrophysics Data System (ADS)
Chandler, Curran; Daga, Vikram; Watkins, James
2009-03-01
Significant improvements in 193 nm photolithography have enabled the extension of device feature sizes beyond the 45 nm and 32 nm nodes, yet uncertainty lies beyond 22 nm features as no single replacement has emerged. Here we show that low molecular weight, nonionic block copolymer surfactant blends are capable of self-assembling into highly ordered domains with feature sizes on the order of 5 nm. These surfactants, most of which lack the required χN for microphase separation on their own, exhibit strong segregation and long-range order upon addition of a component capable of multi-point hydrogen bonding that is specific for one of the blocks in the copolymer. This has been demonstrated by our SAXS data for several Pluronic (PEO-b-PPO-b-PEO) and Brij (PEO-b-[CH2]nCH3) surfactants of various molecular weights and PEO volume fractions. Furthermore, we employ these highly-ordered systems as thin film, nanolithographic etch masks for the transfer of sub-10 nm patterns into silicon-based substrates. Small molecule, hydrogen bonding additives containing aromatic or silsesquioxane structure are also used to tune etch contrast between the blocks which is important for reducing line edge roughness (LER) of such small features.
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NASA Astrophysics Data System (ADS)
Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément
2015-07-01
3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.
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Additional Security Considerations for Grid Management
NASA Technical Reports Server (NTRS)
Eidson, Thomas M.
2003-01-01
The use of Grid computing environments is growing in popularity. A Grid computing environment is primarily a wide area network that encompasses multiple local area networks, where some of the local area networks are managed by different organizations. A Grid computing environment also includes common interfaces for distributed computing software so that the heterogeneous set of machines that make up the Grid can be used more easily. The other key feature of a Grid is that the distributed computing software includes appropriate security technology. The focus of most Grid software is on the security involved with application execution, file transfers, and other remote computing procedures. However, there are other important security issues related to the management of a Grid and the users who use that Grid. This note discusses these additional security issues and makes several suggestions as how they can be managed.
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MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.
Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing
2015-11-01
Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.
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System Complexity Reduction via Feature Selection
ERIC Educational Resources Information Center
Deng, Houtao
2011-01-01
This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…
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High Dimensional Classification Using Features Annealed Independence Rules.
Fan, Jianqing; Fan, Yingying
2008-01-01
Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.
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Predicting couple therapy outcomes based on speech acoustic features
Nasir, Md; Baucom, Brian Robert; Narayanan, Shrikanth
2017-01-01
Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral
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Taylor, Kirsten I; Devereux, Barry J; Acres, Kadia; Randall, Billi; Tyler, Lorraine K
2012-03-01
Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. Copyright © 2011 Elsevier B.V. All rights reserved.
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Botly, Leigh C P; De Rosa, Eve
2012-10-01
The visual search task established the feature integration theory of attention in humans and measures visuospatial attentional contributions to feature binding. We recently demonstrated that the neuromodulator acetylcholine (ACh), from the nucleus basalis magnocellularis (NBM), supports the attentional processes required for feature binding using a rat digging-based task. Additional research has demonstrated cholinergic contributions from the NBM to visuospatial attention in rats. Here, we combined these lines of evidence and employed visual search in rats to examine whether cortical cholinergic input supports visuospatial attention specifically for feature binding. We trained 18 male Long-Evans rats to perform visual search using touch screen-equipped operant chambers. Sessions comprised Feature Search (no feature binding required) and Conjunctive Search (feature binding required) trials using multiple stimulus set sizes. Following acquisition of visual search, 8 rats received bilateral NBM lesions using 192 IgG-saporin to selectively reduce cholinergic afferentation of the neocortex, which we hypothesized would selectively disrupt the visuospatial attentional processes needed for efficient conjunctive visual search. As expected, relative to sham-lesioned rats, ACh-NBM-lesioned rats took significantly longer to locate the target stimulus on Conjunctive Search, but not Feature Search trials, thus demonstrating that cholinergic contributions to visuospatial attention are important for feature binding in rats.
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Importance of cellulase cocktails favoring hydrolysis of cellulose.
Victoria, Juliet; Odaneth, Annamma; Lali, Arvind
2017-07-03
Depolymerization of lignocellulosic biomass is catalyzed by groups of enzymes whose action is influenced by substrate features and the composition of cellulase preparation. Cellulases contain a mixture of variety of enzymes, whose proportions dictate the saccharification of biomass. In the current study, four cellulase preparation varying in their composition were used to hydrolyze two types of alkali-treated biomass (aqueous ammonia-treated rice straw and sodium hydroxide-treated rice straw) to study the effect on catalytic rate, saccharification yields, and sugar release profile. We found that substrate features affected the extent of saccharification but had minimal effect on the sugar release pattern. In addition, complete hydrolysis to glucose was observed with enzyme preparation having at least a cellobiase units (CBU)/carboxymethyl cellulose (CMC) ratio (>0.15), while a modified enzyme ratio can be used for oligosaccharide synthesis. Thus, cellulase preparation with defined ratios of the three main enzymes can improve the saccharification which is of utmost importance in defining the success of lignocellulose-based economies.
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A short feature vector for image matching: The Log-Polar Magnitude feature descriptor
Hast, Anders; Wählby, Carolina; Sintorn, Ida-Maria
2017-01-01
The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images. PMID:29190737
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Feature-Based Retinal Image Registration Using D-Saddle Feature
Hasikin, Khairunnisa; A. Karim, Noor Khairiah; Ahmedy, Fatimah
2017-01-01
Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle. PMID:29204257
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Relative Importance and Additive Effects of Maternal and Infant Risk Factors on Childhood Asthma
Rosas-Salazar, Christian; James, Kristina; Escobar, Gabriel; Gebretsadik, Tebeb; Li, Sherian Xu; Carroll, Kecia N.; Walsh, Eileen; Mitchel, Edward; Das, Suman; Kumar, Rajesh; Yu, Chang; Dupont, William D.; Hartert, Tina V.
2016-01-01
Background Environmental exposures that occur in utero and during early life may contribute to the development of childhood asthma through alteration of the human microbiome. The objectives of this study were to estimate the cumulative effect and relative importance of environmental exposures on the risk of childhood asthma. Methods We conducted a population-based birth cohort study of mother-child dyads who were born between 1995 and 2003 and were continuously enrolled in the PRIMA (Prevention of RSV: Impact on Morbidity and Asthma) cohort. The individual and cumulative impact of maternal urinary tract infections (UTI) during pregnancy, maternal colonization with group B streptococcus (GBS), mode of delivery, infant antibiotic use, and older siblings at home, on the risk of childhood asthma were estimated using logistic regression. Dose-response effect on childhood asthma risk was assessed for continuous risk factors: number of maternal UTIs during pregnancy, courses of infant antibiotics, and number of older siblings at home. We further assessed and compared the relative importance of these exposures on the asthma risk. In a subgroup of children for whom maternal antibiotic use during pregnancy information was available, the effect of maternal antibiotic use on the risk of childhood asthma was estimated. Results Among 136,098 singleton birth infants, 13.29% developed asthma. In both univariate and adjusted analyses, maternal UTI during pregnancy (odds ratio [OR] 1.2, 95% confidence interval [CI] 1.18, 1.25; adjusted OR [AOR] 1.04, 95%CI 1.02, 1.07 for every additional UTI) and infant antibiotic use (OR 1.21, 95%CI 1.20, 1.22; AOR 1.16, 95%CI 1.15, 1.17 for every additional course) were associated with an increased risk of childhood asthma, while having older siblings at home (OR 0.92, 95%CI 0.91, 0.93; AOR 0.85, 95%CI 0.84, 0.87 for each additional sibling) was associated with a decreased risk of childhood asthma, in a dose-dependent manner. Compared with vaginal
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Relative Importance and Additive Effects of Maternal and Infant Risk Factors on Childhood Asthma.
Wu, Pingsheng; Feldman, Amy S; Rosas-Salazar, Christian; James, Kristina; Escobar, Gabriel; Gebretsadik, Tebeb; Li, Sherian Xu; Carroll, Kecia N; Walsh, Eileen; Mitchel, Edward; Das, Suman; Kumar, Rajesh; Yu, Chang; Dupont, William D; Hartert, Tina V
2016-01-01
Environmental exposures that occur in utero and during early life may contribute to the development of childhood asthma through alteration of the human microbiome. The objectives of this study were to estimate the cumulative effect and relative importance of environmental exposures on the risk of childhood asthma. We conducted a population-based birth cohort study of mother-child dyads who were born between 1995 and 2003 and were continuously enrolled in the PRIMA (Prevention of RSV: Impact on Morbidity and Asthma) cohort. The individual and cumulative impact of maternal urinary tract infections (UTI) during pregnancy, maternal colonization with group B streptococcus (GBS), mode of delivery, infant antibiotic use, and older siblings at home, on the risk of childhood asthma were estimated using logistic regression. Dose-response effect on childhood asthma risk was assessed for continuous risk factors: number of maternal UTIs during pregnancy, courses of infant antibiotics, and number of older siblings at home. We further assessed and compared the relative importance of these exposures on the asthma risk. In a subgroup of children for whom maternal antibiotic use during pregnancy information was available, the effect of maternal antibiotic use on the risk of childhood asthma was estimated. Among 136,098 singleton birth infants, 13.29% developed asthma. In both univariate and adjusted analyses, maternal UTI during pregnancy (odds ratio [OR] 1.2, 95% confidence interval [CI] 1.18, 1.25; adjusted OR [AOR] 1.04, 95%CI 1.02, 1.07 for every additional UTI) and infant antibiotic use (OR 1.21, 95%CI 1.20, 1.22; AOR 1.16, 95%CI 1.15, 1.17 for every additional course) were associated with an increased risk of childhood asthma, while having older siblings at home (OR 0.92, 95%CI 0.91, 0.93; AOR 0.85, 95%CI 0.84, 0.87 for each additional sibling) was associated with a decreased risk of childhood asthma, in a dose-dependent manner. Compared with vaginal delivery, C
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Feature Selection for Wheat Yield Prediction
NASA Astrophysics Data System (ADS)
Ruß, Georg; Kruse, Rudolf
Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an everincreasing population by taking advantage of a field’s heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields’ heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.
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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.
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Geist; Dauble
1998-09-01
/ Knowledge of the three-dimensional connectivity between rivers and groundwater within the hyporheic zone can be used to improve the definition of fall chinook salmon (Oncorhynchus tshawytscha) spawning habitat. Information exists on the microhabitat characteristics that define suitable salmon spawning habitat. However, traditional spawning habitat models that use these characteristics to predict available spawning habitat are restricted because they can not account for the heterogeneous nature of rivers. We present a conceptual spawning habitat model for fall chinook salmon that describes how geomorphic features of river channels create hydraulic processes, including hyporheic flows, that influence where salmon spawn in unconstrained reaches of large mainstem alluvial rivers. Two case studies based on empirical data from fall chinook salmon spawning areas in the Hanford Reach of the Columbia River are presented to illustrate important aspects of our conceptual model. We suggest that traditional habitat models and our conceptual model be combined to predict the limits of suitable fall chinook salmon spawning habitat. This approach can incorporate quantitative measures of river channel morphology, including general descriptors of geomorphic features at different spatial scales, in order to understand the processes influencing redd site selection and spawning habitat use. This information is needed in order to protect existing salmon spawning habitat in large rivers, as well as to recover habitat already lost.KEY WORDS: Hyporheic zone; Geomorphology; Spawning habitat; Large rivers; Fall chinook salmon; Habitat management
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Premotor and non-motor features of Parkinson’s disease
Goldman, Jennifer G.; Postuma, Ron
2014-01-01
Purpose of review This review highlights recent advances in premotor and non-motor features in Parkinson’s disease, focusing on these issues in the context of prodromal and early stage Parkinson’s disease. Recent findings While Parkinson’s disease patients experience a wide range of non-motor symptoms throughout the disease course, studies demonstrate that non-motor features are not solely a late manifestation. Indeed, disturbances of smell, sleep, mood, and gastrointestinal function may herald Parkinson’s disease or related synucleinopathies and precede these neurodegenerative conditions by 5 or more years. In addition, other non-motor symptoms such as cognitive impairment are now recognized in incident or de novo Parkinson’s disease cohorts. Many of these non-motor features reflect disturbances in non-dopaminergic systems and early involvement of peripheral and central nervous systems including olfactory, enteric, and brainstem neurons as in Braak’s proposed pathological staging of Parkinson’s disease. Current research focuses on identifying potential biomarkers that may detect persons at risk for Parkinson’s disease and permit early intervention with neuroprotective or disease-modifying therapeutics. Summary Recent studies provide new insights on the frequency, pathophysiology, and importance of non-motor features in Parkinson’s disease as well as the recognition that these non-motor symptoms occur in premotor, early, and later phases of Parkinson’s disease. PMID:24978368
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Homomorphic encryption-based secure SIFT for privacy-preserving feature extraction
NASA Astrophysics Data System (ADS)
Hsu, Chao-Yung; Lu, Chun-Shien; Pei, Soo-Chang
2011-02-01
Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to address the problem of secure SIFT feature extraction and representation in the encrypted domain. Since all the operations in SIFT must be moved to the encrypted domain, we propose a homomorphic encryption-based secure SIFT method for privacy-preserving feature extraction and representation based on Paillier cryptosystem. In particular, homomorphic comparison is a must for SIFT feature detection but is still a challenging issue for homomorphic encryption methods. To conquer this problem, we investigate a quantization-like secure comparison strategy in this paper. Experimental results demonstrate that the proposed homomorphic encryption-based SIFT performs comparably to original SIFT on image benchmarks, while preserving privacy additionally. We believe that this work is an important step toward privacy-preserving multimedia retrieval in an environment, where privacy is a major concern.
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Advanced alerting features: displaying new relevant data and retracting alerts.
Kuperman, G. J.; Hiltz, F. L.; Teich, J. M.
1997-01-01
We added two advanced features to our automated alerting system. The first feature identifies and displays, at the time an alert is reviewed, relevant data filed between the login time of a specimen leading to an alerting result and the time the alert is reviewed. Relevant data is defined as data of the same kind as generated the alert. The other feature retracts alerts when the alerting value is edited and no longer satisfies the alerting criteria. We evaluated the two features for a 14-week period (new relevant data) and a 6-week period (retraction). Of a total of 1104 alerts in the 14-week evaluation, 286 (25.9%) had new relevant data displayed at alert review time. Of the 286, 75.2% were due to additions of comments to the original piece of alerting data; 24.1% were due to new or pending laboratory results of the same type that generated the alert. Two alerts (out of 490) were retracted in a 6 week period. We conclude that in our system, new clinically relevant data is often added between the time of specimen login and the time that an alerting result from that specimen is reviewed. Retractions occur rarely but are important to detect and communicate. PMID:9357625
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Characterizing mammographic images by using generic texture features
2012-01-01
Introduction Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. Methods A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Results Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Conclusions Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy. PMID:22490545
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48 CFR 452.236-70 - Additive or Deductive Items.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 48 Federal Acquisition Regulations System 4 2011-10-01 2011-10-01 false Additive or Deductive... Additive or Deductive Items. As prescribed in 436.205, insert the following provision: Additive or... listed in the schedule) those additive or deductive bid items providing the most features of the work...
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48 CFR 452.236-70 - Additive or Deductive Items.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 48 Federal Acquisition Regulations System 4 2013-10-01 2013-10-01 false Additive or Deductive... Additive or Deductive Items. As prescribed in 436.205, insert the following provision: Additive or... listed in the schedule) those additive or deductive bid items providing the most features of the work...
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48 CFR 452.236-70 - Additive or Deductive Items.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 48 Federal Acquisition Regulations System 4 2012-10-01 2012-10-01 false Additive or Deductive... Additive or Deductive Items. As prescribed in 436.205, insert the following provision: Additive or... listed in the schedule) those additive or deductive bid items providing the most features of the work...
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48 CFR 452.236-70 - Additive or Deductive Items.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Additive or Deductive... Additive or Deductive Items. As prescribed in 436.205, insert the following provision: Additive or... listed in the schedule) those additive or deductive bid items providing the most features of the work...
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48 CFR 452.236-70 - Additive or Deductive Items.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 48 Federal Acquisition Regulations System 4 2014-10-01 2014-10-01 false Additive or Deductive... Additive or Deductive Items. As prescribed in 436.205, insert the following provision: Additive or... listed in the schedule) those additive or deductive bid items providing the most features of the work...
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The new Mobile Command Center at KSC is important addition to emergency preparedness
NASA Technical Reports Server (NTRS)
2000-01-01
Charles Street, Roger Scheidt and Robert ZiBerna, the Emergency Preparedness team at KSC, sit in the conference room inside the Mobile Command Center, a specially equipped vehicle. Nicknamed '''The Brute,''' it also features computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or Cape Canaveral Air Force Station.
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The new Mobile Command Center at KSC is important addition to emergency preparedness
NASA Technical Reports Server (NTRS)
2000-01-01
This new specially equipped vehicle serves as a mobile command center for emergency preparedness staff and other support personnel when needed at KSC or Cape Canaveral Air Force Station. It features a conference room, computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or CCAFS.
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The new Mobile Command Center at KSC is important addition to emergency preparedness
NASA Technical Reports Server (NTRS)
2000-01-01
Robert ZiBerna, Roger Scheidt and Charles Street, the Emergency Preparedness team at KSC, practice for an emergency scenario inside the Mobile Command Center, a specially equipped vehicle. It features a conference room, computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or Cape Canaveral Air Force Station.
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Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.
2009-01-01
In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409
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The feature-weighted receptive field: an interpretable encoding model for complex feature spaces.
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
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Online feature selection with streaming features.
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.
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Exploring KM Features of High-Performance Companies
NASA Astrophysics Data System (ADS)
Wu, Wei-Wen
2007-12-01
For reacting to an increasingly rival business environment, many companies emphasize the importance of knowledge management (KM). It is a favorable way to explore and learn KM features of high-performance companies. However, finding out the critical KM features of high-performance companies is a qualitative analysis problem. To handle this kind of problem, the rough set approach is suitable because it is based on data-mining techniques to discover knowledge without rigorous statistical assumptions. Thus, this paper explored KM features of high-performance companies by using the rough set approach. The results show that high-performance companies stress the importance on both tacit and explicit knowledge, and consider that incentives and evaluations are the essentials to implementing KM.
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Feature Selection and Pedestrian Detection Based on Sparse Representation.
Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei
2015-01-01
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony.
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Feature selection using probabilistic prediction of support vector regression.
Yang, Jian-Bo; Ong, Chong-Jin
2011-06-01
This paper presents a new wrapper-based feature selection method for support vector regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse.
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a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image
NASA Astrophysics Data System (ADS)
Li, L.; Yang, H.; Chen, Q.; Liu, X.
2018-04-01
Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.
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Crowding with conjunctions of simple features.
Põder, Endel; Wagemans, Johan
2007-11-20
Several recent studies have related crowding with the feature integration stage in visual processing. In order to understand the mechanisms involved in this stage, it is important to use stimuli that have several features to integrate, and these features should be clearly defined and measurable. In this study, Gabor patches were used as target and distractor stimuli. The stimuli differed in three dimensions: spatial frequency, orientation, and color. A group of 3, 5, or 7 objects was presented briefly at 4 deg eccentricity of the visual field. The observers' task was to identify the object located in the center of the group. A strong effect of the number of distractors was observed, consistent with various spatial pooling models. The analysis of incorrect responses revealed that these were a mix of feature errors and mislocalizations of the target object. Feature errors were not purely random, but biased by the features of distractors. We propose a simple feature integration model that predicts most of the observed regularities.
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Systems for deep brain stimulation: review of technical features.
Amon, A; Alesch, F
2017-09-01
The use of deep brain stimulation (DBS) is an important treatment option for movement disorders and other medical conditions. Today, three major manufacturers provide implantable systems for DBS. Although the underlying principle is basically the same for all available systems, the differences in the technical features vary considerably. This article outlines aspects regarding the technical features of DBS systems. The differences between voltage and current sources are addressed and their effect on stimulation is shown. To maintain clinical benefit and minimize side effects the stimulation field has to be adapted to the requirements of the patient. Shaping of the stimulation field can be achieved by the electrode design and polarity configuration. Furthermore, the electric signal consisting of stimulation rate, stimulation amplitude and pulse width affect the stimulation field. Interleaving stimulation is an additional concept, which permits improved treatment outcomes. Therefore, the electrode design, the polarity, the electric signal, and the concept of interleaving stimulation are presented. The investigated systems can be also categorized as rechargeable and non-rechargeable, which is briefly discussed. Options for interconnecting different system components from various manufacturers are presented. The present paper summarizes the technical features and their combination possibilities, which can have a major impact on the therapeutic effect.
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Features of Wisdom: Prototypical Attributes of Wise People.
ERIC Educational Resources Information Center
Maciel, Anna G.; And Others
Features of everyday conceptions of a "wise person" were examined, based on a model of wisdom-related knowledge (Baltes & Smith, 1990). The goal was to examine whether the psychological theory underlying this model is consistent with lay conceptions of wisdom, and whether everyday conceptions contain additional features not contained…
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Poliosis overlying a nevus with blue nevus features.
Young, Lorraine C; Van Dyke, Gregory S; Lipton, Shira; Binder, Scott W
2008-02-28
Poliosis is a localized patch of gray or white hair. Because it can be seen with a variety of disorders and drugs, a full history and exam is indicated. Additionally, it can be associated with underlying benign and malignant tumors, necessitating histological identification. We review the lesions that are reported with poliosis. In addition, we will report a case of poliosis overlying an intradermal nevus with congenital as well as blue nevus features. To the best of our knowledge, blue nevus features associated with poliosis have not been previously described.
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The new Mobile Command Center at KSC is important addition to emergency preparedness
NASA Technical Reports Server (NTRS)
2000-01-01
Charles Street, part of the Emergency Preparedness team at KSC, uses a phone on the specially equipped emergency response vehicle. The vehicle, nicknamed '''The Brute,''' serves as a mobile command center for emergency preparedness staff and other support personnel when needed. It features a conference room, computer work stations, mobile telephones and a fax machine. It also can generate power with its onboard generator. Besides being ready to respond in case of emergencies during launches, the vehicle must be ready to help address fires, security threats, chemical spills, terrorist attaches, weather damage or other critical situations that might face KSC or Cape Canaveral Air Force Station.
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Local Feature Selection for Data Classification.
Armanfard, Narges; Reilly, James P; Komeili, Majid
2016-06-01
Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.
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Zhang, Hai-lin; Fu, Shi-hong; Deng, Zhang; Yuan, Jun; Jiang, Hong-yue; Li, Ming-hua; Gao, Xiao-yan; Wang, Jing-lin; Liu, Yong-hua; Yin, Zheng-liu; Yang, Wei-hong; Zhang, Yu-zhen; Feng, Yun; Wang, Huan-yu; Liang, Guo-dong
2013-05-01
To understand the epidemiologic characteristics of dengue fever, imported from Myanmar to the border of Yunnan province, China. Viral molecular epidemiologic features were also studied. Questionnaires were used on each diagnosed, suspected dengue fever, case or unknown cases with fever when coming from Myanmar entering the port and hospitals in Ruili city of Yunnan province. Serum samples of these patients were collected to detect IgM antibody against dengue virus and RT-PCR assay. Homology and phylogenetic tree based on the whole nucleotide sequence of PrM-C and NS5 gene of dengue virus were further analyzed. A total of 103 sera were collected from patients at acute stage in Ruili city in July to November 2008. Among them, 49 cases were confirmed for dengue fever according to IgM and nucleic acid testings. Except one, other 48 cases were all imported into Ruili, from Myanmar. Of those, 18 patients were residents from Mujie city of Myanmar and hospitalized in Ruili and the rest 30 patients were Chinese citizens who had finished business and returned from Myanmar. Two isolates of serum samples from the imported cases were identified and both homology and phylogenetic analysis were performed, using the nucleotide sequences of PrM and NS5 genes. They were divided into dengue type 1 (RLB61) and dengue type 3 (RLC31) and were closer to the dengue virus strains isolated from Southeast Asia countries. It is confirmed that an epidemic of dengue fever which was imported from Myanmar to Ruili city of Yunnan province, China. Evidence also showed that both type I and III epidemic strains of dengue virus did exist in Mujie city of Myanmar in 2008.
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Importance of perceived naturalness for acceptance of food additives and cultured meat.
Siegrist, Michael; Sütterlin, Bernadette
2017-06-01
Four experiments examined some factors influencing the perceived naturalness of food products and their biasing effect on risk perception. The results of Experiment 1a showed that three food additives displaying their respective E-numbers (i.e., codes for food additives in the European Union and Switzerland) decreased perceived naturalness. Experiment 1b demonstrated that mentioning possible health effects decreased the perceived naturalness of a plant-based food additive. This experiment further showed that it would not matter for perceived naturalness whether the food was synthetic or nature-identical. Moreover, the results of Experiments 2 and 3 suggested that the same risk associated with meat consumption was much more acceptable for traditionally produced meat compared with in-vitro meat. Experiment 3 further indicated that the perceived naturalness of the meat (i.e., traditional or cultured meat) had a full mediation effect on participants' evaluation of the acceptability of the risk of colon cancer associated with the meat consumption. Even if the new production method (i.e., cultured meat) was more environmentally friendly and less harmful to animals, the perceived lack of naturalness might reduce the acceptability of the risk associated with such a product. The present study provides evidence that consumers rely on symbolic information when evaluating foods, which may lead to biased judgments and decisions. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Classification of visual and linguistic tasks using eye-movement features.
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).
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A new approach to modeling the influence of image features on fixation selection in scenes
Nuthmann, Antje; Einhäuser, Wolfgang
2015-01-01
Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner. PMID:25752239
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Compton, Michael T; Berez, Chantal; Walker, Elaine F
Family history of psychosis, gender, mode of onset, and age at onset are considered prognostic factors important to clinicians evaluating first-episode psychosis; yet, clinicians have little guidance as to how these four factors differentially predict early-course substance abuse, symptomatology, and functioning. We conducted a "head-to-head comparison" of these four factors regarding their associations with key clinical features at initial hospitalization. We also assessed potential interactions between gender and family history with regard to age at onset of psychosis and symptom severity. Consecutively admitted first-episode patients (n=334) were evaluated in two studies that rigorously assessed a number of early-course variables. Associations among variables of interest were examined using Pearson correlations, χ 2 tests, Student's t-tests, and 2×2 factorial analyses of variance. Substance (nicotine, alcohol, and cannabis) abuse and positive symptom severity were predicted only by male gender. Negative symptom severity and global functioning impairments were predicted by earlier age at onset of psychosis. General psychopathology symptom severity was predicted by both mode of onset and age at onset. Interaction effects were not observed with regard to gender and family history in predicting age at onset or symptom severity. The four prognostic features have differential associations with substance abuse, domains of symptom severity, and global functioning. Gender and age at onset of psychosis appear to be more predictive of clinical features at the time of initial evaluation (and thus presumably longer term outcomes) than the presence of a family history of psychosis and a more gradual mode of onset.
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Automated Quantification of Gradient Defined Features
2008-09-01
defined features in submarine environments. The technique utilizes MATLAB scripts to convert bathymetry data into a gradient dataset, produce gradient...maps, and most importantly, automate the process of defining and characterizing gradient defined features such as flows, faults, landslide scarps, folds...convergent plate margin hosts a series of large serpentinite mud volcanoes (Fig. 1). One of the largest of these active mud volcanoes is Big Blue
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Feature instructions improve face-matching accuracy
Bindemann, Markus
2018-01-01
Identity comparisons of photographs of unfamiliar faces are prone to error but important for applied settings, such as person identification at passport control. Finding techniques to improve face-matching accuracy is therefore an important contemporary research topic. This study investigated whether matching accuracy can be improved by instruction to attend to specific facial features. Experiment 1 showed that instruction to attend to the eyebrows enhanced matching accuracy for optimized same-day same-race face pairs but not for other-race faces. By contrast, accuracy was unaffected by instruction to attend to the eyes, and declined with instruction to attend to ears. Experiment 2 replicated the eyebrow-instruction improvement with a different set of same-race faces, comprising both optimized same-day and more challenging different-day face pairs. These findings suggest that instruction to attend to specific features can enhance face-matching accuracy, but feature selection is crucial and generalization across face sets may be limited. PMID:29543822
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The interaction of feature and space based orienting within the attention set.
Lim, Ahnate; Sinnett, Scott
2014-01-01
The processing of sensory information relies on interacting mechanisms of sustained attention and attentional capture, both of which operate in space and on object features. While evidence indicates that exogenous attentional capture, a mechanism previously understood to be automatic, can be eliminated while concurrently performing a demanding task, we reframe this phenomenon within the theoretical framework of the "attention set" (Most et al., 2005). Consequently, the specific prediction that cuing effects should reappear when feature dimensions of the cue overlap with those in the attention set (i.e., elements of the demanding task) was empirically tested and confirmed using a dual-task paradigm involving both sustained attention and attentional capture, adapted from Santangelo et al. (2007). Participants were required to either detect a centrally presented target presented in a stream of distractors (the primary task), or respond to a spatially cued target (the secondary task). Importantly, the spatial cue could either share features with the target in the centrally presented primary task, or not share any features. Overall, the findings supported the attention set hypothesis showing that a spatial cuing effect was only observed when the peripheral cue shared a feature with objects that were already in the attention set (i.e., the primary task). However, this finding was accompanied by differential attentional orienting dependent on the different types of objects within the attention set, with feature-based orienting occurring for target-related objects, and additional spatial-based orienting for distractor-related objects.
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The interaction of feature and space based orienting within the attention set
Lim, Ahnate; Sinnett, Scott
2014-01-01
The processing of sensory information relies on interacting mechanisms of sustained attention and attentional capture, both of which operate in space and on object features. While evidence indicates that exogenous attentional capture, a mechanism previously understood to be automatic, can be eliminated while concurrently performing a demanding task, we reframe this phenomenon within the theoretical framework of the “attention set” (Most et al., 2005). Consequently, the specific prediction that cuing effects should reappear when feature dimensions of the cue overlap with those in the attention set (i.e., elements of the demanding task) was empirically tested and confirmed using a dual-task paradigm involving both sustained attention and attentional capture, adapted from Santangelo et al. (2007). Participants were required to either detect a centrally presented target presented in a stream of distractors (the primary task), or respond to a spatially cued target (the secondary task). Importantly, the spatial cue could either share features with the target in the centrally presented primary task, or not share any features. Overall, the findings supported the attention set hypothesis showing that a spatial cuing effect was only observed when the peripheral cue shared a feature with objects that were already in the attention set (i.e., the primary task). However, this finding was accompanied by differential attentional orienting dependent on the different types of objects within the attention set, with feature-based orienting occurring for target-related objects, and additional spatial-based orienting for distractor-related objects. PMID:24523682
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47 CFR 68.318 - Additional limitations.
Code of Federal Regulations, 2010 CFR
2010-10-01
... TERMINAL EQUIPMENT TO THE TELEPHONE NETWORK Conditions for Terminal Equipment Approval § 68.318 Additional... incorporate the specified features. (b) Registered terminal equipment with automatic dialing capability. (1... proceeding to dial another number. (6) Network addressing signals shall be transmitted no earlier than: (i...
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47 CFR 68.318 - Additional limitations.
Code of Federal Regulations, 2012 CFR
2012-10-01
... TERMINAL EQUIPMENT TO THE TELEPHONE NETWORK Conditions for Terminal Equipment Approval § 68.318 Additional... incorporate the specified features. (b) Registered terminal equipment with automatic dialing capability. (1... proceeding to dial another number. (6) Network addressing signals shall be transmitted no earlier than: (i...
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47 CFR 68.318 - Additional limitations.
Code of Federal Regulations, 2011 CFR
2011-10-01
... TERMINAL EQUIPMENT TO THE TELEPHONE NETWORK Conditions for Terminal Equipment Approval § 68.318 Additional... incorporate the specified features. (b) Registered terminal equipment with automatic dialing capability. (1... proceeding to dial another number. (6) Network addressing signals shall be transmitted no earlier than: (i...
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47 CFR 68.318 - Additional limitations.
Code of Federal Regulations, 2013 CFR
2013-10-01
... TERMINAL EQUIPMENT TO THE TELEPHONE NETWORK Conditions for Terminal Equipment Approval § 68.318 Additional... incorporate the specified features. (b) Registered terminal equipment with automatic dialing capability. (1... proceeding to dial another number. (6) Network addressing signals shall be transmitted no earlier than: (i...
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47 CFR 68.318 - Additional limitations.
Code of Federal Regulations, 2014 CFR
2014-10-01
... TERMINAL EQUIPMENT TO THE TELEPHONE NETWORK Conditions for Terminal Equipment Approval § 68.318 Additional... incorporate the specified features. (b) Registered terminal equipment with automatic dialing capability. (1... proceeding to dial another number. (6) Network addressing signals shall be transmitted no earlier than: (i...
