Active classifier selection for RGB-D object categorization using a Markov random field ensemble method

NASA Astrophysics Data System (ADS)

Durner, Maximilian; Márton, Zoltán.; Hillenbrand, Ulrich; Ali, Haider; Kleinsteuber, Martin

2017-03-01

In this work, a new ensemble method for the task of category recognition in different environments is presented. The focus is on service robotic perception in an open environment, where the robot's task is to recognize previously unseen objects of predefined categories, based on training on a public dataset. We propose an ensemble learning approach to be able to flexibly combine complementary sources of information (different state-of-the-art descriptors computed on color and depth images), based on a Markov Random Field (MRF). By exploiting its specific characteristics, the MRF ensemble method can also be executed as a Dynamic Classifier Selection (DCS) system. In the experiments, the committee- and topology-dependent performance boost of our ensemble is shown. Despite reduced computational costs and using less information, our strategy performs on the same level as common ensemble approaches. Finally, the impact of large differences between datasets is analyzed.

Classification of breast abnormalities using artificial neural network

NASA Astrophysics Data System (ADS)

Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

2015-05-01

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

Self-reports of induced abortion: an empathetic setting can improve the quality of data.

PubMed Central

Rasch, V; Muhammad, H; Urassa, E; Bergström, S

2000-01-01

OBJECTIVES: This study estimated the proportion of incomplete abortions that are induced in hospital-based settings in Tanzania. METHODS: A cross-sectional questionnaire study was conducted in 2 phases at 3 hospitals in Tanzania. Phase 1 included 302 patients with a diagnosis of incomplete abortion, and phase 2 included 823 such patients. RESULTS: In phase 1, in which cases were classified by clinical criteria and information from the patient, 3.9% to 16.1% of the cases were classified as induced abortion. In phase 2, in which the structured interview was changed to an empathetic dialogue and previously used clinical criteria were omitted, 30.9% to 60.0% of the cases were classified as induced abortion. CONCLUSIONS: An empathetic dialogue improves the quality of data collected among women with induced abortion. PMID:10897196

Automatic discrimination of fine roots in minirhizotron images.

PubMed

Zeng, Guang; Birchfield, Stanley T; Wells, Christina E

2008-01-01

Minirhizotrons provide detailed information on the production, life history and mortality of fine roots. However, manual processing of minirhizotron images is time-consuming, limiting the number and size of experiments that can reasonably be analysed. Previously, an algorithm was developed to automatically detect and measure individual roots in minirhizotron images. Here, species-specific root classifiers were developed to discriminate detected roots from bright background artifacts. Classifiers were developed from training images of peach (Prunus persica), freeman maple (Acer x freemanii) and sweetbay magnolia (Magnolia virginiana) using the Adaboost algorithm. True- and false-positive rates for classifiers were estimated using receiver operating characteristic curves. Classifiers gave true positive rates of 89-94% and false positive rates of 3-7% when applied to nontraining images of the species for which they were developed. The application of a classifier trained on one species to images from another species resulted in little or no reduction in accuracy. These results suggest that a single root classifier can be used to distinguish roots from background objects across multiple minirhizotron experiments. By incorporating root detection and discrimination algorithms into an open-source minirhizotron image analysis application, many analysis tasks that are currently performed by hand can be automated.

Grounding grammatical categories: attention bias in hand space influences grammatical congruency judgment of Chinese nominal classifiers.

PubMed

Lobben, Marit; D'Ascenzo, Stefania

2015-01-01

Embodied cognitive theories predict that linguistic conceptual representations are grounded and continually represented in real world, sensorimotor experiences. However, there is an on-going debate on whether this also holds for abstract concepts. Grammar is the archetype of abstract knowledge, and therefore constitutes a test case against embodied theories of language representation. Former studies have largely focussed on lexical-level embodied representations. In the present study we take the grounding-by-modality idea a step further by using reaction time (RT) data from the linguistic processing of nominal classifiers in Chinese. We take advantage of an independent body of research, which shows that attention in hand space is biased. Specifically, objects near the hand consistently yield shorter RTs as a function of readiness for action on graspable objects within reaching space, and the same biased attention inhibits attentional disengagement. We predicted that this attention bias would equally apply to the graspable object classifier but not to the big object classifier. Chinese speakers (N = 22) judged grammatical congruency of classifier-noun combinations in two conditions: graspable object classifier and big object classifier. We found that RTs for the graspable object classifier were significantly faster in congruent combinations, and significantly slower in incongruent combinations, than the big object classifier. There was no main effect on grammatical violations, but rather an interaction effect of classifier type. Thus, we demonstrate here grammatical category-specific effects pertaining to the semantic content and by extension the visual and tactile modality of acquisition underlying the acquisition of these categories. We conclude that abstract grammatical categories are subjected to the same mechanisms as general cognitive and neurophysiological processes and may therefore be grounded.

Grounding grammatical categories: attention bias in hand space influences grammatical congruency judgment of Chinese nominal classifiers

PubMed Central

Lobben, Marit; D’Ascenzo, Stefania

2015-01-01

Embodied cognitive theories predict that linguistic conceptual representations are grounded and continually represented in real world, sensorimotor experiences. However, there is an on-going debate on whether this also holds for abstract concepts. Grammar is the archetype of abstract knowledge, and therefore constitutes a test case against embodied theories of language representation. Former studies have largely focussed on lexical-level embodied representations. In the present study we take the grounding-by-modality idea a step further by using reaction time (RT) data from the linguistic processing of nominal classifiers in Chinese. We take advantage of an independent body of research, which shows that attention in hand space is biased. Specifically, objects near the hand consistently yield shorter RTs as a function of readiness for action on graspable objects within reaching space, and the same biased attention inhibits attentional disengagement. We predicted that this attention bias would equally apply to the graspable object classifier but not to the big object classifier. Chinese speakers (N = 22) judged grammatical congruency of classifier-noun combinations in two conditions: graspable object classifier and big object classifier. We found that RTs for the graspable object classifier were significantly faster in congruent combinations, and significantly slower in incongruent combinations, than the big object classifier. There was no main effect on grammatical violations, but rather an interaction effect of classifier type. Thus, we demonstrate here grammatical category-specific effects pertaining to the semantic content and by extension the visual and tactile modality of acquisition underlying the acquisition of these categories. We conclude that abstract grammatical categories are subjected to the same mechanisms as general cognitive and neurophysiological processes and may therefore be grounded. PMID:26379611

Smart BIT/TSMD Integration

DTIC Science & Technology

1991-12-01

user using the ’: knn ’ option in the do-scenario command line). An instance of the K-Nearest Neighbor object is first created and initialized before...Navigation Computer HF High Frequency ILS Instrument Landing System KNN K - Nearest Neighbor LRU Line Replaceable Unit MC Mission Computer MTCA...approaches have been investigated here, K-nearest Neighbors ( KNN ) and neural networks (NN). Both approaches require that previously classified examples of

SN2005da: A Spectroscopic and Photometric Analysis of a Peculiar Type Ic Supernova

NASA Astrophysics Data System (ADS)

Williamson, Jacob

2017-12-01

Core collapse supernovae are an important class of objects in stellar evolution research as they are the final life stage of high mass stars. Supernovae in general are classified into several spectral types; this paper explores SN 2005da, classified as a Type Ic, meaning it lacks hydrogen and helium lines. The supernova was originally classified as a broad-lined Type Ic (Type Ic-BL), with expansion velocities near maximum light greater than or approximately equal to 15000 km/s. However, some of the elements present in the spectrum, namely carbon and oxygen, have narrower lines (FWHM approximately equal to 2300 km/s) than other elements, indicating an interaction with a previously ejected envelope. The supernova is also found to have a decay time, with a change in magnitude over 15 days following maximum light of about 1.4 magnitudes, that is significantly faster than typical Type Ic or Ic-BL. This is more akin to a rarer object type known as a Type Ibn, although it lacks the characteristic narrow helium lines of this type. Therefore, SN 2005da appears to be unlike known examples of Type Ic supernovae.

A Tissue Systems Pathology Test Detects Abnormalities Associated with Prevalent High-Grade Dysplasia and Esophageal Cancer in Barrett's Esophagus.

PubMed

Critchley-Thorne, Rebecca J; Davison, Jon M; Prichard, Jeffrey W; Reese, Lia M; Zhang, Yi; Repa, Kathleen; Li, Jinhong; Diehl, David L; Jhala, Nirag C; Ginsberg, Gregory G; DeMarshall, Maureen; Foxwell, Tyler; Jobe, Blair A; Zaidi, Ali H; Duits, Lucas C; Bergman, Jacques J G H M; Rustgi, Anil; Falk, Gary W

2017-02-01

There is a need for improved tools to detect high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in patients with Barrett's esophagus. In previous work, we demonstrated that a 3-tier classifier predicted risk of incident progression in Barrett's esophagus. Our aim was to determine whether this risk classifier could detect a field effect in nondysplastic (ND), indefinite for dysplasia (IND), or low-grade dysplasia (LGD) biopsies from Barrett's esophagus patients with prevalent HGD/EAC. We performed a multi-institutional case-control study to evaluate a previously developed risk classifier that is based upon quantitative image features derived from 9 biomarkers and morphology, and predicts risk for HGD/EAC in Barrett's esophagus patients. The risk classifier was evaluated in ND, IND, and LGD biopsies from Barrett's esophagus patients diagnosed with HGD/EAC on repeat endoscopy (prevalent cases, n = 30, median time to HGD/EAC diagnosis 140.5 days) and nonprogressors (controls, n = 145, median HGD/EAC-free surveillance time 2,015 days). The risk classifier stratified prevalent cases and non-progressor patients into low-, intermediate-, and high-risk classes [OR, 46.0; 95% confidence interval, 14.86-169 (high-risk vs. low-risk); P < 0.0001]. The classifier also provided independent prognostic information that outperformed the subspecialist and generalist diagnosis. A tissue systems pathology test better predicts prevalent HGD/EAC in Barrett's esophagus patients than pathologic variables. The results indicate that molecular and cellular changes associated with malignant transformation in Barrett's esophagus may be detectable as a field effect using the test. A tissue systems pathology test may provide an objective method to facilitate earlier identification of Barrett's esophagus patients requiring therapeutic intervention. Cancer Epidemiol Biomarkers Prev; 26(2); 240-8. ©2016 AACR. ©2016 American Association for Cancer Research.

Moving object localization using optical flow for pedestrian detection from a moving vehicle.

PubMed

Hariyono, Joko; Hoang, Van-Dung; Jo, Kang-Hyun

2014-01-01

This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14 × 14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.

Separability of Abstract-Category and Specific-Exemplar Visual Object Subsystems: Evidence from fMRI Pattern Analysis

PubMed Central

McMenamin, Brenton W.; Deason, Rebecca G.; Steele, Vaughn R.; Koutstaal, Wilma; Marsolek, Chad J.