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Features in visual search combine linearly
Pramod, R. T.; Arun, S. P.
2014-01-01
Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features (intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and co-activation models (based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features—in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search. PMID:24715328
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Automatic lip reading by using multimodal visual features
NASA Astrophysics Data System (ADS)
Takahashi, Shohei; Ohya, Jun
2013-12-01
Since long time ago, speech recognition has been researched, though it does not work well in noisy places such as in the car or in the train. In addition, people with hearing-impaired or difficulties in hearing cannot receive benefits from speech recognition. To recognize the speech automatically, visual information is also important. People understand speeches from not only audio information, but also visual information such as temporal changes in the lip shape. A vision based speech recognition method could work well in noisy places, and could be useful also for people with hearing disabilities. In this paper, we propose an automatic lip-reading method for recognizing the speech by using multimodal visual information without using any audio information such as speech recognition. First, the ASM (Active Shape Model) is used to track and detect the face and lip in a video sequence. Second, the shape, optical flow and spatial frequencies of the lip features are extracted from the lip detected by ASM. Next, the extracted multimodal features are ordered chronologically so that Support Vector Machine is performed in order to learn and classify the spoken words. Experiments for classifying several words show promising results of this proposed method.
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Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.
Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong
2016-04-15
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.
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Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong
2016-01-01
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505
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Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.
Donoho, David; Jin, Jiashun
2008-09-30
In important application fields today-genomics and proteomics are examples-selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, ..., p, let pi(i) denote the two-sided P-value associated with the ith feature Z-score and pi((i)) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p - pi((i)))/sqrt{i/p(1-i/p)}. We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT.
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Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
Donoho, David; Jin, Jiashun
2008-01-01
In important application fields today—genomics and proteomics are examples—selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, …, p, let πi denote the two-sided P-value associated with the ith feature Z-score and π(i) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p − π(i))/i/p(1−i/p). We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT. PMID:18815365
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Build platform that provides mechanical engagement with additive manufacturing prints
DOE Office of Scientific and Technical Information (OSTI.GOV)
Elliott, Amelia M.
A build platform and methods of fabricating an article with such a platform in an extrusion-type additive manufacturing machine are provided. A platform body 202 includes features 204 that extend outward from the body 202. The features 204 define protrusive areas 206 and recessive areas 208 that cooperate to mechanically engage the extruded material that forms the initial layers 220 of an article when the article is being fabricated by a nozzle 12 of the additive manufacturing machine 10.
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NASA Astrophysics Data System (ADS)
Khotimah, Chusnul; Purnami, Santi Wulan; Prastyo, Dedy Dwi; Chosuvivatwong, Virasakdi; Sriplung, Hutcha
2017-11-01
Support Vector Machines (SVMs) has been widely applied for prediction in many fields. Recently, SVM is also developed for survival analysis. In this study, Additive Survival Least Square SVM (A-SURLSSVM) approach is used to analyze cervical cancer dataset and its performance is compared with the Cox model as a benchmark. The comparison is evaluated based on the prognostic index produced: concordance index (c-index), log rank, and hazard ratio. The higher prognostic index represents the better performance of the corresponding methods. This work also applied feature selection to choose important features using backward elimination technique based on the c-index criterion. The cervical cancer dataset consists of 172 patients. The empirical results show that nine out of the twelve features: age at marriage, age of first getting menstruation, age, parity, type of treatment, history of family planning, stadium, long-time of menstruation, and anemia status are selected as relevant features that affect the survival time of cervical cancer patients. In addition, the performance of the proposed method is evaluated through a simulation study with the different number of features and censoring percentages. Two out of three performance measures (c-index and hazard ratio) obtained from A-SURLSSVM consistently yield better results than the ones obtained from Cox model when it is applied on both simulated and cervical cancer data. Moreover, the simulation study showed that A-SURLSSVM performs better when the percentage of censoring data is small.
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Guo, Chengyuan; Wang, Renzhong; Xiao, Chunwang
2012-01-01
Background Global climatic change is generally expected to stimulate net primary production, and consequently increase soil carbon (C) input. The enhanced C input together with potentially increased precipitation may affect soil microbial processes and plant growth. Methodology/Principal Findings To examine the effects of C and water additions on soil microbial properties and plant growth, we conducted an experiment lasting two years in a temperate steppe of northeastern China. We found that soil C and water additions significantly affected microbial properties and stimulated plant growth. Carbon addition significantly increased soil microbial biomass and activity but had a limited effect on microbial community structure. Water addition significantly increased soil microbial activity in the first year but the response to water decreased in the second year. The water-induced changes of microbial activity could be ascribed to decreased soil nitrogen (N) availability and to the shift in soil microbial community structure. However, no water effect on soil microbial activity was visible under C addition during the two years, likely because C addition alleviated nutrient limitation of soil microbes. In addition, C and water additions interacted to affect plant functional group composition. Water addition significantly increased the ratio of grass to forb biomass in C addition plots but showed only minor effects under ambient C levels. Our results suggest that soil microbial activity and plant growth are limited by nutrient (C and N) and water availability, and highlight the importance of nutrient availability in modulating the responses of soil microbes and plants to potentially increased precipitation in the temperate steppe. Conclusions/Significance Increased soil C input and precipitation would show significant effects on soil microbial properties and plant growth in the temperate steppe. These findings will improve our understanding of the responses of soil microbes
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Aging, selective attention, and feature integration.
Plude, D J; Doussard-Roosevelt, J A
1989-03-01
This study used feature-integration theory as a means of determining the point in processing at which selective attention deficits originate. The theory posits an initial stage of processing in which features are registered in parallel and then a serial process in which features are conjoined to form complex stimuli. Performance of young and older adults on feature versus conjunction search is compared. Analyses of reaction times and error rates suggest that elderly adults in addition to young adults, can capitalize on the early parallel processing stage of visual information processing, and that age decrements in visual search arise as a result of the later, serial stage of processing. Analyses of a third, unconfounded, conjunction search condition reveal qualitatively similar modes of conjunction search in young and older adults. The contribution of age-related data limitations is found to be secondary to the contribution of age decrements in selective attention.
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Sensor-oriented feature usability evaluation in fingerprint segmentation
NASA Astrophysics Data System (ADS)
Li, Ying; Yin, Yilong; Yang, Gongping
2013-06-01
Existing fingerprint segmentation methods usually process fingerprint images captured by different sensors with the same feature or feature set. We propose to improve the fingerprint segmentation result in view of an important fact that images from different sensors have different characteristics for segmentation. Feature usability evaluation, which means to evaluate the usability of features to find the personalized feature or feature set for different sensors to improve the performance of segmentation. The need for feature usability evaluation for fingerprint segmentation is raised and analyzed as a new issue. To address this issue, we present a decision-tree-based feature-usability evaluation method, which utilizes a C4.5 decision tree algorithm to evaluate and pick the best suitable feature or feature set for fingerprint segmentation from a typical candidate feature set. We apply the novel method on the FVC2002 database of fingerprint images, which are acquired by four different respective sensors and technologies. Experimental results show that the accuracy of segmentation is improved, and time consumption for feature extraction is dramatically reduced with selected feature(s).
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Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming
2015-01-01
Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.
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Random forest feature selection approach for image segmentation
NASA Astrophysics Data System (ADS)
Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina; Vaida, Mircea Florin
2017-03-01
In the field of image segmentation, discriminative models have shown promising performance. Generally, every such model begins with the extraction of numerous features from annotated images. Most authors create their discriminative model by using many features without using any selection criteria. A more reliable model can be built by using a framework that selects the important variables, from the point of view of the classification, and eliminates the unimportant once. In this article we present a framework for feature selection and data dimensionality reduction. The methodology is built around the random forest (RF) algorithm and its variable importance evaluation. In order to deal with datasets so large as to be practically unmanageable, we propose an algorithm based on RF that reduces the dimension of the database by eliminating irrelevant features. Furthermore, this framework is applied to optimize our discriminative model for brain tumor segmentation.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Zhang, L; Fried, D; Fave, X
Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between twomore » groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity
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Feature extraction via KPCA for classification of gait patterns.
Wu, Jianning; Wang, Jue; Liu, Li
2007-06-01
Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.
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NASA Astrophysics Data System (ADS)
Wang, C.; Gomez-Velez, J. D.; Wilson, J. L.
2017-12-01
Groundwater plays a key role in runoff generation and stream water chemistry from reach to watershed scales. The spatial distribution of ridges and streams can influence the spatial patterns of groundwater recharge and drainage, specially in mountainous terrains where these features are more prominent. However, typical modeling efforts simplify or ignore some of these features due to computational limitations without a systematic investigation of the implications for flow and transport within the watershed. In this study, we investigate the effect of capturing key topographic features on modeled groundwater flow and transport characteristics in a mountainous watershed. We build model scenarios of different topographic complexity levels (TCLs) to capture different levels of representation of streams and ridges in the model. Modeled baseflow and groundwater mean residence time (MRT) are used to quantify the differences among TCLs. Our results show that capturing the streams and ridges has a significant influence on simulated groundwater flow and transport patterns. Topographic complexity controls the proportion of baseflow generated from local, intermediate, and regional flow paths, thus influencing the amount and MRT of basefow flowing into streams of different Horton-Strahler orders. We further simulate the concentration of solute exported into streams from subsurface chemical weathering. The concentration of chemical weathering products in streams is less sensitive to model TCL due to the thermodynamic constraint on the equilibrium concentration of the chemical weathering. We also tested the influence of geology on the effect of TCL. The effect of TCL is consistent under different geological conditions; however, it is enhanced in models with low hydraulic conductivity because more of the flow is forced into shallow and local flow paths. All of these changes can affect our ability to interpret environmental tracer data and predict bio- and geo-chemical evolution of
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Preferred features of urban parks and forests
Herbert W. Schroeder
1982-01-01
To make the most efficient use of scarce recreation resources, urban forest managers need to know what features of recreation sites are the most important for creating high-quality recreation environments. In this study, observers viewed photographs of urban forest sites in the Chicago area and described the features of the sites that they liked and disliked. Natural...
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Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features
Zhang, Yaoyun; Jiang, Min; Wang, Jingqi; Xu, Hua
2016-01-01
Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926
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Community Perceptions of Specific Skin Features of Possible Melanoma
ERIC Educational Resources Information Center
Baade, Peter D.; Balanda, Kevin P.; Stanton, Warren R.; Lowe, John B.; Del Mar, Chris B.
2004-01-01
Background: Melanoma can be curable if detected early. One component of detecting melanoma is an awareness of the important features of the disease. It is currently not clear which features the community view as indicative of melanoma. Objective: To investigate which features of the skin members of an urban community believe may indicate skin…
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Vegetation-terrain feature relationships in southeast Arizona
NASA Technical Reports Server (NTRS)
Schrumpf, B. J. (Principal Investigator); Mouat, D. A.
1972-01-01
There are no author-identified significant results in this report. Studies of relationships of vegetation distribution to geomorphic characteristics of the landscape and of plant phenological patterns to vegetation identification of satellite imagery indicate that there exists positive relationships between certain plant species and certain terrain features. Not all species were found to exhibit positive relationships with all terrain feature variables, but enough positive relationships seem to exist to indicate that terrain feature variable-vegetation relationship studies have a definite place in plant ecological investigations. Even more importantly, the vegetation groups examined appeared to be successfully discriminated by the terrain feature variables. This would seem to indicate that spatial interpretations of vegetation groups may be possible. While vegetational distributions aren't determined by terrain feature differences, terrain features do mirror factors which directly influence vegetational response and hence distribution. As a result, those environmental features which can be readily and rapidly ascertained on relatively small-scale imagery may prove to be valuable indicators of vegetation distribution.
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Buri, Marcus V.; Domingues, Tatiana M.; Paredes-Gamero, Edgar J.; Casaes-Rodrigues, Rafael L.; Rodrigues, Elaine Guadelupe; Miranda, Antonio
2013-01-01
Many reports have shown that antimicrobial peptides exhibit anticancer abilities. Gomesin (Gm) exhibits potent cytotoxic activity against cancer cells by a membrane pore formation induced after well-orchestrated intracellular mechanisms. In this report, the replacements of the Cys by Ser or Thr, and the use D-amino acids in the Gm structure were done to investigate the importance of the resistance to degradation of the molecule with its cytotoxicity. [Thr2,6,11,15]-Gm, and [Ser2,6,11,15]-Gm exhibits low cytotoxicity, and low resistance to degradation, and after 24 h are present in localized area near to the membrane. Conversely, the use of D-amino acids in the analogue [D-Thr2,6,11,15]-D-Gm confers resistance to degradation, increases its potency, and maintained this peptide spread in the cytosol similarly to what happens with Gm. Replacements of Cys by Thr and Gln by L- or D-Pro ([D-Thr2,6,11,15, Pro9]-D-Gm, and [Thr2,6,11,15, D-Pro9]-Gm), which induced a similar β-hairpin conformation, also increase their resistance to degradation, and cytotoxicity, but after 24 h they are not present spread in the cytosol, exhibiting lower cytotoxicity in comparison to Gm. Additionally, chloroquine, a lysosomal enzyme inhibitor potentiated the effect of the peptides. Furthermore, the binding and internalization of peptides was determined, but a direct correlation among these factors was not observed. However, cholesterol ablation, which increase fluidity of cellular membrane, also increase cytotoxicity and internalization of peptides. β-hairpin spatial conformation, and intracellular localization/target, and the capability of entry are important properties of gomesin cytotoxicity. PMID:24312251
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Vertical Feature Mask Feature Classification Flag Extraction
Atmospheric Science Data Center
2013-03-28
Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...
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Dissociative features in posttraumatic stress disorder: A latent profile analysis.
Műllerová, Jana; Hansen, Maj; Contractor, Ateka A; Elhai, Jon D; Armour, Cherie
2016-09-01
The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes the dissociative subtype of posttraumatic stress disorder (PTSD) in terms of the individual meeting the criteria for PTSD and additionally reporting symptoms of depersonalization and/or derealization. The current study aimed to examine whether a dissociative PTSD profile may include alternative features of dissociation and whether it could be differentiated from a nondissociative PTSD profile on certain psychopathologies and demographics. Data from 309 trauma-exposed participants, collected through Amazon Mechanical Turk, were subjected to latent profile analysis. Regression analyses were used to examine the predictors of latent classes. Three discrete profiles named Baseline, PTSD, and Dissociative profile were uncovered. All examined features of dissociation were significantly elevated in the Dissociative profile. Anxiety, male sex, being employed, and having a minority racial background significantly predicted the Dissociative profile relative to the PTSD profile. The study points to the importance of alternative symptoms of dissociation in the dissociative PTSD subtype beyond the symptoms of depersonalization and derealization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
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Combined rule extraction and feature elimination in supervised classification.
Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E
2012-09-01
There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.
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Speech recognition features for EEG signal description in detection of neonatal seizures.
Temko, A; Boylan, G; Marnane, W; Lightbody, G
2010-01-01
In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.
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Sentiment analysis of feature ranking methods for classification accuracy
NASA Astrophysics Data System (ADS)
Joseph, Shashank; Mugauri, Calvin; Sumathy, S.
2017-11-01
Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.
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Automated simultaneous multiple feature classification of MTI data
NASA Astrophysics Data System (ADS)
Harvey, Neal R.; Theiler, James P.; Balick, Lee K.; Pope, Paul A.; Szymanski, John J.; Perkins, Simon J.; Porter, Reid B.; Brumby, Steven P.; Bloch, Jeffrey J.; David, Nancy A.; Galassi, Mark C.
2002-08-01
Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.
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Feature engineering for MEDLINE citation categorization with MeSH.
Jimeno Yepes, Antonio Jose; Plaza, Laura; Carrillo-de-Albornoz, Jorge; Mork, James G; Aronson, Alan R
2015-04-08
Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific
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Precise Modelling of Telluric Features in Astronomical Spectra
NASA Astrophysics Data System (ADS)
Seifahrt, A.; Käufl, H. U.; Zängl, G.; Bean, J.; Richter, M.; Siebenmorgen, R.
2010-12-01
Ground-based astronomical observations suffer from the disturbing effects of the Earth's atmosphere. Oxygen, water vapour and a number of atmospheric trace gases absorb and emit light at discrete frequencies, shaping observing bands in the near- and mid-infrared and leaving their fingerprints - telluric absorption and emission lines - in astronomical spectra. The standard approach of removing the absorption lines is to observe a telluric standard star: a time-consuming and often imperfect solution. Alternatively, the spectral features of the Earth's atmosphere can be modelled using a radiative transfer code, often delivering a satisfying solution that removes these features without additional observations. In addition the model also provides a precise wavelength solution and an instrumental profile.
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Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection
NASA Astrophysics Data System (ADS)
Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant
2014-03-01
Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by
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Physical Features of Visual Images Affect Macaque Monkey’s Preference for These Images
Funahashi, Shintaro
2016-01-01
Animals exhibit different degrees of preference toward various visual stimuli. In addition, it has been shown that strongly preferred stimuli can often act as a reward. The aim of the present study was to determine what features determine the strength of the preference for visual stimuli in order to examine neural mechanisms of preference judgment. We used 50 color photographs obtained from the Flickr Material Database (FMD) as original stimuli. Four macaque monkeys performed a simple choice task, in which two stimuli selected randomly from among the 50 stimuli were simultaneously presented on a monitor and monkeys were required to choose either stimulus by eye movements. We considered that the monkeys preferred the chosen stimulus if it continued to look at the stimulus for an additional 6 s and calculated a choice ratio for each stimulus. Each monkey exhibited a different choice ratio for each of the original 50 stimuli. They tended to select clear, colorful and in-focus stimuli. Complexity and clarity were stronger determinants of preference than colorfulness. Images that included greater amounts of spatial frequency components were selected more frequently. These results indicate that particular physical features of the stimulus can affect the strength of a monkey’s preference and that the complexity, clarity and colorfulness of the stimulus are important determinants of this preference. Neurophysiological studies would be needed to examine whether these features of visual stimuli produce more activation in neurons that participate in this preference judgment. PMID:27853424
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Automated Extraction of Secondary Flow Features
NASA Technical Reports Server (NTRS)
Dorney, Suzanne M.; Haimes, Robert
2005-01-01
The use of Computational Fluid Dynamics (CFD) has become standard practice in the design and development of the major components used for air and space propulsion. To aid in the post-processing and analysis phase of CFD many researchers now use automated feature extraction utilities. These tools can be used to detect the existence of such features as shocks, vortex cores and separation and re-attachment lines. The existence of secondary flow is another feature of significant importance to CFD engineers. Although the concept of secondary flow is relatively understood there is no commonly accepted mathematical definition for secondary flow. This paper will present a definition for secondary flow and one approach for automatically detecting and visualizing secondary flow.
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Text feature extraction based on deep learning: a review.
Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan
2017-01-01
Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.
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Trajectory analysis via a geometric feature space approach
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rintoul, Mark D.; Wilson, Andrew T.
This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as the total distance traveled and the distance between start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally,more » these features can generally be mapped easily to behaviors of interest to humans who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.« less
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Trajectory analysis via a geometric feature space approach
Rintoul, Mark D.; Wilson, Andrew T.
2015-10-05
This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as the total distance traveled and the distance between start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally,more » these features can generally be mapped easily to behaviors of interest to humans who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.« less
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Interstitial pneumonia with autoimmune features: an additional risk factor for ARDS?
Grasselli, Giacomo; Vergnano, Beatrice; Pozzi, Maria Rosa; Sala, Vittoria; D'Andrea, Gabriele; Scaravilli, Vittorio; Mantero, Marco; Pesci, Alberto; Pesenti, Antonio
2017-09-18
Interstitial pneumonia with autoimmune features (IPAF) identifies a recently recognized autoimmune syndrome characterized by interstitial lung disease and autoantibodies positivity, but absence of a specific connective tissue disease diagnosis or alternative etiology. We retrospectively reviewed the clinical presentation, diagnostic workup and management of seven critically ill patients who met diagnostic criteria for IPAF. We compared baseline characteristics and clinical outcome of IPAF patients with those of the population of ARDS patients admitted in the same period. Seven consecutive patients with IPAF admitted to intensive care unit for acute respiratory distress syndrome (ARDS) were compared with 78 patients with ARDS secondary to a known risk factor and with eight ARDS patients without recognized risk factors. Five IPAF patients (71%) survived and were discharged alive from ICU: Their survival rate was equal to that of patients with a known risk factor (71%), while the subgroup of patients without risk factors had a markedly lower survival (38%). According to the Berlin definition criteria, ARDS was severe in four IPAF patients and moderate in the remaining three. All had multiple organ dysfunction at presentation. The most frequent autoantibody detected was anti-SSA/Ro52. All patients required prolonged mechanical ventilation (median duration 49 days, range 10-88); four received extracorporeal membrane oxygenation and one received low-flow extracorporeal CO 2 removal. All patients received immunosuppressive therapy. This is the first description of a cohort of critical patients meeting the diagnostic criteria for IPAF presenting with ARDS. This diagnosis should be considered in any critically ill patient with interstitial lung disease of unknown origin. While management is challenging and level of support high, survival appears to be good and comparable to that of patients with ARDS associated with a known clinical insult.
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Feature-Selective Attentional Modulations in Human Frontoparietal Cortex.
Ester, Edward F; Sutterer, David W; Serences, John T; Awh, Edward
2016-08-03
Control over visual selection has long been framed in terms of a dichotomy between "source" and "site," where top-down feedback signals originating in frontoparietal cortical areas modulate or bias sensory processing in posterior visual areas. This distinction is motivated in part by observations that frontoparietal cortical areas encode task-level variables (e.g., what stimulus is currently relevant or what motor outputs are appropriate), while posterior sensory areas encode continuous or analog feature representations. Here, we present evidence that challenges this distinction. We used fMRI, a roving searchlight analysis, and an inverted encoding model to examine representations of an elementary feature property (orientation) across the entire human cortical sheet while participants attended either the orientation or luminance of a peripheral grating. Orientation-selective representations were present in a multitude of visual, parietal, and prefrontal cortical areas, including portions of the medial occipital cortex, the lateral parietal cortex, and the superior precentral sulcus (thought to contain the human homolog of the macaque frontal eye fields). Additionally, representations in many-but not all-of these regions were stronger when participants were instructed to attend orientation relative to luminance. Collectively, these findings challenge models that posit a strict segregation between sources and sites of attentional control on the basis of representational properties by demonstrating that simple feature values are encoded by cortical regions throughout the visual processing hierarchy, and that representations in many of these areas are modulated by attention. Influential models of visual attention posit a distinction between top-down control and bottom-up sensory processing networks. These models are motivated in part by demonstrations showing that frontoparietal cortical areas associated with top-down control represent abstract or categorical stimulus
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A feature refinement approach for statistical interior CT reconstruction
NASA Astrophysics Data System (ADS)
Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong
2016-07-01
Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)—minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.
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A feature refinement approach for statistical interior CT reconstruction.
Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong
2016-07-21
Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)-minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.
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NASA Astrophysics Data System (ADS)
Hargitai, Henrik
2016-10-01
We have created a metacatalog, or catalog or catalogs, of surface features of Mars that also includes the actual data in the catalogs listed. The goal is to make mesoscale surface feature databases available in one place, in a GIS-ready format. The databases can be directly imported to ArcGIS or other GIS platforms, like Google Mars. Some of the catalogs in our database are also ingested into the JMARS platform.All catalogs have been previously published in a peer-reviewed journal, but they may contain updates of the published catalogs. Many of the catalogs are "integrated", i.e. they merge databases or information from various papers on the same topic, including references to each individual features listed.Where available, we have included shapefiles with polygon or linear features, however, most of the catalogs only contain point data of their center points and morphological data.One of the unexpected results of the planetary feature metacatalog is that some features have been described by several papers, using different, i.e., conflicting designations. This shows the need for the development of an identification system suitable for mesoscale (100s m to km sized) features that tracks papers and thus prevents multiple naming of the same feature.The feature database can be used for multicriteria analysis of a terrain, thus enables easy distribution pattern analysis and the correlation of the distribution of different landforms and features on Mars. Such catalog makes a scientific evaluation of potential landing sites easier and more effective during the selection process and also supports automated landing site selections.The catalog is accessible at https://planetarydatabase.wordpress.com/.
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Additive manufacturing of optical components
NASA Astrophysics Data System (ADS)
Heinrich, Andreas; Rank, Manuel; Maillard, Philippe; Suckow, Anne; Bauckhage, Yannick; Rößler, Patrick; Lang, Johannes; Shariff, Fatin; Pekrul, Sven
2016-08-01
The development of additive manufacturing methods has enlarged rapidly in recent years. Thereby, the work mainly focuses on the realization of mechanical components, but the additive manufacturing technology offers a high potential in the field of optics as well. Owing to new design possibilities, completely new solutions are possible. This article briefly reviews and compares the most important additive manufacturing methods for polymer optics. Additionally, it points out the characteristics of additive manufactured polymer optics. Thereby, surface quality is of crucial importance. In order to improve it, appropriate post-processing steps are necessary (e.g. robot polishing or coating), which will be discussed. An essential part of this paper deals with various additive manufactured optical components and their use, especially in optical systems for shape metrology (e.g. borehole sensor, tilt sensor, freeform surface sensor, fisheye lens). The examples should demonstrate the potentials and limitations of optical components produced by additive manufacturing.
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Cigarette Design Features: Effects on Emission Levels, User Perception, and Behavior.
Talhout, Reinskje; Richter, Patricia A; Stepanov, Irina; Watson, Christina V; Watson, Clifford H
2018-01-01
This paper describes the effects of non-tobacco, physical cigarette design features on smoke emissions, product appeal, and smoking behaviors - 3 factors that determine smoker's exposure and related health risks. We reviewed available evidence for the impact of filter ventilation, new filter types, and cigarettes dimensions on toxic emissions, smoker's perceptions, and behavior. For evidence sources we used scientific literature and websites providing product characteristics and marketing information. Whereas filter ventilation results in lower machine-generated emissions, it also leads to perceptions of lighter taste and relative safety in smokers who can unwittingly employ more intense smoking behavior to obtain the desired amount of nicotine and sensory appeal. Filter additives that modify smoke emissions can also modify sensory cues, resulting in changes in smoking behavior. Flavor capsules increase the cigarette's appeal and novelty, and lead to misperceptions of reduced harm. Slim cigarettes have lower yields of some smoke emissions, but smoking behavior can be more intense than with standard cigarettes. Physical design features significantly impact machine-measured emission yields in cigarette smoke, product appeal, smoking behaviors, and exposures in smokers. The influence of current and emerging design features is important in understanding the effectiveness of regulatory actions to reduce smoking-related harm.
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Monitoring Geothermal Features in Yellowstone National Park with ATLAS Multispectral Imagery
NASA Technical Reports Server (NTRS)
Spruce, Joseph; Berglund, Judith
2000-01-01
The National Park Service (NPS) must produce an Environmental Impact Statement for each proposed development in the vicinity of known geothermal resource areas (KGRAs) in Yellowstone National Park. In addition, the NPS monitors indicator KGRAs for environmental quality and is still in the process of mapping many geothermal areas. The NPS currently maps geothermal features with field survey techniques. High resolution aerial multispectral remote sensing in the visible, NIR, SWIR, and thermal spectral regions could enable YNP geothermal features to be mapped more quickly and in greater detail In response, Yellowstone Ecosystems Studies, in partnership with NASA's Commercial Remote Sensing Program, is conducting a study on the use of Airborne Terrestrial Applications Sensor (ATLAS) multispectral data for monitoring geothermal features in the Upper Geyser Basin. ATLAS data were acquired at 2.5 meter resolution on August 17, 2000. These data were processed into land cover classifications and relative temperature maps. For sufficiently large features, the ATLAS data can map geothermal areas in terms of geyser pools and hot springs, plus multiple categories of geothermal runoff that are apparently indicative of temperature gradients and microbial matting communities. In addition, the ATLAS maps clearly identify geyserite areas. The thermal bands contributed to classification success and to the computation of relative temperature. With masking techniques, one can assess the influence of geothermal features on the Firehole River. Preliminary results appear to confirm ATLAS data utility for mapping and monitoring geothermal features. Future work will include classification refinement and additional validation.
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Designing attractive gamification features for collaborative storytelling websites.
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.
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Clinical and diagnostic features of delayed hypoxic leukoencephalopathy.
Shprecher, David R; Flanigan, Kevin M; Smith, A Gordon; Smith, Shawn M; Schenkenberg, Thomas; Steffens, John
2008-01-01
Delayed hypoxic leukoencephalopathy is an underrecognized syndrome of delayed demyelination, which is important to consider when delayed onset of neuropsychiatric symptoms follows a hypoxic event. The authors describe clinical and diagnostic features of three such cases, review the pathophysiology of delayed hypoxic leukoencephalopathy, and discuss features which may help distinguish it from toxic leukoencephalopathy.
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NASA Astrophysics Data System (ADS)
Hayana Hasibuan, Eka; Mawengkang, Herman; Efendi, Syahril
2017-12-01
The use of Partical Swarm Optimization Algorithm in this research is to optimize the feature weights on the Voting Feature Interval 5 algorithm so that we can find the model of using PSO algorithm with VFI 5. Optimization of feature weight on Diabetes or Dyspesia data is considered important because it is very closely related to the livelihood of many people, so if there is any inaccuracy in determining the most dominant feature weight in the data will cause death. Increased accuracy by using PSO Algorithm ie fold 1 from 92.31% to 96.15% increase accuracy of 3.8%, accuracy of fold 2 on Algorithm VFI5 of 92.52% as well as generated on PSO Algorithm means accuracy fixed, then in fold 3 increase accuracy of 85.19% Increased to 96.29% Accuracy increased by 11%. The total accuracy of all three trials increased by 14%. In general the Partical Swarm Optimization algorithm has succeeded in increasing the accuracy to several fold, therefore it can be concluded the PSO algorithm is well used in optimizing the VFI5 Classification Algorithm.
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Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan
2014-01-01
Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
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Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi
2014-01-01
Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236
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Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan
2003-12-01
The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the
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Schädler, Marc René; Kollmeier, Birger
2015-04-01
To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.
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Feature selection for examining behavior by pathology laboratories.
Hawkins, S; Williams, G; Baxter, R
2001-08-01
Australia has a universal health insurance scheme called Medicare, which is managed by Australia's Health Insurance Commission. Medicare payments for pathology services generate voluminous transaction data on patients, doctors and pathology laboratories. The Health Insurance Commission (HIC) currently uses predictive models to monitor compliance with regulatory requirements. The HIC commissioned a project to investigate the generation of new features from the data. Feature generation has not appeared as an important step in the knowledge discovery in databases (KDD) literature. New interesting features for use in predictive modeling are generated. These features were summarized, visualized and used as inputs for clustering and outlier detection methods. Data organization and data transformation methods are described for the efficient access and manipulation of these new features.
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Wavelet-based energy features for glaucomatous image classification.
Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha
2012-01-01
Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.
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A bootstrap based Neyman-Pearson test for identifying variable importance.
Ditzler, Gregory; Polikar, Robi; Rosen, Gail
2015-04-01
Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any FS algorithm, regardless of the FS criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. We provide freely available software implementations of the proposed methodology.
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Mobile personal health records: an evaluation of features and functionality.
Kharrazi, Hadi; Chisholm, Robin; VanNasdale, Dean; Thompson, Benjamin
2012-09-01
To evaluate stand-alone mobile personal health record (mPHR) applications for the three leading cellular phone platforms (iOS, BlackBerry, and Android), assessing each for content, function, security, and marketing characteristics. Nineteen stand-alone mPHR applications (8 for iOS, 5 for BlackBerry, and 6 for Android) were identified and evaluated. Main criteria used to include mPHRs were: operating standalone on a mobile platform; not requiring external connectivity; and covering a wide range of health topics. Selected mPHRs were analyzed considering product characteristics, data elements, and application features. We also reviewed additional features such as marketing tactics. Within and between the different mobile platforms attributes for the mPHR were highly variable. None of the mPHRs contained all attributes included in our evaluation. The top four mPHRs contained 13 of the 14 features omitting only the in-case-of emergency feature. Surprisingly, seven mPHRs lacked basic security measures as important as password protection. The mPHRs were relatively inexpensive: ranging from no cost to $9.99. The mPHR application cost varied in some instances based on whether it supported single or multiple users. Ten mPHRs supported multiple user profiles. Notably, eight mPHRs used scare tactics as marketing strategy. mPHR is an emerging health care technology. The majority of existing mPHR apps is limited by at least one of the attributes considered for this study; however, as the mobile market continues to expand it is likely that more comprehensive mPHRs will be developed in the near future. New advancements in mobile technology can be utilized to enhance mPHRs by long-term patient empowerment features. Marketing strategies for mPHRs should target specific subpopulations and avoid scare tactics. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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A feature selection approach towards progressive vector transmission over the Internet
NASA Astrophysics Data System (ADS)
Miao, Ru; Song, Jia; Feng, Min
2017-09-01
WebGIS has been applied for visualizing and sharing geospatial information popularly over the Internet. In order to improve the efficiency of the client applications, the web-based progressive vector transmission approach is proposed. Important features should be selected and transferred firstly, and the methods for measuring the importance of features should be further considered in the progressive transmission. However, studies on progressive transmission for large-volume vector data have mostly focused on map generalization in the field of cartography, but rarely discussed on the selection of geographic features quantitatively. This paper applies information theory for measuring the feature importance of vector maps. A measurement model for the amount of information of vector features is defined based upon the amount of information for dealing with feature selection issues. The measurement model involves geometry factor, spatial distribution factor and thematic attribute factor. Moreover, a real-time transport protocol (RTP)-based progressive transmission method is then presented to improve the transmission of vector data. To clearly demonstrate the essential methodology and key techniques, a prototype for web-based progressive vector transmission is presented, and an experiment of progressive selection and transmission for vector features is conducted. The experimental results indicate that our approach clearly improves the performance and end-user experience of delivering and manipulating large vector data over the Internet.
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Liu, Z; Sun, J; Smith, M; Smith, L; Warr, R
2013-11-01
Computer-assisted diagnosis (CAD) of malignant melanoma (MM) has been advocated to help clinicians to achieve a more objective and reliable assessment. However, conventional CAD systems examine only the features extracted from digital photographs of lesions. Failure to incorporate patients' personal information constrains the applicability in clinical settings. To develop a new CAD system to improve the performance of automatic diagnosis of melanoma, which, for the first time, incorporates digital features of lesions with important patient metadata into a learning process. Thirty-two features were extracted from digital photographs to characterize skin lesions. Patients' personal information, such as age, gender and, lesion site, and their combinations, was quantified as metadata. The integration of digital features and metadata was realized through an extended Laplacian eigenmap, a dimensionality-reduction method grouping lesions with similar digital features and metadata into the same classes. The diagnosis reached 82.1% sensitivity and 86.1% specificity when only multidimensional digital features were used, but improved to 95.2% sensitivity and 91.0% specificity after metadata were incorporated appropriately. The proposed system achieves a level of sensitivity comparable with experienced dermatologists aided by conventional dermoscopes. This demonstrates the potential of our method for assisting clinicians in diagnosing melanoma, and the benefit it could provide to patients and hospitals by greatly reducing unnecessary excisions of benign naevi. This paper proposes an enhanced CAD system incorporating clinical metadata into the learning process for automatic classification of melanoma. Results demonstrate that the additional metadata and the mechanism to incorporate them are useful for improving CAD of melanoma. © 2013 British Association of Dermatologists.
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A Reduced Set of Features for Chronic Kidney Disease Prediction
Misir, Rajesh; Mitra, Malay; Samanta, Ranjit Kumar
2017-01-01
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs. PMID:28706750
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Zadpoor, Amir A
2017-07-25
Recent advances in additive manufacturing (AM) techniques in terms of accuracy, reliability, the range of processable materials, and commercial availability have made them promising candidates for production of functional parts including those used in the biomedical industry. The complexity-for-free feature offered by AM means that very complex designs become feasible to manufacture, while batch-size-indifference enables fabrication of fully patient-specific medical devices. Design for AM (DfAM) approaches aim to fully utilize those features for development of medical devices with substantially enhanced performance and biomaterials with unprecedented combinations of favorable properties that originate from complex geometrical designs at the micro-scale. This paper reviews the most important approaches in DfAM particularly those applicable to additive bio-manufacturing including image-based design pipelines, parametric and non-parametric designs, metamaterials, rational and computationally enabled design, topology optimization, and bio-inspired design. Areas with limited research have been identified and suggestions have been made for future research. The paper concludes with a brief discussion on the practical aspects of DfAM and the potential of combining AM with subtractive and formative manufacturing processes in so-called hybrid manufacturing processes.
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Zadpoor, Amir A.
2017-01-01
Recent advances in additive manufacturing (AM) techniques in terms of accuracy, reliability, the range of processable materials, and commercial availability have made them promising candidates for production of functional parts including those used in the biomedical industry. The complexity-for-free feature offered by AM means that very complex designs become feasible to manufacture, while batch-size-indifference enables fabrication of fully patient-specific medical devices. Design for AM (DfAM) approaches aim to fully utilize those features for development of medical devices with substantially enhanced performance and biomaterials with unprecedented combinations of favorable properties that originate from complex geometrical designs at the micro-scale. This paper reviews the most important approaches in DfAM particularly those applicable to additive bio-manufacturing including image-based design pipelines, parametric and non-parametric designs, metamaterials, rational and computationally enabled design, topology optimization, and bio-inspired design. Areas with limited research have been identified and suggestions have been made for future research. The paper concludes with a brief discussion on the practical aspects of DfAM and the potential of combining AM with subtractive and formative manufacturing processes in so-called hybrid manufacturing processes. PMID:28757572
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Semantic Feature Distinctiveness and Frequency
ERIC Educational Resources Information Center
Lamb, Katherine M.
2012-01-01
Lexical access is the process in which basic components of meaning in language, the lexical entries (words) are activated. This activation is based on the organization and representational structure of the lexical entries. Semantic features of words, which are the prominent semantic characteristics of a word concept, provide important information…
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Abrams, Daniel A; Nicol, Trent; White-Schwoch, Travis; Zecker, Steven; Kraus, Nina
2017-05-01
Speech perception relies on a listener's ability to simultaneously resolve multiple temporal features in the speech signal. Little is known regarding neural mechanisms that enable the simultaneous coding of concurrent temporal features in speech. Here we show that two categories of temporal features in speech, the low-frequency speech envelope and periodicity cues, are processed by distinct neural mechanisms within the same population of cortical neurons. We measured population activity in primary auditory cortex of anesthetized guinea pig in response to three variants of a naturally produced sentence. Results show that the envelope of population responses closely tracks the speech envelope, and this cortical activity more closely reflects wider bandwidths of the speech envelope compared to narrow bands. Additionally, neuronal populations represent the fundamental frequency of speech robustly with phase-locked responses. Importantly, these two temporal features of speech are simultaneously observed within neuronal ensembles in auditory cortex in response to clear, conversation, and compressed speech exemplars. Results show that auditory cortical neurons are adept at simultaneously resolving multiple temporal features in extended speech sentences using discrete coding mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.
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Gynaecomastia as a presenting feature of thyrotoxicosis
Chan, W; Yeung, V; Chow, C; So, W; Cockram, C
1999-01-01
The association between gynaecomastia and thyrotoxicosis is well recognised. However, the reported frequency of the association is variable, partly depending upon the defining criteria used. Here we report two patients with thyrotoxicosis in whom gynaecomastia was the presenting feature. Both patients had other contributing factors, which are assumed to have predisposed to gynaecomastia. In both patients, the gynaecomastia resolved with successful treatment of the thyrotoxicosis. When gynaecomastia is a presenting, or prominent feature of thyrotoxicosis, the possibility of additional underlying pathology should be considered. Keywords: gynaecomastia; thyrotoxicosis; sex hormone PMID:10715765
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Visual Pattern Analysis in Histopathology Images Using Bag of Features
NASA Astrophysics Data System (ADS)
Cruz-Roa, Angel; Caicedo, Juan C.; González, Fabio A.
This paper presents a framework to analyse visual patterns in a collection of medical images in a two stage procedure. First, a set of representative visual patterns from the image collection is obtained by constructing a visual-word dictionary under a bag-of-features approach. Second, an analysis of the relationships between visual patterns and semantic concepts in the image collection is performed. The most important visual patterns for each semantic concept are identified using correlation analysis. A matrix visualization of the structure and organization of the image collection is generated using a cluster analysis. The experimental evaluation was conducted on a histopathology image collection and results showed clear relationships between visual patterns and semantic concepts, that in addition, are of easy interpretation and understanding.
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Interpretation of fingerprint image quality features extracted by self-organizing maps
NASA Astrophysics Data System (ADS)
Danov, Ivan; Olsen, Martin A.; Busch, Christoph
2014-05-01
Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.
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Modeling crash injury severity by road feature to improve safety.
Penmetsa, Praveena; Pulugurtha, Srinivas S
2018-01-02
The objective of this research is 2-fold: to (a) model and identify critical road features (or locations) based on crash injury severity and compare it with crash frequency and (b) model and identify drivers who are more likely to contribute to crashes by road feature. Crash data from 2011 to 2013 were obtained from the Highway Safety Information System (HSIS) for the state of North Carolina. Twenty-three different road features were considered, analyzed, and compared with each other as well as no road feature. A multinomial logit (MNL) model was developed and odds ratios were estimated to investigate the effect of road features on crash injury severity. Among the many road features, underpass, end or beginning of a divided highway, and on-ramp terminal on crossroad are the top 3 critical road features. Intersection crashes are frequent but are not highly likely to result in severe injuries compared to critical road features. Roundabouts are least likely to result in both severe and moderate injuries. Female drivers are more likely to be involved in crashes at intersections (4-way and T) compared to male drivers. Adult drivers are more likely to be involved in crashes at underpasses. Older drivers are 1.6 times more likely to be involved in a crash at the end or beginning of a divided highway. The findings from this research help to identify critical road features that need to be given priority. As an example, additional advanced warning signs and providing enlarged or highly retroreflective signs that grab the attention of older drivers may help in making locations such as end or beginning of a divided highway much safer. Educating drivers about the necessary skill sets required at critical road features in addition to engineering solutions may further help them adopt safe driving behaviors on the road.
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Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.
Nallon, Eric C; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Li, Qiliang
2016-01-19
A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.
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Learning discriminative functional network features of schizophrenia
NASA Astrophysics Data System (ADS)
Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar
2017-03-01
Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.
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Park, Hyunjin; Yang, Jin-ju; Seo, Jongbum; Choi, Yu-yong; Lee, Kun-ho; Lee, Jong-min
2014-04-01
Cortical features derived from magnetic resonance imaging (MRI) provide important information to account for human intelligence. Cortical thickness, surface area, sulcal depth, and mean curvature were considered to explain human intelligence. One region of interest (ROI) of a cortical structure consisting of thousands of vertices contained thousands of measurements, and typically, one mean value (first order moment), was used to represent a chosen ROI, which led to a potentially significant loss of information. We proposed a technological improvement to account for human intelligence in which a second moment (variance) in addition to the mean value was adopted to represent a chosen ROI, so that the loss of information would be less severe. Two computed moments for the chosen ROIs were analyzed with partial least squares regression (PLSR). Cortical features for 78 adults were measured and analyzed in conjunction with the full-scale intelligence quotient (FSIQ). Our results showed that 45% of the variance of the FSIQ could be explained using the combination of four cortical features using two moments per chosen ROI. Our results showed improvement over using a mean value for each ROI, which explained 37% of the variance of FSIQ using the same set of cortical measurements. Our results suggest that using additional second order moments is potentially better than using mean values of chosen ROIs for regression analysis to account for human intelligence. Copyright © 2014 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Zargari, Abolfazl; Du, Yue; Thai, Theresa C.; Gunderson, Camille C.; Moore, Kathleen; Mannel, Robert S.; Liu, Hong; Zheng, Bin; Qiu, Yuchen
2018-02-01
The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.
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Channel and feature selection in multifunction myoelectric control.
Khushaba, Rami N; Al-Jumaily, Adel
2007-01-01
Real time controlling devices based on myoelectric singles (MES) is one of the challenging research problems. This paper presents a new approach to reduce the computational cost of real time systems driven by Myoelectric signals (MES) (a.k.a Electromyography--EMG). The new approach evaluates the significance of feature/channel selection on MES pattern recognition. Particle Swarm Optimization (PSO), an evolutionary computational technique, is employed to search the feature/channel space for important subsets. These important subsets will be evaluated using a multilayer perceptron trained with back propagation neural network (BPNN). Practical results acquired from tests done on six subjects' datasets of MES signals measured in a noninvasive manner using surface electrodes are presented. It is proved that minimum error rates can be achieved by considering the correct combination of features/channels, thus providing a feasible system for practical implementation purpose for rehabilitation of patients.
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Clinical and molecular features of human rhinovirus C
Bochkov, Yury A.; Gern, James E.
2012-01-01
A newly discovered group of human rhinoviruses (HRVs) has been classified as the HRV-C species based on distinct genomic features. HRV-Cs circulate worldwide, and are important causes of upper and lower respiratory illnesses. Methods to culture and produce these viruses have recently been developed, and should enable identification of unique features of HRV-C replication and biology. PMID:22285901
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Enhancing the Pronunciation of English Suprasegmental Features through Reflective Learning Method
ERIC Educational Resources Information Center
Suwartono
2014-01-01
Suprasegmental features are of paramount importance in spoken English. Yet, these pronunciation features are marginalised in EFL/ESL teaching-learning. This article reported a study that was aimed at improving the students' mastery of English suprasegmental features through the use of reflective learning method. The study adopted Kemmis and…
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Optical security features for plastic card documents
NASA Astrophysics Data System (ADS)
Hossick Schott, Joachim
1998-04-01
Print-on-demand is currently a major trend in the production of paper based documents. This fully digital production philosophy will likely have ramifications also for the secure identification document market. Here, plastic cards increasingly replace traditionally paper based security sensitive documents such as drivers licenses and passports. The information content of plastic cards can be made highly secure by using chip cards. However, printed and other optical security features will continue to play an important role, both for machine readable and visual inspection. Therefore, on-demand high resolution print technologies, laser engraving, luminescent pigments and laminated features such as holograms, kinegrams or phase gratings will have to be considered for the production of secure identification documents. Very important are also basic optical, surface and material durability properties of the laminates as well as the strength and nature of the adhesion between the layers. This presentation will address some of the specific problems encountered when optical security features such as high resolution printing and laser engraving are to be integrated in the on-demand production of secure plastic card identification documents.
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External Standards or Standard Addition? Selecting and Validating a Method of Standardization
NASA Astrophysics Data System (ADS)
Harvey, David T.
2002-05-01
A common feature of many problem-based laboratories in analytical chemistry is a lengthy independent project involving the analysis of "real-world" samples. Students research the literature, adapting and developing a method suitable for their analyte, sample matrix, and problem scenario. Because these projects encompass the complete analytical process, students must consider issues such as obtaining a representative sample, selecting a method of analysis, developing a suitable standardization, validating results, and implementing appropriate quality assessment/quality control practices. Most textbooks and monographs suitable for an undergraduate course in analytical chemistry, however, provide only limited coverage of these important topics. The need for short laboratory experiments emphasizing important facets of method development, such as selecting a method of standardization, is evident. The experiment reported here, which is suitable for an introductory course in analytical chemistry, illustrates the importance of matrix effects when selecting a method of standardization. Students also learn how a spike recovery is used to validate an analytical method, and obtain a practical experience in the difference between performing an external standardization and a standard addition.
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Prominent feature extraction for review analysis: an empirical study
NASA Astrophysics Data System (ADS)
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.
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Finlayson, Nonie J.; Golomb, Julie D.
2016-01-01
A fundamental aspect of human visual perception is the ability to recognize and locate objects in the environment. Importantly, our environment is predominantly three-dimensional (3D), but while there is considerable research exploring the binding of object features and location, it is unknown how depth information interacts with features in the object binding process. A recent paradigm called the spatial congruency bias demonstrated that 2D location is fundamentally bound to object features (Golomb, Kupitz, & Thiemann, 2014), such that irrelevant location information biases judgments of object features, but irrelevant feature information does not bias judgments of location or other features. Here, using the spatial congruency bias paradigm, we asked whether depth is processed as another type of location, or more like other features. We initially found that depth cued by binocular disparity biased judgments of object color. However, this result seemed to be driven more by the disparity differences than the depth percept: Depth cued by occlusion and size did not bias color judgments, whereas vertical disparity information (with no depth percept) did bias color judgments. Our results suggest that despite the 3D nature of our visual environment, only 2D location information – not position-in-depth – seems to be automatically bound to object features, with depth information processed more similarly to other features than to 2D location. PMID:27468654
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Finlayson, Nonie J; Golomb, Julie D
2016-10-01
A fundamental aspect of human visual perception is the ability to recognize and locate objects in the environment. Importantly, our environment is predominantly three-dimensional (3D), but while there is considerable research exploring the binding of object features and location, it is unknown how depth information interacts with features in the object binding process. A recent paradigm called the spatial congruency bias demonstrated that 2D location is fundamentally bound to object features, such that irrelevant location information biases judgments of object features, but irrelevant feature information does not bias judgments of location or other features. Here, using the spatial congruency bias paradigm, we asked whether depth is processed as another type of location, or more like other features. We initially found that depth cued by binocular disparity biased judgments of object color. However, this result seemed to be driven more by the disparity differences than the depth percept: Depth cued by occlusion and size did not bias color judgments, whereas vertical disparity information (with no depth percept) did bias color judgments. Our results suggest that despite the 3D nature of our visual environment, only 2D location information - not position-in-depth - seems to be automatically bound to object features, with depth information processed more similarly to other features than to 2D location. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Additional Improvements to the NASA Lewis Ice Accretion Code LEWICE
NASA Technical Reports Server (NTRS)
Wright, William B.; Bidwell, Colin S.
1995-01-01
Due to the feedback of the user community, three major features have been added to the NASA Lewis ice accretion code LEWICE. These features include: first, further improvements to the numerics of the code so that more time steps can be run and so that the code is more stable; second, inclusion and refinement of the roughness prediction model described in an earlier paper; third, inclusion of multi-element trajectory and ice accretion capabilities to LEWICE. This paper will describe each of these advancements in full and make comparisons with the experimental data available. Further refinement of these features and inclusion of additional features will be performed as more feedback is received.
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Epidemiology and clinical features of imported malaria in East London.
Francis, Benjamin C; Gonzalo, Ximena; Duggineni, Sirisha; Thomas, Janice M; NicFhogartaigh, Caoimhe; Babiker, Zahir Osman Eltahir
2016-06-01
Malaria is the most common imported tropical disease in the United Kingdom (UK). The overall mortality is low but inter-regional differences have been observed. We conducted a 2-year retrospective review of clinical and laboratory records of patients with malaria attending three acute hospitals in East London from 1 April 2013 through 31 March 2015. Epidemiological and clinical characteristics of imported malaria were described and risk factors associated with severe falciparum malaria were explored. In total, 133 patients with laboratory-confirmed malaria were identified including three requiring critical care admission but no deaths. The median age at presentation was 41 years (IQR 30-50). The majority of patients were males (64.7%, 86/133) and had Black or Black British ethnicity (67.5%, 79/117). West Africa was the most frequent region of travel (70.4%, 76/108). Chemoprophylaxis use was poor (25.3%, 20/79). The interval between arriving in the UK and presenting to hospital was short (median 10 days; IQR 5-15.5, n = 84). July-September was the peak season of presentation (34.6%, 46/133). Plasmodium falciparum was the commonest species (76.7%, 102/133) and 31.4% (32/102) of these patients had parasitaemia >2%. Severe falciparum malaria was documented in 36.3% (37/102) of patients and the October-March season presentation was associated with an increased risk of severity (OR 3.00; 95% CI 1.30-6.93). Black patients appeared to have reduced risk of severe falciparum malaria (OR 0.46; 95% CI 0.16-1.35) but this was not statistically significant. HIV sero-status was determined in only 27.1% (36/133) of cases. Only 8.5% (10/117) of all malaria patients were treated as outpatients. Clinicians need to raise awareness on malaria prevention strategies, improve rates of HIV testing in tropical travellers, and familiarise themselves with ambulatory management of malaria. The relationship between season of presentation, ethnicity and severity of falciparum malaria
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An adaptive multi-feature segmentation model for infrared image
NASA Astrophysics Data System (ADS)
Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa
2016-04-01
Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.
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Kistler, Christine E; Crutchfield, Trisha M; Sutfin, Erin L; Ranney, Leah M; Berman, Micah L; Zarkin, Gary A; Goldstein, Adam O
2017-06-07
To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18-25, n = 11; middle-age group aged 26-64, n = 9; and women's group aged 26-64, n = 9). We conducted five individual older adult interviews (aged 68-80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women's group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts.
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Frank, Andrew Oliver; De Souza, Lorraine H
2018-05-01
To describe the clinical features of electric powered indoor/outdoor wheelchair users with a muscular dystrophy, likely to influence optimal prescription; reflecting features of muscular dystrophies, conditions secondary to disability, and comorbidities impacting on equipment provision. Cross-sectional retrospective case note review of recipients of electric powered indoor/outdoor wheelchairs provided by a specialist regional wheelchair service. Data on demography, diagnostic/clinical, and wheelchair prescription were systematically extracted. Fifty-one men and 14 women, mean age 23.7 (range 10-67, s.d. 12.95) years, were studied. Forty had Duchenne muscular dystrophy, 22 had other forms of muscular dystrophy, and three were unclassified. Twenty-seven were aged under 19. Notable clinical features included problematic pain (10), cardiomyopathy (5), and ventilatory failure (4). Features related to disability were (kypho)scoliosis (20) and edema/cellulitis (3) whilst comorbidities included back pain (5). Comparison of younger with older users revealed younger users had more features of muscular dystrophy affecting electric powered chair provision (56%) whilst older users had more comorbidity (37%). Tilt-in-space was prescribed for 81% of users, specialized seating for 55% and complex controls for 16%. Muscular dystrophy users were prescribed electric powered indoor/outdoor chairs with many additional features reflecting the consequences of profound muscle weakness. In addition to facilitating independence and participation, electric powered indoor/outdoor chairs have major therapeutic benefits. Implications for rehabilitation Powered wheelchairs have therapeutic benefits in managing muscular dystrophy pain and weakness. The use of specialized seating needs careful consideration in supporting progressive muscle weakness and the management of scoliosis. Pain, discomfort, pressure risk, and muscle fatigue may be reduced by use of tilt-in-space.
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A prototype feature system for feature retrieval using relationships
Choi, J.; Usery, E.L.
2009-01-01
Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features.
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Additive Manufacturing Design Considerations for Liquid Engine Components
NASA Technical Reports Server (NTRS)
Whitten, Dave; Hissam, Andy; Baker, Kevin; Rice, Darron
2014-01-01
The Marshall Space Flight Center's Propulsion Systems Department has gained significant experience in the last year designing, building, and testing liquid engine components using additive manufacturing. The department has developed valve, duct, turbo-machinery, and combustion device components using this technology. Many valuable lessons were learned during this process. These lessons will be the focus of this presentation. We will present criteria for selecting part candidates for additive manufacturing. Some part characteristics are 'tailor made' for this process. Selecting the right parts for the process is the first step to maximizing productivity gains. We will also present specific lessons we learned about feature geometry that can and cannot be produced using additive manufacturing machines. Most liquid engine components were made using a two-step process. The base part was made using additive manufacturing and then traditional machining processes were used to produce the final part. The presentation will describe design accommodations needed to make the base part and lessons we learned about which features could be built directly and which require the final machine process. Tolerance capabilities, surface finish, and material thickness allowances will also be covered. Additive Manufacturing can produce internal passages that cannot be made using traditional approaches. It can also eliminate a significant amount of manpower by reducing part count and leveraging model-based design and analysis techniques. Information will be shared about performance enhancements and design efficiencies we experienced for certain categories of engine parts.
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Integrated feature extraction and selection for neuroimage classification
NASA Astrophysics Data System (ADS)
Fan, Yong; Shen, Dinggang
2009-02-01
Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.
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Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals
Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu
2012-01-01
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017
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Features and characterization needs of rubber composite structures
NASA Technical Reports Server (NTRS)
Tabaddor, Farhad
1989-01-01
Some of the major unique features of rubber composite structures are outlined. The features covered are those related to the material properties, but the analytical features are also briefly discussed. It is essential to recognize these features at the planning stage of any long-range analytical, experimental, or application program. The development of a general and comprehensive program which fully accounts for all the important characteristics of tires, under all the relevant modes of operation, may present a prohibitively expensive and impractical task at the near future. There is therefore a need to develop application methodologies which can utilize the less general models, beyond their theoretical limitations and yet with reasonable reliability, by proper mix of analytical, experimental, and testing activities.
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Eye movement identification based on accumulated time feature
NASA Astrophysics Data System (ADS)
Guo, Baobao; Wu, Qiang; Sun, Jiande; Yan, Hua
2017-06-01
Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.
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Low-Dimensional Feature Representation for Instrument Identification
NASA Astrophysics Data System (ADS)
Ihara, Mizuki; Maeda, Shin-Ichi; Ikeda, Kazushi; Ishii, Shin
For monophonic music instrument identification, various feature extraction and selection methods have been proposed. One of the issues toward instrument identification is that the same spectrum is not always observed even in the same instrument due to the difference of the recording condition. Therefore, it is important to find non-redundant instrument-specific features that maintain information essential for high-quality instrument identification to apply them to various instrumental music analyses. For such a dimensionality reduction method, the authors propose the utilization of linear projection methods: local Fisher discriminant analysis (LFDA) and LFDA combined with principal component analysis (PCA). After experimentally clarifying that raw power spectra are actually good for instrument classification, the authors reduced the feature dimensionality by LFDA or by PCA followed by LFDA (PCA-LFDA). The reduced features achieved reasonably high identification performance that was comparable or higher than those by the power spectra and those achieved by other existing studies. These results demonstrated that our LFDA and PCA-LFDA can successfully extract low-dimensional instrument features that maintain the characteristic information of the instruments.
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ON THE MISALIGNMENT BETWEEN CHROMOSPHERIC FEATURES AND THE MAGNETIC FIELD ON THE SUN
DOE Office of Scientific and Technical Information (OSTI.GOV)
Martínez-Sykora, Juan; Pontieu, Bart De; Hansteen, Viggo
2016-11-01
Observations of the upper chromosphere show an enormous amount of intricate fine structure. Much of this comes in the form of linear features, which are most often assumed to be well aligned with the direction of the magnetic field in the low plasma β regime that is thought to dominate the upper chromosphere. We use advanced radiative magnetohydrodynamic simulations, including the effects of ion-neutral interactions (using the generalized Ohm’s law) in the partially ionized chromosphere, to show that the magnetic field is often not well aligned with chromospheric features. This occurs where the ambipolar diffusion is large, i.e., ions andmore » neutral populations decouple as the ion-neutral collision frequency drops, allowing the field to slip through the neutral population; where currents perpendicular to the field are strong; and where thermodynamic timescales are longer than or similar to those of ambipolar diffusion. We find this often happens in dynamic spicule or fibril-like features at the top of the chromosphere. This has important consequences for field extrapolation methods, which increasingly use such upper chromospheric features to help constrain the chromospheric magnetic field: our results invalidate the underlying assumption that these features are aligned with the field. In addition, our results cast doubt on results from 1D hydrodynamic models, which assume that plasma remains on the same field lines. Finally, our simulations show that ambipolar diffusion significantly alters the amount of free energy available in the coronal part of our simulated volume, which is likely to have consequences for studies of flare initiation.« less
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Optimized feature-detection for on-board vision-based surveillance
NASA Astrophysics Data System (ADS)
Gond, Laetitia; Monnin, David; Schneider, Armin
2012-06-01
The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.
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Blumrosen, Gaddi; Luttwak, Ami
2013-01-01
Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs. PMID:23979481
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Blumrosen, Gaddi; Luttwak, Ami
2013-08-23
Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.
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Integration of heterogeneous features for remote sensing scene classification
NASA Astrophysics Data System (ADS)
Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang
2018-01-01
Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.
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Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry
Stanislawski, Larry V.; Buttenfield, Barbara P.; Raposo, Paulo; Cameron, Madeline; Falgout, Jeff T.
2015-01-01
Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features through cartographic generalization (Regnauld and McMaster 2008, Stanislawski et al. 2014). A variety of algorithms exist to simplify linear cartographic features, and all of the methods affect the positional accuracy of the features (Shahriari and Tao 2002, Regnauld and McMaster 2008, Stanislawski et al. 2012). In general, simplification operations are controlled by one or more tolerance parameters that limit the amount of positional change the operation can make to features. Using a single tolerance value can have varying levels of positional change on features; depending on local shape, texture, or geometric characteristics of the original features (McMaster and Shea 1992, Shahriari and Tao 2002, Buttenfield et al. 2010). Consequently, numerous researchers have advocated calibration of simplification parameters to control quantifiable properties of resulting changes to the features (Li and Openshaw 1990, Raposo 2013, Tobler 1988, Veregin 2000, and Buttenfield, 1986, 1989).This research identifies relations between local topographic conditions and geometric characteristics of linear features that are available in the National Hydrography Dataset (NHD). The NHD is a comprehensive vector dataset of surface 18 th ICA Workshop on Generalisation and Multiple Representation, Rio de Janiero, Brazil 2015 2 water features within the United States that is maintained by the U.S. Geological Survey (USGS). In this paper, geometric characteristics of cartographic representations for natural stream and river features are summarized for subbasin watersheds within entire regions of the
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NASA Astrophysics Data System (ADS)
Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael
2017-05-01
Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.
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Stimulus information contaminates summation tests of independent neural representations of features
NASA Technical Reports Server (NTRS)
Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.
2002-01-01
Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.
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Consolidation & Factors Influencing Sintering Process in Polymer Powder Based Additive Manufacturing
NASA Astrophysics Data System (ADS)
Sagar, M. B.; Elangovan, K.
2017-08-01
Additive Manufacturing (AM) is two decade old technology; where parts are build layer manufacturing method directly from a CAD template. Over the years, AM techniques changes the future way of part fabrication with enhanced intricacy and custom-made features are aimed. Commercially polymers, metals, ceramic and metal-polymer composites are in practice where polymers enhanced the expectations in AM and are considered as a kind of next industrial revolution. Growing trend in polymer application motivated to study their feasibility and properties. Laser sintering, Heat sintering and Inhibition sintering are the most successful AM techniques for polymers but having least application. The presentation gives up selective sintering of powder polymers and listed commercially available polymer materials. Important significant factors for effective processing and analytical approaches to access them are discussed.
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Westerling, Anna M; Haikala, Veikko E; Bell, J Simon; Airaksinen, Marja S
2010-01-01
To determine Finnish community pharmacy owners' requirements for the next generation of software systems. Descriptive, nonexperimental, cross-sectional study. Finland during December 2006. 308 independent pharmacy owners. Survey listing 126 features that could potentially be included in the new information technology (IT) system. The list was grouped into five categories: (1) drug information and patient counseling, (2) medication safety, (3) interprofessional collaboration, (4) pharmacy services, and (5) pharmacy internal processes. Perceived value of potential features for a new IT system. The survey was mailed to all independent pharmacy owners in Finland (n = 580; response rate 53% [n = 308]). Respondents gave priority to logistical functions and functions related to drug information and patient care. The highest rated individual features were tracking product expiry (rated as very or quite important by 100% of respondents), computerized drug-drug interaction screening (99%), an electronic version of the national pharmaceutical reference book (97%), and a checklist-type drug information database to assist patient counseling (95%). In addition to the high ranking for logistical features, Finnish pharmacy owners put a priority on support for cognitive pharmaceutical services in the next IT system. Although the importance of logistical functions is understandable, the owners demonstrated a commitment to strategic health policy goals when planning their business IT system.
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Computed Tomography Inspection and Analysis for Additive Manufacturing Components
NASA Technical Reports Server (NTRS)
Beshears, Ronald D.
2017-01-01
Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws and geometric features were inspected using a 2-megavolt linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed to determine the impact of additive manufacturing on inspectability of objects with complex geometries.
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Clinical features of schizophrenia in a woman with hyperandrogenism.
Kopala, L C; Lewine, R; Good, K P; Fluker, M; Martzke, J S; Lapointe, J S; Honer, W G
1997-01-01
Ample evidence supports sex differences in the clinical features of schizophrenia. In this regard, estrogen may contribute to later onset and less severe course of illness in women. Direct investigation of hormonal status in schizophrenia is extremely difficult. The present report documents the clinical features of schizophrenia in a young woman with long-standing hyperandrogenism related to polycystic ovarian disease. We postulate that hyperandrogenism contributed to a relatively early onset, olfactory dysfunction, and other clinical features of schizophrenia more commonly associated with men. Additionally, acute estrogen depletion following cessation of oral contraceptives may have precipitated psychosis, while recommencement of oral contraceptives could have contributed to subsequent improvement in symptoms. PMID:9002393
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Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin
2017-01-01
Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its
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Enhancing facial features by using clear facial features
NASA Astrophysics Data System (ADS)
Rofoo, Fanar Fareed Hanna
2017-09-01
The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.
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Kistler, Christine E.; Crutchfield, Trisha M.; Sutfin, Erin L.; Ranney, Leah M.; Berman, Micah L.; Zarkin, Gary A.; Goldstein, Adam O.