2014-01-01

Previous research indicates that dissociable neural subsystems underlie abstract-category (AC) recognition and priming of objects (e.g., cat, piano) and specific-exemplar (SE) recognition and priming of objects (e.g., a calico cat, a different calico cat, a grand piano, etc.). However, the degree of separability between these subsystems is not known, despite the importance of this issue for assessing relevant theories. Visual object representations are widely distributed in visual cortex, thus a multivariate pattern analysis (MVPA) approach to analyzing functional magnetic resonance imaging (fMRI) data may be critical for assessing the separability of different kinds of visual object processing. Here we examined the neural representations of visual object categories and visual object exemplars using multi-voxel pattern analyses of brain activity elicited in visual object processing areas during a repetition-priming task. In the encoding phase, participants viewed visual objects and the printed names of other objects. In the subsequent test phase, participants identified objects that were either same-exemplar primed, different-exemplar primed, word-primed, or unprimed. In visual object processing areas, classifiers were trained to distinguish same-exemplar primed objects from word-primed objects. Then, the abilities of these classifiers to discriminate different-exemplar primed objects and word-primed objects (reflecting AC priming) and to discriminate same-exemplar primed objects and different-exemplar primed objects (reflecting SE priming) was assessed. Results indicated that (a) repetition priming in occipital-temporal regions is organized asymmetrically, such that AC priming is more prevalent in the left hemisphere and SE priming is more prevalent in the right hemisphere, and (b) AC and SE subsystems are weakly modular, not strongly modular or unified. PMID:25528436

Separability of abstract-category and specific-exemplar visual object subsystems: evidence from fMRI pattern analysis.

PubMed

McMenamin, Brenton W; Deason, Rebecca G; Steele, Vaughn R; Koutstaal, Wilma; Marsolek, Chad J

2015-02-01

Previous research indicates that dissociable neural subsystems underlie abstract-category (AC) recognition and priming of objects (e.g., cat, piano) and specific-exemplar (SE) recognition and priming of objects (e.g., a calico cat, a different calico cat, a grand piano, etc.). However, the degree of separability between these subsystems is not known, despite the importance of this issue for assessing relevant theories. Visual object representations are widely distributed in visual cortex, thus a multivariate pattern analysis (MVPA) approach to analyzing functional magnetic resonance imaging (fMRI) data may be critical for assessing the separability of different kinds of visual object processing. Here we examined the neural representations of visual object categories and visual object exemplars using multi-voxel pattern analyses of brain activity elicited in visual object processing areas during a repetition-priming task. In the encoding phase, participants viewed visual objects and the printed names of other objects. In the subsequent test phase, participants identified objects that were either same-exemplar primed, different-exemplar primed, word-primed, or unprimed. In visual object processing areas, classifiers were trained to distinguish same-exemplar primed objects from word-primed objects. Then, the abilities of these classifiers to discriminate different-exemplar primed objects and word-primed objects (reflecting AC priming) and to discriminate same-exemplar primed objects and different-exemplar primed objects (reflecting SE priming) was assessed. Results indicated that (a) repetition priming in occipital-temporal regions is organized asymmetrically, such that AC priming is more prevalent in the left hemisphere and SE priming is more prevalent in the right hemisphere, and (b) AC and SE subsystems are weakly modular, not strongly modular or unified. Copyright © 2014 Elsevier Inc. All rights reserved.

Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System

NASA Technical Reports Server (NTRS)

Lin, Tsung Han (Hank)

2011-01-01

JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.

Extracting duration information in a picture category decoding task using hidden Markov Models

NASA Astrophysics Data System (ADS)

Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg

2016-04-01

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.

PubMed

Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi

2014-01-01

In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Search for an Appropriate Behavior within the Emotional Regulation in Virtual Creatures Using a Learning Classifier System

PubMed Central

Rosales, Jonathan-Hernando; Cervantes, José-Antonio

2017-01-01

Emotion regulation is a process by which human beings control emotional behaviors. From neuroscientific evidence, this mechanism is the product of conscious or unconscious processes. In particular, the mechanism generated by a conscious process needs a priori components to be computed. The behaviors generated by previous experiences are among these components. These behaviors need to be adapted to fulfill the objectives in a specific situation. The problem we address is how to endow virtual creatures with emotion regulation in order to compute an appropriate behavior in a specific emotional situation. This problem is clearly important and we have not identified ways to solve this problem in the current literature. In our proposal, we show a way to generate the appropriate behavior in an emotional situation using a learning classifier system (LCS). We illustrate the function of our proposal in unknown and known situations by means of two case studies. Our results demonstrate that it is possible to converge to the appropriate behavior even in the first case; that is, when the system does not have previous experiences and in situations where some previous information is available our proposal proves to be a very powerful tool. PMID:29209362

Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).

Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling.

PubMed

Senden, R; Savelberg, H H C M; Grimm, B; Heyligers, I C; Meijer, K

2012-06-01

This study investigated whether the Tinetti scale, as a subjective measure for fall risk, is associated with objectively measured gait characteristics. It is studied whether gait parameters are different for groups that are stratified for fall risk using the Tinetti scale. Moreover, the discriminative power of gait parameters to classify elderly according to the Tinetti scale is investigated. Gait of 50 elderly with a Tinneti>24 and 50 elderly with a Tinetti≤24 was analyzed using acceleration-based gait analysis. Validated algorithms were used to derive spatio-temporal gait parameters, harmonic ratio, inter-stride amplitude variability and root mean square (RMS) from the accelerometer data. Clear differences in gait were found between the groups. All gait parameters correlated with the Tinetti scale (r-range: 0.20-0.73). Only walking speed, step length and RMS showed moderate to strong correlations and high discriminative power to classify elderly according to the Tinetti scale. It is concluded that subtle gait changes that have previously been related to fall risk are not captured by the subjective assessment. It is therefore worthwhile to include objective gait assessment in fall risk screening. Copyright © 2012 Elsevier B.V. All rights reserved.

The Biology of Linguistic Expression Impacts Neural Correlates for Spatial Language

PubMed Central

Emmorey, Karen; McCullough, Stephen; Mehta, Sonya; Ponto, Laura L. B.; Grabowski, Thomas J.

2013-01-01

Biological differences between signed and spoken languages may be most evident in the expression of spatial information. PET was used to investigate the neural substrates supporting the production of spatial language in American Sign Language as expressed by classifier constructions, in which handshape indicates object type and the location/motion of the hand iconically depicts the location/motion of a referent object. Deaf native signers performed a picture description task in which they overtly named objects or produced classifier constructions that varied in location, motion, or object type. In contrast to the expression of location and motion, the production of both lexical signs and object type classifier morphemes engaged left inferior frontal cortex and left inferior temporal cortex, supporting the hypothesis that unlike the location and motion components of a classifier construction, classifier handshapes are categorical morphemes that are retrieved via left hemisphere language regions. In addition, lexical signs engaged the anterior temporal lobes to a greater extent than classifier constructions, which we suggest reflects increased semantic processing required to name individual objects compared with simply indicating the type of object. Both location and motion classifier constructions engaged bilateral superior parietal cortex, with some evidence that the expression of static locations differentially engaged the left intraparietal sulcus. We argue that bilateral parietal activation reflects the biological underpinnings of sign language. To express spatial information, signers must transform visual–spatial representations into a body-centered reference frame and reach toward target locations within signing space. PMID:23249348

Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.

PubMed

Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

2011-01-01

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10(-6). Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful.

Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs

PubMed Central

Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

2011-01-01

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10−6. Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful. PMID:22195129

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ye, Q.-Z., E-mail: tom6740@gmail.com

We present the results of BVRI photometry and classification of 53 unusual asteroids, including 35 near-Earth asteroids (NEAs), 6 high eccentricity/inclination asteroids, and 12 recently identified asteroid-pair candidates. Most of these asteroids were not classified prior to this work. For the few asteroids that have been previously studied, the results are generally in agreement. In addition to observing and classifying these objects, we merge the results from severalphotometric/spectroscopic surveys to create the largest-ever sample with 449 spectrally classified NEAs for statistical analysis. We identify a 'transition point' of the relative number of C/X-like and S-like NEAs at H {approx} 18more » {r_reversible} D {approx} 1 km with confidence level at {approx}95% or higher. We find that the C/X-like:S-like ratio for 18 {<=} H < 22 is about twice as high as that of H < 18 (0.33 {+-} 0.04 versus 0.17 {+-} 0.02), virtually supporting the hypothesis that smaller NEAs generally have less weathered surfaces (therefore less reddish appearance) due to younger collision ages.« less

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.

PubMed

Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian

2017-01-01

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.

Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

2015-12-01

This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.

PubMed

Ochs, Christopher; He, Zhe; Zheng, Ling; Geller, James; Perl, Yehoshua; Hripcsak, George; Musen, Mark A

2016-06-01

An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one ontology at a time. To improve the efficiency of the Abstraction-Network-based QA methodology, we introduced a QA framework that uses uniform Abstraction Network derivation techniques and QA methodologies that are applicable to whole families of structurally similar ontologies. For the family-based framework to be successful, it is necessary to develop a method for classifying ontologies into structurally similar families. We now describe a structural meta-ontology that classifies ontologies according to certain structural features that are commonly used in the modeling of ontologies (e.g., object properties) and that are important for Abstraction Network derivation. Each class of the structural meta-ontology represents a family of ontologies with identical structural features, indicating which types of Abstraction Networks and QA methodologies are potentially applicable to all of the ontologies in the family. We derive a collection of 81 families, corresponding to classes of the structural meta-ontology, that enable a flexible, streamlined family-based QA methodology, offering multiple choices for classifying an ontology. The structure of 373 ontologies from the NCBO BioPortal is analyzed and each ontology is classified into multiple families modeled by the structural meta-ontology. Copyright © 2016 Elsevier Inc. All rights reserved.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

PubMed

Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

2016-10-25

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

PubMed Central

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Women with previous fragility fractures can be classified based on bone microarchitecture and finite element analysis measured with HR-pQCT.

PubMed

Nishiyama, K K; Macdonald, H M; Hanley, D A; Boyd, S K

2013-05-01

High-resolution peripheral quantitative computed tomography (HR-pQCT) measurements of distal radius and tibia bone microarchitecture and finite element (FE) estimates of bone strength performed well at classifying postmenopausal women with and without previous fracture. The HR-pQCT measurements outperformed dual energy x-ray absorptiometry (DXA) at classifying forearm fractures and fractures at other skeletal sites. Areal bone mineral density (aBMD) is the primary measurement used to assess osteoporosis and fracture risk; however, it does not take into account bone microarchitecture, which also contributes to bone strength. Thus, our objective was to determine if bone microarchitecture measured with HR-pQCT and FE estimates of bone strength could classify women with and without low-trauma fractures. We used HR-pQCT to assess bone microarchitecture at the distal radius and tibia in 44 postmenopausal women with a history of low-trauma fracture and 88 age-matched controls from the Calgary cohort of the Canadian Multicentre Osteoporosis Study (CaMos) study. We estimated bone strength using FE analysis and simulated distal radius aBMD from the HR-pQCT scans. Femoral neck (FN) and lumbar spine (LS) aBMD were measured with DXA. We used support vector machines (SVM) and a tenfold cross-validation to classify the fracture cases and controls and to determine accuracy. The combination of HR-pQCT measures of microarchitecture and FE estimates of bone strength had the highest area under the receiver operating characteristic (ROC) curve of 0.82 when classifying forearm fractures compared to an area under the curve (AUC) of 0.71 from DXA-derived aBMD of the forearm and 0.63 from FN and spine DXA. For all fracture types, FE estimates of bone strength at the forearm alone resulted in an AUC of 0.69. Models based on HR-pQCT measurements of bone microarchitecture and estimates of bone strength performed better than DXA-derived aBMD at classifying women with and without prior fracture. In future, these models may improve prediction of individuals at risk of low-trauma fracture.

"Scissors, Paper, Stone": Perceptual Foundations of Noun Classifier Systems.

ERIC Educational Resources Information Center

Erbaugh, Mary S.

While all languages use shape to classify unfamiliar objects, some languages as diverse as Mandarin, Thai, Japanese, Mohawk, and American Sign Language lexicalize these and other types of description as noun classifiers. Classification does not develop from a fixed set of features in the object, but is discourse-sensitive and invoked when it would…

Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

NASA Astrophysics Data System (ADS)

Addink, Elisabeth A.; Van Coillie, Frieke M. B.; De Jong, Steven M.

2012-04-01

Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received considerable attention over the past 15 years for analyzing and interpreting remote sensing imagery. In contrast to traditional image analysis, GEOBIA works more like the human eye-brain combination does. The latter uses the object's color (spectral information), size, texture, shape and occurrence to other image objects to interpret and analyze what we see. GEOBIA starts by segmenting the image grouping together pixels into objects and next uses a wide range of object properties to classify the objects or to extract object's properties from the image. Significant advances and improvements in image analysis and interpretation are made thanks to GEOBIA. In June 2010 the third conference on GEOBIA took place at the Ghent University after successful previous meetings in Calgary (2008) and Salzburg (2006). This special issue presents a selection of the 2010 conference papers that are worked out as full research papers for JAG. The papers cover GEOBIA applications as well as innovative methods and techniques. The topics range from vegetation mapping, forest parameter estimation, tree crown identification, urban mapping, land cover change, feature selection methods and the effects of image compression on segmentation. From the original 94 conference papers, 26 full research manuscripts were submitted; nine papers were selected and are presented in this special issue. Selection was done on the basis of quality and topic of the studies. The next GEOBIA conference will take place in Rio de Janeiro from 7 to 9 May 2012 where we hope to welcome even more scientists working in the field of GEOBIA.

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

PubMed

Onorati, Francesco; Regalia, Giulia; Caborni, Chiara; Migliorini, Matteo; Bender, Daniel; Poh, Ming-Zher; Frazier, Cherise; Kovitch Thropp, Eliana; Mynatt, Elizabeth D; Bidwell, Jonathan; Mai, Roberto; LaFrance, W Curt; Blum, Andrew S; Friedman, Daniel; Loddenkemper, Tobias; Mohammadpour-Touserkani, Fatemeh; Reinsberger, Claus; Tognetti, Simone; Picard, Rosalind W

2017-11-01

New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Treesearch

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

Persistence of the Intuitive Conception of Living Things in Adolescence

NASA Astrophysics Data System (ADS)

Babai, Reuven; Sekal, Rachel; Stavy, Ruth

2010-02-01

This study investigated whether intuitive, naive conceptions of "living things" based on objects' mobility (movement = alive) persist into adolescence and affect 10th graders' accuracy of responses and reaction times during object classification. Most of the 58 students classified the test objects correctly as living/nonliving, yet they demonstrated significantly longer reaction times for classifying plants compared to animals and for classifying dynamic objects compared to static inanimate objects. Findings indicated that, despite prior learning in biology, the intuitive conception of living things persists up to age 15-16 years, affecting related reasoning processes. Consideration of these findings may help educators in their decisions about the nature of examples they use in their classrooms.

Employing Machine-Learning Methods to Study Young Stellar Objects

NASA Astrophysics Data System (ADS)

Moore, Nicholas

2018-01-01

Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.

Grounding by Attention Simulation in Peripersonal Space: Pupils Dilate to Pinch Grip But Not Big Size Nominal Classifier.

PubMed

Lobben, Marit; Bochynska, Agata

2018-03-01

Grammatical categories represent implicit knowledge, and it is not known if such abstract linguistic knowledge can be continuously grounded in real-life experiences, nor is it known what types of mental states can be simulated. A former study showed that attention bias in peripersonal space (PPS) affects reaction times in grammatical congruency judgments of nominal classifiers, suggesting that simulated semantics may include reenactment of attention. In this study, we contrasted a Chinese nominal classifier used with nouns denoting pinch grip objects with a classifier for nouns with big object referents in a pupil dilation experiment. Twenty Chinese native speakers read grammatical and ungrammatical classifier-noun combinations and made grammaticality judgment while their pupillary responses were measured. It was found that their pupils dilated significantly more to the pinch grip classifier than to the big object classifier, indicating attention simulation in PPS. Pupil dilations were also significantly larger with congruent trials on the whole than in incongruent trials, but crucially, congruency and classifier semantics were independent of each other. No such effects were found in controls. Copyright © 2017 Cognitive Science Society, Inc.

Rethinking Trends in Instructional Objectives: Exploring the Alignment of Objectives with Activities and Assessment in Higher Education--A Case Study

ERIC Educational Resources Information Center

Yamanaka, Akio; Wu, Leon Yufeng

2014-01-01

This study explored higher education level syllabi to identify trends in educational objectives. Bloom's Taxonomy and various strategic models were used to classify 714 objectives from 114 sections of courses administered through a Midwest teacher education institution in the United States. 1229 verbs and verb phrases were classified through the…

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

Soluble Mediators and Clinical Features Discern Risk of Transitioning to Classified Disease in Relatives of Systemic Lupus Erythematosus Patients

PubMed Central

Munroe, Melissa E.; Young, Kendra A.; Kamen, Diane L.; Guthridge, Joel M.; Niewold, Timothy B.; Costenbader, Karen H.; Weisman, Michael H.; Ishimori, Mariko L.; Wallace, Daniel J.; Gilkeson, Gary S.; Karp, David R.; Harley, John B.; Norris, Jill M.; James, Judith A.

2016-01-01

Objective Systemic lupus erythematosus (SLE) and other autoimmune diseases cause significant morbidity. Identifying populations at risk of developing SLE is essential to curtail irreversible inflammatory damage. The objective of this study was to identify factors associated with transition to classified disease that inform SLE risk. Methods Previously identified lupus patient blood relatives with < 4 American College of Rheumatology SLE classification criteria at baseline (n=409) were enrolled in this follow-up study. Participants provided detailed family, demographic, and clinical information, including the SLE-specific portion of the Connective Tissue Disease Screening Questionnaire (SLE-CSQ). Plasma samples were tested for the presence of lupus-associated autoantibodies and 52 soluble mediators. Generalized estimating equations (GEE) were applied to identify factors anticipating disease transition. Results Forty-five relatives (11%) transitioned to classified SLE during follow-up (mean time=6.4 years). Relatives who transitioned displayed more lupus-associated autoantibody specificities and higher SLE-CSQ scores (p<0.0001) at baseline than non-transitioned relatives. Importantly, they also had elevated baseline plasma levels of inflammatory mediators, including B-lymphocyte stimulator (BLyS), stem cell factor (SCF), and interferon-associated chemokines (p≤0.02), with concurrent decreases in levels of regulatory mediators, tumor growth factor (TGF)-β and interleukin (IL)-10 (p≤0.03). GEE revealed that baseline SLE-CSQ or ACR scores and plasma levels of SCF and TGF-β (p≤0.03), but not autoantibodies, were significant and independent predictors of SLE transition. Conclusions Altered levels of soluble mediators anticipate transition to classified disease in lupus relatives. Thus, immune perturbations precede SLE classification and can help identify high-risk relatives for rheumatology referral and potential enrollment in prevention trials. PMID:27863174

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Methods, systems and devices for detecting threatening objects and for classifying magnetic data

DOEpatents

Kotter, Dale K [Shelley, ID; Roybal, Lyle G [Idaho Falls, ID; Rohrbaugh, David T [Idaho Falls, ID; Spencer, David F [Idaho Falls, ID

2012-01-24

A method for detecting threatening objects in a security screening system. The method includes a step of classifying unique features of magnetic data as representing a threatening object. Another step includes acquiring magnetic data. Another step includes determining if the acquired magnetic data comprises a unique feature.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petersen, C.A., Westinghouse Hanford

The overall objective of this report is to provide a technical basis to support a U.S. Nuclear Regulatory Commission determination to classify the low-activity waste from the Hanford Site single-shell and double-shell tanks as `incidental` wastes after removal of additional radionuclides and immobilization.The proposed processing method, in addition to the previous radionuclide removal efforts, will remove the largest practical amount of total site radioactivity, attributable to high-level wastes, for disposal in a deep geologic repository. The remainder of the waste would be considered `incidental` waste and could be disposed onsite.

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

PubMed Central

Kavitha, Muthu Subash; Ganesh Kumar, Pugalendhi; Park, Soon-Yong; Huh, Kyung-Hoe; Heo, Min-Suk; Kurita, Takio; Asano, Akira; An, Seo-Yong

2016-01-01

Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. Results: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942–0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. Conclusions: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage. PMID:27186991

Detecting individual memories through the neural decoding of memory states and past experience.

PubMed

Rissman, Jesse; Greely, Henry T; Wagner, Anthony D

2010-05-25

A wealth of neuroscientific evidence indicates that our brains respond differently to previously encountered than to novel stimuli. There has been an upswell of interest in the prospect that functional MRI (fMRI), when coupled with multivariate data analysis techniques, might allow the presence or absence of individual memories to be detected from brain activity patterns. This could have profound implications for forensic investigations and legal proceedings, and thus the merits and limitations of such an approach are in critical need of empirical evaluation. We conducted two experiments to investigate whether neural signatures of recognition memory can be reliably decoded from fMRI data. In Exp. 1, participants were scanned while making explicit recognition judgments for studied and novel faces. Multivoxel pattern analysis (MVPA) revealed a robust ability to classify whether a given face was subjectively experienced as old or new, as well as whether recognition was accompanied by recollection, strong familiarity, or weak familiarity. Moreover, a participant's subjective mnemonic experiences could be reliably decoded even when the classifier was trained on the brain data from other individuals. In contrast, the ability to classify a face's objective old/new status, when holding subjective status constant, was severely limited. This important boundary condition was further evidenced in Exp. 2, which demonstrated that mnemonic decoding is poor when memory is indirectly (implicitly) probed. Thus, although subjective memory states can be decoded quite accurately under controlled experimental conditions, fMRI has uncertain utility for objectively detecting an individual's past experiences.

Cladistical Analysis of the Jovian and Saturnian Satellite Systems

NASA Astrophysics Data System (ADS)

Holt, Timothy. R.; Brown, Adrian. J.; Nesvorný, David; Horner, Jonathan; Carter, Brad

2018-06-01

Jupiter and Saturn each have complex systems of satellites and rings. These satellites can be classified into dynamical groups, implying similar formation scenarios. Recently, a larger number of additional irregular satellites have been discovered around both gas giants that have yet to be classified. The aim of this paper is to examine the relationships between the satellites and rings of the gas giants, using an analytical technique called cladistics. Cladistics is traditionally used to examine relationships between living organisms, the “tree of life.” In this work, we perform the first cladistical study of objects in a planetary science context. Our method uses the orbital, physical, and compositional characteristics of satellites to classify the objects in the Jovian and Saturnian systems. We find that the major relationships between the satellites in the two systems, such as families, as presented in previous studies, are broadly preserved. In addition, based on our analysis of the Jovian system, we identify a new retrograde irregular family, the Iocaste family, and suggest that the Phoebe family of the Saturnian system can be further divided into two subfamilies. We also propose that the Saturnian irregular families be renamed, to be consistent with the convention used in Jovian families. Using cladistics, we are also able to assign the new unclassified irregular satellites into families. Taken together, the results of this study demonstrate the potential use of the cladistical technique in the investigation of relationships between orbital bodies.

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

PubMed

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Olivine-rich asteroids in the near-Earth space

NASA Astrophysics Data System (ADS)

Popescu, Marcel; Perna, D.; Barucci, M. A.; Fornasier, S.; Doressoundiram, A.; Lantz, C.; Merlin, F.; Belskaya, I. N.; Fulchignoni, M.