2017-01-01
To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18–25, n = 11; middle-age group aged 26–64, n = 9; and women’s group aged 26–64, n = 9). We conducted five individual older adult interviews (aged 68–80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women’s group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts. PMID:28590444
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14 CFR 35.7 - Features and characteristics.
Code of Federal Regulations, 2011 CFR
2011-01-01
... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. No. 35-8, 73 FR 63346, Oct. 24...
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14 CFR 35.7 - Features and characteristics.
Code of Federal Regulations, 2012 CFR
2012-01-01
... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. 35-8, 73 FR 63346, Oct. 24, 2008] ...
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14 CFR 35.7 - Features and characteristics.
Code of Federal Regulations, 2013 CFR
2013-01-01
... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. 35-8, 73 FR 63346, Oct. 24, 2008] ...
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14 CFR 35.7 - Features and characteristics.
Code of Federal Regulations, 2010 CFR
2010-01-01
... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. No. 35-8, 73 FR 63346, Oct. 24...
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14 CFR 35.7 - Features and characteristics.
Code of Federal Regulations, 2014 CFR
2014-01-01
... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. 35-8, 73 FR 63346, Oct. 24, 2008] ...
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2013-01-01
Adult and juvenile dermatomyositis share the hallmark features of pathognomic skin rash and muscle inflammation, but are heterogeneous disorders with a range of additional disease features and complications. The frequency of important clinical features such as calcinosis, interstitial lung disease and malignancy varies markedly between adult and juvenile disease. These differences may reflect different disease triggers between children and adults, but whilst various viral and other environmental triggers have been implicated, results are so far conflicting. Myositis-specific autoantibodies can be detected in both adults and children with idiopathic inflammatory myopathies. They are associated with specific disease phenotypes and complications, and divide patients into clinically homogenous subgroups. Interestingly, whilst the same autoantibodies are found in both adults and children, the disease features remain different within autoantibody subgroups, particularly with regard to life-threatening disease associations, such as malignancy and rapidly progressive interstitial lung disease. Our understanding of the mechanisms that underlie these differences is limited by a lack of studies directly comparing adults and children. Dermatomyositis is an autoimmune disease, which is believed to develop as a result of an environmental trigger in a genetically predisposed individual. Age-specific host immune responses and muscle physiology may be additional complicating factors that have significant impact on disease presentation. Further study into this area may produce new insights into disease pathogenesis. PMID:23566358
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Economic indicators selection for crime rates forecasting using cooperative feature selection
NASA Astrophysics Data System (ADS)
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina
2013-04-01
Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.
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Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio
2018-02-01
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.
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PrAS: Prediction of amidation sites using multiple feature extraction.
Wang, Tong; Zheng, Wei; Wuyun, Qiqige; Wu, Zhenfeng; Ruan, Jishou; Hu, Gang; Gao, Jianzhao
2017-02-01
Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at https://sourceforge.net/p/praspkg. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Anxious mood narrows attention in feature space.
Wegbreit, Ezra; Franconeri, Steven; Beeman, Mark
2015-01-01
Spatial attention can operate like a spotlight whose scope can vary depending on task demands. Emotional states contribute to the spatial extent of attentional selection, with the spotlight focused more narrowly during anxious moods and more broadly during happy moods. In addition to visual space, attention can also operate over features, and we show here that mood states may also influence attentional scope in feature space. After anxious or happy mood inductions, participants focused their attention to identify a central target while ignoring flanking items. Flankers were sometimes coloured differently than targets, so focusing attention on target colour should lead to relatively less interference. Compared to happy and neutral moods, when anxious, participants showed reduced interference when colour isolated targets from flankers, but showed more interference when flankers and targets were the same colour. This pattern reveals that the anxious mood caused these individuals to attend to the irrelevant feature in both cases, regardless of its benefit or detriment. In contrast, participants showed no effect of colour on interference when happy, suggesting that positive mood did not influence attention in feature space. These mood effects on feature-based attention provide a theoretical bridge between previous findings concerning spatial and conceptual attention.
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Reducing Communication in Algebraic Multigrid Using Additive Variants
DOE Office of Scientific and Technical Information (OSTI.GOV)
Vassilevski, Panayot S.; Yang, Ulrike Meier
Algebraic multigrid (AMG) has proven to be an effective scalable solver on many high performance computers. However, its increasing communication complexity on coarser levels has shown to seriously impact its performance on computers with high communication cost. Moreover, additive AMG variants provide not only increased parallelism as well as decreased numbers of messages per cycle but also generally exhibit slower convergence. Here we present various new additive variants with convergence rates that are significantly improved compared to the classical additive algebraic multigrid method and investigate their potential for decreased communication, and improved communication-computation overlap, features that are essential for goodmore » performance on future exascale architectures.« less
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Reducing Communication in Algebraic Multigrid Using Additive Variants
Vassilevski, Panayot S.; Yang, Ulrike Meier
2014-02-12
Algebraic multigrid (AMG) has proven to be an effective scalable solver on many high performance computers. However, its increasing communication complexity on coarser levels has shown to seriously impact its performance on computers with high communication cost. Moreover, additive AMG variants provide not only increased parallelism as well as decreased numbers of messages per cycle but also generally exhibit slower convergence. Here we present various new additive variants with convergence rates that are significantly improved compared to the classical additive algebraic multigrid method and investigate their potential for decreased communication, and improved communication-computation overlap, features that are essential for goodmore » performance on future exascale architectures.« less
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Iris recognition using possibilistic fuzzy matching on local features.
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.
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Arabic writer identification based on diacritic's features
NASA Astrophysics Data System (ADS)
Maliki, Makki; Al-Jawad, Naseer; Jassim, Sabah A.
2012-06-01
Natural languages like Arabic, Kurdish, Farsi (Persian), Urdu, and any other similar languages have many features, which make them different from other languages like Latin's script. One of these important features is diacritics. These diacritics are classified as: compulsory like dots which are used to identify/differentiate letters, and optional like short vowels which are used to emphasis consonants. Most indigenous and well trained writers often do not use all or some of these second class of diacritics, and expert readers can infer their presence within the context of the writer text. In this paper, we investigate the use of diacritics shapes and other characteristic as parameters of feature vectors for Arabic writer identification/verification. Segmentation techniques are used to extract the diacritics-based feature vectors from examples of Arabic handwritten text. The results of evaluation test will be presented, which has been carried out on an in-house database of 50 writers. Also the viability of using diacritics for writer recognition will be demonstrated.
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Clinical report of a 17q12 microdeletion with additionally unreported clinical features.
Roberts, Jennifer L; Gandomi, Stephanie K; Parra, Melissa; Lu, Ira; Gau, Chia-Ling; Dasouki, Majed; Butler, Merlin G
2014-01-01
Copy number variations involving the 17q12 region have been associated with developmental and speech delay, autism, aggression, self-injury, biting and hitting, oppositional defiance, inappropriate language, and auditory hallucinations. We present a tall-appearing 17-year-old boy with marfanoid habitus, hypermobile joints, mild scoliosis, pectus deformity, widely spaced nipples, pes cavus, autism spectrum disorder, intellectual disability, and psychiatric manifestations including physical and verbal aggression, obsessive-compulsive behaviors, and oppositional defiance. An echocardiogram showed borderline increased aortic root size. An abdominal ultrasound revealed a small pancreas, mild splenomegaly with a 1.3 cm accessory splenule, and normal kidneys and liver. A testing panel for Marfan, aneurysm, and related disorders was negative. Subsequently, a 400 K array-based comparative genomic hybridization (aCGH) + SNP analysis was performed which identified a de novo suspected pathogenic deletion on chromosome 17q12 encompassing 28 genes. Despite the limited number of cases described in the literature with 17q12 rearrangements, our proband's phenotypic features both overlap and expand on previously reported cases. Since syndrome-specific DNA sequencing studies failed to provide an explanation for this patient's unusual habitus, we postulate that this case represents an expansion of the 17q12 microdeletion phenotype. Further analysis of the deleted interval is recommended for new genotype-phenotype correlations.
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ORNL Lightweighting Research Featured on MotorWeek
None
2018-06-06
PBS MotorWeek, television's longest running automotive series, featured ORNL lightweighting research for vehicle applications in an episode that aired in early April 2014. The crew captured footage of research including development of new metal alloys, additive manufacturing, carbon fiber production, advanced batteries, power electronics components, and neutron imaging applications for materials evaluation.
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FSMRank: feature selection algorithm for learning to rank.
Lai, Han-Jiang; Pan, Yan; Tang, Yong; Yu, Rong
2013-06-01
In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T(2)). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.
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Feature selection gait-based gender classification under different circumstances
NASA Astrophysics Data System (ADS)
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.
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Automatic feature-based grouping during multiple object tracking.
Erlikhman, Gennady; Keane, Brian P; Mettler, Everett; Horowitz, Todd S; Kellman, Philip J
2013-12-01
Contour interpolation automatically binds targets with distractors to impair multiple object tracking (Keane, Mettler, Tsoi, & Kellman, 2011). Is interpolation special in this regard or can other features produce the same effect? To address this question, we examined the influence of eight features on tracking: color, contrast polarity, orientation, size, shape, depth, interpolation, and a combination (shape, color, size). In each case, subjects tracked 4 of 8 objects that began as undifferentiated shapes, changed features as motion began (to enable grouping), and returned to their undifferentiated states before halting. We found that intertarget grouping improved performance for all feature types except orientation and interpolation (Experiment 1 and Experiment 2). Most importantly, target-distractor grouping impaired performance for color, size, shape, combination, and interpolation. The impairments were, at times, large (>15% decrement in accuracy) and occurred relative to a homogeneous condition in which all objects had the same features at each moment of a trial (Experiment 2), and relative to a "diversity" condition in which targets and distractors had different features at each moment (Experiment 3). We conclude that feature-based grouping occurs for a variety of features besides interpolation, even when irrelevant to task instructions and contrary to the task demands, suggesting that interpolation is not unique in promoting automatic grouping in tracking tasks. Our results also imply that various kinds of features are encoded automatically and in parallel during tracking.
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Real-Time Feature Tracking Using Homography
NASA Technical Reports Server (NTRS)
Clouse, Daniel S.; Cheng, Yang; Ansar, Adnan I.; Trotz, David C.; Padgett, Curtis W.
2010-01-01
This software finds feature point correspondences in sequences of images. It is designed for feature matching in aerial imagery. Feature matching is a fundamental step in a number of important image processing operations: calibrating the cameras in a camera array, stabilizing images in aerial movies, geo-registration of images, and generating high-fidelity surface maps from aerial movies. The method uses a Shi-Tomasi corner detector and normalized cross-correlation. This process is likely to result in the production of some mismatches. The feature set is cleaned up using the assumption that there is a large planar patch visible in both images. At high altitude, this assumption is often reasonable. A mathematical transformation, called an homography, is developed that allows us to predict the position in image 2 of any point on the plane in image 1. Any feature pair that is inconsistent with the homography is thrown out. The output of the process is a set of feature pairs, and the homography. The algorithms in this innovation are well known, but the new implementation improves the process in several ways. It runs in real-time at 2 Hz on 64-megapixel imagery. The new Shi-Tomasi corner detector tries to produce the requested number of features by automatically adjusting the minimum distance between found features. The homography-finding code now uses an implementation of the RANSAC algorithm that adjusts the number of iterations automatically to achieve a pre-set probability of missing a set of inliers. The new interface allows the caller to pass in a set of predetermined points in one of the images. This allows the ability to track the same set of points through multiple frames.
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Prediction of interface residue based on the features of residue interaction network.
Jiao, Xiong; Ranganathan, Shoba
2017-11-07
Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Olney, R S; Hoyme, H E; Roche, F; Ferguson, K; Hintz, S; Madan, A
2001-11-01
Schinzel phocomelia syndrome is characterized by limb/pelvis hypoplasia/aplasia: specifically, intercalary limb deficiencies and absent or hypoplastic pelvic bones. The phenotype is similar to that described in a related multiple malformation syndrome known as Al-Awadi/Raas-Rothschild syndrome. The additional important feature of large parietooccipital skull defects without meningocele, encephalocele, or other brain malformation has thus far been reported only in children with Schinzel phocomelia syndrome. We recently evaluated a boy affected with Schinzel phocomelia born to nonconsanguineous healthy parents of Mexican origin. A third-trimester fetal ultrasound scan showed severe limb deficiencies and an absent pelvis. The infant died shortly after birth. Dysmorphology examination, radiographs, and autopsy revealed quadrilateral intercalary limb deficiencies with preaxial toe polydactyly; an absent pelvis and a 7 x 3-cm skull defect; and extraskeletal anomalies including microtia, telecanthus, micropenis with cryptorchidism, renal cysts, stenosis of the colon, and a cleft alveolar ridge. A normal 46,XY karyotype was demonstrated, and autosomal recessive inheritance was presumed on the basis of previously reported families. This case report emphasizes the importance of recognizing severe pelvic and skull deficiencies (either post- or prenatally) in differentiating infants with Schinzel phocomelia from other multiple malformation syndromes that feature intercalary limb defects, including thalidomide embryopathy and Roberts-SC phocomelia. Copyright 2001 Wiley-Liss, Inc.
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NASA Astrophysics Data System (ADS)
Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai
2017-02-01
It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.
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Endomyocardial fibrosis in Sudan: clinical and echocardiographic features
Khalil, Siddiq Ibrahim; Khalil, Suha; El Tigani, Salma; Saad, Hanan A
2017-01-01
Summary Objective: Endomyocardial fibrosis (EMF) is a rare disease and is often an underdiagnosed and forgotten cardiomyopathy. The objective of this study was to document the current frequency of EMF in Sudan by defining and selecting cases from patients attending the echocardiography laboratory. Additionally we aimed to create an EMF registry for Sudan. Methods: The study started in January 2007 and is on-going. All the patients attending our echocardiography clinics in four different hospitals in Khartoum, Sudan, were included. Transthoracic echocardiography was used as the main diagnostic and selection tool. The diagnosis of EMF was based on predefined criteria and definitions, and was further supported by additional clinical, ECG, laboratory and chest X-ray findings. Results: Out of 4 332 cases studied, 23 (0.5%) were found to have features of EMF. Females constituted 52% and the age range was 24 to 67 years. All patients presented with dyspnoea grades III–IV. Advanced heart failure with gross fluid overload was seen in 54% of cases and ascites was seen in 30%. EMF was biventricular in 53%, left ventricular in 29% and right ventricular in 18% of cases. Apical and ventricular wall fibrosis was found in all cases, followed by atrial enlargement, atrioventricular valve incompetence, ventricular cavity obliteration, restrictive flow pattern and pericardial effusion. Additional echocardiographic features are defined and discussed. Conclusion: Although a rare disease, cases of EMF can be identified in Sudan if a high index of suspicion is observed. New echocardiographic features of ventricular wall layering, endocardial fibrous shelf and endomyocardiopericarial fibrosis were identified and are discussed. PMID:28906536
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A Photographic Atlas of Rock Breakdown Features in Geomorphic Environments
NASA Technical Reports Server (NTRS)
Bourke, Mary C. (Editor); Brearley, J. Alexander; Haas, Randall; Viles, Heather A.
2007-01-01
A primary goal of geomorphological enquiry is to make genetic associations between process and form. In rock breakdown studies, the links between process, inheritance and lithology are not well constrained. In particular, there is a need to establish an understanding of feature persistence. That is, to determine the extent to which in situ rock breakdown (e.g., aeolian abrasion or salt weathering) masks signatures of earlier geomorphic transport processes (e.g., fluvial transport or crater ejecta). Equally important is the extent to which breakdown during geomorphic transport masks the imprint of past weathering. The use of rock features in this way raises the important question: Can features on the surface of a rock reliably indicate its geomorphic history? This has not been determined for rock surfaces on Earth or other planets. A first step towards constraining the links between process, inheritance, and morphology is to identify pristine features produced by different process regimes. The purpose of this atlas is to provide a comprehensive image collection of breakdown features commonly observed on boulders in different geomorphic environments. The atlas is intended as a tool for planetary geoscientists and their students to assist in identifying features found on rocks on planetary surfaces. In compiling this atlas, we have attempted to include features that have formed 'recently' and where the potential for modification by another geomorphic process is low. However, we acknowledge that this is, in fact, difficult to achieve when selecting rocks in their natural environment. We group breakdown features according to their formative environment and process. In selecting images for inclusion in the atlas we were mindful to cover a wide range of climatic zones. For example, in the weathering chapter, clast features are shown from locations such as the hyper-arid polar desert of Antarctica and the semi-arid canyons of central Australia. This is important as some
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Relearning To Teach Arithmetic Addition and Subtraction: A Teacher's Study Guide.
ERIC Educational Resources Information Center
Russell, Susan Jo
This package features videotapes and a study guide that are designed to help teachers revisit the operations of addition and subtraction and consider how students can develop meaningful approaches to these operations. The study guides' sessions are on addition, subtraction, the teacher's role, and goals for students and teachers. The readings in…
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Ebrahimi, Farideh; Setarehdan, Seyed-Kamaledin; Ayala-Moyeda, Jose; Nazeran, Homer
2013-10-01
The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time-frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time-frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time-frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV
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Terrain-driven unstructured mesh development through semi-automatic vertical feature extraction
NASA Astrophysics Data System (ADS)
Bilskie, Matthew V.; Coggin, David; Hagen, Scott C.; Medeiros, Stephen C.
2015-12-01
A semi-automated vertical feature terrain extraction algorithm is described and applied to a two-dimensional, depth-integrated, shallow water equation inundation model. The extracted features describe what are commonly sub-mesh scale elevation details (ridge and valleys), which may be ignored in standard practice because adequate mesh resolution cannot be afforded. The extraction algorithm is semi-automated, requires minimal human intervention, and is reproducible. A lidar-derived digital elevation model (DEM) of coastal Mississippi and Alabama serves as the source data for the vertical feature extraction. Unstructured mesh nodes and element edges are aligned to the vertical features and an interpolation algorithm aimed at minimizing topographic elevation error assigns elevations to mesh nodes via the DEM. The end result is a mesh that accurately represents the bare earth surface as derived from lidar with element resolution in the floodplain ranging from 15 m to 200 m. To examine the influence of the inclusion of vertical features on overland flooding, two additional meshes were developed, one without crest elevations of the features and another with vertical features withheld. All three meshes were incorporated into a SWAN+ADCIRC model simulation of Hurricane Katrina. Each of the three models resulted in similar validation statistics when compared to observed time-series water levels at gages and post-storm collected high water marks. Simulated water level peaks yielded an R2 of 0.97 and upper and lower 95% confidence interval of ∼ ± 0.60 m. From the validation at the gages and HWM locations, it was not clear which of the three model experiments performed best in terms of accuracy. Examination of inundation extent among the three model results were compared to debris lines derived from NOAA post-event aerial imagery, and the mesh including vertical features showed higher accuracy. The comparison of model results to debris lines demonstrates that additional
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Morphological Features and Important Parameters of Large Optic Discs for Diagnosing Glaucoma
Okimoto, Satoshi; Yamashita, Keiko; Shibata, Tetsuo; Kiuchi, Yoshiaki
2015-01-01
Purpose To compare the optic disc parameters of glaucomatous eyes to those of non-glaucomatous eyes with large discs. Methods We studied 225 consecutive eyes with large optic discs (>2.82 mm2): 91 eyes with glaucoma and 134 eyes without glaucoma. An eye was diagnosed with glaucoma when visual field defects were detected by the Humphrey Field Analyzer. All of the Heidelberg Retina Tomograph II (HRT II) parameters were compared between the non-glaucomatous and glaucomatous eyes. A logistic regression analysis of the HRT II parameters was used to establish a new formula for diagnosing glaucoma, and the sensitivity and specificity of the Moorfields Regression Analysis (MRA) was compared to the findings made by our analyses. Results The mean disc area was 3.44±0.50 mm2 in the non-glaucomatous group and 3.40±0.52 mm2 in the glaucoma group. The cup area, cup volume, cup-to-disc area ratio, linear cup/disc ratio, mean cup depth, and the maximum cup depth were significantly larger in glaucomatous eyes than in the non-glaucomatous eyes. The rim area, rim volume, cup shape measurement, mean retinal nerve fiber layer (RNFL) thickness, and RFNL cross-sectional area were significantly smaller in glaucomatous eyes than in non-glaucomatous eyes. The cup-to-disc area ratio, the height variation contour (HVC), and the RNFL cross-sectional area were important parameters for diagnosing the early stage glaucoma, and the cup-to-disc area ratio and cup volume were useful for diagnosing advanced stage glaucoma in eyes with a large optic disc. The new formula had higher sensitivity and specificity for diagnosing glaucoma than MRA. Conclusions The cup-to-disc area ratio, HVC, RNFL cross-sectional area, and cup volume were important parameters for diagnosing glaucoma in eyes with a large optic disc. The important disc parameters to diagnose glaucoma depend on the stage of glaucoma in patients with large discs. PMID:25798580
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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.
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Barbosa, Gleyce Moreno; Dos Santos, Eldio Gonçalves; Capella, Francielle Neves Carvalho; Homsani, Fortune; de Pointis Marçal, Carina; Dos Santos Valle, Roberta; de Araújo Abi-Chacra, Érika; Braga-Silva, Lys Adriana; de Oliveira Sales, Marcelo Henrique; da Silva Neto, Inácio Domingos; da Veiga, Venicio Feo; Dos Santos, André Luis Souza; Holandino, Carla
2017-02-01
Available treatments against human fungal pathogens present high levels of resistance, motivating the development of new antifungal therapies. In this context, the present work aimed to analyze direct electric current (DC) antifungal action, using an in vitro apparatus equipped with platinum electrodes. Candida albicans yeast cells were submitted to three distinct conditions of DC treatment (anodic flow-AF; electroionic flow-EIF; and cathodic flow-CF), as well as different charges, ranging from 0.03 to 2.40 C. Our results indicated C. albicans presented distinct sensibility depending on the DC intensity and polarity applied. Both the colony-forming unit assay and the cytometry flow with propidium iodide indicated a drastic reduction on cellular viability after AF treatment with 0.15 C, while CF- and EIF-treated cells stayed alive when DC doses were increased up to 2.40 C. Additionally, transmission electron microscopy revealed important ultrastructural alterations in AF-treated yeasts, including cell structure disorganization, ruptures in plasmatic membrane, and cytoplasmic rarefaction. This work emphasizes the importance of physical parameters (polarity and doses) in cellular damage, and brings new evidence for using electrotherapy to treat C. albicans pathology process. Bioelectromagnetics. 38:95-108, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
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Zuo, Chunlai; Chumbalkar, Vaibhav; Ells, Peter F; Bonville, Daniel J; Lee, Hwajeong
2017-09-01
Idiopathic noncirrhotic portal hypertension (INCPH) is associated with histologic changes secondary to obliterative portal venopathy without cirrhosis. We studied the prevalence of individual histological features of INCPH in liver biopsies obtained incidentally during unrelated elective procedures and in elective liver biopsies with the diagnosis of fatty liver disease. A total of 53 incidental liver biopsies obtained intraoperatively during unrelated elective procedures and an additional 28 elective biopsies with the diagnosis of fatty liver disease without portal hypertension and cirrhosis were studied. Various histologic features of INCPH were evaluated. Shunt vessel (30%), phlebosclerosis (27%), increased number of portal vessels (19%) and incomplete septa (17%) were common in these liver biopsies after confounding factors such as co-existing fatty liver disease or fibrosis were excluded. At least one feature of INCPH was noted in 90% of the biopsies. Eight (10%) biopsies showed 5-6 features of INCPH. In total, 11 (14%) of 81 patients had risk factors associated with INCPH, including hypercoagulability, autoimmune disease, exposure to drugs, and infections. No patient had portal hypertension at the end of the follow-up. The histologic features of INCPH are seen in incidental liver biopsies and fatty liver disease without portal hypertension. Ten percent of the biopsies show 5-6 features of INCPH without portal hypertension. Interpreting histologic features in the right clinical context is important for proper patient care.
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Issues and special features of animal health research
2011-01-01
In the rapidly changing context of research on animal health, INRA launched a collective discussion on the challenges facing the field, its distinguishing features, and synergies with biomedical research. As has been declared forcibly by the heads of WHO, FAO and OIE, the challenges facing animal health, beyond diseases transmissible to humans, are critically important and involve food security, agriculture economics, and the ensemble of economic activities associated with agriculture. There are in addition issues related to public health (zoonoses, xenobiotics, antimicrobial resistance), the environment, and animal welfare. Animal health research is distinguished by particular methodologies and scientific questions that stem from the specific biological features of domestic species and from animal husbandry practices. It generally does not explore the same scientific questions as research on human biology, even when the same pathogens are being studied, and the discipline is rooted in a very specific agricultural and economic context. Generic and methodological synergies nevertheless exist with biomedical research, particularly with regard to tools and biological models. Certain domestic species furthermore present more functional similarities with humans than laboratory rodents. The singularity of animal health research in relation to biomedical research should be taken into account in the organization, evaluation, and funding of the field through a policy that clearly recognizes the specific issues at stake. At the same time, the One Health approach should facilitate closer collaboration between biomedical and animal health research at the level of research teams and programmes. PMID:21864344
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Issues and special features of animal health research.
Ducrot, Christian; Bed'hom, Bertrand; Béringue, Vincent; Coulon, Jean-Baptiste; Fourichon, Christine; Guérin, Jean-Luc; Krebs, Stéphane; Rainard, Pascal; Schwartz-Cornil, Isabelle; Torny, Didier; Vayssier-Taussat, Muriel; Zientara, Stephan; Zundel, Etienne; Pineau, Thierry
2011-08-24
In the rapidly changing context of research on animal health, INRA launched a collective discussion on the challenges facing the field, its distinguishing features, and synergies with biomedical research. As has been declared forcibly by the heads of WHO, FAO and OIE, the challenges facing animal health, beyond diseases transmissible to humans, are critically important and involve food security, agriculture economics, and the ensemble of economic activities associated with agriculture. There are in addition issues related to public health (zoonoses, xenobiotics, antimicrobial resistance), the environment, and animal welfare.Animal health research is distinguished by particular methodologies and scientific questions that stem from the specific biological features of domestic species and from animal husbandry practices. It generally does not explore the same scientific questions as research on human biology, even when the same pathogens are being studied, and the discipline is rooted in a very specific agricultural and economic context.Generic and methodological synergies nevertheless exist with biomedical research, particularly with regard to tools and biological models. Certain domestic species furthermore present more functional similarities with humans than laboratory rodents.The singularity of animal health research in relation to biomedical research should be taken into account in the organization, evaluation, and funding of the field through a policy that clearly recognizes the specific issues at stake. At the same time, the One Health approach should facilitate closer collaboration between biomedical and animal health research at the level of research teams and programmes.
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Queensland tick typhus: three cases with unusual clinical features.
Wilson, P A; Tierney, L; Lai, K; Graves, S
2013-07-01
Queensland tick typhus (QTT), caused by Rickettsia australis, is usually a relatively mild illness but can occasionally be severe. We describe three cases of probable QTT with unusual clinical features, namely splenic infarction, fulminant myopericarditis and severe leukocytoclastic vasculitis. QTT may present with uncommon clinical features in addition to the more common manifestations. A high index of suspicion enables specific antibiotic therapy that may hasten recovery. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.
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NASA Astrophysics Data System (ADS)
Soltanian-Zadeh, Hamid; Windham, Joe P.; Peck, Donald J.
1997-04-01
This paper presents development and performance evaluation of an MRI feature space method. The method is useful for: identification of tissue types; segmentation of tissues; and quantitative measurements on tissues, to obtain information that can be used in decision making (diagnosis, treatment planning, and evaluation of treatment). The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of Gadolinium) were acquired for ten tumor patients. (2) Images were analyed by two image analysts according to the following algorithm. The intracranial brain tissues were segmented from the scalp and background. The additive noise was suppressed using a multi-dimensional non-linear edge- preserving filter which preserves partial volume information on average. Image nonuniformities were corrected using a modified lowpass filtering approach. The resulting images were used to generate and visualize an optimal feature space. Cluster centers were identified on the feature space. Then images were segmented into normal tissues and different zones of the tumor. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image analysis results were compared to each other and to the biopsy results. Pre- and post-surgery feature spaces were also compared. The proposed algorithm made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. Based on the clusters marked for each zone, the method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- to post-surgery and radiation
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Zheng, Jiaping; Yu, Hong
2016-01-01
Background Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients’ notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians’ agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen’s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term
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NASA Astrophysics Data System (ADS)
Wang, Yongbo; Sheng, Yehua; Lu, Guonian; Tian, Peng; Zhang, Kai
2008-04-01
Surface reconstruction is an important task in the field of 3d-GIS, computer aided design and computer graphics (CAD & CG), virtual simulation and so on. Based on available incremental surface reconstruction methods, a feature-constrained surface reconstruction approach for point cloud is presented. Firstly features are extracted from point cloud under the rules of curvature extremes and minimum spanning tree. By projecting local sample points to the fitted tangent planes and using extracted features to guide and constrain the process of local triangulation and surface propagation, topological relationship among sample points can be achieved. For the constructed models, a process named consistent normal adjustment and regularization is adopted to adjust normal of each face so that the correct surface model is achieved. Experiments show that the presented approach inherits the convenient implementation and high efficiency of traditional incremental surface reconstruction method, meanwhile, it avoids improper propagation of normal across sharp edges, which means the applicability of incremental surface reconstruction is greatly improved. Above all, appropriate k-neighborhood can help to recognize un-sufficient sampled areas and boundary parts, the presented approach can be used to reconstruct both open and close surfaces without additional interference.
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Identifying significant environmental features using feature recognition.
DOT National Transportation Integrated Search
2015-10-01
The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, : including those r...
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Predictive Value of Morphological Features in Patients with Autism versus Normal Controls
ERIC Educational Resources Information Center
Ozgen, H.; Hellemann, G. S.; de Jonge, M. V.; Beemer, F. A.; van Engeland, H.
2013-01-01
We investigated the predictive power of morphological features in 224 autistic patients and 224 matched-pairs controls. To assess the relationship between the morphological features and autism, we used the receiver operator curves (ROC). In addition, we used recursive partitioning (RP) to determine a specific pattern of abnormalities that is…
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Intrinsic two-dimensional features as textons
NASA Technical Reports Server (NTRS)
Barth, E.; Zetzsche, C.; Rentschler, I.
1998-01-01
We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.
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Qualification of security printing features
NASA Astrophysics Data System (ADS)
Simske, Steven J.; Aronoff, Jason S.; Arnabat, Jordi
2006-02-01
This paper describes the statistical and hardware processes involved in qualifying two related printing features for their deployment in product (e.g. document and package) security. The first is a multi-colored tiling feature that can also be combined with microtext to provide additional forms of security protection. The color information is authenticated automatically with a variety of handheld, desktop and production scanners. The microtext is authenticated either following magnification or manually by a field inspector. The second security feature can also be tile-based. It involves the use of two inks that provide the same visual color, but differ in their transparency to infrared (IR) wavelengths. One of the inks is effectively transparent to IR wavelengths, allowing emitted IR light to pass through. The other ink is effectively opaque to IR wavelengths. These inks allow the printing of a seemingly uniform, or spot, color over a (truly) uniform IR emitting ink layer. The combination converts a uniform covert ink and a spot color to a variable data region capable of encoding identification sequences with high density. Also, it allows the extension of variable data printing for security to ostensibly static printed regions, affording greater security protection while meeting branding and marketing specifications.
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Yenuganti, Vengala Rao; Vanselow, Jens
2017-05-01
Cell culture models are essential for the detailed study of molecular processes. We analyze the dynamics of changes in a culture model of bovine granulosa cells. The cells were cultured for up to 8 days and analyzed for steroid production and gene expression. According to the expression of the marker genes CDH1, CDH2 and VIM, the cells maintained their mesenchymal character throughout the time of culture. In contrast, the levels of functionally important transcripts and of estradiol and progesterone production were rapidly down-regulated but showed a substantial up-regulation from day 4. FOXL2, a marker for granulosa cell identity, was also rapidly down-regulated after plating but completely recovered towards the end of culture. In contrast, expression of the Sertoli cell marker SOX9 and the lesion/inflammation marker PTGS2 increased during the first 2 days after plating but gradually decreased later on. We conclude that only long-term culture conditions (>4 days) allow the cells to recover from plating stress and to re-acquire characteristic granulosa cell features.
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Zhang, Chenwang; Gao, Liuze; Xu, Eugene Yujun
2016-11-01
Spermatogenesis is one of the fundamental processes of sexual reproduction, present in almost all metazoan animals. Like many other reproductive traits, developmental features and traits of spermatogenesis are under strong selective pressure to change, both at morphological and underlying molecular levels. Yet evidence suggests that some fundamental features of spermatogenesis may be ancient and conserved among metazoan species. Identifying the underlying conserved molecular mechanisms could reveal core components of metazoan spermatogenic machinery and provide novel insight into causes of human infertility. Conserved RNA-binding proteins and their interacting RNA network emerge to be a common theme important for animal sperm development. We review research on the recent addition to the RNA family - Long non-coding RNA (lncRNA) and its roles in spermatogenesis in the context of the expanding RNA-protein network. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Deterministic object tracking using Gaussian ringlet and directional edge features
NASA Astrophysics Data System (ADS)
Krieger, Evan W.; Sidike, Paheding; Aspiras, Theus; Asari, Vijayan K.