2018-06-01

In the framework of a 30-night spectroscopic survey of small near-Earth asteroids (NEAs), we present new results regarding the identification of olivine-rich objects. The following NEAs were classified as A-type using visible spectra obtained with 3.6-m New Technology Telescope: (293726) 2007 RQ17, (444584) 2006 UK, 2012 NP, 2014 YS34, 2015 HB117, 2015 LH, 2015 TB179, 2015 TW144. We determined a relative abundance of 5.4 per cent (8 out of 147 observed targets) A-types at a 100-m size range of NEA population. The ratio is at least five times larger compared with the previously known A-types, which represent less than ˜ 1 per cent of NEAs taxonomically classified. By taking into account that part of our targets may not be confirmed as olivine-rich asteroids by their near-infrared spectra, or they can have a nebular origin, our result provides an upper-limit estimation of mantle fragments at size ranges below 300 m. Our findings are compared with the `battered-to-bits' scenario, claiming that at small sizes the olivine-rich objects should be more abundant when compared with basaltic and iron ones.

Establishment of a Comprehensive List of Candidate Antiaging Medicinal Herb Used in Korean Medicine by Text Mining of the Classical Korean Medical Literature, “Dongeuibogam,” and Preliminary Evaluation of the Antiaging Effects of These Herbs

PubMed Central

Choi, Moo Jin; Choi, Byung Tae; Shin, Hwa Kyoung; Shin, Byung Cheul; Han, Yoo Kyoung; Baek, Jin Ung

2015-01-01

The major objectives of this study were to provide a list of candidate antiaging medicinal herbs that have been widely utilized in Korean medicine and to organize preliminary data for the benefit of experimental and clinical researchers to develop new drug therapies by analyzing previous studies. “Dongeuibogam,” a representative source of the Korean medicine literature, was selected to investigate candidate antiaging medicinal herbs and to identify appropriate terms that describe the specific antiaging effects that these herbs are predicted to elicit. In addition, we aimed to review previous studies that referenced the selected candidate antiaging medicinal herbs. From our chosen source, “Dongeuibogam,” we were able to screen 102 terms describing antiaging effects, which were further classified into 11 subtypes. Ninety-seven candidate antiaging medicinal herbs were selected using the criterion that their antiaging effects were described using the same terms as those employed in “Dongeuibogam.” These candidates were classified into 11 subtypes. Of the 97 candidate antiaging medicinal herbs selected, 47 are widely used by Korean medical doctors in Korea and were selected for further analysis of their antiaging effects. Overall, we found an average of 7.7 previous studies per candidate herb that described their antiaging effects. PMID:25861371

Automatic identification of fault surfaces through Object Based Image Analysis of a Digital Elevation Model in the submarine area of the North Aegean Basin

NASA Astrophysics Data System (ADS)

Argyropoulou, Evangelia

2015-04-01

The current study was focused on the seafloor morphology of the North Aegean Basin in Greece, through Object Based Image Analysis (OBIA) using a Digital Elevation Model. The goal was the automatic extraction of morphologic and morphotectonic features, resulting into fault surface extraction. An Object Based Image Analysis approach was developed based on the bathymetric data and the extracted features, based on morphological criteria, were compared with the corresponding landforms derived through tectonic analysis. A digital elevation model of 150 meters spatial resolution was used. At first, slope, profile curvature, and percentile were extracted from this bathymetry grid. The OBIA approach was developed within the eCognition environment. Four segmentation levels were created having as a target "level 4". At level 4, the final classes of geomorphological features were classified: discontinuities, fault-like features and fault surfaces. On previous levels, additional landforms were also classified, such as continental platform and continental slope. The results of the developed approach were evaluated by two methods. At first, classification stability measures were computed within eCognition. Then, qualitative and quantitative comparison of the results took place with a reference tectonic map which has been created manually based on the analysis of seismic profiles. The results of this comparison were satisfactory, a fact which determines the correctness of the developed OBIA approach.

Online decoding of object-based attention using real-time fMRI.

PubMed

Niazi, Adnan M; van den Broek, Philip L C; Klanke, Stefan; Barth, Markus; Poel, Mannes; Desain, Peter; van Gerven, Marcel A J

2014-01-01

Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for moment-to-moment decoding of attention to spatially overlapped objects belonging to two different object categories. First, a whole-brain classifier was trained on pictures of faces and places. Subjects then saw transparently overlapped pictures of a face and a place, and attended to only one of them while ignoring the other. The category of the attended object, face or place, was decoded on a scan-by-scan basis using the previously trained decoder. The decoder performed at 77.6% accuracy indicating that despite competing bottom-up sensory input, object-based visual attention biased neural patterns towards that of the attended object. Furthermore, a comparison between different classification approaches indicated that the representation of faces and places is distributed rather than focal. This implies that real-time decoding of object-based attention requires a multivariate decoding approach that can detect these distributed patterns of cortical activity. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Classifying Higher Education Institutions in Korea: A Performance-Based Approach

ERIC Educational Resources Information Center

Shin, Jung Cheol

2009-01-01

The purpose of this study was to classify higher education institutions according to institutional performance rather than predetermined benchmarks. Institutional performance was defined as research performance and classified using Hierarchical Cluster Analysis, a statistical method that classifies objects according to specified classification…

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Exploiting the HASH Planetary Nebula Research Platform

NASA Astrophysics Data System (ADS)

Parker, Quentin A.; Bojičić, Ivan; Frew, David J.

2017-10-01

The HASH (Hong Kong/ AAO/ Strasbourg/ Hα) planetary nebula research platform is a unique data repository with a graphical interface and SQL capability that offers the community powerful, new ways to undertake Galactic PN studies. HASH currently contains multi-wavelength images, spectra, positions, sizes, morphologies and other data whenever available for 2401 true, 447 likely, and 692 possible Galactic PNe, for a total of 3540 objects. An additional 620 Galactic post-AGB stars, pre-PNe, and PPN candidates are included. All objects were classified and evaluated following the precepts and procedures established and developed by our group over the last 15 years. The complete database contains over 6,700 Galactic objects including the many mimics and related phenomena previously mistaken or confused with PNe. Curation and updating currently occurs on a weekly basis to keep the repository as up to date as possible until the official release of HASH v1 planned in the near future.

Parenting styles as a tobacco-use protective factor among Brazilian adolescents.

PubMed

Tondowski, Cláudia S; Bedendo, André; Zuquetto, Carla; Locatelli, Danilo P; Opaleye, Emérita S; Noto, Ana R

2015-12-01

The objective was to evaluate the relationship between tobacco use (previous month and frequent use), parenting styles and parental smoking behavior in a sample of high school students. Participants were recruited from public and private high schools from 27 Brazilian state capitals (N = 17,246). The overall prevalence of tobacco use in life was 25.2%; 15.3% in the previous year; 8.6% in the previous month; and 3.2% for frequent use. Tobacco use by the parents was reported by 28.6% of the students. Regarding parenting styles, 39.2% were classified as negligent, 33.3% authoritative, 15.6% as indulgent and 11.9% authoritarian. Compared to adolescents with authoritative parents, those with negligent or indulgent parents were more prone to report tobacco use during the last month or frequent use. This study showed an association between parenting styles and tobacco use by high school students. Authoritative parents were associated with protection from frequent and previous month tobacco use among adolescents.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Subclinical delusional ideation and a self-reference bias in everyday reasoning.

PubMed

Galbraith, Niall; Manktelow, Ken; Morris, Neil

2008-02-01

Previous studies (e.g. Moller & Husby, 2000; Blackwood et al., 2004) have revealed that delusional thinking is accompanied by an exaggerated focus upon the self and upon stimuli that are perceived to be related to the self. The objective was to examine whether those high in subclinical delusional ideation exhibit a heightened tendency for self-reference. Using a mixed design, healthy individuals, classified into high- and low-scoring groups on the Peters et al. Delusions Inventory (Peters, Day, & Garety, 1996), were compared on everyday reasoning tasks across three experiments. High-PDI scorers, in contrast to the low-PDI group, rated self-referent objections to everyday arguments as stronger than other-referent objections and formulated more self-referent assertion-based objections to everyday arguments. The findings support the notion that subclinical delusional ideation is linked to a self-reference bias, which is evident in the sort of everyday thinking that people engage in when forming or evaluating their beliefs and which may contribute to delusion formation.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2014 CFR

2014-07-01

... engaged in manufacturing orthopedic devices to prescription in a retail environment (previously classified... for facilities primarily engaged in web search portals; 541712—Research and Development in the... engaged in Guided missile and space vehicle engine research and development (previously classified under...

Recognition of aspect-dependent three-dimensional objects by an echolocating Atlantic bottlenose dolphin.

PubMed

Helweg, D A; Roitblat, H L; Nachtigall, P E; Hautus, M J

1996-01-01

We examined the ability of a bottlenose dolphin (Tursiops truncatus) to recognize aspect-dependent objects using echolocation. An aspect-dependent object such as a cube produces acoustically different echoes at different angles relative to the echolocation signal. The dolphin recognized the objects even though the objects were free to rotate and sway. A linear discriminant analysis and nearest centroid classifier could classify the objects using average amplitude, center frequency, and bandwidth of object echoes. The results show that dolphins can use varying acoustic properties to recognize constant objects and suggest that aspect-independent representations may be formed by combining information gleaned from multiple echoes.

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

PubMed Central

dos Santos, Matheus; Ribeiro, Pedro Otávio; Núñez, Pedro; Botelho, Silvia

2017-01-01

The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper. PMID:28961163

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Insomnia with objective short sleep duration is associated with longer duration of insomnia in the Freiburg Insomnia Cohort compared to insomnia with normal sleep duration, but not with hypertension

PubMed Central

Johann, Anna F.; Hertenstein, Elisabeth; Kyle, Simon D.; Baglioni, Chiara; Feige, Bernd; Nissen, Christoph; McGinness, Alastair J.; Riemann, Dieter; Spiegelhalder, Kai

2017-01-01

Study objectives To replicate the association between insomnia with objective short sleep duration and hypertension, type 2 diabetes and duration of insomnia. Design Retrospective case-control study. Setting Department of Psychiatry and Psychotherapy, Medical Center—University of Freiburg. Participants 328 patients with primary insomnia classified according to DSM-IV criteria (125 males, 203 females, 44.3 ± 12.2 years). Interventions N/A Measurements All participants were investigated using polysomnography, blood pressure measurements, and fasting routine laboratory. Results Insomnia patients with short sleep duration (< 6 hours) in the first night of laboratory sleep presented with a longer duration of insomnia compared to those with normal sleep duration (≥ 6 hours) in the first night of laboratory sleep. Insomnia patients who were categorised as short sleepers in either night were not more likely to suffer from hypertension (systolic blood pressure of ≥ 140 mm Hg, diastolic blood pressure of ≥ 90 mm Hg, or a previously established diagnosis). Data analysis showed that insomnia patients with objective short sleep duration were not more likely to suffer from type 2 diabetes (fasting plasma glucose level of ≥ 126 mg/dl, or a previously established diagnosis). However, the diabetes analysis was only based on a very small number of diabetes cases. As a new finding, insomnia patients who were categorised as short sleepers in either night presented with increases in liver enzyme levels. Conclusions The finding on insomnia duration supports the concept of two distinct sub-groups of insomnia, namely insomnia with, and without, objectively determined short sleep duration. However, our data challenges previous findings that insomnia patients with short sleep duration are more likely to suffer from hypertension. PMID:28746413

Spatial Statistics for Tumor Cell Counting and Classification

NASA Astrophysics Data System (ADS)

Wirjadi, Oliver; Kim, Yoo-Jin; Breuel, Thomas

To count and classify cells in histological sections is a standard task in histology. One example is the grading of meningiomas, benign tumors of the meninges, which requires to assess the fraction of proliferating cells in an image. As this process is very time consuming when performed manually, automation is required. To address such problems, we propose a novel application of Markov point process methods in computer vision, leading to algorithms for computing the locations of circular objects in images. In contrast to previous algorithms using such spatial statistics methods in image analysis, the present one is fully trainable. This is achieved by combining point process methods with statistical classifiers. Using simulated data, the method proposed in this paper will be shown to be more accurate and more robust to noise than standard image processing methods. On the publicly available SIMCEP benchmark for cell image analysis algorithms, the cell count performance of the present paper is significantly more accurate than results published elsewhere, especially when cells form dense clusters. Furthermore, the proposed system performs as well as a state-of-the-art algorithm for the computer-aided histological grading of meningiomas when combined with a simple k-nearest neighbor classifier for identifying proliferating cells.

Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

PubMed Central

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

NASA Technical Reports Server (NTRS)

McGlynn, Thomas; Suchkov, A. A.; Winter, E. L.; Hanisch, R. J.; White, R. L.; Ochsenbein, F.; Derriere, S.; Voges, W.; Corcoran, M. F.

2004-01-01

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

He 2-104 - A symbiotic proto-planetary nebula?

NASA Technical Reports Server (NTRS)

Schwarz, Hugo E.; Aspin, Colin; Lutz, Julie H.

1989-01-01

CCD observations are presented for He 2-104, an object previously classified as both PN and symbiotic star, which show that this is in fact a protoplanetary nebula (PPN) with a dynamical age of about 800 yr. The presence of highly collimated jets, extending over 75 arcsec on the sky, combined with an energy distribution showing a hot as well as a cool component, indicates that He 2-104 is a binary PPN. Since the primary is probably a Mira with a 400-d period (as reported by Whitelock, 1988), it is proposed that the system is a symbiotic PPN.

Multiple hypotheses image segmentation and classification with application to dietary assessment.

PubMed

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J; Delp, Edward J

2015-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain

PubMed Central

Chen, Quanjing; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. PMID:25595179

A Proposal to Develop Interactive Classification Technology

NASA Technical Reports Server (NTRS)

deBessonet, Cary

1998-01-01

Research for the first year was oriented towards: 1) the design of an interactive classification tool (ICT); and 2) the development of an appropriate theory of inference for use in ICT technology. The general objective was to develop a theory of classification that could accommodate a diverse array of objects, including events and their constituent objects. Throughout this report, the term "object" is to be interpreted in a broad sense to cover any kind of object, including living beings, non-living physical things, events, even ideas and concepts. The idea was to produce a theory that could serve as the uniting fabric of a base technology capable of being implemented in a variety of automated systems. The decision was made to employ two technologies under development by the principal investigator, namely, SMS (Symbolic Manipulation System) and SL (Symbolic Language) [see debessonet, 1991, for detailed descriptions of SMS and SL]. The plan was to enhance and modify these technologies for use in an ICT environment. As a means of giving focus and direction to the proposed research, the investigators decided to design an interactive, classificatory tool for use in building accessible knowledge bases for selected domains. Accordingly, the proposed research was divisible into tasks that included: 1) the design of technology for classifying domain objects and for building knowledge bases from the results automatically; 2) the development of a scheme of inference capable of drawing upon previously processed classificatory schemes and knowledge bases; and 3) the design of a query/ search module for accessing the knowledge bases built by the inclusive system. The interactive tool for classifying domain objects was to be designed initially for textual corpora with a view to having the technology eventually be used in robots to build sentential knowledge bases that would be supported by inference engines specially designed for the natural or man-made environments in which the robots would be called upon to operate.

Interspeaker Variation in Habitual Speaking Rate: Additional Evidence

ERIC Educational Resources Information Center

Tsao, Ying-Chiao; Weismer, Gary; Iqbal, Kamran

2006-01-01

Purpose: The purpose of the present study was to test the hypothesis that talkers previously classified by Y.-C. Tsao and G. Weismer (1997) as habitually fast versus habitually slow would show differences in the way they manipulated articulation rate across the rate continuum. Method: Thirty talkers previously classified by Tsao and Weismer (1997)…

Point and Compact Hα Sources in the Interior of M33

NASA Astrophysics Data System (ADS)

Moody, J. Ward; Hintz, Eric G.; Joner, Michael D.; Roming, Peter W. A.; Hintz, Maureen L.

2017-12-01

A variety of interesting objects such as Wolf-Rayet stars, tight OB associations, planetary nebulae, X-ray binaries, etc., can be discovered as point or compact sources in Hα surveys. How these objects distribute through a galaxy sheds light on the galaxy star formation rate and history, mass distribution, and dynamics. The nearby galaxy M33 is an excellent place to study the distribution of Hα-bright point sources in a flocculant spiral galaxy. We have reprocessed an archived WIYN continuum-subtracted Hα image of the inner 6.‧5 × 6.‧5 of M33 and, employing both eye and machine searches, have tabulated sources with a flux greater than approximately 10-15 erg cm-2s-1. We have effectively recovered previously mapped H II regions and have identified 152 unresolved point sources and 122 marginally resolved compact sources, of which 39 have not been previously identified in any archive. An additional 99 Hα sources were found to have sufficient archival flux values to generate a Spectral Energy Distribution. Using the SED, flux values, Hα flux value, and compactness, we classified 67 of these sources.

LCC: Light Curves Classifier

NASA Astrophysics Data System (ADS)

Vo, Martin

2017-08-01

Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing.

PubMed

Riniker, Sereina; Fechner, Nikolas; Landrum, Gregory A

2013-11-25

The concept of data fusion - the combination of information from different sources describing the same object with the expectation to generate a more accurate representation - has found application in a very broad range of disciplines. In the context of ligand-based virtual screening (VS), data fusion has been applied to combine knowledge from either different active molecules or different fingerprints to improve similarity search performance. Machine-learning (ML) methods based on fusion of multiple homogeneous classifiers, in particular random forests, have also been widely applied in the ML literature. The heterogeneous version of classifier fusion - fusing the predictions from different model types - has been less explored. Here, we investigate heterogeneous classifier fusion for ligand-based VS using three different ML methods, RF, naïve Bayes (NB), and logistic regression (LR), with four 2D fingerprints, atom pairs, topological torsions, RDKit fingerprint, and circular fingerprint. The methods are compared using a previously developed benchmarking platform for 2D fingerprints which is extended to ML methods in this article. The original data sets are filtered for difficulty, and a new set of challenging data sets from ChEMBL is added. Data sets were also generated for a second use case: starting from a small set of related actives instead of diverse actives. The final fused model consistently outperforms the other approaches across the broad variety of targets studied, indicating that heterogeneous classifier fusion is a very promising approach for ligand-based VS. The new data sets together with the adapted source code for ML methods are provided in the Supporting Information .

Ensemble candidate classification for the LOTAAS pulsar survey

NASA Astrophysics Data System (ADS)

Tan, C. M.; Lyon, R. J.; Stappers, B. W.; Cooper, S.; Hessels, J. W. T.; Kondratiev, V. I.; Michilli, D.; Sanidas, S.

2018-03-01

One of the biggest challenges arising from modern large-scale pulsar surveys is the number of candidates generated. Here, we implemented several improvements to the machine learning (ML) classifier previously used by the LOFAR Tied-Array All-Sky Survey (LOTAAS) to look for new pulsars via filtering the candidates obtained during periodicity searches. To assist the ML algorithm, we have introduced new features which capture the frequency and time evolution of the signal and improved the signal-to-noise calculation accounting for broad profiles. We enhanced the ML classifier by including a third class characterizing RFI instances, allowing candidates arising from RFI to be isolated, reducing the false positive return rate. We also introduced a new training data set used by the ML algorithm that includes a large sample of pulsars misclassified by the previous classifier. Lastly, we developed an ensemble classifier comprised of five different Decision Trees. Taken together these updates improve the pulsar recall rate by 2.5 per cent, while also improving the ability to identify pulsars with wide pulse profiles, often misclassified by the previous classifier. The new ensemble classifier is also able to reduce the percentage of false positive candidates identified from each LOTAAS pointing from 2.5 per cent (˜500 candidates) to 1.1 per cent (˜220 candidates).

Deep learning for EEG-Based preference classification

NASA Astrophysics Data System (ADS)

Teo, Jason; Hou, Chew Lin; Mountstephens, James

2017-10-01

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows

NASA Astrophysics Data System (ADS)

Wilschut, Liesbeth I.; Heesterbeek, Johan A. P.; Begon, Mike; de Jong, Steven M.; Ageyev, Vladimir; Laudisoit, Anne; Addink, Elisabeth A.

2018-02-01

In Kazakhstan, plague outbreaks occur when its main host, the great gerbil, exceeds an abundance threshold. These live in family groups in burrows, which can be mapped using remote sensing. Occupancy (percentage of burrows occupied) is a good proxy for abundance and hence the possibility of an outbreak. Here we use time series of satellite images to estimate occupancy remotely. In April and September 2013, 872 burrows were identified in the field as either occupied or empty. For satellite images acquired between April and August, 'burrow objects' were identified and matched to the field burrows. The burrow objects were represented by 25 different polygon types, then classified (using a majority vote from 10 Random Forests) as occupied or empty, using Normalized Difference Vegetation Indices (NDVI) calculated for all images. Throughout the season NDVI values were higher for empty than for occupied burrows. Occupancy status of individual burrows that were continuously occupied or empty, was classified with producer's and user's accuracy values of 63 and 64% for the optimum polygon. Occupancy level was predicted very well and differed 2% from the observed occupancy. This establishes firmly the principle that occupancy can be estimated using satellite images with the potential to predict plague outbreaks over extensive areas with much greater ease and accuracy than previously.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

PubMed

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

A clinical analysis of two indolent lymphoma entities: mantle cell lymphoma and marginal zone lymphoma (including the mucosa-associated lymphoid tissue and monocytoid B-cell subcategories): a Southwest Oncology Group study.

PubMed

Fisher, R I; Dahlberg, S; Nathwani, B N; Banks, P M; Miller, T P; Grogan, T M

1995-02-15

The objectives of this study were (1) to determine the clinical presentation and natural history associated with two newly recognized pathologic entities termed mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL), including the mucosa-associated lymphoid tissue (MALT) and monocytoid B-cell subcategories, and (2) to determine whether these entities differ clinically from the other relatively indolent non-Hodgkin's lymphomas with which they have been previously classified. We reviewed the conventional pathology and clinical course of 376 patients who had no prior therapy; had stage III/IV disease; were classified as Working Formulation categories A, B, C, D, or E; and received cyclophosphamide, doxorubicin, vincristine, prednisone (CHOP) on Southwest Oncology Group (SWOG) studies no. 7204, 7426, or 7713. All slides were reviewed by the three pathologists who reached a consensus diagnosis. Age, sex, performance status, bone marrow and/or gastrointestinal involvement, failure-free survival, and overall survival were compared among all the categories. We found that (1) MCL and MZL each represent approximately 10% of stage III or IV patients previously classified as Working Formulation categories A through E and treated with CHOP on SWOG clinical trials; (2) the failure-free survival and overall survival of patients with MZL is the same as that of patients with Working Formulation categories A through E, but the failure-free survival and overall survival of the monocytoid B-cell patients were higher than that of the MALT lymphoma patients (P = .009 and .007, respectively); and (3) the failure-free survival and overall survival of patients with MCL is significantly worse than that of patients with Working Formulation categories A through E (P = .0002 and .0001, respectively). In conclusion, patients with advanced stage MALT lymphomas may have a more aggressive course than previously recognized. Patients with MCL do not have an indolent lymphoma and are candidates for innovative therapy.