2017-10-01
Challenges currently existing for intensity-based histogram feature tracking methods in wide area motion imagery (WAMI) data include object structural information distortions, background variations, and object scale change. These issues are caused by different pavement or ground types and from changing the sensor or altitude. All of these challenges need to be overcome in order to have a robust object tracker, while attaining a computation time appropriate for real-time processing. To achieve this, we present a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), which employs Kirsch kernel filtering for edge features and a ringlet feature mapping for rotational invariance. The method also includes an automatic scale change component to obtain accurate object boundaries and improvements for lowering computation times. We evaluated the DRIFT algorithm on two challenging WAMI datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness and efficiency. Additional evaluations on general tracking video sequences are performed using the Visual Tracker Benchmark and Visual Object Tracking 2014 databases to demonstrate the algorithms ability with additional challenges in long complex sequences including scale change. Experimental results show that the proposed approach yields competitive results compared to state-of-the-art object tracking methods on the testing datasets.
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Colorectal Cancer: The Importance of Early Detection
... of this page please turn JavaScript on. Feature: Colorectal Cancer The Importance of Early Detection Past Issues / Summer ... Cancer of the colon or rectum is called colorectal cancer. The colon and the rectum are part of ...
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Kruskal-Wallis-based computationally efficient feature selection for face recognition.
Ali Khan, Sajid; Hussain, Ayyaz; Basit, Abdul; Akram, Sheeraz
2014-01-01
Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.
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Sutcliffe, Katy; Melendez-Torres, G J; Burchett, Helen E D; Richardson, Michelle; Rees, Rebecca; Thomas, James
2018-03-14
Extensive research effort shows that weight management programmes (WMPs) targeting both diet and exercise are broadly effective. However, the critical features of WMPs remain unclear. To develop a deeper understanding of WMPs critical features, we undertook a systematic review of qualitative evidence. We sought to understand from a service-user perspective how programmes are experienced, and may be effective, on the ground. We identified qualitative studies from existing reviews and updated the searches of one review. We included UK studies capturing the views of adult WMP users. Thematic analysis was used inductively to code and synthesize the evidence. Service users were emphatic that supportive relationships, with service providers or WMP peers, are the most critical aspect of WMPs. Supportive relationships were described as providing an extrinsic motivator or "hook" which helped to overcome barriers such as scepticism about dietary advice or a lack confidence to engage in physical activity. The evidence revealed that service-users' understandings of the critical features of WMPs differ from the focus of health promotion guidance or descriptions of evaluated programmes which largely emphasize educational or goal setting aspects of WMPs. Existing programme guidance may not therefore fully address the needs of service users. The study illustrates that the perspectives of service users can reveal unanticipated intervention mechanisms or underemphasized critical features and underscores the value of a holistic understanding about "what happens" in complex psychosocial interventions such as WMPs. © 2017 The Authors Health Expectations published by John Wiley & Sons Ltd.
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Estimating standard errors in feature network models.
Frank, Laurence E; Heiser, Willem J
2007-05-01
Feature network models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods employed in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best-fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard error is evaluated using Monte Carlo techniques.
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Exploring Martian Impact Craters: Why They are Important for the Search for Life
NASA Technical Reports Server (NTRS)
Schwenzer, S. P.; Abramov, O.; Allen, C. C.; Clifford, S.; Filiberto, J.; Kring, D. A.; Lasue, J.; McGovern, P. J.; Newsom, H. E.; Treiman, A. H.;
2010-01-01
Fluvial features and evidence for aqueous alteration indicate that Mars was wet, at least partially and/or periodically, in the Noachian. Also, impact cratering appears to have been the dominant geological process [1] during that epoch. Thus, investigation of Noachian craters will further our understanding of this geologic process, its effects on the water-bearing Martian crust, and any life that may have been present at the time. Impact events disturbed and heated the water- and/or ice-bearing crust, likely initiated long-lived hydrothermal systems [2-4], and formed crater lakes [5], creating environments suitable for life [6]. Thus, Noachian impact craters are particularly important exploration targets because they provide a window into warm, water-rich environments of the past which were possibly conducive to life. In addition to the presence of lake deposits, assessment of the presence of hydrothermal deposits in the walls, floors and uplifts of craters is important in the search for life on Mars. Impact craters are also important for astrobiological exploration in other ways. For example, smaller craters can be used as natural excavation pits, and so can provide information and samples that would otherwise be inaccessible (e.g., [7]). In addition, larger (> 75 km) craters can excavate material from a potentially habitable region, even on present-day Mars, located beneath a >5-km deep cryosphere.
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Facial soft biometric features for forensic face recognition.
Tome, Pedro; Vera-Rodriguez, Ruben; Fierrez, Julian; Ortega-Garcia, Javier
2015-12-01
This paper proposes a functional feature-based approach useful for real forensic caseworks, based on the shape, orientation and size of facial traits, which can be considered as a soft biometric approach. The motivation of this work is to provide a set of facial features, which can be understood by non-experts such as judges and support the work of forensic examiners who, in practice, carry out a thorough manual comparison of face images paying special attention to the similarities and differences in shape and size of various facial traits. This new approach constitutes a tool that automatically converts a set of facial landmarks to a set of features (shape and size) corresponding to facial regions of forensic value. These features are furthermore evaluated in a population to generate statistics to support forensic examiners. The proposed features can also be used as additional information that can improve the performance of traditional face recognition systems. These features follow the forensic methodology and are obtained in a continuous and discrete manner from raw images. A statistical analysis is also carried out to study the stability, discrimination power and correlation of the proposed facial features on two realistic databases: MORPH and ATVS Forensic DB. Finally, the performance of both continuous and discrete features is analyzed using different similarity measures. Experimental results show high discrimination power and good recognition performance, especially for continuous features. A final fusion of the best systems configurations achieves rank 10 match results of 100% for ATVS database and 75% for MORPH database demonstrating the benefits of using this information in practice. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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A judicious multiple hypothesis tracker with interacting feature extraction
NASA Astrophysics Data System (ADS)
McAnanama, James G.; Kirubarajan, T.
2009-05-01
The multiple hypotheses tracker (mht) is recognized as an optimal tracking method due to the enumeration of all possible measurement-to-track associations, which does not involve any approximation in its original formulation. However, its practical implementation is limited by the NP-hard nature of this enumeration. As a result, a number of maintenance techniques such as pruning and merging have been proposed to bound the computational complexity. It is possible to improve the performance of a tracker, mht or not, using feature information (e.g., signal strength, size, type) in addition to kinematic data. However, in most tracking systems, the extraction of features from the raw sensor data is typically independent of the subsequent association and filtering stages. In this paper, a new approach, called the Judicious Multi Hypotheses Tracker (jmht), whereby there is an interaction between feature extraction and the mht, is presented. The measure of the quality of feature extraction is input into measurement-to-track association while the prediction step feeds back the parameters to be used in the next round of feature extraction. The motivation for this forward and backward interaction between feature extraction and tracking is to improve the performance in both steps. This approach allows for a more rational partitioning of the feature space and removes unlikely features from the assignment problem. Simulation results demonstrate the benefits of the proposed approach.
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Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation
Siraj, Maheyzah Md; Zainal, Anazida; Elshoush, Huwaida Tagelsir; Elhaj, Fatin
2016-01-01
Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset. PMID:27893821
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Learning to Solve Addition and Subtraction Word Problems in English as an Imported Language
ERIC Educational Resources Information Center
Verzosa, Debbie Bautista; Mulligan, Joanne
2013-01-01
This paper reports an intervention phase of a design study aimed to assist second-grade Filipino children in solving addition word problems in English, a language they primarily encounter only in school. With Filipino as the medium of instruction, an out-of-school pedagogical intervention providing linguistic and representational scaffolds was…
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Resta, V.; Peláez, R. J.; Afonso, C. N.
2014-03-28
This work studies the changes in the optical response and morphological features of 6 ± 1 nm diameter Au nanoparticles (NPs) when covered by a layer of a-Al{sub 2}O{sub 3} by pulsed laser deposition (PLD). The laser fluence used for ablating the Al{sub 2}O{sub 3} target is varied in order to modify the kinetic energy (KE) of the species bombarding the NPs during their coverage. When the ion KE < 200 eV, the structural features and optical properties of the NPs are close to those of uncovered ones. Otherwise, a shift to the blue and a strong damping of the surface plasmon resonance is observed asmore » fluence is increased. There are two processes responsible for these changes, both related to aluminum ions arriving to the substrate during the coverage process, i.e., sputtering of the metal and implantation of aluminum species in the metal. Both processes have been simulated using standard models for ion bombardment, the calculated effective implanted depths allow explaining the observed changes in the optical response, and the use of a size-dependent sputtering coefficient for the Au NPs predicts the experimental sputtering fractions. In spite of the work is based on PLD, the concepts investigated and conclusions can straightforwardly be extrapolated to other physical vapor deposition techniques or processes involving ion bombardment of metal NPs by ions having KE > 200 eV.« less
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NASA Astrophysics Data System (ADS)
Parshin, Dmitry A.
2018-05-01
The additive process of forming a semicircular arched structure by means of layer-by-layer addition of material to its inner surface is simulated. The impact of this process running mode on the development of the technological stresses fields in the structure being formed under the action of gravity under properties of the material creep and aging is examined. In the framework of the linear mechanics of accreted solids a mathematical model of the process under study is offered and numerical experiments are conducted. It is shown that the stress-strain state of the additively formed heavy objects decisively depends on their formation mode. Various practically important trends and features of this dependence are studied.
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Enhanced Exoplanet Biosignature from an Interferometer Addition to Low Resolution Spectrographs
NASA Astrophysics Data System (ADS)
Erskine, D. J.; Muirhead, P. S.; Vanderburg, A. M.; Szentgyorgyi, A.
2017-12-01
The absorption spectral signature of many atmospheric molecules consists of a group of 40 or so lines that are approximately periodic due to the physics of molecular vibration. This is fortuitous for detecting atmospheric features in an exoEarth, since it has a similar periodic nature as an interferometer's transmission, which is sinusoidal. The period (in wavenumbers) of the interferometer is selectable, being inversely proportional to the delay (in cm). We show that the addition of a small interferometer of 0.6 cm delay to an existing dispersive spectrograph can greatly enhance the detection of molecular features, by several orders of magnitude for initially low resolution spectrographs. We simulate the Gemini Planet Imager measuring a telluric spectrum having native resolution of 40 and 70 in the 1.65 micron and 2 micron bands. These low resolutions are insufficient to resolve the fine features of the molecular feature group. However, the addition of a 0.6 cm delay outside the spectrograph and in series with it increases the local amplitude of the signal to a level similar to a R=4400 (at 1.65 micron) or R=3900 (at 2 micron) classical spectrograph. Prepared by LLNL under Contract DE-AC52-07NA27344.
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Latent feature decompositions for integrative analysis of multi-platform genomic data
Gregory, Karl B.; Momin, Amin A.; Coombes, Kevin R.; Baladandayuthapani, Veerabhadran
2015-01-01
Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to, a glioblastoma multiforme dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between
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A deep learning framework for modeling structural features of RNA-binding protein targets
Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang
2016-01-01
RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https
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News video story segmentation method using fusion of audio-visual features
NASA Astrophysics Data System (ADS)
Wen, Jun; Wu, Ling-da; Zeng, Pu; Luan, Xi-dao; Xie, Yu-xiang
2007-11-01
News story segmentation is an important aspect for news video analysis. This paper presents a method for news video story segmentation. Different form prior works, which base on visual features transform, the proposed technique uses audio features as baseline and fuses visual features with it to refine the results. At first, it selects silence clips as audio features candidate points, and selects shot boundaries and anchor shots as two kinds of visual features candidate points. Then this paper selects audio feature candidates as cues and develops different fusion method, which effectively using diverse type visual candidates to refine audio candidates, to get story boundaries. Experiment results show that this method has high efficiency and adaptability to different kinds of news video.
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Using input feature information to improve ultraviolet retrieval in neural networks
NASA Astrophysics Data System (ADS)
Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina
2017-09-01
In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.
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Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm
Kokaly, Raymond F.; Skidmore, Andrew K
2015-01-01
Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic C-H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the
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Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm
NASA Astrophysics Data System (ADS)
Kokaly, Raymond F.; Skidmore, Andrew K.
2015-12-01
Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic Csbnd H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences
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Courseware Components and Features: Preferences of Faculty in the Human Sciences
ERIC Educational Resources Information Center
Causin, Gina Fe G.; Robertson, Lona J.; Ryan, Bill
2008-01-01
This project gathered information on the important components and features of distance education courseware identified by faculty teaching in the Great Plains Interactive Distance Education Alliance. Respondents indicated that they were most interested in features that helped with course management, allowed them to update and post course materials…
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THE ROLE OF THE HIPPOCAMPUS IN OBJECT DISCRIMINATION BASED ON VISUAL FEATURES.
Levcik, David; Nekovarova, Tereza; Antosova, Eliska; Stuchlik, Ales; Klement, Daniel
2018-06-07
The role of rodent hippocampus has been intensively studied in different cognitive tasks. However, its role in discrimination of objects remains controversial due to conflicting findings. We tested whether the number and type of features available for the identification of objects might affect the strategy (hippocampal-independent vs. hippocampal-dependent) that rats adopt to solve object discrimination tasks. We trained rats to discriminate 2D visual objects presented on a computer screen. The objects were defined either by their shape only or by multiple-features (a combination of filling pattern and brightness in addition to the shape). Our data showed that objects displayed as simple geometric shapes are not discriminated by trained rats after their hippocampi had been bilaterally inactivated by the GABA A -agonist muscimol. On the other hand, objects containing a specific combination of non-geometric features in addition to the shape are discriminated even without the hippocampus. Our results suggest that the involvement of the hippocampus in visual object discrimination depends on the abundance of object's features. Copyright © 2018. Published by Elsevier Inc.
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Object attributes combine additively in visual search
Pramod, R. T.; Arun, S. P.
2016-01-01
We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes. PMID:26967014
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Object attributes combine additively in visual search.
Pramod, R T; Arun, S P
2016-01-01
We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.
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Structural optimization under overhang constraints imposed by additive manufacturing technologies
NASA Astrophysics Data System (ADS)
Allaire, G.; Dapogny, C.; Estevez, R.; Faure, A.; Michailidis, G.
2017-12-01
This article addresses one of the major constraints imposed by additive manufacturing processes on shape optimization problems - that of overhangs, i.e. large regions hanging over void without sufficient support from the lower structure. After revisiting the 'classical' geometric criteria used in the literature, based on the angle between the structural boundary and the build direction, we propose a new mechanical constraint functional, which mimics the layer by layer construction process featured by additive manufacturing technologies, and thereby appeals to the physical origin of the difficulties caused by overhangs. This constraint, as well as some variants, is precisely defined; their shape derivatives are computed in the sense of Hadamard's method, and numerical strategies are extensively discussed, in two and three space dimensions, to efficiently deal with the appearance of overhang features in the course of shape optimization processes.
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Quantitative imaging features: extension of the oncology medical image database
NASA Astrophysics Data System (ADS)
Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.
2015-03-01
Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.
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Coloretti, Fabio; Tabanelli, Giulia; Chiavari, Cristiana; Lanciotti, Rosalba; Grazia, Luigi; Gardini, Fausto; Montanari, Chiara
2014-03-01
The aim was to evaluate the effect of wine addition during manufacturing of dry fermented sausages, in terms of safety aspects (biogenic amine accumulation), aroma profile and sensory characteristics. Three batches of salami were produced: without wine addition and with 7.5% or 15% (v/w) of white wine. The fermented sausages showed characteristics that can increase product diversification. Some of the sensory features (i.e. increased salty perception) can represent an important strategy because of the trend to reduce salt intake for health reasons. The presence of wine immediately reduced the pH and is a source of ethanol, which can have an inhibitory effect against undesirable microflora. The microbiological results observed regarding Enterobacteriaceae and enterococci were encouraging. The addition of wine did not negatively affect the ripening time or increase the presence of biogenic amines. The samples containing wine showed reduced concentrations of putrescine. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Distel, M A; Middeldorp, C M; Trull, T J; Derom, C A; Willemsen, G; Boomsma, D I
2011-04-01
Traumatic life events are generally more common in patients with borderline personality disorder (BPD) than in non-patients or patients with other personality disorders. This study investigates whether exposure to life events moderates the genetic architecture of BPD features. As the presence of genotype-environment correlation (rGE) can lead to spurious findings of genotype-environment interaction (G × E), we also test whether BPD features increase the likelihood of exposure to life events. The extent to which an individual is at risk to develop BPD was assessed with the Personality Assessment Inventory - Borderline features scale (PAI-BOR). Life events under study were a divorce/break-up, traffic accident, violent assault, sexual assault, robbery and job loss. Data were available for 5083 twins and 1285 non-twin siblings. Gene-environment interaction and correlation were assessed by using structural equation modelling (SEM) and the co-twin control design. There was evidence for both gene-environment interaction and correlation. Additive genetic influences on BPD features interacted with the exposure to sexual assault, with genetic variance being lower in exposed individuals. In individuals who had experienced a divorce/break-up, violent assault, sexual assault or job loss, environmental variance for BPD features was higher, leading to a lower heritability of BPD features in exposed individuals. Gene-environment correlation was present for some life events. The genes that influence BPD features thus also increased the likelihood of being exposed to certain life events. To our knowledge, this study is the first to test the joint effect of genetic and environmental influences and the exposure to life events on BPD features in the general population. Our results indicate the importance of both genetic vulnerability and life events.
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Crowell, Kevin L; Slysz, Gordon W; Baker, Erin S; LaMarche, Brian L; Monroe, Matthew E; Ibrahim, Yehia M; Payne, Samuel H; Anderson, Gordon A; Smith, Richard D
2013-11-01
The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.
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Feature selection from a facial image for distinction of sasang constitution.
Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho
2009-09-01
Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.
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Tumors of the Testis: Morphologic Features and Molecular Alterations.
Howitt, Brooke E; Berney, Daniel M
2015-12-01
This article reviews the most frequently encountered tumor of the testis; pure and mixed malignant testicular germ cell tumors (TGCT), with emphasis on adult (postpubertal) TGCTs and their differential diagnoses. We additionally review TGCT in the postchemotherapy setting, and findings to be integrated into the surgical pathology report, including staging of testicular tumors and other problematic issues. The clinical features, gross pathologic findings, key histologic features, common differential diagnoses, the use of immunohistochemistry, and molecular alterations in TGCTs are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.
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Robust Feature Selection Technique using Rank Aggregation.
Sarkar, Chandrima; Cooley, Sarah; Srivastava, Jaideep
2014-01-01
Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases while classifiers exploit different statistical properties of data for evaluation. In numerous situations this can put researchers into dilemma as to which feature selection method and a classifiers to choose from a vast range of choices. In this paper, we propose a technique that aggregates the consensus properties of various feature selection methods to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable towards achieving similar and ideally higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the Robustness Index (RI). We perform an extensive empirical evaluation of our technique on eight data sets with different dimensions including Arrythmia, Lung Cancer, Madelon, mfeat-fourier, internet-ads, Leukemia-3c and Embryonal Tumor and a real world data set namely Acute Myeloid Leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that compared to other techniques our algorithm improves the classification accuracy by approximately 3-4% (in data set with less than 500 features) and by more than 5% (in data set with more than 500 features), across a wide range of classifiers.
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Kim, Yoon Jae; Heo, Jeong; Park, Kwang Suk; Kim, Sungwan
2016-08-01
Arrhythmia refers to a group of conditions in which the heartbeat is irregular, fast, or slow due to abnormal electrical activity in the heart. Some types of arrhythmia such as ventricular fibrillation may result in cardiac arrest or death. Thus, arrhythmia detection becomes an important issue, and various studies have been conducted. Additionally, an arrhythmia detection algorithm for portable devices such as mobile phones has recently been developed because of increasing interest in e-health care. This paper proposes a novel classification approach and features, which are validated for improved real-time arrhythmia monitoring. The classification approach that was employed for arrhythmia detection is based on the concept of ensemble learning and the Taguchi method and has the advantage of being accurate and computationally efficient. The electrocardiography (ECG) data for arrhythmia detection was obtained from the MIT-BIH Arrhythmia Database (n=48). A novel feature, namely the heart rate variability calculated from 5s segments of ECG, which was not considered previously, was used. The novel classification approach and feature demonstrated arrhythmia detection accuracy of 89.13%. When the same data was classified using the conventional support vector machine (SVM), the obtained accuracy was 91.69%, 88.14%, and 88.74% for Gaussian, linear, and polynomial kernels, respectively. In terms of computation time, the proposed classifier was 5821.7 times faster than conventional SVM. In conclusion, the proposed classifier and feature showed performance comparable to those of previous studies, while the computational complexity and update interval were highly reduced. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Lombaert, Herve; Grady, Leo; Polimeni, Jonathan R.; Cheriet, Farida
2013-01-01
Existing methods for surface matching are limited by the trade-off between precision and computational efficiency. Here we present an improved algorithm for dense vertex-to-vertex correspondence that uses direct matching of features defined on a surface and improves it by using spectral correspondence as a regularization. This algorithm has the speed of both feature matching and spectral matching while exhibiting greatly improved precision (distance errors of 1.4%). The method, FOCUSR, incorporates implicitly such additional features to calculate the correspondence and relies on the smoothness of the lowest-frequency harmonics of a graph Laplacian to spatially regularize the features. In its simplest form, FOCUSR is an improved spectral correspondence method that nonrigidly deforms spectral embeddings. We provide here a full realization of spectral correspondence where virtually any feature can be used as additional information using weights on graph edges, but also on graph nodes and as extra embedded coordinates. As an example, the full power of FOCUSR is demonstrated in a real case scenario with the challenging task of brain surface matching across several individuals. Our results show that combining features and regularizing them in a spectral embedding greatly improves the matching precision (to a sub-millimeter level) while performing at much greater speed than existing methods. PMID:23868776
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Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder
Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi
2018-01-01
Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931
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How important is vehicle safety for older consumers in the vehicle purchase process?
Koppel, Sjaan; Clark, Belinda; Hoareau, Effie; Charlton, Judith L; Newstead, Stuart V
2013-01-01
This study aimed to investigate the importance of vehicle safety to older consumers in the vehicle purchase process. Older (n = 102), middle-aged (n = 791), and younger (n = 109) participants throughout the eastern Australian states of Victoria, New South Wales, and Queensland who had recently purchased a new or used vehicle completed an online questionnaire about their vehicle purchase process. When asked to list the 3 most important considerations in the vehicle purchase process (in an open-ended format), older consumers were mostly likely to list price as their most important consideration (43%). Similarly, when presented with a list of vehicle factors (such as price, design, Australasian New Car Assessment Program [ANCAP] rating), older consumers were most likely to identify price as the most important vehicle factor (36%). When presented with a list of vehicle features (such as automatic transmission, braking, air bags), older consumers in the current study were most likely to identify an antilock braking system (41%) as the most important vehicle feature, and 50 percent of older consumers identified a safety-related vehicle feature as the highest priority vehicle feature (50%). When asked to list up to 3 factors that make a vehicle safe, older consumers in the current study were most likely to list braking systems (35%), air bags (22%), and the driver's behavior or skill (11%). When asked about the influence of safety in the new vehicle purchase process, one third of older consumers reported that all new vehicles are safe (33%) and almost half of the older consumers rated their vehicle as safer than average (49%). A logistic regression model was developed to predict the profile of older consumers more likely to assign a higher priority to safety features in the vehicle purchasing process. The model predicted that the importance of safety-related features was influenced by several variables, including older consumers' beliefs that they could protect themselves
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Prevailing Torque Locking Feature in Threaded Fasteners Using Anaerobic Adhesive
NASA Technical Reports Server (NTRS)
Hernandez, Alan; Hess, Daniel P.
2016-01-01
This paper presents results from tests to assess the use of anaerobic adhesive for providing a prevailing torque locking feature in threaded fasteners. Test procedures are developed and tests are performed on three fastener materials, four anaerobic adhesives, and both unseated assembly conditions. Five to ten samples are tested for each combination. Tests for initial use, reuse without additional adhesive, and reuse with additional adhesive are performed for all samples. A 48-hour cure time was used for all initial use and reuse tests. Test data are presented as removal torque versus removal angle with the specification required prevailing torque range added for performance assessment. Percent specification pass rates for the all combinations of fastener material, adhesive, and assembly condition are tabulated and reveal use of anaerobic adhesive as a prevailing torque locking feature is viable. Although not every possible fastener material and anaerobic adhesive combination provides prevailing torque values within specification, any combination can be assessed using the test procedures presented. Reuse without additional anaerobic adhesive generally provides some prevailing torque, and in some cases within specification. Reuse with additional adhesive often provides comparable removal torque data as in initial use.
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NASA Astrophysics Data System (ADS)
Kuhn, Sascha; Burr, August; Kübler, Michael; Deckert, Matthias; Bleesen, Christoph
2011-02-01
In this paper the replication qualities of periodically and randomly arranged micro-features molded in the injection molding process and their effects on surface properties are studied. The features are molded in PC, PMMA and PP at different mold wall temperatures in order to point out the necessity and profitability of a variotherm mold wall temperature control system. A one-dimensional heat conduction model is proposed to predict the cycle times of the variotherm injection molding processes. With regard to these processes, the molding results are compared to the molded surface feature heights using an atomic force microscope. In addition, the effects of the molded surface features on macroscopic surfaces are characterized in terms of light reflection using a spectrometer and in terms of water wettability by measuring the static contact angle. Furthermore, due to the sensitivity of the surface features on the molded parts, their durability is compared in a scratch test with a diamond tip. This leads to successful implementation in applications in which the optical appearance, in terms of gloss and reflection, and the water repellence, in terms of drag flow and adhesion, are of importance.
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Orthogonal system of fractural and integrated diagnostic features in vibration analysis
NASA Astrophysics Data System (ADS)
Kostyukov, V. N.; Boychenko, S. N.
2017-08-01
The paper presents the results obtained in the studies of the orthogonality of the vibration diagnostic features system comprising the integrated features, particularly - root mean square values of vibration acceleration, vibration velocity, vibration displacement and fractal feature (Hurst exponent). To diagnose the condition of the equipment by the vibration signal, the orthogonality of the vibration diagnostic features is important. The fact of orthogonality shows that the system of features is not superfluous and allows the maximum coverage of the state space of the object being diagnosed. This, in turn, increases reliability of the machinery condition monitoring results. The studies were carried out on the models of vibration signals using the programming language R.
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Extracted facial feature of racial closely related faces
NASA Astrophysics Data System (ADS)
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.
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Ecosystem features determine seagrass community response to sea otter foraging
Hessing-Lewis, Margot; Rechsteiner, Erin U.; Hughes, Brent B.; Tinker, M. Tim; Monteith, Zachary L.; Olson, Angeleen M.; Henderson, Matthew Morgan; Watson, Jane C.
2017-01-01
Comparing sea otter recovery in California (CA) and British Columbia (BC) reveals key ecosystem properties that shape top-down effects in seagrass communities. We review potential ecosystem drivers of sea otter foraging in CA and BC seagrass beds, including the role of coastline complexity and environmental stress on sea otter effects. In BC, we find greater species richness across seagrass trophic assemblages. Furthermore, Cancer spp. crabs, an important link in the seagrass trophic cascade observed in CA, are less common. Additionally, the more recent reintroduction of sea otters, more complex coastline, and reduced environmental stress in BC seagrass habitats supports the hypotheses that sea otter foraging pressure is currently reduced there. In order to manage the ecosystem features that lead to regional differences in top predator effects in seagrass communities, we review our findings, their spatial and temporal constraints, and present a social-ecological framework for future research.
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Mala, S.; Latha, K.
2014-01-01
Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185
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Mala, S; Latha, K
2014-01-01
Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.
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Ash, Samuel Y; Harmouche, Rola; Ross, James C; Diaz, Alejandro A; Rahaghi, Farbod N; Sanchez-Ferrero, Gonzalo Vegas; Putman, Rachel K; Hunninghake, Gary M; Onieva, Jorge Onieva; Martinez, Fernando J; Choi, Augustine M; Bowler, Russell P; Lynch, David A; Hatabu, Hiroto; Bhatt, Surya P; Dransfield, Mark T; Wells, J Michael; Rosas, Ivan O; San Jose Estepar, Raul; Washko, George R
2018-06-05
Purpose To determine if interstitial features at chest CT enhance the effect of emphysema on clinical disease severity in smokers without clinical pulmonary fibrosis. Materials and Methods In this retrospective cohort study, an objective CT analysis tool was used to measure interstitial features (reticular changes, honeycombing, centrilobular nodules, linear scar, nodular changes, subpleural lines, and ground-glass opacities) and emphysema in 8266 participants in a study of chronic obstructive pulmonary disease (COPD) called COPDGene (recruited between October 2006 and January 2011). Additive differences in patients with emphysema with interstitial features and in those without interstitial features were analyzed by using t tests, multivariable linear regression, and Kaplan-Meier analysis. Multivariable linear and Cox regression were used to determine if interstitial features modified the effect of continuously measured emphysema on clinical measures of disease severity and mortality. Results Compared with individuals with emphysema alone, those with emphysema and interstitial features had a higher percentage predicted forced expiratory volume in 1 second (absolute difference, 6.4%; P < .001), a lower percentage predicted diffusing capacity of lung for carbon monoxide (DLCO) (absolute difference, 7.4%; P = .034), a 0.019 higher right ventricular-to-left ventricular (RVLV) volume ratio (P = .029), a 43.2-m shorter 6-minute walk distance (6MWD) (P < .001), a 5.9-point higher St George's Respiratory Questionnaire (SGRQ) score (P < .001), and 82% higher mortality (P < .001). In addition, interstitial features modified the effect of emphysema on percentage predicted DLCO, RVLV volume ratio, 6WMD, SGRQ score, and mortality (P for interaction < .05 for all). Conclusion In smokers, the combined presence of interstitial features and emphysema was associated with worse clinical disease severity and higher mortality than was emphysema alone. In addition, interstitial features
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Nearest neighbor 3D segmentation with context features
NASA Astrophysics Data System (ADS)
Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes
2018-03-01
Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.
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Adaptive feature selection using v-shaped binary particle swarm optimization.
Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong
2017-01-01
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.
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Adaptive feature selection using v-shaped binary particle swarm optimization
Dong, Hongbin; Zhou, Xiurong
2017-01-01
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850
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Mid-Infrared Silicate Dust Features in Seyfert 1 Spectra
NASA Astrophysics Data System (ADS)
Thompson, Grant D.; Levenson, N. A.; Sirocky, M. M.; Uddin, S.
2007-12-01
Silicate dust emission dominates the mid-infrared spectra of galaxies, and the dust produces two spectral features, at 10 and 18 μm. These features' strengths (in emission or absorption) and peak wavelengths reveal the geometry of the dust distribution, and they are sensitive to the dust composition. We examine mid-infrared spectra of 32 Seyfert 1 active galactic nuclei (AGN), observed with the Infrared Spectrograph aboard the Spitzer Space Telescope. In the spectra, we typically find the shorter-wavelength feature in emission, at an average peak wavelength of 10.0 μm, although it is known historically as the "9.7 μm" feature. In addition, peak wavelength increases with feature strength. The 10 and 18 μm feature strengths together are sensitive to the dust geometry surrounding the central heating engine. Numerical calculations of radiative transfer distinguish between clumpy and smooth distributions, and we find that the surroundings of these AGN (the obscuring "tori" of unified AGN schemes) are clumpy. Polycyclic aromatic hydrocarbon (PAH) features are associated with star formation, and we find strong PAH emission (luminosity ≥ 1042 erg/s) in only four sources, three of which show independent evidence for starbursts. We will explore the effects of luminosity on dust geometry and chemistry in a comparison sample of quasars. We acknowledge work supported by the NSF under grant number 0237291.
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Radiographic Features of Acute Patellar Tendon Rupture.