Classification of visual and linguistic tasks using eye-movement features.

PubMed

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).

Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data

NASA Astrophysics Data System (ADS)

Hon, Marc; Stello, Dennis; Yu, Jie

2018-05-01

Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower frequency resolution spectra expected from these missions. Additionally, we provide new classifications for 8633 Kepler red giants, out of which 426 have previously not been classified using asteroseismology. This brings the total to 14983 Kepler red giants classified with our new neural network. We also verify that our classifiers are remarkably robust to suboptimal data, including low signal-to-noise and incorrect training truth labels.

Prediction of three social cognitive-motivational structure types.

PubMed

Malerstein, A J; Ahern, M M; Pulos, S

2001-10-01

Previously, using interviews from Baumrind's longitudinal study, three cognitive-motivational structures (CMSs) were predicted in 68 adolescents from caregiving settings and from the CMS types of their mothers, based on the mothers' interviews elicited six years earlier. CMS theory proposes that during Piaget's Concrete Operational Period care-receiving influences the child's adoption of a social cognitive style, which corresponds to one of Piaget's stages of cognitive development. One who is classified as an Operational experiences the caregiving setting as tuned to the child's long-term interests, becomes focused on function and control of function and grasps the distinctions between and gradations of social attributes. One classified as future Intuitive experiences the caregiving as insufficient or unreliable and becomes focused on getting and having, and assesses social situations based on current striking dimensions. A person classified as being future Symbolic experiences the caregiving as out of tune with the self or the world, becomes focused on identity and emotional closeness, and may define self or object by a single attribute. This previous study did not distinguish between the influence of caregiving (including mothers' CMS) on the formation of adolescent CMS type and the possible constancy of CMS type from ages 9 to 15 years. The current study was designed to distinguish between these two possibilities, using data from 67 of the same mothers. Mothers' interviews were purged of descriptions of her child's behavior. Another interview was composed of the purged descriptions of child behavior. This was also done for interviews held when the child was 4 and 15 as well as at 9. From interviews with descriptions of child behavior purged, mother's CMS type at the child's age of 4 and 9 yr. agreed with her adolescent's previously assigned CMS type (p<.05), and caregiving setting at 9 years predicted the adolescent's CMS type (p<.05). From interviews composed of descriptions of only the child's behavior, adolescent CMS type agreed with previously assigned adolescent CMS type (p<.01). Findings were consonant with the idea that CMS type formation is influenced at about Age 9 and sufficiently established to be recognized at Age 15.

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

NASA Astrophysics Data System (ADS)

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

2018-04-01

Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.

Use of Chest Wall Electromyography to Detect Respiratory Effort during Polysomnography

PubMed Central

Berry, Richard B.; Ryals, Scott; Girdhar, Ankur; Wagner, Mary H.

2016-01-01

Study Objectives: To evaluate the ability of chest wall EMG (CW-EMG) using surface electrodes to classify apneas as obstructive, mixed, or central compared to classification using dual channel uncalibrated respiratory inductance plethysmography (RIP). Methods: CW-EMG was recorded from electrodes in the eighth intercostal space at the right mid-axillary line. Consecutive adult clinical sleep studies were retrospectively reviewed, and the first 60 studies with at least 10 obstructive and 10 mixed or central apneas and technically adequate tracings were selected. Four obstructive and six central or mixed apneas (as classified by previous clinical scoring) were randomly selected. A blinded experienced scorer classified the apneas on the basis of tracings showing either RIP channels or the CW-EMG channel. The agreement using the two classification methods was determined by kappa analysis and intraclass correlation. Results: The percentage agreement was 89.5%, the kappa statistic was 0.83 (95% confidence interval 0.79 to 0.87), and the intraclass correlation was 0.83, showing good agreement. Of the 249 apneas classified as central by RIP, 26 were classified as obstructive (10.4%) and 7 as mixed (2.8%) by CW-EMG. Of the 229 events classified as central by CW-EMG, 7 (3.1%) were classified as obstructive and 6 (2.6%) as mixed by RIP. Conclusions: Monitoring CW-EMG may provide a clinically useful method of detection of respiratory effort when used with RIP and can prevent false classification of apneas as central. RIP can rarely detect respiratory effort not easily discernible by CW-EMG and the combination of the two methods is more likely to avoid apnea misclassification. Citation: Berry RB, Ryals S, Girdhar A, Wagner MH. Use of chest wall electromyography to detect respiratory effort during polysomnography. J Clin Sleep Med 2016;12(9):1239–1244. PMID:27306391

Potential Use of Food/Activity, Parenting Style, and Caregiver Feeding Style Measurement Tools with American Indian Families: A Brief Report.

PubMed

Hughes, Sheryl O; Hayes, Jenna T; Sigman-Grant, Madeleine; VanBrackle, Angela

2017-02-01

Objective To provide preliminary descriptive data on caregiver and child weight status, parenting styles, feeding styles, and feeding practices of a small American Indian sample. Methods Participants included a subsample of American Indian caregivers (n = 23) identified from a larger study that was conducted in five states. Using previously validated instruments, means, standard deviations, and ranges for general parenting styles, feeding styles, and feeding practices were explored. Results In general, most caregivers reported healthy feeding practices. Most caregivers scored higher on responsive compared to restrictive or permissive in general parenting. Of the sample, 12 caregivers (52.2 %) were classified in the indulgent feeding style category, 5 caregivers (21.7 %) were classified as authoritative, 5 (21.7 %) uninvolved, and 1 (4.3 %) authoritarian. Conclusions More investigations are needed to explore questions raised by this study about using common tools that measure childhood obesity with American Indian families.

Prediction of Happy-Sad mood from daily behaviors and previous sleep history.

PubMed

Sano, Akane; Yu, Amy Z; McHill, Andrew W; Phillips, Andrew J K; Taylor, Sara; Jaques, Natasha; Klerman, Elizabeth B; Picard, Rosalind W

2015-01-01

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

From Chair to "Chair": A Representational Shift Account of Object Labeling Effects on Memory

ERIC Educational Resources Information Center

Lupyan, Gary

2008-01-01

What are the consequences of calling things by their names? Six experiments investigated how classifying familiar objects with basic-level names (chairs, tables, and lamps) affected recognition memory. Memory was found to be worse for items that were overtly classified with the category name--as reflected by lower hit rates--compared with items…

Obscenity detection using haar-like features and Gentle Adaboost classifier.

PubMed

Mustafa, Rashed; Min, Yang; Zhu, Dingju

2014-01-01

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Special object extraction from medieval books using superpixels and bag-of-features

NASA Astrophysics Data System (ADS)

Yang, Ying; Rushmeier, Holly

2017-01-01

We propose a method to extract special objects in images of medieval books, which generally represent, for example, figures and capital letters. Instead of working on the single-pixel level, we consider superpixels as the basic classification units for improved time efficiency. More specifically, we classify superpixels into different categories/objects by using a bag-of-features approach, where a superpixel category classifier is trained with the local features of the superpixels of the training images. With the trained classifier, we are able to assign the category labels to the superpixels of a historical document image under test. Finally, special objects can easily be identified and extracted after analyzing the categorization results. Experimental results demonstrate that, as compared to the state-of-the-art algorithms, our method provides comparable performance for some historical books but greatly outperforms them in terms of generality and computational time.

Object recognition based on Google's reverse image search and image similarity

NASA Astrophysics Data System (ADS)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

NASA Astrophysics Data System (ADS)

Kozoderov, V. V.; Kondranin, T. V.; Dmitriev, E. V.

2017-12-01

The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the " K-weighted neighbors" method that is close to the nonparametric Bayesian classifier.

Insomnia with objective short sleep duration is associated with longer duration of insomnia in the Freiburg Insomnia Cohort compared to insomnia with normal sleep duration, but not with hypertension.

PubMed

Johann, Anna F; Hertenstein, Elisabeth; Kyle, Simon D; Baglioni, Chiara; Feige, Bernd; Nissen, Christoph; McGinness, Alastair J; Riemann, Dieter; Spiegelhalder, Kai

2017-01-01

To replicate the association between insomnia with objective short sleep duration and hypertension, type 2 diabetes and duration of insomnia. Retrospective case-control study. Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg. 328 patients with primary insomnia classified according to DSM-IV criteria (125 males, 203 females, 44.3 ± 12.2 years). N/A. All participants were investigated using polysomnography, blood pressure measurements, and fasting routine laboratory. Insomnia patients with short sleep duration (< 6 hours) in the first night of laboratory sleep presented with a longer duration of insomnia compared to those with normal sleep duration (≥ 6 hours) in the first night of laboratory sleep. Insomnia patients who were categorised as short sleepers in either night were not more likely to suffer from hypertension (systolic blood pressure of ≥ 140 mm Hg, diastolic blood pressure of ≥ 90 mm Hg, or a previously established diagnosis). Data analysis showed that insomnia patients with objective short sleep duration were not more likely to suffer from type 2 diabetes (fasting plasma glucose level of ≥ 126 mg/dl, or a previously established diagnosis). However, the diabetes analysis was only based on a very small number of diabetes cases. As a new finding, insomnia patients who were categorised as short sleepers in either night presented with increases in liver enzyme levels. The finding on insomnia duration supports the concept of two distinct sub-groups of insomnia, namely insomnia with, and without, objectively determined short sleep duration. However, our data challenges previous findings that insomnia patients with short sleep duration are more likely to suffer from hypertension.

FMRI-adaptation to highly-rendered color photographs of animals and manipulable artifacts during a classification task.

PubMed

Chouinard, Philippe A; Goodale, Melvyn A

2012-02-01

We used fMRI to identify brain areas that adapted to either animals or manipulable artifacts while participants classified highly-rendered color photographs into subcategories. Several key brain areas adapted more strongly to one class of objects compared to the other. Namely, we observed stronger adaptation for animals in the lingual gyrus bilaterally, which are known to analyze the color of objects, and in the right frontal operculum and in the anterior insular cortex bilaterally, which are known to process emotional content. In contrast, the left anterior intraparietal sulcus, which is important for configuring the hand to match the three-dimensional structure of objects during grasping, adapted more strongly to manipulable artifacts. Contrary to what a previous study has found using gray-scale photographs, we did not replicate categorical-specific adaptation in the lateral fusiform gyrus for animals and categorical-specific adaptation in the medial fusiform gyrus for manipulable artifacts. Both categories of objects adapted strongly in the fusiform gyrus without any clear preference in location along its medial-lateral axis. We think that this is because the fusiform gyrus has an important role to play in color processing and hence its responsiveness to color stimuli could be very different than its responsiveness to gray-scale photographs. Nevertheless, on the basis of what we found, we propose that the recognition and subsequent classification of animals may depend primarily on perceptual properties, such as their color, and on their emotional content whereas other factors, such as their function, may play a greater role for classifying manipulable artifacts. Copyright © 2011 Elsevier Inc. All rights reserved.

The structure of the distant Kuiper belt in a Nice model scenario

NASA Astrophysics Data System (ADS)

Pike, Rosemary E.; Lawler, Samantha; Brasser, Ramon; Shankman, Cory; Alexandersen, Mike; Kavelaars, J. J.