Fazal, Muhammad Ali; Moonot, Pradeep; Haddad, Fares
2015-11-01
The purpose of our study was to assess soft tissue features of acute patellar tendon rupture on lateral knee radiograph that would facilitate early diagnosis. The participants were divided into two groups of 35 patients each. There were 28 men and seven women with a mean age of 46 years in the control group and 26 men and nine women with a mean age of 47 years in the rupture group. The lateral knee radiograph of each patient was evaluated for Insall-Salvati ratio for patella alta, increased density of the infrapatellar fat pad, appearance of the soft tissue margin of the patellar tendon and bony avulsions. In the rupture group there were three consistent soft tissue radiographic features in addition to patellar alta. These were increased density of infrapatellar fat pad; loss of sharp, well-defined linear margins of the patellar tendon and angulated wavy margin of the patellar tendon while in the control group these features were not observed. The soft tissue radiographic features described in the rupture group are consistent and reliable. When coupled with careful clinical assessment, these will aid in early diagnosis and further imaging will be seldom required. © 2015 Chinese Orthopaedic Association and Wiley Publishing Asia Pty Ltd.
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Fatal methanol poisoning: features of liver histopathology.
Akhgari, Maryam; Panahianpour, Mohammad Hadi; Bazmi, Elham; Etemadi-Aleagha, Afshar; Mahdavi, Amirhosein; Nazari, Saeed Hashemi
2013-03-01
Methanol poisoning has become a considerable problem in Iran. Liver can show some features of poisoning after methanol ingestion. Therefore, our concern was to examine liver tissue histopathology in fatal methanol poisoning cases in Iranian population. In this study, 44 cases of fatal methanol poisoning were identified in a year. The histological changes of the liver were reviewed. The most striking features of liver damage by light microscopy were micro-vesicular steatosis, macro-vesicular steatosis, focal hepatocyte necrosis, mild intra-hepatocyte bile stasis, feathery degeneration and hydropic degeneration. Blood and vitreous humor methanol concentrations were examined to confirm the proposed history of methanol poisoning. The majority of cases were men (86.36%). In conclusion, methanol poisoning can cause histological changes in liver tissues. Most importantly in cases with mean blood and vitreous humor methanol levels greater than 127 ± 38.9 mg/dL more than one pathologic features were detected.
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Interpretable Topic Features for Post-ICU Mortality Prediction.
Luo, Yen-Fu; Rumshisky, Anna
2016-01-01
Electronic health records provide valuable resources for understanding the correlation between various diseases and mortality. The analysis of post-discharge mortality is critical for healthcare professionals to follow up potential causes of death after a patient is discharged from the hospital and give prompt treatment. Moreover, it may reduce the cost derived from readmissions and improve the quality of healthcare. Our work focused on post-discharge ICU mortality prediction. In addition to features derived from physiological measurements, we incorporated ICD-9-CM hierarchy into Bayesian topic model learning and extracted topic features from medical notes. We achieved highest AUCs of 0.835 and 0.829 for 30-day and 6-month post-discharge mortality prediction using baseline and topic proportions derived from Labeled-LDA. Moreover, our work emphasized the interpretability of topic features derived from topic model which may facilitates the understanding and investigation of the complexity between mortality and diseases.
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Antisocial features and "faking bad": A critical note.
Niesten, Isabella J M; Nentjes, Lieke; Merckelbach, Harald; Bernstein, David P
2015-01-01
We critically review the literature on antisocial personality features and symptom fabrication (i.e., faking bad; e.g., malingering). A widespread assumption is that these constructs are intimately related. Some studies have, indeed, found that antisocial individuals score higher on instruments detecting faking bad, but others have been unable to replicate this pattern. In addition, studies exploring whether antisocial individuals are especially talented in faking bad have generally come up with null results. The notion of an intrinsic link between antisocial features and faking bad is difficult to test and research in this domain is sensitive to selection bias. We argue that research on faking bad would profit from further theoretical articulation. One topic that deserves scrutiny is how antisocial features affect the cognitive dissonance typically induced by faking bad. We illustrate our points with preliminary data and discuss their implications. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Metalinguistic Knowledge of Salient vs. Unsalient Features: Evidence from the Arabic Construct State
ERIC Educational Resources Information Center
Azaz, Mahmoud
2017-01-01
This study examined to what extent English-speaking learners of Arabic demonstrated varied metalinguistic knowledge of a salient feature (head-direction) vs. an unsalient feature (definiteness) in the Arabic construct state. In addition, it examined whether this knowledge was utilized in form-focused task performance. In the target construction,…
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Acoustic features of objects matched by an echolocating bottlenose dolphin.
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.
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The Role of Attention in the Binding of Surface Features to Locations
Hyun, Joo-seok; Woodman, Geoffrey F.; Luck, Steven J.
2013-01-01
Previous studies have proposed that attention is not necessary for detecting simple features but is necessary for binding them to spatial locations. The present study tested this hypothesis, using the N2pc component of the event-related potential waveform as a measure of the allocation of attention. A simple feature detection condition, in which observers reported whether a target color was present or not, was compared with feature-location binding conditions, in which observers reported the location of the target color. A larger N2pc component was observed in the binding conditions than in the detection condition, indicating that additional attentional resources are needed to bind a feature to a location than to detect the feature independently of its location. This finding supports theories of attention in which attention plays a special role in binding features. PMID:24235876
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Prostate cancer detection: Fusion of cytological and textural features.
Nguyen, Kien; Jain, Anil K; Sabata, Bikash
2011-01-01
A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification.
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ECG feature extraction and disease diagnosis.
Bhyri, Channappa; Hamde, S T; Waghmare, L M
2011-01-01
An important factor to consider when using findings on electrocardiograms for clinical decision making is that the waveforms are influenced by normal physiological and technical factors as well as by pathophysiological factors. In this paper, we propose a method for the feature extraction and heart disease diagnosis using wavelet transform (WT) technique and LabVIEW (Laboratory Virtual Instrument Engineering workbench). LabVIEW signal processing tools are used to denoise the signal before applying the developed algorithm for feature extraction. First, we have developed an algorithm for R-peak detection using Haar wavelet. After 4th level decomposition of the ECG signal, the detailed coefficient is squared and the standard deviation of the squared detailed coefficient is used as the threshold for detection of R-peaks. Second, we have used daubechies (db6) wavelet for the low resolution signals. After cross checking the R-peak location in 4th level, low resolution signal of daubechies wavelet P waves and T waves are detected. Other features of diagnostic importance, mainly heart rate, R-wave width, Q-wave width, T-wave amplitude and duration, ST segment and frontal plane axis are also extracted and scoring pattern is applied for the purpose of heart disease diagnosis. In this study, detection of tachycardia, bradycardia, left ventricular hypertrophy, right ventricular hypertrophy and myocardial infarction have been considered. In this work, CSE ECG data base which contains 5000 samples recorded at a sampling frequency of 500 Hz and the ECG data base created by the S.G.G.S. Institute of Engineering and Technology, Nanded (Maharashtra) have been used.
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Sensorineural Deafness, Distinctive Facial Features and Abnormal Cranial Bones
Gad, Alona; Laurino, Mercy; Maravilla, Kenneth R.; Matsushita, Mark; Raskind, Wendy H.
2008-01-01
The Waardenburg syndromes (WS) account for approximately 2% of congenital sensorineural deafness. This heterogeneous group of diseases currently can be categorized into four major subtypes (WS types 1-4) on the basis of characteristic clinical features. Multiple genes have been implicated in WS, and mutations in some genes can cause more than one WS subtype. In addition to eye, hair and skin pigmentary abnormalities, dystopia canthorum and broad nasal bridge are seen in WS type 1. Mutations in the PAX3 gene are responsible for the condition in the majority of these patients. In addition, mutations in PAX3 have been found in WS type 3 that is distinguished by musculoskeletal abnormalities, and in a family with a rare subtype of WS, craniofacial-deafness-hand syndrome (CDHS), characterized by dysmorphic facial features, hand abnormalities, and absent or hypoplastic nasal and wrist bones. Here we describe a woman who shares some, but not all features of WS type 3 and CDHS, and who also has abnormal cranial bones. All sinuses were hypoplastic, and the cochlea were small. No sequence alteration in PAX3 was found. These observations broaden the clinical range of WS and suggest there may be genetic heterogeneity even within the CDHS subtype. PMID:18553554
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Code of Federal Regulations, 2014 CFR
2014-07-01
... refiners, gasoline importers and producers and importers of certified ethanol denaturant. 80.1630 Section...) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline Sulfur § 80.1630 Sampling and testing requirements for refiners, gasoline importers and producers and importers of certified ethanol denaturant. (a) Sample and...
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Munitions related feature extraction from LIDAR data.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Roberts, Barry L.
2010-06-01
The characterization of former military munitions ranges is critical in the identification of areas likely to contain residual unexploded ordnance (UXO). Although these ranges are large, often covering tens-of-thousands of acres, the actual target areas represent only a small fraction of the sites. The challenge is that many of these sites do not have records indicating locations of former target areas. The identification of target areas is critical in the characterization and remediation of these sites. The Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) of the DoD have been developing and implementing techniquesmore » for the efficient characterization of large munitions ranges. As part of this process, high-resolution LIDAR terrain data sets have been collected over several former ranges. These data sets have been shown to contain information relating to former munitions usage at these ranges, specifically terrain cratering due to high-explosives detonations. The location and relative intensity of crater features can provide information critical in reconstructing the usage history of a range, and indicate areas most likely to contain UXO. We have developed an automated procedure using an adaptation of the Circular Hough Transform for the identification of crater features in LIDAR terrain data. The Circular Hough Transform is highly adept at finding circular features (craters) in noisy terrain data sets. This technique has the ability to find features of a specific radius providing a means of filtering features based on expected scale and providing additional spatial characterization of the identified feature. This method of automated crater identification has been applied to several former munitions ranges with positive results.« less
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Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.
Ju, Zhe; He, Jian-Jun
2018-06-01
Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutarylation sites accurately. In this study, a novel bioinformatics tool named GlutPred is developed to predict glutarylation sites by using multiple feature extraction and maximum relevance minimum redundancy feature selection. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode glutarylation sites. And the maximum relevance minimum redundancy method and the incremental feature selection algorithm are adopted to remove the redundant features. On the other hand, a biased support vector machine algorithm is used to handle the imbalanced problem in glutarylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of GlutPred achieves a satisfactory performance with a Sensitivity of 64.80%, a Specificity of 76.60%, an Accuracy of 74.90% and a Matthew's correlation coefficient of 0.3194. Feature analysis shows that some k-spaced amino acid pair features play the most important roles in the prediction of glutarylation sites. The conclusions derived from this study might provide some clues for understanding the molecular mechanisms of glutarylation. Copyright © 2018 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Zender, J. J.; Kariyappa, R.; Giono, G.; Bergmann, M.; Delouille, V.; Damé, L.; Hochedez, J.-F.; Kumara, S. T.
2017-09-01
Context. The magnetic field plays a dominant role in the solar irradiance variability. Determining the contribution of various magnetic features to this variability is important in the context of heliospheric studies and Sun-Earth connection. Aims: We studied the solar irradiance variability and its association with the underlying magnetic field for a period of five years (January 2011-January 2016). We used observations from the Large Yield Radiometer (LYRA), the Sun Watcher with Active Pixel System detector and Image Processing (SWAP) on board PROBA2, the Atmospheric Imaging Assembly (AIA), and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Methods: The Spatial Possibilistic Clustering Algorithm (SPoCA) is applied to the extreme ultraviolet (EUV) observations obtained from the AIA to segregate coronal features by creating segmentation maps of active regions (ARs), coronal holes (CHs) and the quiet sun (QS). Further, these maps are applied to the full-disk SWAP intensity images and the full-disk (FD) HMI line-of-sight (LOS) magnetograms to isolate the SWAP coronal features and photospheric magnetic counterparts, respectively. We then computed full-disk and feature-wise averages of EUV intensity and line of sight (LOS) magnetic flux density over ARs/CHs/QS/FD. The variability in these quantities is compared with that of LYRA irradiance values. Results: Variations in the quantities resulting from the segmentation, namely the integrated intensity and the total magnetic flux density of ARs/CHs/QS/FD regions, are compared with the LYRA irradiance variations. We find that the EUV intensity over ARs/CHs/QS/FD is well correlated with the underlying magnetic field. In addition, variations in the full-disk integrated intensity and magnetic flux density values are correlated with the LYRA irradiance variations. Conclusions: Using the segmented coronal features observed in the EUV wavelengths as proxies to isolate the underlying
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Additional security features for optically variable foils
NASA Astrophysics Data System (ADS)
Marshall, Allan C.; Russo, Frank
1998-04-01
For thousands of years, man has exploited the attraction and radiance of pure gold to adorn articles of great significance. Today, designers decorate packaging with metallic gold foils to maintain the prestige of luxury items such as perfumes, chocolates, wine and whisky, and to add visible appeal and value to wide range of products. However, today's products do not call for the hand beaten gold leaf of the Ancient Egyptians, instead a rapid production technology exists which makes use of accurately coated thin polymer films and vacuum deposited metallic layers. Stamping Foils Technology is highly versatile since several different layers may be combined into one product, each providing a different function. Not only can a foil bring visual appeal to an article, it can provide physical and chemical resistance properties and also protect an article from human forms of interference, such as counterfeiting, copying or tampering. Stamping foils have proved to be a highly effective vehicle for applying optical devices to items requiring this type of protection. Credit cards, bank notes, personal identification documents and more recently high value packaged items such as software and perfumes are protected by optically variable devices applied using stamping foil technology.
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Semantic data association for planar features in outdoor 6D-SLAM using lidar
NASA Astrophysics Data System (ADS)
Ulas, C.; Temeltas, H.
2013-05-01
Simultaneous Localization and Mapping (SLAM) is a fundamental problem of the autonomous systems in GPS (Global Navigation System) denied environments. The traditional probabilistic SLAM methods uses point features as landmarks and hold all the feature positions in their state vector in addition to the robot pose. The bottleneck of the point-feature based SLAM methods is the data association problem, which are mostly based on a statistical measure. The data association performance is very critical for a robust SLAM method since all the filtering strategies are applied after a known correspondence. For point-features, two different but very close landmarks in the same scene might be confused while giving the correspondence decision when their positions and error covariance matrix are solely taking into account. Instead of using the point features, planar features can be considered as an alternative landmark model in the SLAM problem to be able to provide a more consistent data association. Planes contain rich information for the solution of the data association problem and can be distinguished easily with respect to point features. In addition, planar maps are very compact since an environment has only very limited number of planar structures. The planar features does not have to be large structures like building wall or roofs; the small plane segments can also be used as landmarks like billboards, traffic posts and some part of the bridges in urban areas. In this paper, a probabilistic plane-feature extraction method from 3DLiDAR data and the data association based on the extracted semantic information of the planar features is introduced. The experimental results show that the semantic data association provides very satisfactory result in outdoor 6D-SLAM.
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Frank, Till D.; Carmody, Aimée M.; Kholodenko, Boris N.
2012-01-01
We derive a statistical model of transcriptional activation using equilibrium thermodynamics of chemical reactions. We examine to what extent this statistical model predicts synergy effects of cooperative activation of gene expression. We determine parameter domains in which greater-than-additive and less-than-additive effects are predicted for cooperative regulation by two activators. We show that the statistical approach can be used to identify different causes of synergistic greater-than-additive effects: nonlinearities of the thermostatistical transcriptional machinery and three-body interactions between RNA polymerase and two activators. In particular, our model-based analysis suggests that at low transcription factor concentrations cooperative activation cannot yield synergistic greater-than-additive effects, i.e., DNA transcription can only exhibit less-than-additive effects. Accordingly, transcriptional activity turns from synergistic greater-than-additive responses at relatively high transcription factor concentrations into less-than-additive responses at relatively low concentrations. In addition, two types of re-entrant phenomena are predicted. First, our analysis predicts that under particular circumstances transcriptional activity will feature a sequence of less-than-additive, greater-than-additive, and eventually less-than-additive effects when for fixed activator concentrations the regulatory impact of activators on the binding of RNA polymerase to the promoter increases from weak, to moderate, to strong. Second, for appropriate promoter conditions when activator concentrations are increased then the aforementioned re-entrant sequence of less-than-additive, greater-than-additive, and less-than-additive effects is predicted as well. Finally, our model-based analysis suggests that even for weak activators that individually induce only negligible increases in promoter activity, promoter activity can exhibit greater-than-additive responses when
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Forensic detection of noise addition in digital images
NASA Astrophysics Data System (ADS)
Cao, Gang; Zhao, Yao; Ni, Rongrong; Ou, Bo; Wang, Yongbin
2014-03-01
We proposed a technique to detect the global addition of noise to a digital image. As an anti-forensics tool, noise addition is typically used to disguise the visual traces of image tampering or to remove the statistical artifacts left behind by other operations. As such, the blind detection of noise addition has become imperative as well as beneficial to authenticate the image content and recover the image processing history, which is the goal of general forensics techniques. Specifically, the special image blocks, including constant and strip ones, are used to construct the features for identifying noise addition manipulation. The influence of noising on blockwise pixel value distribution is formulated and analyzed formally. The methodology of detectability recognition followed by binary decision is proposed to ensure the applicability and reliability of noising detection. Extensive experimental results demonstrate the efficacy of our proposed noising detector.
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Poltev, V; Anisimov, V M; Dominguez, V; Gonzalez, E; Deriabina, A; Garcia, D; Rivas, F; Polteva, N A
2018-02-01
Deciphering the mechanism of functioning of DNA as the carrier of genetic information requires identifying inherent factors determining its structure and function. Following this path, our previous DFT studies attributed the origin of unique conformational characteristics of right-handed Watson-Crick duplexes (WCDs) to the conformational profile of deoxydinucleoside monophosphates (dDMPs) serving as the minimal repeating units of DNA strand. According to those findings, the directionality of the sugar-phosphate chain and the characteristic ranges of dihedral angles of energy minima combined with the geometric differences between purines and pyrimidines determine the dependence on base sequence of the three-dimensional (3D) structure of WCDs. This work extends our computational study to complementary deoxydinucleotide-monophosphates (cdDMPs) of non-standard conformation, including those of Z-family, Hoogsteen duplexes, parallel-stranded structures, and duplexes with mispaired bases. For most of these systems, except Z-conformation, computations closely reproduce experimental data within the tolerance of characteristic limits of dihedral parameters for each conformation family. Computation of cdDMPs with Z-conformation reveals that their experimental structures do not correspond to the internal energy minimum. This finding establishes the leading role of external factors in formation of the Z-conformation. Energy minima of cdDMPs of non-Watson-Crick duplexes demonstrate different sequence-dependence features than those known for WCDs. The obtained results provide evidence that the biologically important regularities of 3D structure distinguish WCDs from duplexes having non-Watson-Crick nucleotide pairing.
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Cognitive and noncognitive neurological features of young-onset dementia.
Kelley, Brendan J; Boeve, Bradley F; Josephs, Keith A
2009-01-01
The rarity of young-onset dementia (YOD), the broad differential diagnosis and unusual clinical presentations present unique challenges to correctly recognize the condition and establish an accurate diagnosis. Limited data exist regarding clinical features associated with dementia prior to the age of 45. We retrospectively assessed cognitive and noncognitive neurological characteristics of 235 patients who presented for evaluation of YOD to investigate the clinical characteristics of YOD compared to later-onset dementias and to identify clinical features associated with specific etiologies that may aid in the evaluation of YOD. Multiple cognitive domains were affected in most patients, and no significant differences in affected domains existed between groups. Early psychiatric and behavioral features occurred at very high frequencies. Nearly 80% of this YOD cohort had additional noncognitive symptoms or signs as a feature of their disease. Chorea was strongly associated with Huntington disease. Parkinsonism was not seen in patients having an autoimmune/inflammatory etiology. The rarity of YOD and the high frequency of early psychiatric features led to frequent misdiagnosis early in the clinical course. The high frequency of noncognitive symptoms and signs may aid clinicians in distinguishing patients requiring a more extensive evaluation for YOD.
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Code of Federal Regulations, 2014 CFR
2014-07-01
... requirements for importers who import gasoline into the United States by truck. 80.1349 Section 80.1349... FUELS AND FUEL ADDITIVES Gasoline Benzene Sampling, Testing and Retention Requirements § 80.1349 Alternative sampling and testing requirements for importers who import gasoline into the United States by...
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Varying irrelevant phonetic features hinders learning of the feature being trained.
Antoniou, Mark; Wong, Patrick C M
2016-01-01
Learning to distinguish nonnative words that differ in a critical phonetic feature can be difficult. Speech training studies typically employ methods that explicitly direct the learner's attention to the relevant nonnative feature to be learned. However, studies on vision have demonstrated that perceptual learning may occur implicitly, by exposing learners to stimulus features, even if they are irrelevant to the task, and it has recently been suggested that this task-irrelevant perceptual learning framework also applies to speech. In this study, subjects took part in a seven-day training regimen to learn to distinguish one of two nonnative features, namely, voice onset time or lexical tone, using explicit training methods consistent with most speech training studies. Critically, half of the subjects were exposed to stimuli that varied not only in the relevant feature, but in the irrelevant feature as well. The results showed that subjects who were trained with stimuli that varied in the relevant feature and held the irrelevant feature constant achieved the best learning outcomes. Varying both features hindered learning and generalization to new stimuli.
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Additive manufacturing method for SRF components of various geometries
Rimmer, Robert; Frigola, Pedro E; Murokh, Alex Y
2015-05-05
An additive manufacturing method for forming nearly monolithic SRF niobium cavities and end group components of arbitrary shape with features such as optimized wall thickness and integral stiffeners, greatly reducing the cost and technical variability of conventional cavity construction. The additive manufacturing method for forming an SRF cavity, includes atomizing niobium to form a niobium powder, feeding the niobium powder into an electron beam melter under a vacuum, melting the niobium powder under a vacuum in the electron beam melter to form an SRF cavity; and polishing the inside surface of the SRF cavity.
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Feature Selection from a Facial Image for Distinction of Sasang Constitution
Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun
2009-01-01
Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013
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Chen, Jinying; Zheng, Jiaping; Yu, Hong
2016-11-30
Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians' agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen's kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for
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Face Alignment via Regressing Local Binary Features.
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.
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Constrained clusters of gene expression profiles with pathological features.
Sese, Jun; Kurokawa, Yukinori; Monden, Morito; Kato, Kikuya; Morishita, Shinichi
2004-11-22
Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC-Clustering revealed informative gene expression clusters, which could be annotated with various pathological features, such as 'tumor' and 'man', or 'except tumor' and 'normal liver function'. In contrast, the k-means method overlooked these clusters.
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Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.
Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng
2018-01-01
In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.
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Magnetic field feature extraction and selection for indoor location estimation.
Galván-Tejada, Carlos E; García-Vázquez, Juan Pablo; Brena, Ramon F
2014-06-20
User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.
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Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat
2015-06-01
Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.
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2011-01-01
Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite
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Asymmetric Michael Addition Mediated by Chiral Ionic Liquids.
Suzuki, Yumiko
2018-06-01
Chiral ionic liquids with a focus on their applications in asymmetric Michael additions and related reactions were reviewed. The examples were classified on the basis of the mode of asymmetric induction (e.g., external induction/non-covalent interaction or internal induction/covalent bond formation), the roles in reactions (as a solvent or catalyst), and their structural features (e.g., imidazolium-based chiral cations, other chiral oniums; proline derivatives). Most of the reactions with high chiral induction are Michael addition of ketones or aldehydes to chalcones or nitrostyrenes where proline-derived chiral ionic liquids catalyze the reaction through enamine/ iminium formation. Many reports demonstrate the recyclability of ionic liquid-tagged pyrrolidines.
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NASA Astrophysics Data System (ADS)
Emaminejad, Nastaran; Wahi-Anwar, Muhammad; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael
2018-02-01
Translation of radiomics into clinical practice requires confidence in its interpretations. This may be obtained via understanding and overcoming the limitations in current radiomic approaches. Currently there is a lack of standardization in radiomic feature extraction. In this study we examined a few factors that are potential sources of inconsistency in characterizing lung nodules, such as 1)different choices of parameters and algorithms in feature calculation, 2)two CT image dose levels, 3)different CT reconstruction algorithms (WFBP, denoised WFBP, and Iterative). We investigated the effect of variation of these factors on entropy textural feature of lung nodules. CT images of 19 lung nodules identified from our lung cancer screening program were identified by a CAD tool and contours provided. The radiomics features were extracted by calculating 36 GLCM based and 4 histogram based entropy features in addition to 2 intensity based features. A robustness index was calculated across different image acquisition parameters to illustrate the reproducibility of features. Most GLCM based and all histogram based entropy features were robust across two CT image dose levels. Denoising of images slightly improved robustness of some entropy features at WFBP. Iterative reconstruction resulted in improvement of robustness in a fewer times and caused more variation in entropy feature values and their robustness. Within different choices of parameters and algorithms texture features showed a wide range of variation, as much as 75% for individual nodules. Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study.
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Illusory conjunctions in simultanagnosia: coarse coding of visual feature location?
McCrea, Simon M; Buxbaum, Laurel J; Coslett, H Branch
2006-01-01
Simultanagnosia is a disorder characterized by an inability to see more than one object at a time. We report a simultanagnosic patient (ED) with bilateral posterior infarctions who produced frequent illusory conjunctions on tasks involving form and surface features (e.g., a red T) and form alone. ED also produced "blend" errors in which features of one familiar perceptual unit appeared to migrate to another familiar perceptual unit (e.g., "RO" read as "PQ"). ED often misread scrambled letter strings as a familiar word (e.g., "hmoe" read as "home"). Finally, ED's success in reporting two letters in an array was inversely related to the distance between the letters. These findings are consistent with the hypothesis that ED's illusory reflect coarse coding of visual feature location that is ameliorated in part by top-down information from object and word recognition systems; the findings are also consistent, however, with Treisman's Feature Integration Theory. Finally, the data provide additional support for the claim that the dorsal parieto-occipital cortex is implicated in the binding of visual feature information.
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Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks
NASA Astrophysics Data System (ADS)
Sun, Wei; Chang, K. C.
2005-05-01
Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.
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The analysis of image feature robustness using cometcloud
Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin
2012-01-01
The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759
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Feature extraction from multiple data sources using genetic programming
NASA Astrophysics Data System (ADS)
Szymanski, John J.; Brumby, Steven P.; Pope, Paul A.; Eads, Damian R.; Esch-Mosher, Diana M.; Galassi, Mark C.; Harvey, Neal R.; McCulloch, Hersey D.; Perkins, Simon J.; Porter, Reid B.; Theiler, James P.; Young, Aaron C.; Bloch, Jeffrey J.; David, Nancy A.
2002-08-01
Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.
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Visual search for feature and conjunction targets with an attention deficit.
Arguin, M; Joanette, Y; Cavanagh, P
1993-01-01
Abstract Brain-damaged subjects who had previously been identified as suffering from a visual attention deficit for contralesional stimulation were tested on a series of visual search tasks. The experiments examined the hypothesis that the processing of single features is preattentive but that feature integration, necessary for the correct perception of conjunctions of features, requires attention (Treisman & Gelade, 1980 Treisman & Sato, 1990). Subjects searched for a feature target (orientation or color) or for a conjunction target (orientation and color) in unilateral displays in which the number of items presented was variable. Ocular fixation was controlled so that trials on which eye movements occurred were cancelled. While brain-damaged subjects with a visual attention disorder (VAD subjects) performed similarly to normal controls in feature search tasks, they showed a marked deficit in conjunction search. Specifically, VAD subjects exhibited an important reduction of their serial search rates for a conjunction target with contralesional displays. In support of Treisman's feature integration theory, a visual attention deficit leads to a marked impairment in feature integration whereas it does not appear to affect feature encoding.
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Undercut feature recognition for core and cavity generation
NASA Astrophysics Data System (ADS)
Yusof, Mursyidah Md; Salman Abu Mansor, Mohd
2018-01-01
Core and cavity is one of the important components in injection mould where the quality of the final product is mostly dependent on it. In the industry, with years of experience and skill, mould designers commonly use commercial CAD software to design the core and cavity which is time consuming. This paper proposes an algorithm that detect possible undercut features and generate the core and cavity. Two approaches are presented; edge convexity and face connectivity approach. The edge convexity approach is used to recognize undercut features while face connectivity is used to divide the faces into top and bottom region.
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Coding visual features extracted from video sequences.
Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano
2014-05-01
Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Poeschla, Eric, E-mail: poeschla.eric@mayo.edu
Central initiation of plus strand synthesis is a conserved feature of lentiviruses and certain other retroelements. This complication of the standard reverse transcription mechanism produces a transient “central DNA flap” in the viral cDNA, which has been proposed to mediate its subsequent nuclear import. This model has assumed that the important feature is the flapped DNA structure itself rather than the process that produces it. Recently, an alternative kinetic model was proposed. It posits that central plus strand synthesis functions to accelerate conversion to the double-stranded state, thereby helping HIV-1 to evade single-strand DNA-targeting antiviral restrictions such as APOBEC3 proteins,more » and perhaps to avoid innate immune sensor mechanisms. The model is consistent with evidence that lentiviruses must often synthesize their cDNAs when dNTP concentrations are limiting and with data linking reverse transcription and uncoating. There may be additional kinetic advantages for the artificial genomes of lentiviral gene therapy vectors. - Highlights: • Two main functional models for HIV central plus strand synthesis have been proposed. • In one, a transient central DNA flap in the viral cDNA mediates HIV-1 nuclear import. • In the other, multiple kinetic consequences are emphasized. • One is defense against APOBEC3G, which deaminates single-stranded DNA. • Future questions pertain to antiviral restriction, uncoating and nuclear import.« less
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Prostate cancer detection: Fusion of cytological and textural features
Nguyen, Kien; Jain, Anil K.; Sabata, Bikash
2011-01-01
A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification. PMID:22811959
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A graph-Laplacian-based feature extraction algorithm for neural spike sorting.
Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos
2009-01-01
Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.
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Effect of Dissolution Kinetics on Feature Size in Dip-Pen Nanolithography
NASA Astrophysics Data System (ADS)
Weeks, B. L.; Noy, A.; Miller, A. E.; de Yoreo, J. J.
2002-06-01
We have investigated the effects of humidity, tip speed, and dwell time on feature size during dip pen nanolithography. Our results indicate a transition between two distinct deposition regimes occurs at a dwell time independent of humidity. While feature size increases with humidity, the relative increase is independent of dwell time. The results are described by a model that accounts for detachment and reattachment at the tip. The model suggests that, at short dwell times (high speed), the most important parameter controlling the feature size is the activation energy for thiol detachment.
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NASA Astrophysics Data System (ADS)
Yang, Wei; Zhang, Su; Li, Wenying; Chen, Yaqing; Lu, Hongtao; Chen, Wufan; Chen, Yazhu
2010-04-01
Various computerized features extracted from breast ultrasound images are useful in assessing the malignancy of breast tumors. However, the underlying relationship between the computerized features and tumor malignancy may not be linear in nature. We use the decision tree ensemble trained by the cost-sensitive boosting algorithm to approximate the target function for malignancy assessment and to reflect this relationship qualitatively. Partial dependence plots are employed to explore and visualize the effect of features on the output of the decision tree ensemble. In the experiments, 31 image features are extracted to quantify the sonographic characteristics of breast tumors. Patient age is used as an external feature because of its high clinical importance. The area under the receiver-operating characteristic curve of the tree ensembles can reach 0.95 with sensitivity of 0.95 (61/64) at the associated specificity 0.74 (77/104). The partial dependence plots of the four most important features are demonstrated to show the influence of the features on malignancy, and they are in accord with the empirical observations. The results can provide visual and qualitative references on the computerized image features for physicians, and can be useful for enhancing the interpretability of computer-aided diagnosis systems for breast ultrasound.
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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
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Novotny, Vojtech; Nacu, Aliona; Kvistad, Christopher E; Fromm, Annette; Neckelmann, Gesche F; Khanevski, Andrej N; Tobro, Haakon; Waje-Andreassen, Ulrike; Naess, Halvor; Thomassen, Lars; Logallo, Nicola
2017-11-08
Contrast-enhanced sonothrombolysis (CEST) seems to be a safe and promising treatment in acute ischemic stroke. It remains unknown if temporal bone features may influence the efficacy of CEST. We investigated the association between different temporal bone features on admission computed tomography (CT) scan and the outcome in acute ischemic stroke patients included in the randomized Norwegian Sonothrombolysis in Acute Stroke Study (NOR-SASS). Patients diagnosed as stroke mimics and those with infratentorial stroke or with incorrect insonation were excluded. We retrospectively assessed temporal bone heterogeneity (presence of diploë), diploë ratio, thickness, and density on admission CT scans. National institute of Health Stroke Scale (NIHSS) at 24 h and modified Rankin Scale (mRS) at 3 months were correlated with CT findings both in CEST and sham CEST patients. A total of 99 patients were included of which 52 were assigned to CEST and 47 to sham CEST. Approximately 20% patients had a heterogeneous temporal bone in both the CEST and sham CEST group. All temporal bone CT features studied were associated with female sex. In the CEST group, temporal bone heterogeneity (p = 0.006) and higher temporal bone diploë ratio (p = 0.002) were associated with higher NIHSS at 24 h. There was no association between temporal bone features and mRS at 3 months. Approximately 20% of acute ischemic stroke patients have heterogeneous temporal bone and may be resistant to standard 2-MHz transcranial Doppler ultrasound treatment. Sonothrombolysis resistance may easily be predicted by admission CT for better selection.