2016-10-01

By utilizing a well-sampled migration model and characterized survey detections, we demonstrate that the Nice-model scenario results in consistent populations of scattering trans-Neptunian objects (TNOs) and several resonant TNO populations, but fails to reproduce the large population of 5:1 resonators discovered in surveys. We examine in detail the TNO populations implanted by the Nice model simulation from Brasser and Morbidelli (2013, B&M). This analysis focuses on the region from 25-155 AU, probing the classical, scattering, detached, and major resonant populations. Additional integrations were necessary to classify the test particles and determine population sizes and characteristics. The classified simulation objects are compared to the real TNOs from the Canada-France Ecliptic Plane Survey (CFEPS), CFEPS high latitude fields, and the Alexandersen (2016) survey. These surveys all include a detailed characterization of survey depth, pointing, and tracking efficiency, which allows detailed testing of this independently produced model of TNO populations. In the B&M model, the regions of the outer Solar System populated via capture of scattering objects are consistent with survey constraints. The scattering TNOs and most n:1 resonant populations have consistent orbital distributions and population sizes with the real detections, as well as a starting disk mass consistent with expectations. The B&M 5:1 resonators have a consistent orbital distribution with the real detections and previous models. However, the B&M 5:1 Neptune resonance is underpopulated by a factor of ~100 and would require a starting proto-planetesimal disk with a mass of ~100 Earth masses. The large population in the 5:1 Neptune resonance is unexplained by scattering capture in a Nice-model scenario, however this model accurately produces the TNO subpopulations that result from scattering object capture and provides additional insight into sub-population orbital distributions.

Automatic staging of bladder cancer on CT urography

NASA Astrophysics Data System (ADS)

Garapati, Sankeerth S.; Hadjiiski, Lubomir M.; Cha, Kenny H.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Alva, Ajjai; Paramagul, Chintana; Wei, Jun; Zhou, Chuan

2016-03-01

Correct staging of bladder cancer is crucial for the decision of neoadjuvant chemotherapy treatment and minimizing the risk of under- or over-treatment. Subjectivity and variability of clinicians in utilizing available diagnostic information may lead to inaccuracy in staging bladder cancer. An objective decision support system that merges the information in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate and consistent staging assessments. In this study, we developed a preliminary method to stage bladder cancer. With IRB approval, 42 bladder cancer cases with CTU scans were collected from patient files. The cases were classified into two classes based on pathological stage T2, which is the decision threshold for neoadjuvant chemotherapy treatment (i.e. for stage >=T2) clinically. There were 21 cancers below stage T2 and 21 cancers at stage T2 or above. All 42 lesions were automatically segmented using our auto-initialized cascaded level sets (AI-CALS) method. Morphological features were extracted, which were selected and merged by linear discriminant analysis (LDA) classifier. A leave-one-case-out resampling scheme was used to train and test the classifier using the 42 lesions. The classification accuracy was quantified using the area under the ROC curve (Az). The average training Az was 0.97 and the test Az was 0.85. The classifier consistently selected the lesion volume, a gray level feature and a contrast feature. This predictive model shows promise for assisting in assessing the bladder cancer stage.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

Modulation of microsaccades by spatial frequency during object categorization.

PubMed

Craddock, Matt; Oppermann, Frank; Müller, Matthias M; Martinovic, Jasna

2017-01-01

The organization of visual processing into a coarse-to-fine information processing based on the spatial frequency properties of the input forms an important facet of the object recognition process. During visual object categorization tasks, microsaccades occur frequently. One potential functional role of these eye movements is to resolve high spatial frequency information. To assess this hypothesis, we examined the rate, amplitude and speed of microsaccades in an object categorization task in which participants viewed object and non-object images and classified them as showing either natural objects, man-made objects or non-objects. Images were presented unfiltered (broadband; BB) or filtered to contain only low (LSF) or high spatial frequency (HSF) information. This allowed us to examine whether microsaccades were modulated independently by the presence of a high-level feature - the presence of an object - and by low-level stimulus characteristics - spatial frequency. We found a bimodal distribution of saccades based on their amplitude, with a split between smaller and larger microsaccades at 0.4° of visual angle. The rate of larger saccades (⩾0.4°) was higher for objects than non-objects, and higher for objects with high spatial frequency content (HSF and BB objects) than for LSF objects. No effects were observed for smaller microsaccades (<0.4°). This is consistent with a role for larger microsaccades in resolving HSF information for object identification, and previous evidence that more microsaccades are directed towards informative image regions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Logo recognition in video by line profile classification

NASA Astrophysics Data System (ADS)

den Hollander, Richard J. M.; Hanjalic, Alan

2003-12-01

We present an extension to earlier work on recognizing logos in video stills. The logo instances considered here are rigid planar objects observed at a distance in the scene, so the possible perspective transformation can be approximated by an affine transformation. For this reason we can classify the logos by matching (invariant) line profiles. We enhance our previous method by considering multiple line profiles instead of a single profile of the logo. The positions of the lines are based on maxima in the Hough transform space of the segmented logo foreground image. Experiments are performed on MPEG1 sport video sequences to show the performance of the proposed method.

Robust online tracking via adaptive samples selection with saliency detection

NASA Astrophysics Data System (ADS)

Yan, Jia; Chen, Xi; Zhu, QiuPing

2013-12-01

Online tracking has shown to be successful in tracking of previously unknown objects. However, there are two important factors which lead to drift problem of online tracking, the one is how to select the exact labeled samples even when the target locations are inaccurate, and the other is how to handle the confusors which have similar features with the target. In this article, we propose a robust online tracking algorithm with adaptive samples selection based on saliency detection to overcome the drift problem. To deal with the problem of degrading the classifiers using mis-aligned samples, we introduce the saliency detection method to our tracking problem. Saliency maps and the strong classifiers are combined to extract the most correct positive samples. Our approach employs a simple yet saliency detection algorithm based on image spectral residual analysis. Furthermore, instead of using the random patches as the negative samples, we propose a reasonable selection criterion, in which both the saliency confidence and similarity are considered with the benefits that confusors in the surrounding background are incorporated into the classifiers update process before the drift occurs. The tracking task is formulated as a binary classification via online boosting framework. Experiment results in several challenging video sequences demonstrate the accuracy and stability of our tracker.

Selection of the best features for leukocytes classification in blood smear microscopic images

NASA Astrophysics Data System (ADS)

Sarrafzadeh, Omid; Rabbani, Hossein; Talebi, Ardeshir; Banaem, Hossein Usefi

2014-03-01

Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain.

PubMed

Chen, Quanjing; Garcea, Frank E; Mahon, Bradford Z

2016-04-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter

NASA Astrophysics Data System (ADS)

Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio

2012-01-01

Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.

Real-time reliability measure-driven multi-hypothesis tracking using 2D and 3D features

NASA Astrophysics Data System (ADS)

Zúñiga, Marcos D.; Brémond, François; Thonnat, Monique

2011-12-01

We propose a new multi-target tracking approach, which is able to reliably track multiple objects even with poor segmentation results due to noisy environments. The approach takes advantage of a new dual object model combining 2D and 3D features through reliability measures. In order to obtain these 3D features, a new classifier associates an object class label to each moving region (e.g. person, vehicle), a parallelepiped model and visual reliability measures of its attributes. These reliability measures allow to properly weight the contribution of noisy, erroneous or false data in order to better maintain the integrity of the object dynamics model. Then, a new multi-target tracking algorithm uses these object descriptions to generate tracking hypotheses about the objects moving in the scene. This tracking approach is able to manage many-to-many visual target correspondences. For achieving this characteristic, the algorithm takes advantage of 3D models for merging dissociated visual evidence (moving regions) potentially corresponding to the same real object, according to previously obtained information. The tracking approach has been validated using video surveillance benchmarks publicly accessible. The obtained performance is real time and the results are competitive compared with other tracking algorithms, with minimal (or null) reconfiguration effort between different videos.

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier

NASA Astrophysics Data System (ADS)

Hashemi, H.; Tax, D. M. J.; Duin, R. P. W.; Javaherian, A.; de Groot, P.

2008-11-01

Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA). In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and support vector classifier (SVC) are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.

Selective visual attention in object detection processes

NASA Astrophysics Data System (ADS)

Paletta, Lucas; Goyal, Anurag; Greindl, Christian

2003-03-01

Object detection is an enabling technology that plays a key role in many application areas, such as content based media retrieval. Attentive cognitive vision systems are here proposed where the focus of attention is directed towards the most relevant target. The most promising information is interpreted in a sequential process that dynamically makes use of knowledge and that enables spatial reasoning on the local object information. The presented work proposes an innovative application of attention mechanisms for object detection which is most general in its understanding of information and action selection. The attentive detection system uses a cascade of increasingly complex classifiers for the stepwise identification of regions of interest (ROIs) and recursively refined object hypotheses. While the most coarse classifiers are used to determine first approximations on a region of interest in the input image, more complex classifiers are used for more refined ROIs to give more confident estimates. Objects are modelled by local appearance based representations and in terms of posterior distributions of the object samples in eigenspace. The discrimination function to discern between objects is modeled by a radial basis functions (RBF) network that has been compared with alternative networks and been proved consistent and superior to other artifical neural networks for appearance based object recognition. The experiments were led for the automatic detection of brand objects in Formula One broadcasts within the European Commission's cognitive vision project DETECT.

A semi-automated image analysis procedure for in situ plankton imaging systems.

PubMed

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.

A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems

PubMed Central

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C.; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M.

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups. PMID:26010260

Research on Objectives for High-School Biology

ERIC Educational Resources Information Center

Korgan, John J., Jr.; Wilson, John T.

1973-01-01

Describes procedures to develop instructional objectives for high school biology. Two kinds of objectives are identified as pre-objectives and performance objectives. Models to classify these in branches of biology and to ensure quality control are provided. (PS)

Appearances Aren't Everything: Shape Classifiers and Referential Processing in Cantonese

ERIC Educational Resources Information Center

Tsang, Cara; Chambers, Craig G.

2011-01-01

Cantonese shape classifiers encode perceptual information that is characteristic of their associated nouns, although certain nouns are exceptional. For example, the classifier "tiu" occurs primarily with nouns for long-narrow-flexible objects (e.g., scarves, snakes, and ropes) and also occurs with the noun for a (short, rigid) key. In 3…

Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

PubMed Central

Min, Yang; Zhu, Dingju

2014-01-01

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier. PMID:25003153

Primeval very low-mass stars and brown dwarfs - II. The most metal-poor substellar object

NASA Astrophysics Data System (ADS)

Zhang, Z. H.; Homeier, D.; Pinfield, D. J.; Lodieu, N.; Jones, H. R. A.; Allard, F.; Pavlenko, Ya. V.

2017-06-01

SDSS J010448.46+153501.8 has previously been classified as an sdM9.5 subdwarf. However, its very blue J - K colour (-0.15 ± 0.17) suggests a much lower metallicity compared to normal sdM9.5 subdwarfs. Here, we re-classify this object as a usdL1.5 subdwarf based on a new optical and near-infrared spectrum obtained with X-shooter on the Very Large Telescope. Spectral fitting with BT-Settl models leads to Teff = 2450 ± 150 K, [Fe/H] = -2.4 ± 0.2 and log g = 5.5 ± 0.25. We estimate a mass for SDSS J010448.46+153501.8 of 0.086 ± 0.0015 M⊙ which is just below the hydrogen-burning minimum mass at [Fe/H] = -2.4 (˜0.088 M⊙) according to evolutionary models. Our analysis thus shows SDSS J010448.46+153501.8 to be the most metal-poor and highest mass substellar object known to-date. We found that SDSS J010448.46+153501.8 is joined by another five known L subdwarfs (2MASS J05325346+8246465, 2MASS J06164006-6407194, SDSS J125637.16-022452.2, ULAS J151913.03-000030.0 and 2MASS J16262034+3925190) in a 'halo brown dwarf transition zone' in the Teff-[Fe/H] plane, which represents a narrow mass range in which unsteady nuclear fusion occurs. This halo brown dwarf transition zone forms a 'substellar subdwarf gap' for mid L to early T types.