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MPEG content summarization based on compressed domain feature analysis
NASA Astrophysics Data System (ADS)
Sugano, Masaru; Nakajima, Yasuyuki; Yanagihara, Hiromasa
2003-11-01
This paper addresses automatic summarization of MPEG audiovisual content on compressed domain. By analyzing semantically important low-level and mid-level audiovisual features, our method universally summarizes the MPEG-1/-2 contents in the form of digest or highlight. The former is a shortened version of an original, while the latter is an aggregation of important or interesting events. In our proposal, first, the incoming MPEG stream is segmented into shots and the above features are derived from each shot. Then the features are adaptively evaluated in an integrated manner, and finally the qualified shots are aggregated into a summary. Since all the processes are performed completely on compressed domain, summarization is achieved at very low computational cost. The experimental results show that news highlights and sports highlights in TV baseball games can be successfully extracted according to simple shot transition models. As for digest extraction, subjective evaluation proves that meaningful shots are extracted from content without a priori knowledge, even if it contains multiple genres of programs. Our method also has the advantage of generating an MPEG-7 based description such as summary and audiovisual segments in the course of summarization.
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Mkpojiogu, Emmanuel O C; Hashim, Nor Laily
2016-01-01
Customer satisfaction is the result of product quality and viability. The place of the perceived satisfaction of users/customers for a software product cannot be neglected especially in today competitive market environment as it drives the loyalty of customers and promotes high profitability and return on investment. Therefore understanding the importance of requirements as it is associated with the satisfaction of users/customers when their requirements are met is worth the pain considering. It is necessary to know the relationship between customer satisfactions when their requirements are met (or their dissatisfaction when their requirements are unmet) and the importance of such requirement. So many works have been carried out on customer satisfaction in connection with the importance of requirements but the relationship between customer satisfaction scores (coefficients) of the Kano model and users/customers self-stated requirements importance have not been sufficiently explored. In this study, an attempt is made to unravel the underlying relationship existing between Kano model's customer satisfaction indexes and users/customers self reported requirements importance. The results of the study indicate some interesting associations between these considered variables. These bivariate associations reveal that customer satisfaction index (SI), and average satisfaction coefficient (ASC) and customer dissatisfaction index (DI) and average satisfaction coefficient (ASC) are highly correlated (r = 96 %) and thus ASC can be used in place of either SI or DI in representing customer satisfaction scores. Also, these Kano model's customer satisfaction variables (SI, DI, and ASC) are each associated with self-stated requirements importance (IMP). Further analysis indicates that the value customers or users place on requirements that are met or on features that are incorporated into a product influences the level of satisfaction such customers derive from the product. The
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Feature selection for the classification of traced neurons.
López-Cabrera, José D; Lorenzo-Ginori, Juan V
2018-06-01
The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.
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Frequency of the first feature in action sequences influences feature binding.
Mattson, Paul S; Fournier, Lisa R; Behmer, Lawrence P
2012-10-01
We investigated whether binding among perception and action feature codes is a preliminary step toward creating a more durable memory trace of an action event. If so, increasing the frequency of a particular event (e.g., a stimulus requiring a movement with the left or right hand in an up or down direction) should increase the strength and speed of feature binding for this event. The results from two experiments, using a partial-repetition paradigm, confirmed that feature binding increased in strength and/or occurred earlier for a high-frequency (e.g., left hand moving up) than for a low-frequency (e.g., right hand moving down) event. Moreover, increasing the frequency of the first-specified feature in the action sequence alone (e.g., "left" hand) increased the strength and/or speed of action feature binding (e.g., between the "left" hand and movement in an "up" or "down" direction). The latter finding suggests an update to the theory of event coding, as not all features in the action sequence equally determine binding strength. We conclude that action planning involves serial binding of features in the order of action feature execution (i.e., associations among features are not bidirectional but are directional), which can lead to a more durable memory trace. This is consistent with physiological evidence suggesting that serial order is preserved in an action plan executed from memory and that the first feature in the action sequence may be critical in preserving this serial order.
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NASA Astrophysics Data System (ADS)
Su, Guoshao; Shi, Yanjiong; Feng, Xiating; Jiang, Jianqing; Zhang, Jie; Jiang, Quan
2018-02-01
Rockbursts are markedly characterized by the ejection of rock fragments from host rocks at certain speeds. The rockburst process is always accompanied by acoustic signals that include acoustic emissions (AE) and sounds. A deep insight into the evolutionary features of AE and sound signals is important to improve the accuracy of rockburst prediction. To investigate the evolutionary features of AE and sound signals, rockburst tests on granite rock specimens under true-triaxial loading conditions were performed using an improved rockburst testing system, and the AE and sounds during rockburst development were recorded and analyzed. The results show that the evolutionary features of the AE and sound signals were obvious and similar. On the eve of a rockburst, a `quiescent period' could be observed in both the evolutionary process of the AE hits and the sound waveform. Furthermore, the time-dependent fractal dimensions of the AE hits and sound amplitude both showed a tendency to continuously decrease on the eve of the rockbursts. In addition, on the eve of the rockbursts, the main frequency of the AE and sound signals both showed decreasing trends, and the frequency spectrum distributions were both characterized by low amplitudes, wide frequency bands and multiple peak shapes. Thus, the evolutionary features of sound signals on the eve of rockbursts, as well as that of AE signals, can be used as beneficial information for rockburst prediction.
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ERIC Educational Resources Information Center
Webb, Tessa M.; Beech, John R.; Mayall, Kate M.; Andrews, Antony S.
2006-01-01
The relative importance of internal and external letter features of words in children's developing reading was investigated to clarify further the nature of early featural analysis. In Experiment 1, 72 6-, 8-, and 10-year-olds read aloud words displayed as wholes, external features only (central features missing, thereby preserving word shape…
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Yu, Zhiqiang; Paul, Rakesh; Bhattacharya, Chandrabali; Bozeman, Trevor C; Rishel, Michael J; Hecht, Sidney M
2015-05-19
We have shown previously that the bleomycin (BLM) carbohydrate moiety can recapitulate the tumor cell targeting effects of the entire BLM molecule, that BLM itself is modular in nature consisting of a DNA-cleaving aglycone which is delivered selectively to the interior of tumor cells by its carbohydrate moiety, and that there are disaccharides structurally related to the BLM disaccharide which are more efficient than the natural disaccharide at tumor cell targeting/uptake. Because BLM sugars can deliver molecular cargoes selectively to tumor cells, and thus potentially form the basis for a novel antitumor strategy, it seemed important to consider additional structural features capable of affecting the efficiency of tumor cell recognition and delivery. These included the effects of sugar polyvalency and net charge (at physiological pH) on tumor cell recognition, internalization, and trafficking. Since these parameters have been shown to affect cell surface recognition, internalization, and distribution in other contexts, this study has sought to define the effects of these structural features on tumor cell recognition by bleomycin and its disaccharide. We demonstrate that both can have a significant effect on tumor cell binding/internalization, and present data which suggests that the metal ions normally bound by bleomycin following clinical administration may significantly contribute to the efficiency of tumor cell uptake, in addition to their characterized function in DNA cleavage. A BLM disaccharide-Cy5** conjugate incorporating the positively charged dipeptide d-Lys-d-Lys was found to associate with both the mitochondria and the nuclear envelope of DU145 cells, suggesting possible cellular targets for BLM disaccharide-cytotoxin conjugates.
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Feature-to-Feature Inference Under Conditions of Cue Restriction and Dimensional Correlation.
Lancaster, Matthew E; Homa, Donald
2017-01-01
The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.
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Zeng, Guixiang; Sakaki, Shigeyoshi
2011-06-06
Through detailed calculations by density functional theory and second-order Møller-Plesset perturbation theory (MP2) to fourth-order Møller-Plesset perturbation theory including single, double, and quadruple excitations [MP4(SDQ)] methods, we investigated the oxidative addition of the B-Br bond of dibromo(trimethylsiloxy)borane [Br(2)B(OSiMe(3))] to Pt(0) and Pd(0) complexes [M(PMe(3))(2)] (M = Pt or Pd) directly yielding a trans bromoboryl complex trans-[MBr{BBr(OSiMe(3))}(PMe(3))(2)]. Two reaction pathways are found for this reaction: One is a nucleophilic attack pathway which directly leads to the trans product, and the other is a stepwise reaction pathway which occurs through successive cis oxidative addition of the B-Br bond to [M(PMe(3))(2)] and thermal cis-trans isomerization. In the Pt system, the former course occurs with a much smaller energy barrier (E(a) = 5.8 kcal/mol) than the latter one (E(a) = 20.7 kcal/mol), where the DFT-calculated E(a) value is presented hereafter. In the Pd system, only the latter course is found in which the rate-determining steps is the cis-trans isomerization with the E(a) of 15.1 kcal/mol. Interestingly, the thermal cis-trans isomerization occurs on the singlet potential energy surface against our expectation. This unexpected result is understood in terms of the strong donation ability of the boryl group. Detailed analyses of electronic processes in all these reaction steps as well as remarkable characteristic features of [Br(2)B(OSiMe(3))] are also provided. © 2011 American Chemical Society
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Improved Feature Matching for Mobile Devices with IMU.
Masiero, Andrea; Vettore, Antonio
2016-08-05
Thanks to the recent diffusion of low-cost high-resolution digital cameras and to the development of mostly automated procedures for image-based 3D reconstruction, the popularity of photogrammetry for environment surveys is constantly increasing in the last years. Automatic feature matching is an important step in order to successfully complete the photogrammetric 3D reconstruction: this step is the fundamental basis for the subsequent estimation of the geometry of the scene. This paper reconsiders the feature matching problem when dealing with smart mobile devices (e.g., when using the standard camera embedded in a smartphone as imaging sensor). More specifically, this paper aims at exploiting the information on camera movements provided by the inertial navigation system (INS) in order to make the feature matching step more robust and, possibly, computationally more efficient. First, a revised version of the affine scale-invariant feature transform (ASIFT) is considered: this version reduces the computational complexity of the original ASIFT, while still ensuring an increase of correct feature matches with respect to the SIFT. Furthermore, a new two-step procedure for the estimation of the essential matrix E (and the camera pose) is proposed in order to increase its estimation robustness and computational efficiency.
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Wilkening, Jennifer L; Ray, Chris; Varner, Johanna
2015-01-01
The American pika (Ochotona princeps) is considered a sentinel species for detecting ecological effects of climate change. Pikas are declining within a large portion of their range, and ongoing research suggests loss of sub-surface ice as a mechanism. However, no studies have demonstrated physiological responses of pikas to sub-surface ice features. Here we present the first analysis of physiological stress in pikas living in and adjacent to habitats underlain by ice. Fresh fecal samples were collected non-invasively from two adjacent sites in the Rocky Mountains (one with sub-surface ice and one without) and analyzed for glucocorticoid metabolites (GCM). We also measured sub-surface microclimates in each habitat. Results indicate lower GCM concentration in sites with sub-surface ice, suggesting that pikas are less stressed in favorable microclimates resulting from sub-surface ice features. GCM response was well predicted by habitat characteristics associated with sub-surface ice features, such as lower mean summer temperatures. These results suggest that pikas inhabiting areas without sub-surface ice features are experiencing higher levels of physiological stress and may be more susceptible to changing climates. Although post-deposition environmental effects can confound analyses based on fecal GCM, we found no evidence for such effects in this study. Sub-surface ice features are key to water cycling and storage and will likely represent an increasingly important component of water resources in a warming climate. Fecal samples collected from additional watersheds as part of current pika monitoring programs could be used to further characterize relationships between pika stress and sub-surface ice features.
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Wilkening, Jennifer L.; Ray, Chris; Varner, Johanna
2015-01-01
The American pika (Ochotona princeps) is considered a sentinel species for detecting ecological effects of climate change. Pikas are declining within a large portion of their range, and ongoing research suggests loss of sub-surface ice as a mechanism. However, no studies have demonstrated physiological responses of pikas to sub-surface ice features. Here we present the first analysis of physiological stress in pikas living in and adjacent to habitats underlain by ice. Fresh fecal samples were collected non-invasively from two adjacent sites in the Rocky Mountains (one with sub-surface ice and one without) and analyzed for glucocorticoid metabolites (GCM). We also measured sub-surface microclimates in each habitat. Results indicate lower GCM concentration in sites with sub-surface ice, suggesting that pikas are less stressed in favorable microclimates resulting from sub-surface ice features. GCM response was well predicted by habitat characteristics associated with sub-surface ice features, such as lower mean summer temperatures. These results suggest that pikas inhabiting areas without sub-surface ice features are experiencing higher levels of physiological stress and may be more susceptible to changing climates. Although post-deposition environmental effects can confound analyses based on fecal GCM, we found no evidence for such effects in this study. Sub-surface ice features are key to water cycling and storage and will likely represent an increasingly important component of water resources in a warming climate. Fecal samples collected from additional watersheds as part of current pika monitoring programs could be used to further characterize relationships between pika stress and sub-surface ice features. PMID:25803587
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Factors that affect micro-tooling features created by direct printing approach
NASA Astrophysics Data System (ADS)
Kumbhani, Mayur N.
Current market required faster pace production of smaller, better, and improved products in shorter amount of time. Traditional high-rate manufacturing process such as hot embossing, injection molding, compression molding, etc. use tooling to replicate feature on a products. Miniaturization of many product in the field of biomedical, electronics, optical, and microfluidic is occurring on a daily bases. There is a constant need to produce cheaper, and faster tooling, which can be utilize by existing manufacturing processes. Traditionally, in order to manufacture micron size tooling features processes such as micro-machining, Electrical Discharge Machining (EDM), etc. are utilized. Due to a higher difficulty to produce smaller size features, and longer production cycle time, various additive manufacturing approaches are proposed, e.g. selective laser sintering (SLS), inkjet printing (3DP), fused deposition modeling (FDM), etc. were proposed. Most of these approaches can produce net shaped products from different materials such as metal, ceramic, or polymers. Several attempts were made to produce tooling features using additive manufacturing approaches. Most of these produced tooling were not cost effective, and the life cycle of these tooling was reported short. In this research, a method to produce tooling features using direct printing approach, where highly filled feedstock was dispensed on a substrate. This research evaluated different natural binders, such as guar gum, xanthan gum, and sodium carboxymethyl cellulose (NaCMC) and their combinations were evaluated. The best binder combination was then use to evaluate effect of different metal (316L stainless steel (3 mum), 316 stainless steel (45 mum), and 304 stainless steel (45 mum)) particle size on feature quality. Finally, the effect of direct printing process variables such as dispensing tip internal diameter (500 mum, and 333 mum) at different printing speeds were evaluated.
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Rust, Roland T; Thompson, Debora Viana; Hamilton, Rebecca W
2006-02-01
Consider a coffeemaker that offers 12 drink options, a car with more than 700 features on the dashboard, and a mouse pad that's also a clock, calculator, and FM radio. All are examples of "feature bloat", or "featuritis", the result of an almost irresistible temptation to load products with lots of bells and whistles. The problem is that the more features a product boasts, the harder it is to use. Manufacturers that increase a product's capability--the number of useful functions it can perform--at the expense of its usability are exposing their customers to feature fatigue. The authors have conducted three studies to gain a better understanding of how consumers weigh a product's capability relative to its usability. They found that even though consumers know that products with more features are harder to use, they initially choose high-feature models. They also pile on more features when given the chance to customize a product for their needs. Once consumers have actually worked with a product, however, usability starts to matter more to them than capability. For managers in consumer products companies, these findings present a dilemma: Should they maximize initial sales by designing high-feature models, which consumers consistently choose, or should they limit the number of features in order to enhance the lifetime value of their customers? The authors' analytical model guides companies toward a happy middle ground: maximizing the net present value of the typical customer's profit stream. The authors also advise companies to build simpler products, help consumers learn which products suit their needs, develop products that do one thing very well, and design market research in which consumers use actual products or prototypes.
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Comparative study of palm print authentication system using geometric features
NASA Astrophysics Data System (ADS)
Shreyas, Kamath K. M.; Rajeev, Srijith; Panetta, Karen; Agaian, Sos S.
2017-05-01
Biometrics, particularly palm print authentication has been a stimulating research area due to its abundance of features. Stable features and effective matching are the most crucial steps for an authentication system. In conventional palm print authentication systems, matching is based on flexion creases, friction ridges, and minutiae points. Currently, contactless palm print imaging is an emerging technology. However, they tend to involve fluctuations in the image quality and texture loss due to factors such as varying illumination conditions, occlusions, noise, pose, and ghosting. These variations decrease the performance of the authentication systems. Furthermore, real-time palm print authentication in large databases continue to be a challenging task. In order to effectively solve these problems, features which are invariant to these anomalies are required. This paper proposes a robust palm print matching framework by making a comparative study of different local geometric features such as Difference-of-Gaussian, Hessian, Hessian-Laplace, Harris-Laplace, and Multiscale Harris for feature detection. These detectors are coupled with Scale Invariant Feature Transformation (SIFT) descriptor to describe the identified features. Additionally, a two-stage refinement process is carried out to obtain the best stable matches. Computer simulations demonstrate that the accuracy of the system has increased effectively with an EER of 0.86% when Harris-Laplace detector is used on IITD database.
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Research of image retrieval technology based on color feature
NASA Astrophysics Data System (ADS)
Fu, Yanjun; Jiang, Guangyu; Chen, Fengying
2009-10-01
Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram
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Identification of particle-laden flow features from wavelet decomposition
NASA Astrophysics Data System (ADS)
Jackson, A.; Turnbull, B.
2017-12-01
A wavelet decomposition based technique is applied to air pressure data obtained from laboratory-scale powder snow avalanches. This technique is shown to be a powerful tool for identifying both repeatable and chaotic features at any frequency within the signal. Additionally, this technique is demonstrated to be a robust method for the removal of noise from the signal as well as being capable of removing other contaminants from the signal. Whilst powder snow avalanches are the focus of the experiments analysed here, the features identified can provide insight to other particle-laden gravity currents and the technique described is applicable to a wide variety of experimental signals.
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Interpersonal Features and Functions of Nonsuicidal Self-Injury
ERIC Educational Resources Information Center
Muehlenkamp, Jennifer; Brausch, Amy; Quigley, Katherine; Whitlock, Janis
2013-01-01
Etiological models of nonsuicidal self-injury (NSSI) suggest interpersonal features may be important to understand this behavior, but social functions and correlates have not been extensively studied. This study addresses existing limitations by examining interpersonal correlates and functions of NSSI within a stratified random sample of 1,243…
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How Methodological Features Affect Effect Sizes in Education
ERIC Educational Resources Information Center
Cheung, Alan; Slavin, Robert
2016-01-01
As evidence-based reform becomes increasingly important in educational policy, it is becoming essential to understand how research design might contribute to reported effect sizes in experiments evaluating educational programs. The purpose of this study was to examine how methodological features such as types of publication, sample sizes, and…
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Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer
Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.
2016-01-01
Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980
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A Ruby API to query the Ensembl database for genomic features.
Strozzi, Francesco; Aerts, Jan
2011-04-01
The Ensembl database makes genomic features available via its Genome Browser. It is also possible to access the underlying data through a Perl API for advanced querying. We have developed a full-featured Ruby API to the Ensembl databases, providing the same functionality as the Perl interface with additional features. A single Ruby API is used to access different releases of the Ensembl databases and is also able to query multi-species databases. Most functionality of the API is provided using the ActiveRecord pattern. The library depends on introspection to make it release independent. The API is available through the Rubygem system and can be installed with the command gem install ruby-ensembl-api.
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Investigation of kinematic features for dismount detection and tracking
NASA Astrophysics Data System (ADS)
Narayanaswami, Ranga; Tyurina, Anastasia; Diel, David; Mehra, Raman K.; Chinn, Janice M.
2012-05-01
With recent changes in threats and methods of warfighting and the use of unmanned aircrafts, ISR (Intelligence, Surveillance and Reconnaissance) activities have become critical to the military's efforts to maintain situational awareness and neutralize the enemy's activities. The identification and tracking of dismounts from surveillance video is an important step in this direction. Our approach combines advanced ultra fast registration techniques to identify moving objects with a classification algorithm based on both static and kinematic features of the objects. Our objective was to push the acceptable resolution beyond the capability of industry standard feature extraction methods such as SIFT (Scale Invariant Feature Transform) based features and inspired by it, SURF (Speeded-Up Robust Feature). Both of these methods utilize single frame images. We exploited the temporal component of the video signal to develop kinematic features. Of particular interest were the easily distinguishable frequencies characteristic of bipedal human versus quadrupedal animal motion. We examine limits of performance, frame rates and resolution required for human, animal and vehicles discrimination. A few seconds of video signal with the acceptable frame rate allow us to lower resolution requirements for individual frames as much as by a factor of five, which translates into the corresponding increase of the acceptable standoff distance between the sensor and the object of interest.
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[Imported malaria and HIV infection in Madrid. Clinical and epidemiological features].
Ramírez-Olivencia, G; Herrero, M D; Subirats, M; de Juanes, J R; Peña, J M; Puente, S
2012-01-01
Few data are available in Spain data on human immunodeficiency virus (HIV) patients coinfected with malaria. This study has aimed to determine the epidemiological and clinical characteristics of imported malaria in patients coinfected with HIV. A case-series retrospective study was performed using the patient's medical records. The study population consisted on patients diagnosed with malaria attended in our center from january 1, 2002 to december 31, 2007. A total of 484 episodes of malaria, 398 of which were included in this study, were identified. Co-infection with HIV was described in 32 cases. All of them occurred in individuals presumably with some degree of semi-immunity. In the coinfected group, there were 13 cases (40.6%) asymptomatic, whereas this event occurred in 99 cases of patients not coinfected (37.2%) (P=0.707). The greater presence of anemia in co-infected patients (62.5% vs 32.3% in non-coinfected [P=0.001]) stands out. In present study, the clinical presentation forms were similar, regardless of the presence or absence of HIV infection. Although the study population does not reflect all possible scenarios of malaria and HIV coinfection, our results indicate the reality of patients attended in the Autonomous Community of Madrid. Copyright © 2011 Elsevier España, S.L. All rights reserved.
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A human performance evaluation of graphic symbol-design features.
Samet, M G; Geiselman, R E; Landee, B M
1982-06-01
16 subjects learned each of two tactical display symbol sets (conventional symbols and iconic symbols) in turn and were then shown a series of graphic displays containing various symbol configurations. For each display, the subject was asked questions corresponding to different behavioral processes relating to symbol use (identification, search, comparison, pattern recognition). The results indicated that: (a) conventional symbols yielded faster pattern-recognition performance than iconic symbols, and iconic symbols did not yield faster identification than conventional symbols, and (b) the portrayal of additional feature information (through the use of perimeter density or vector projection coding) slowed processing of the core symbol information in four tasks, but certain symbol-design features created less perceptual interference and had greater correspondence with the portrayal of specific tactical concepts than others. The results were discussed in terms of the complexities involved in the selection of symbol design features for use in graphic tactical displays.
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Estimating Missing Features to Improve Multimedia Information Retrieval
DOE Office of Scientific and Technical Information (OSTI.GOV)
Bagherjeiran, A; Love, N S; Kamath, C
Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features.more » In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query.« less
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Defying Intuition: Demonstrating the Importance of the Empirical Technique.
ERIC Educational Resources Information Center
Kohn, Art
1992-01-01
Describes a classroom activity featuring a simple stay-switch probability game. Contends that the exercise helps students see the importance of empirically validating beliefs. Includes full instructions for conducting and discussing the exercise. (CFR)
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Assessment of features for automatic CTG analysis based on expert annotation.
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.
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Asymmetric Michael Addition Mediated by Chiral Ionic Liquids
Suzuki, Yumiko
2018-01-01
Chiral ionic liquids with a focus on their applications in asymmetric Michael additions and related reactions were reviewed. The examples were classified on the basis of the mode of asymmetric induction (e.g., external induction/non-covalent interaction or internal induction/covalent bond formation), the roles in reactions (as a solvent or catalyst), and their structural features (e.g., imidazolium-based chiral cations, other chiral oniums; proline derivatives). Most of the reactions with high chiral induction are Michael addition of ketones or aldehydes to chalcones or nitrostyrenes where proline-derived chiral ionic liquids catalyze the reaction through enamine/ iminium formation. Many reports demonstrate the recyclability of ionic liquid-tagged pyrrolidines. PMID:29861702
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Chimeric Mice with Competent Hematopoietic Immunity Reproduce Key Features of Severe Lassa Fever.
Oestereich, Lisa; Lüdtke, Anja; Ruibal, Paula; Pallasch, Elisa; Kerber, Romy; Rieger, Toni; Wurr, Stephanie; Bockholt, Sabrina; Pérez-Girón, José V; Krasemann, Susanne; Günther, Stephan; Muñoz-Fontela, César
2016-05-01
Lassa fever (LASF) is a highly severe viral syndrome endemic to West African countries. Despite the annual high morbidity and mortality caused by LASF, very little is known about the pathophysiology of the disease. Basic research on LASF has been precluded due to the lack of relevant small animal models that reproduce the human disease. Immunocompetent laboratory mice are resistant to infection with Lassa virus (LASV) and, to date, only immunodeficient mice, or mice expressing human HLA, have shown some degree of susceptibility to experimental infection. Here, transplantation of wild-type bone marrow cells into irradiated type I interferon receptor knockout mice (IFNAR-/-) was used to generate chimeric mice that reproduced important features of severe LASF in humans. This included high lethality, liver damage, vascular leakage and systemic virus dissemination. In addition, this model indicated that T cell-mediated immunopathology was an important component of LASF pathogenesis that was directly correlated with vascular leakage. Our strategy allows easy generation of a suitable small animal model to test new vaccines and antivirals and to dissect the basic components of LASF pathophysiology.
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Cloud and surface textural features in polar regions
NASA Technical Reports Server (NTRS)
Welch, Ronald M.; Kuo, Kwo-Sen; Sengupta, Sailes K.
1990-01-01
The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.
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Deep feature extraction and combination for synthetic aperture radar target classification
NASA Astrophysics Data System (ADS)
Amrani, Moussa; Jiang, Feng
2017-10-01
Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.
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Fang, Chunying; Li, Haifeng; Ma, Lin; Zhang, Mancai
2017-01-01
Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S -transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S -transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F -score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.
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Adaptability: An Important Capacity for Effective Teachers
ERIC Educational Resources Information Center
Collie, Rebecca J.; Martin, Andrew J.
2016-01-01
A defining feature of teaching work is that it involves novelty, change, and uncertainty on a daily basis. Being able to respond effectively to this change is known as adaptability. In this article, we discuss the importance of adaptability for teachers and their healthy and effective functioning in the workplace. We discuss approaches for…
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Extracting intrinsic functional networks with feature-based group independent component analysis.
Calhoun, Vince D; Allen, Elena
2013-04-01
There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro
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Parsa, Soroush; Ccanto, Raúl; Olivera, Edgar; Scurrah, María; Alcázar, Jesús; Rosenheim, Jay A.
2012-01-01
Background Pest impact on an agricultural field is jointly influenced by local and landscape features. Rarely, however, are these features studied together. The present study applies a “facilitated ecoinformatics” approach to jointly screen many local and landscape features of suspected importance to Andean potato weevils (Premnotrypes spp.), the most serious pests of potatoes in the high Andes. Methodology/Principal Findings We generated a comprehensive list of predictors of weevil damage, including both local and landscape features deemed important by farmers and researchers. To test their importance, we assembled an observational dataset measuring these features across 138 randomly-selected potato fields in Huancavelica, Peru. Data for local features were generated primarily by participating farmers who were trained to maintain records of their management operations. An information theoretic approach to modeling the data resulted in 131,071 models, the best of which explained 40.2–46.4% of the observed variance in infestations. The best model considering both local and landscape features strongly outperformed the best models considering them in isolation. Multi-model inferences confirmed many, but not all of the expected patterns, and suggested gaps in local knowledge for Andean potato weevils. The most important predictors were the field's perimeter-to-area ratio, the number of nearby potato storage units, the amount of potatoes planted in close proximity to the field, and the number of insecticide treatments made early in the season. Conclusions/Significance Results underscored the need to refine the timing of insecticide applications and to explore adjustments in potato hilling as potential control tactics for Andean weevils. We believe our study illustrates the potential of ecoinformatics research to help streamline IPM learning in agricultural learning collaboratives. PMID:22693551
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Features and Historical Aspects of the Philippines Educational System
ERIC Educational Resources Information Center
Musa, Sajid; Ziatdinov, Rushan
2012-01-01
This article deals with the features of the Philippine educational system. Additionally, brief and concise information will be given on how the educational system came into existence, the organization and the structure of the system itself. This paper also tackles the obstacles and problems observed in the past and up to the present, and gives…
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Perceptual approaches to finding features in data
NASA Astrophysics Data System (ADS)
Rogowitz, Bernice E.
2013-03-01
Electronic imaging applications hinge on the ability to discover features in data. For example, doctors examine diagnostic images for tumors, broken bones and changes in metabolic activity. Financial analysts explore visualizations of market data to find correlations, outliers and interaction effects. Seismologists look for signatures in geological data to tell them where to drill or where an earthquake may begin. These data are very diverse, including images, numbers, graphs, 3-D graphics, and text, and are growing exponentially, largely through the rise in automatic data collection technologies such as sensors and digital imaging. This paper explores important trends in the art and science of finding features in data, such as the tension between bottom-up and top-down processing, the semantics of features, and the integration of human- and algorithm-based approaches. This story is told from the perspective of the IS and T/SPIE Conference on Human Vision and Electronic Imaging (HVEI), which has fostered research at the intersection between human perception and the evolution of new technologies.
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Feature selection and classification model construction on type 2 diabetic patients' data.
Huang, Yue; McCullagh, Paul; Black, Norman; Harper, Roy
2007-11-01
Diabetes affects between 2% and 4% of the global population (up to 10% in the over 65 age group), and its avoidance and effective treatment are undoubtedly crucial public health and health economics issues in the 21st century. The aim of this research was to identify significant factors influencing diabetes control, by applying feature selection to a working patient management system to assist with ranking, classification and knowledge discovery. The classification models can be used to determine individuals in the population with poor diabetes control status based on physiological and examination factors. The diabetic patients' information was collected by Ulster Community and Hospitals Trust (UCHT) from year 2000 to 2004 as part of clinical management. In order to discover key predictors and latent knowledge, data mining techniques were applied. To improve computational efficiency, a feature selection technique, feature selection via supervised model construction (FSSMC), an optimisation of ReliefF, was used to rank the important attributes affecting diabetic control. After selecting suitable features, three complementary classification techniques (Naïve Bayes, IB1 and C4.5) were applied to the data to predict how well the patients' condition was controlled. FSSMC identified patients' 'age', 'diagnosis duration', the need for 'insulin treatment', 'random blood glucose' measurement and 'diet treatment' as the most important factors influencing blood glucose control. Using the reduced features, a best predictive accuracy of 95% and sensitivity of 98% was achieved. The influence of factors, such as 'type of care' delivered, the use of 'home monitoring', and the importance of 'smoking' on outcome can contribute to domain knowledge in diabetes control. In the care of patients with diabetes, the more important factors identified: patients' 'age', 'diagnosis duration' and 'family history', are beyond the control of physicians. Treatment methods such as 'insulin', 'diet
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Wallis, Thomas S A; Funke, Christina M; Ecker, Alexander S; Gatys, Leon A; Wichmann, Felix A; Bethge, Matthias
2017-10-01
Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.
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Using high spectral resolution spectrophotometry to study broad mineral absorption features on Mars
NASA Technical Reports Server (NTRS)
Blaney, D. L.; Crisp, D.
1993-01-01
Traditionally telescopic measurements of mineralogic absorption features have been made using relatively low to moderate (R=30-300) spectral resolution. Mineralogic absorption features tend to be broad so high resolution spectroscopy (R greater than 10,000) does not provide significant additional compositional information. Low to moderate resolution spectroscopy allows an observer to obtain data over a wide wavelength range (hundreds to thousands of wavenumbers) compared to the several wavenumber intervals that are collected using high resolution spectrometers. However, spectrophotometry at high resolution has major advantages over lower resolution spectroscopy in situations that are applicable to studies of the Martian surface, i.e., at wavelengths where relatively weak surface absorption features and atmospheric gas absorption features both occur.
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Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing
2018-06-01
Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Classification Influence of Features on Given Emotions and Its Application in Feature Selection
NASA Astrophysics Data System (ADS)
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.
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von Braunmühl, T; Hartmann, D; Tietze, J K; Cekovic, D; Kunte, C; Ruzicka, T; Berking, C; Sattler, E C
2016-11-01
Optical coherence tomography (OCT) has become a valuable non-invasive tool in the in vivo diagnosis of non-melanoma skin cancer, especially of basal cell carcinoma (BCC). Due to an updated software-supported algorithm, a new en-face mode - similar to the horizontal en-face mode in high-definition OCT and reflectance confocal microscopy - surface-parallel imaging is possible which, in combination with the established slice mode of frequency domain (FD-)OCT, may offer additional information in the diagnosis of BCC. To define characteristic morphologic features of BCC using the new en-face mode in addition to the conventional cross-sectional imaging mode for three-dimensional imaging of BCC in FD-OCT. A total of 33 BCC were examined preoperatively by imaging in en-face mode as well as cross-sectional mode in FD-OCT. Characteristic features were evaluated and correlated with histopathology findings. Features established in the cross-sectional imaging mode as well as additional features were present in the en-face mode of FD-OCT: lobulated structures (100%), dark peritumoral rim (75%), bright peritumoral stroma (96%), branching vessels (90%), compressed fibrous bundles between lobulated nests ('star shaped') (78%), and intranodular small bright dots (51%). These features were also evaluated according to the histopathological subtype. In the en-face mode, the lobulated structures with compressed fibrous bundles of the BCC were more distinct than in the slice mode. FD-OCT with a new depiction for horizontal and vertical imaging modes offers additional information in the diagnosis of BCC, especially in nodular BCC, and enhances the possibility of the evaluation of morphologic tumour features. © 2016 European Academy of Dermatology and Venereology.
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Feature extraction inspired by V1 in visual cortex
NASA Astrophysics Data System (ADS)
Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang
2018-04-01
Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.
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Chen, Yifei; Sun, Yuxing; Han, Bing-Qing
2015-01-01
Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.
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Walsh-Hadamard transform kernel-based feature vector for shot boundary detection.
Lakshmi, Priya G G; Domnic, S
2014-12-01
Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.
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Competing features influence children's attention to number.
Chan, Jenny Yun-Chen; Mazzocco, Michèle M M
2017-04-01
Spontaneous focus on numerosity (SFON), an attentional process that some consider distinct from number knowledge, predicts later mathematical skills. Here we assessed the "spontaneity" and malleability of SFON using a picture-matching task. We asked children to view a target picture and to choose which of four other pictures matched the target. We tested whether attention to number (defined as number-based matches) was affected by (a) age, (b) the presence of very noticeable (or salient) features among alternative match choices, and (c) the examiner's use of motor actions to emphasize numerosity. Although adults attended to number more frequently than did preschoolers, the salience of competing features affected responses to number in both age groups. Specifically, number-based matches were more likely when alternative choices matched the target on features of low versus high salience (e.g., the relative location within a picture frame vs. color). In addition, adults' attention to number was more frequent if their first exposure to number-based matches occurred with alternative choices that matched the target on low salience features. This order by salience interaction was not observed among children. Simply observing motor actions that emphasized number (i.e., tapping stimuli) did not enhance children's attention to number. The results extend previous findings on SFON and provide evidence for the contextual influences on, and malleability of, attention to number. Copyright © 2016 Elsevier Inc. All rights reserved.
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Dedifferentiated Liposarcoma With Epithelioid/Epithelial Features.
Makise, Naohiro; Yoshida, Akihiko; Komiyama, Motokiyo; Nakatani, Fumihiko; Yonemori, Kan; Kawai, Akira; Fukayama, Masashi; Hiraoka, Nobuyoshi
2017-11-01
Dedifferentiated liposarcoma (DDLPS) demonstrates a variety of growth patterns, and their histologic resemblance to other spindle cell mesenchymal tumors has been widely recognized. However, epithelioid morphology in DDLPS has only rarely been documented. Here, we report 6 cases of DDLPS with striking epithelioid/epithelial features. The patients were 5 men and 1 woman with a median age of 61 years. All tumors were located in the internal trunk. During follow-up of 1 to 41 months, local recurrence, distant metastases, and tumor-related death occurred in 4, 2, and 4 patients, respectively. Beside well-differentiated liposarcoma component and conventional high-grade spindle cell morphology, all tumors focally exhibited growth comprising small or large epithelioid cells in diffuse or sheet-like proliferation. Rhabdoid cells were present in 2 cases. All 5 tumors tested harbored MDM2 amplification. Cytokeratin and/or epithelial membrane antigen were at least focally positive in all 5 tumors tested. One case contained a small focus of novel heterologous epithelial differentiation with acinar structures, wherein cytokeratin, MOC31, and claudin-4 were diffusely expressed and H3K27me3 expression was lost. DDLPS with epithelioid/epithelial features may lead to misdiagnosis of carcinoma or mesothelioma, and their diagnosis should be based on correlation with clinicopathologic and molecular findings. The epithelioid morphology in DDLPS may suggest an aggressive behavior based on this small series. In addition, we document 2 cases of MDM2-amplified undifferentiated neoplasm with epithelioid features in the internal trunk that lacked association with well-differentiated liposarcoma histology and showed rapid clinical course. Whether these latter tumors belong to DDLPS with epithelioid features requires further study.
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EFS: an ensemble feature selection tool implemented as R-package and web-application.
Neumann, Ursula; Genze, Nikita; Heider, Dominik
2017-01-01
Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.
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Deep Learning Methods for Underwater Target Feature Extraction and Recognition
Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang
2018-01-01
The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407
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Tablet and Smartphone Accessibility Features in the Low Vision Rehabilitation
Irvine, Danielle; Zemke, Alex; Pusateri, Gregg; Gerlach, Leah; Chun, Rob; Jay, Walter M.
2014-01-01
Abstract Tablet and smartphone use is rapidly increasing in developed countries. With this upsurge in popularity, the devices themselves are becoming more user-friendly for all consumers, including the visually impaired. Traditionally, visually impaired patients have received optical rehabilitation in the forms of microscopes, stand magnifiers, handheld magnifiers, telemicroscopes, and electronic magnification such as closed circuit televisions (CCTVs). In addition to the optical and financial limitations of traditional devices, patients do not always view them as being socially acceptable. For this reason, devices are often underutilised by patients due to lack of use in public forums or when among peers. By incorporating smartphones and tablets into a patient’s low vision rehabilitation, in addition to traditional devices, one provides versatile and mainstream options, which may also be less expensive. This article explains exactly what the accessibility features of tablets and smartphones are for the blind and visually impaired, how to access them, and provides an introduction on usage of the features. PMID:27928274
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Xu, Juan; Li, Chuan-Xing; Li, Yong-Sheng; Lv, Jun-Ying; Ma, Ye; Shao, Ting-Ting; Xu, Liang-De; Wang, Ying-Ying; Du, Lei; Zhang, Yun-Peng; Jiang, Wei; Li, Chun-Quan; Xiao, Yun; Li, Xia
2011-02-01
Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.
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Hemorrhage detection in MRI brain images using images features
NASA Astrophysics Data System (ADS)
Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela
2013-11-01
The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.
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Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan
2012-12-01
A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.
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The Role of Attention for Context-Context Binding of Intrinsic and Extrinsic Features
ERIC Educational Resources Information Center
Boywitt, C. Dennis; Meiser, Thorsten
2012-01-01
There is converging evidence that the feeling of conscious recollection is usually accompanied by the bound retrieval of context features of the encoding episode (e.g., Meiser, Sattler, & Weiber, 2008). Recently, however, important limiting conditions have been identified for the binding between context features in memory. For example, focusing on…
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Robust extrema features for time-series data analysis.
Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N
2013-06-01
The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.
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Neural Architecture for Feature Binding in Visual Working Memory.
Schneegans, Sebastian; Bays, Paul M
2017-04-05
Binding refers to the operation that groups different features together into objects. We propose a neural architecture for feature binding in visual working memory that employs populations of neurons with conjunction responses. We tested this model using cued recall tasks, in which subjects had to memorize object arrays composed of simple visual features (color, orientation, and location). After a brief delay, one feature of one item was given as a cue, and the observer had to report, on a continuous scale, one or two other features of the cued item. Binding failure in this task is associated with swap errors, in which observers report an item other than the one indicated by the cue. We observed that the probability of swapping two items strongly correlated with the items' similarity in the cue feature dimension, and found a strong correlation between swap errors occurring in spatial and nonspatial report. The neural model explains both swap errors and response variability as results of decoding noisy neural activity, and can account for the behavioral results in quantitative detail. We then used the model to compare alternative mechanisms for binding nonspatial features. We found the behavioral results fully consistent with a model in which nonspatial features are bound exclusively via their shared location, with no indication of direct binding between color and orientation. These results provide evidence for a special role of location in feature binding, and the model explains how this special role could be realized in the neural system. SIGNIFICANCE STATEMENT The problem of feature binding is of central importance in understanding the mechanisms of working memory. How do we remember not only that we saw a red and a round object, but that these features belong together to a single object rather than to different objects in our environment? Here we present evidence for a neural mechanism for feature binding in working memory, based on encoding of visual
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Quick Survey of Smartphone Features and Functions
Fox, Brent I.; Felkey, Bill G.
2014-01-01
What do you do when you leave the house without your smartphone? Do you sleep with it beside your bed? For us, these devices are as much a part of our lives as the belts around our waists. But, how long has it been since you surveyed the market? We provide a topical update on the current features and functions of these immensely important devices. PMID:24958977
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Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom
2016-01-01
The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640
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Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom
2016-01-01
The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.
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Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel
2017-01-01
Finding a good predictive model for a high-dimensional data set can be challenging. For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable. This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial. We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features. As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures. We conclude that the Pearson correlation has the best theoretical and empirical properties. Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features. Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy.
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Can two dots form a Gestalt? Measuring emergent features with the capacity coefficient.
Hawkins, Robert X D; Houpt, Joseph W; Eidels, Ami; Townsend, James T
2016-09-01
While there is widespread agreement among vision researchers on the importance of some local aspects of visual stimuli, such as hue and intensity, there is no general consensus on a full set of basic sources of information used in perceptual tasks or how they are processed. Gestalt theories place particular value on emergent features, which are based on the higher-order relationships among elements of a stimulus rather than local properties. Thus, arbitrating between different accounts of features is an important step in arbitrating between local and Gestalt theories of perception in general. In this paper, we present the capacity coefficient from Systems Factorial Technology (SFT) as a quantitative approach for formalizing and rigorously testing predictions made by local and Gestalt theories of features. As a simple, easily controlled domain for testing this approach, we focus on the local feature of location and the emergent features of Orientation and Proximity in a pair of dots. We introduce a redundant-target change detection task to compare our capacity measure on (1) trials where the configuration of the dots changed along with their location against (2) trials where the amount of local location change was exactly the same, but there was no change in the configuration. Our results, in conjunction with our modeling tools, favor the Gestalt account of emergent features. We conclude by suggesting several candidate information-processing models that incorporate emergent features, which follow from our approach. Copyright © 2015 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki
2017-09-01
Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.
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A Novel Robot Visual Homing Method Based on SIFT Features
Zhu, Qidan; Liu, Chuanjia; Cai, Chengtao
2015-01-01
Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method. The algorithm is novel in using SIFT features as landmarks instead of the pixels in the horizon region of the panoramic image. In addition, to further improve the matching accuracy of landmarks in the homing algorithm, a novel mismatching elimination algorithm, based on the distribution characteristics of landmarks in the catadioptric panoramic image, is proposed. Experiments on image databases and on a real scene confirm the effectiveness of the proposed method. PMID:26473880
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Critical Song Features for Auditory Pattern Recognition in Crickets
Meckenhäuser, Gundula; Hennig, R. Matthias; Nawrot, Martin P.
2013-01-01
Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females’ phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale. PMID:23437054
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Incorporating Feature-Based Annotations into Automatically Generated Knowledge Representations
NASA Astrophysics Data System (ADS)
Lumb, L. I.; Lederman, J. I.; Aldridge, K. D.
2006-12-01
Earth Science Markup Language (ESML) is efficient and effective in representing scientific data in an XML- based formalism. However, features of the data being represented are not accounted for in ESML. Such features might derive from events (e.g., a gap in data collection due to instrument servicing), identifications (e.g., a scientifically interesting area/volume in an image), or some other source. In order to account for features in an ESML context, we consider them from the perspective of annotation, i.e., the addition of information to existing documents without changing the originals. Although it is possible to extend ESML to incorporate feature-based annotations internally (e.g., by extending the XML schema for ESML), there are a number of complicating factors that we identify. Rather than pursuing the ESML-extension approach, we focus on an external representation for feature-based annotations via XML Pointer Language (XPointer). In previous work (Lumb &Aldridge, HPCS 2006, IEEE, doi:10.1109/HPCS.2006.26), we have shown that it is possible to extract relationships from ESML-based representations, and capture the results in the Resource Description Format (RDF). Thus we explore and report on this same requirement for XPointer-based annotations of ESML representations. As in our past efforts, the Global Geodynamics Project (GGP) allows us to illustrate with a real-world example this approach for introducing annotations into automatically generated knowledge representations.
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Prochorskaite, Agne; Couch, Chris; Malys, Naglis; Maliene, Vida
2016-01-07
It is widely recognised that the quantity and sustainability of new homes in the UK need to increase. However, it is important that sustainable housing is regarded holistically, and not merely in environmental terms, and incorporates elements that enhance the quality of life, health and well-being of its users. This paper focuses on the "soft" features of sustainable housing, that is, the non-technological components of sustainable housing and neighbourhood design that can impact occupants' health and well-being. Aims of the study are to ascertain the relative level of importance that key housing stakeholders attach to these features and to investigate whether the opinions of housing users and housing providers are aligned with regards to their importance. An online survey was carried out to gauge the level of importance that the key stakeholders, such as housing users, local authorities, housing associations, and developers (n = 235), attach to these features. Results revealed that while suitable indoor space was the feature regarded as most important by all stakeholders, there were also a number of disparities in opinion between housing users and housing providers (and among the different types of providers). This implies a scope for initiatives to achieve a better alignment between housing users and providers.
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Sporns, Peter B; Niederstadt, Thomas; Heindel, Walter; Raschke, Michael J; Hartensuer, René; Dittrich, Ralf; Hanning, Uta
2018-01-26
Cervical artery dissection (CAD) is an important etiology of ischemic stroke and early recognition is vital to protect patients from the major complication of cerebral embolization by administration of anticoagulants. The etiology of arterial dissections differ and can be either spontaneous or traumatic. Even though the historical gold standard is still catheter angiography, recent studies suggest a good performance of computed tomography angiography (CTA) for detection of CAD. We conducted this research to evaluate the variety and frequency of possible imaging signs of spontaneous and traumatic CAD and to guide neuroradiologists' decision making. Retrospective review of the database of our multiple injured patients admitted to the Department of Trauma, Hand, and Reconstructive Surgery of the University Hospital Münster in Germany (a level 1 trauma center) for patients with traumatic CAD (tCAD) and of our stroke database (2008-2015) for patients with spontaneous CAD (sCAD) and CT/CTA on initial clinical work-up. All images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two etiologies. This study included 145 patients (99 male, 46 female; 45 ± 18.8 years of age), consisting of 126 dissected arteries with a traumatic and 43 with spontaneous etiology. Intimal flaps were more frequently observed after traumatic etiology (58.1% tCADs, 6.9% sCADs; p < 0.001); additionally, multivessel dissections were much more frequent in trauma patients (3 sCADs, 21 tCADs) and only less than half (42%) of the patients with traumatic dissections showed cervical spine fractures. Neuroradiologists should be aware that intimal flaps and multivessel dissections are more common after a traumatic etiology. In addition, it seems important to conduct a CTA in a trauma setting, even if no cervical spine fracture is detected.
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Features: Real-Time Adaptive Feature and Document Learning for Web Search.
ERIC Educational Resources Information Center
Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai
2001-01-01
Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…
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MEVTV Workshop on Tectonic Features on Mars
NASA Technical Reports Server (NTRS)
Watters, Thomas R. (Editor); Golombek, Matthew P. (Editor)
1989-01-01
The state of knowledge of tectonic features on Mars was determined and kinematic and mechanical models were assessed for their origin. Three sessions were held: wrinkle ridges and compressional structure; strike-slip faults; and extensional structures. Each session began with an overview of the features under discussion. In the case of wrinkle ridges and extensional structures, the overview was followed by keynote addresses by specialists working on similar structures on the Earth. The first session of the workshop focused on the controversy over the relative importance of folding, faulting, and intrusive volcanism in the origin of wrinkle ridges. The session ended with discussions of the origin of compressional flank structures associated with Martian volcanoes and the relationship between the volcanic complexes and the inferred regional stress field. The second day of the workshop began with the presentation and discussion of evidence for strike-slip faults on Mars at various scales. In the last session, the discussion of extensional structures ranged from the origin of grabens, tension cracks, and pit-crater chains to the origin of Valles Marineris canyons. Shear and tensile modes of brittle failure in the formation of extensional features and the role of these failure modes in the formation of pit-crater chains and the canyons of Valles Marineris were debated. The relationship of extensional features to other surface processes, such as carbonate dissolution (karst) were also discussed.
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Cholangiocarcinoma: classification, diagnosis, staging, imaging features, and management.
Oliveira, Irai S; Kilcoyne, Aoife; Everett, Jamie M; Mino-Kenudson, Mari; Harisinghani, Mukesh G; Ganesan, Karthik
2017-06-01
Cholangiocarcinoma is a relatively uncommon malignant neoplasm with poor prognosis. The distinction between extrahepatic and intrahepatic subtypes is important as epidemiological features, biologic and pathologic characteristics, and clinical course are different for both entities. This review study focuses on the role imaging plays in the diagnosis, classification, staging, and post-treatment assessment of cholangiocarcinoma.
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The Linguistic Special Features of the Sami Education
ERIC Educational Resources Information Center
Keskitalo, Pigga; Maatta, Kaarina
2011-01-01
This article focuses on the features of Sami language instruction at the first school grades in Norway. The most important part is to describe what kind of challenges Sami language instruction at the first grades as an indigenous people's language and with the status of a minority language has. This situation introduces some differences and…
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Feature Screening for Ultrahigh Dimensional Categorical Data with Applications.
Huang, Danyang; Li, Runze; Wang, Hansheng
2014-01-01
Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effects. We further show that the proposed procedure possesses screening consistency property in the terminology of Fan and Lv (2008). We investigate the finite sample performance of the proposed procedure by Monte Carlo simulation studies, and illustrate the proposed method by two empirical datasets.
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Collective feature selection to identify crucial epistatic variants.
Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D
2018-01-01
Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger
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Optimal Inspection of Imports to Prevent Invasive Pest Introduction.
Chen, Cuicui; Epanchin-Niell, Rebecca S; Haight, Robert G
2018-03-01
The United States imports more than 1 billion live plants annually-an important and growing pathway for introduction of damaging nonnative invertebrates and pathogens. Inspection of imports is one safeguard for reducing pest introductions, but capacity constraints limit inspection effort. We develop an optimal sampling strategy to minimize the costs of pest introductions from trade by posing inspection as an acceptance sampling problem that incorporates key features of the decision context, including (i) simultaneous inspection of many heterogeneous lots, (ii) a lot-specific sampling effort, (iii) a budget constraint that limits total inspection effort, (iv) inspection error, and (v) an objective of minimizing cost from accepted defective units. We derive a formula for expected number of accepted infested units (expected slippage) given lot size, sample size, infestation rate, and detection rate, and we formulate and analyze the inspector's optimization problem of allocating a sampling budget among incoming lots to minimize the cost of slippage. We conduct an empirical analysis of live plant inspection, including estimation of plant infestation rates from historical data, and find that inspections optimally target the largest lots with the highest plant infestation rates, leaving some lots unsampled. We also consider that USDA-APHIS, which administers inspections, may want to continue inspecting all lots at a baseline level; we find that allocating any additional capacity, beyond a comprehensive baseline inspection, to the largest lots with the highest infestation rates allows inspectors to meet the dual goals of minimizing the costs of slippage and maintaining baseline sampling without substantial compromise. © 2017 Society for Risk Analysis.
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Is magnetic topology important for heating the solar atmosphere?
Parnell, Clare E; Stevenson, Julie E H; Threlfall, James; Edwards, Sarah J
2015-05-28
Magnetic fields permeate the entire solar atmosphere weaving an extremely complex pattern on both local and global scales. In order to understand the nature of this tangled web of magnetic fields, its magnetic skeleton, which forms the boundaries between topologically distinct flux domains, may be determined. The magnetic skeleton consists of null points, separatrix surfaces, spines and separators. The skeleton is often used to clearly visualize key elements of the magnetic configuration, but parts of the skeleton are also locations where currents and waves may collect and dissipate. In this review, the nature of the magnetic skeleton on both global and local scales, over solar cycle time scales, is explained. The behaviour of wave pulses in the vicinity of both nulls and separators is discussed and so too is the formation of current layers and reconnection at the same features. Each of these processes leads to heating of the solar atmosphere, but collectively do they provide enough heat, spread over a wide enough area, to explain the energy losses throughout the solar atmosphere? Here, we consider this question for the three different solar regions: active regions, open-field regions and the quiet Sun. We find that the heating of active regions and open-field regions is highly unlikely to be due to reconnection or wave dissipation at topological features, but it is possible that these may play a role in the heating of the quiet Sun. In active regions, the absence of a complex topology may play an important role in allowing large energies to build up and then, subsequently, be explosively released in the form of a solar flare. Additionally, knowledge of the intricate boundaries of open-field regions (which the magnetic skeleton provides) could be very important in determining the main acceleration mechanism(s) of the solar wind. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
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Construction of Lower Limbs Rehabilitation System Based on Bodily Features and EMG
NASA Astrophysics Data System (ADS)
Kushida, Daisuke; Kanazawa, Tomohiro; Kitamura, Akira
In rehabilitation, there are two roles of reinforcement of the expansion of the motion range and muscular power. The diseased part is requested to be operated by the external force in the former, and the load corresponding to patient's muscular power is requested to be given in the latter. Recently, there are a lot of researches that try such rehabilitation by the machine. The principal object is put on the motion control of the machine in those researches. The most important thing is a mechanism that patient's state is quantitatively evaluated. This paper proposes the mechanism that presumes the patient recovery by relating bodily features to EMG of the diseased part in rehabilitation. In addition, a new rehabilitation system, that contained the self adjustment of the load using those mechanisms and the consideration of fatigue, is proposed. Effectiveness of the proposed rehabilitation system is verified by the simulation work.
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Face-space architectures: evidence for the use of independent color-based features.
Nestor, Adrian; Plaut, David C; Behrmann, Marlene
2013-07-01
The concept of psychological face space lies at the core of many theories of face recognition and representation. To date, much of the understanding of face space has been based on principal component analysis (PCA); the structure of the psychological space is thought to reflect some important aspects of a physical face space characterized by PCA applications to face images. In the present experiments, we investigated alternative accounts of face space and found that independent component analysis provided the best fit to human judgments of face similarity and identification. Thus, our results challenge an influential approach to the study of human face space and provide evidence for the role of statistically independent features in face encoding. In addition, our findings support the use of color information in the representation of facial identity, and we thus argue for the inclusion of such information in theoretical and computational constructs of face space.
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Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.
Yildiz, Izzet B; Mesgarani, Nima; Deneve, Sophie
2016-12-07
A primary goal of auditory neuroscience is to identify the sound features extracted and represented by auditory neurons. Linear encoding models, which describe neural responses as a function of the stimulus, have been primarily used for this purpose. Here, we provide theoretical arguments and experimental evidence in support of an alternative approach, based on decoding the stimulus from the neural response. We used a Bayesian normative approach to predict the responses of neurons detecting relevant auditory features, despite ambiguities and noise. We compared the model predictions to recordings from the primary auditory cortex of ferrets and found that: (1) the decoding filters of auditory neurons resemble the filters learned from the statistics of speech sounds; (2) the decoding model captures the dynamics of responses better than a linear encoding model of similar complexity; and (3) the decoding model accounts for the accuracy with which the stimulus is represented in neural activity, whereas linear encoding model performs very poorly. Most importantly, our model predicts that neuronal responses are fundamentally shaped by "explaining away," a divisive competition between alternative interpretations of the auditory scene. Neural responses in the auditory cortex are dynamic, nonlinear, and hard to predict. Traditionally, encoding models have been used to describe neural responses as a function of the stimulus. However, in addition to external stimulation, neural activity is strongly modulated by the responses of other neurons in the network. We hypothesized that auditory neurons aim to collectively decode their stimulus. In particular, a stimulus feature that is decoded (or explained away) by one neuron is not explained by another. We demonstrated that this novel Bayesian decoding model is better at capturing the dynamic responses of cortical neurons in ferrets. Whereas the linear encoding model poorly reflects selectivity of neurons, the decoding model can
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When do letter features migrate? A boundary condition for feature-integration theory.
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.
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Multimodal emotional state recognition using sequence-dependent deep hierarchical features.
Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan
2015-12-01
Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Liu, Aiming; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi
2017-01-01
Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. PMID:29117100
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Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi
2017-11-08
Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.
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Feature extraction and selection strategies for automated target recognition
NASA Astrophysics Data System (ADS)
Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin
2010-04-01
Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.
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On the use of INS to improve Feature Matching
NASA Astrophysics Data System (ADS)
Masiero, A.; Guarnieri, A.; Vettore, A.; Pirotti, F.
2014-11-01
The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of features properly matched in different images to compute the 3D positions of objects by means of triangulation. Hence, correct feature matching is of fundamental importance to ensure good quality 3D reconstructions. Matching methods are based on the appearance of features, that can change as a consequence of variations of camera position and orientation, and environment illumination. For this reason, several methods have been developed in recent years in order to provide feature descriptors robust (ideally invariant) to such variations, e.g. Scale-Invariant Feature Transform (SIFT), Affine SIFT, Hessian affine and Harris affine detectors, Maximally Stable Extremal Regions (MSER). This work deals with the integration of information provided by the INS in the feature matching procedure: a previously developed navigation algorithm is used to constantly estimate the device position and orientation. Then, such information is exploited to estimate the transformation of feature regions between two camera views. This allows to compare regions from different images but associated to the same feature as seen by the same point of view, hence significantly easing the comparison of feature characteristics and, consequently, improving matching. SIFT-like descriptors are used in order to ensure good matching results in presence of illumination variations and to compensate the approximations related to the estimation
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[The EU Portal: Implementation, importance, and features].
von Aschen, Harald; Krafft, Hartmut
2017-08-01
The European Medicines Agency (EMA) is developing a web-based EU portal with a database "at Union level as a single entry point for the submission of data and information relating to clinical trials in accordance with" the new EU regulation No. 536/2014. The specifications are mostly published, but some documents are still missing. Because the project is integrated and has dependencies on other projects, this could result in other specification upgrades. The IT solution is under ongoing development until project completion in quarter III of 2019. The EU Portal and the database will be audited. If the audit is successful, the new regulation will come into force in October 2018. The use of the EU Portal will then be mandatory with some transition rules. The software development of the portal is restricted to the regulation and the derived requirements. It is not possible to implement any national requirements. We describe in this paper the current key functionalities of the portal and try to derive requirements for a national IT system.On 16.06.2017 the EMA Management Board announced that the development of the new portal has been delayed and it is foreseen that the new regulation can come into effect in 2019 at the earliest. The press release can be found here: http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/news/2017/06/news_detail_002764.jsp%26mid=WC0b01ac058004d5c1 (accessed: 12.07.2017).
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2016-01-01
We have shown previously that the bleomycin (BLM) carbohydrate moiety can recapitulate the tumor cell targeting effects of the entire BLM molecule, that BLM itself is modular in nature consisting of a DNA-cleaving aglycone which is delivered selectively to the interior of tumor cells by its carbohydrate moiety, and that there are disaccharides structurally related to the BLM disaccharide which are more efficient than the natural disaccharide at tumor cell targeting/uptake. Because BLM sugars can deliver molecular cargoes selectively to tumor cells, and thus potentially form the basis for a novel antitumor strategy, it seemed important to consider additional structural features capable of affecting the efficiency of tumor cell recognition and delivery. These included the effects of sugar polyvalency and net charge (at physiological pH) on tumor cell recognition, internalization, and trafficking. Since these parameters have been shown to affect cell surface recognition, internalization, and distribution in other contexts, this study has sought to define the effects of these structural features on tumor cell recognition by bleomycin and its disaccharide. We demonstrate that both can have a significant effect on tumor cell binding/internalization, and present data which suggests that the metal ions normally bound by bleomycin following clinical administration may significantly contribute to the efficiency of tumor cell uptake, in addition to their characterized function in DNA cleavage. A BLM disaccharide-Cy5** conjugate incorporating the positively charged dipeptide d-Lys-d-Lys was found to associate with both the mitochondria and the nuclear envelope of DU145 cells, suggesting possible cellular targets for BLM disaccharide–cytotoxin conjugates. PMID:25905565
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A flower image retrieval method based on ROI feature.
Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan
2004-07-01
Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).
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Zhao, Yu-Xiang; Chou, Chien-Hsing
2016-01-01
In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346
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An adaptation study of internal and external features in facial representations.
Hills, Charlotte; Romano, Kali; Davies-Thompson, Jodie; Barton, Jason J S
2014-07-01
Prior work suggests that internal features contribute more than external features to face processing. Whether this asymmetry is also true of the mental representations of faces is not known. We used face adaptation to determine whether the internal and external features of faces contribute differently to the representation of facial identity, whether this was affected by familiarity, and whether the results differed if the features were presented in isolation or as part of a whole face. In a first experiment, subjects performed a study of identity adaptation for famous and novel faces, in which the adapting stimuli were whole faces, the internal features alone, or the external features alone. In a second experiment, the same faces were used, but the adapting internal and external features were superimposed on whole faces that were ambiguous to identity. The first experiment showed larger aftereffects for unfamiliar faces, and greater aftereffects from internal than from external features, and the latter was true for both familiar and unfamiliar faces. When internal and external features were presented in a whole-face context in the second experiment, aftereffects from either internal or external features was less than that from the whole face, and did not differ from each other. While we reproduce the greater importance of internal features when presented in isolation, we find this is equally true for familiar and unfamiliar faces. The dominant influence of internal features is reduced when integrated into a whole-face context, suggesting another facet of expert face processing. Copyright © 2014 Elsevier B.V. All rights reserved.
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Recovering faces from memory: the distracting influence of external facial features.
Frowd, Charlie D; Skelton, Faye; Atherton, Chris; Pitchford, Melanie; Hepton, Gemma; Holden, Laura; McIntyre, Alex H; Hancock, Peter J B
2012-06-01
Recognition memory for unfamiliar faces is facilitated when contextual cues (e.g., head pose, background environment, hair and clothing) are consistent between study and test. By contrast, inconsistencies in external features, especially hair, promote errors in unfamiliar face-matching tasks. For the construction of facial composites, as carried out by witnesses and victims of crime, the role of external features (hair, ears, and neck) is less clear, although research does suggest their involvement. Here, over three experiments, we investigate the impact of external features for recovering facial memories using a modern, recognition-based composite system, EvoFIT. Participant-constructors inspected an unfamiliar target face and, one day later, repeatedly selected items from arrays of whole faces, with "breeding," to "evolve" a composite with EvoFIT; further participants (evaluators) named the resulting composites. In Experiment 1, the important internal-features (eyes, brows, nose, and mouth) were constructed more identifiably when the visual presence of external features was decreased by Gaussian blur during construction: higher blur yielded more identifiable internal-features. In Experiment 2, increasing the visible extent of external features (to match the target's) in the presented face-arrays also improved internal-features quality, although less so than when external features were masked throughout construction. Experiment 3 demonstrated that masking external-features promoted substantially more identifiable images than using the previous method of blurring external-features. Overall, the research indicates that external features are a distractive rather than a beneficial cue for face construction; the results also provide a much better method to construct composites, one that should dramatically increase identification of offenders.
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Additives for cement compositions based on modified peat
DOE Office of Scientific and Technical Information (OSTI.GOV)
Kopanitsa, Natalya, E-mail: kopanitsa@mail.ru; Sarkisov, Yurij, E-mail: sarkisov@tsuab.ru; Gorshkova, Aleksandra, E-mail: kasatkina.alexandra@gmail.com
High quality competitive dry building mixes require modifying additives for various purposes to be included in their composition. There is insufficient amount of quality additives having stable properties for controlling the properties of cement compositions produced in Russia. Using of foreign modifying additives leads to significant increasing of the final cost of the product. The cost of imported modifiers in the composition of the dry building mixes can be up to 90% of the material cost, depending on the composition complexity. Thus, the problem of import substitution becomes relevant, especially in recent years, due to difficult economic situation. The articlemore » discusses the possibility of using local raw materials as a basis for obtaining dry building mixtures components. The properties of organo-mineral additives for cement compositions based on thermally modified peat raw materials are studied. Studies of the structure and composition of the additives are carried out by physicochemical research methods: electron microscopy and X-ray analysis. Results of experimental research showed that the peat additives contribute to improving of cement-sand mortar strength and hydrophysical properties.« less