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2013-09-30

N0001411WX21394 Steve W. Martin SPAWAR Systems Center Pacific 53366 Front St. San Diego, CA 92152-6551 phone: (619) 553-9882 email: Steve.W.Martin...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, particularly S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

PubMed

Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi

2018-03-24

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.

A Label Propagation Approach for Detecting Buried Objects in Handheld GPR Data

DTIC Science & Technology

2016-04-17

regions of interest that correspond to locations with anomalous signatures. Second, a classifier (or an ensemble of classifiers ) is used to assign a...investigated for almost two decades and several classifiers have been developed. Most of these methods are based on the supervised learning paradigm where...labeled target and clutter signatures are needed to train a classifier to discriminate between the two classes. Typically, large and diverse labeled

The impact of differences between subjective and objective social class on life satisfaction among the Korean population in early old age: Analysis of Korean longitudinal study on aging.

PubMed

Choi, Young; Kim, Jae-Hyun; Park, Eun-Cheol

2016-01-01

Several previous studies have established the relationship between the effects of socioeconomic status or subjective social strata on life satisfaction. However, no previous study has examined the relationship between social class and life satisfaction in terms of a disparity between subjective and objective social status. To investigate the relationship between differences in subjective and objective social class and life satisfaction. Data from the Korean Longitudinal Study of Aging with 8252 participants aged 45 or older was used. Life satisfaction was measured by the question, "How satisfied are you with your quality of life?" The main independent variable was differences in objective (income and education) and subjective social class, which was classified according to nine categories (ranging from high-high to low-low). This association was investigated by linear mixed model due to two waves data nested within individuals. Lower social class (income, education, subjective social class) was associated with dissatisfaction. The impact of objective and subjective social class on life satisfaction varied according to the level of differences in objective and subjective social class. Namely, an individual's life satisfaction declined as objective social classes decreased at the same level of subjective social class (i.e., HH, MH, LH). In both dimensions of objective social class (education and income), an individual's life satisfaction declined as subjective social class decreased by one level (i.e., HH, HM, HL). Our findings indicated that social supports is needed to improve the life satisfaction among the population aged 45 or more with low social class. The government should place increased focus on policies that encourage not only the life satisfaction of the Korean elderly with low objective social class, but also subjective social class. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

RYR2, PTDSS1 and AREG genes are implicated in a Lebanese population-based study of copy number variation in autism

PubMed Central

Soueid, Jihane; Kourtian, Silva; Makhoul, Nadine J.; Makoukji, Joelle; Haddad, Sariah; Ghanem, Simona S.; Kobeissy, Firas; Boustany, Rose-Mary

2016-01-01

Autism Spectrum Disorders (ASDs) are a group of neurodevelopmental disorders characterized by ritualistic-repetitive behaviors and impaired verbal and non-verbal communication. Objectives were to determine the contribution of genetic variation to ASDs in the Lebanese. Affymetrix Cytogenetics Whole-Genome 2.7 M and CytoScan™ HD Arrays were used to detect CNVs in 41 Lebanese autistic children and 35 non-autistic, developmentally delayed and intellectually disabled patients. 33 normal participants were used as controls. 16 de novo CNVs and 57 inherited CNVs, including recognized pathogenic 16p11.2 duplications and 2p16.3 deletions were identified. A duplication at 1q43 classified as likely pathogenic encompasses RYR2 as a potential ASD candidate gene. This previously identified CNV has been classified as both pathogenic, and, of uncertain significance. A duplication of unknown significance at 10q11.22, proposed as a modulator for phenotypic disease expression in Rett syndrome, was also identified. The novel potential autism susceptibility genes PTDSS1 and AREG were uncovered and warrant further genetic and functional analyses. Previously described and novel genetic targets in ASD were identified in Lebanese families with autism. These findings may lead to improved diagnosis of ASDs and informed genetic counseling, and may also lead to untapped therapeutic targets applicable to Lebanese and non-Lebanese patients. PMID:26742492

A decision-theoretic approach to identifying future high-cost patients.

PubMed

Pietz, Kenneth; Byrne, Margaret M; Petersen, Laura A

2006-09-01

The objective of this study was to develop and evaluate a method of allocating funding for very-high-cost (VHC) patients among hospitals. Diagnostic cost groups (DCGs) were used for risk adjustment. The patient population consisted of 253,013 veterans who used Department of Veterans Affairs (VA) medical care services in fiscal year (FY) 2003 (October 1, 2002-September 30, 2003) in a network of 8 VA hospitals. We defined VHC as greater than 75,000 dollars (0.81%). The upper fifth percentile was also used for comparison. A Bayesian decision rule for classifying patients as VHC/not VHC using DCGs was developed and evaluated. The method uses FY 2003 DCGs to allocate VHC funds for FY 2004. We also used FY 2002 DCGs to allocate VHC funds for FY 2003 for comparison. The resulting allocation was compared with using the allocation of VHC patients among the hospitals in the previous year. The decision rule identified DCG 17 as the optimal cutoff for identifying VHC patients for the next year. The previous year's allocation came closest to the actual distribution of VHC patients. The decision-theoretic approach may provide insight into the economic consequences of classifying a patient as VHC or not VHC. More research is needed into methods of identifying future VHC patients so that capitation plans can fairly reimburse healthcare systems for appropriately treating these patients.

How Can Dolphins Recognize Fish According to Their Echoes? A Statistical Analysis of Fish Echoes

PubMed Central

Yovel, Yossi; Au, Whitlow W. L.

2010-01-01

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification. PMID:21124908

How can dolphins recognize fish according to their echoes? A statistical analysis of fish echoes.

PubMed

Yovel, Yossi; Au, Whitlow W L

2010-11-19

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification.

Engine classification using vibrations measured by Laser Doppler Vibrometer on different surfaces

NASA Astrophysics Data System (ADS)

Wei, J.; Liu, Chi-Him; Zhu, Zhigang; Vongsy, Karmon; Mendoza-Schrock, Olga

2015-05-01

In our previous studies, vehicle surfaces' vibrations caused by operating engines measured by Laser Doppler Vibrometer (LDV) have been effectively exploited in order to classify vehicles of different types, e.g., vans, 2-door sedans, 4-door sedans, trucks, and buses, as well as different types of engines, such as Inline-four engines, V-6 engines, 1-axle diesel engines, and 2-axle diesel engines. The results are achieved by employing methods based on an array of machine learning classifiers such as AdaBoost, random forests, neural network, and support vector machines. To achieve effective classification performance, we seek to find a more reliable approach to pick authentic vibrations of vehicle engines from a trustworthy surface. Compared with vibrations directly taken from the uncooperative vehicle surfaces that are rigidly connected to the engines, these vibrations are much weaker in magnitudes. In this work we conducted a systematic study on different types of objects. We tested different types of engines ranging from electric shavers, electric fans, and coffee machines among different surfaces such as a white board, cement wall, and steel case to investigate the characteristics of the LDV signals of these surfaces, in both the time and spectral domains. Preliminary results in engine classification using several machine learning algorithms point to the right direction on the choice of type of object surfaces to be planted for LDV measurements.

Classifier-Guided Sampling for Complex Energy System Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Backlund, Peter B.; Eddy, John P.

2015-09-01

This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of omore » bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.« less

Fast object reconstruction in block-based compressive low-light-level imaging

NASA Astrophysics Data System (ADS)

Ke, Jun; Sui, Dong; Wei, Ping

2014-11-01

In this paper we propose a simply yet effective and efficient method for long-term object tracking. Different from traditional visual tracking method which mainly depends on frame-to-frame correspondence, we combine high-level semantic information with low-level correspondences. Our framework is formulated in a confidence selection framework, which allows our system to recover from drift and partly deal with occlusion problem. To summarize, our algorithm can be roughly decomposed in a initialization stage and a tracking stage. In the initialization stage, an offline classifier is trained to get the object appearance information in category level. When the video stream is coming, the pre-trained offline classifier is used for detecting the potential target and initializing the tracking stage. In the tracking stage, it consists of three parts which are online tracking part, offline tracking part and confidence judgment part. Online tracking part captures the specific target appearance information while detection part localizes the object based on the pre-trained offline classifier. Since there is no data dependence between online tracking and offline detection, these two parts are running in parallel to significantly improve the processing speed. A confidence selection mechanism is proposed to optimize the object location. Besides, we also propose a simple mechanism to judge the absence of the object. If the target is lost, the pre-trained offline classifier is utilized to re-initialize the whole algorithm as long as the target is re-located. During experiment, we evaluate our method on several challenging video sequences and demonstrate competitive results.

Minimum distance classification in remote sensing

NASA Technical Reports Server (NTRS)

Wacker, A. G.; Landgrebe, D. A.

1972-01-01

The utilization of minimum distance classification methods in remote sensing problems, such as crop species identification, is considered. Literature concerning both minimum distance classification problems and distance measures is reviewed. Experimental results are presented for several examples. The objective of these examples is to: (a) compare the sample classification accuracy of a minimum distance classifier, with the vector classification accuracy of a maximum likelihood classifier, and (b) compare the accuracy of a parametric minimum distance classifier with that of a nonparametric one. Results show the minimum distance classifier performance is 5% to 10% better than that of the maximum likelihood classifier. The nonparametric classifier is only slightly better than the parametric version.

The classification of normal screening mammograms

NASA Astrophysics Data System (ADS)

Ang, Zoey Z. Y.; Rawashdeh, Mohammad A.; Heard, Robert; Brennan, Patrick C.; Lee, Warwick; Lewis, Sarah J.

2016-03-01

Rationale and objectives: To understand how breast screen readers classify the difficulty of normal screening mammograms using common lexicon describing normal appearances. Cases were also assessed on their suitability for a single reader strategy. Materials and Methods: 15 breast readers were asked to interpret a test set of 29 normal screening mammogram cases and classify them by rating the difficulty of the case on a five-point Likert scale, identifying the salient features and assessing their suitability for single reading. Using the False Positive Fractions from a previous study, the 29 cases were classified into 10 "low", 10 "medium" and nine "high" difficulties. Data was analyzed with descriptive statistics. Spearman's correlation was used to test the strength of association between the difficulty of the cases and the readers' recommendation for single reading strategy. Results: The ratings from readers in this study corresponded to the known difficulty level of cases for the 'low' and 'high' difficulty cases. Uniform ductal pattern and density, symmetrical mammographic features and the absence of micro-calcifications were the main reasons associated with 'low' difficulty cases. The 'high' difficulty cases were described as having `dense breasts'. There was a statistically significant negative correlation between the difficulty of the cases and readers' recommendation for single reading (r = -0.475, P = 0.009). Conclusion: The findings demonstrated potential relationships between certain mammographic features and the difficulty for readers to classify mammograms as 'normal'. The standard Australian practice of double reading was deemed more suitable for most cases. There was an inverse moderate association between the difficulty of the cases and the recommendations for single reading.

Improved semi-supervised online boosting for object tracking

NASA Astrophysics Data System (ADS)

Li, Yicui; Qi, Lin; Tan, Shukun

2016-10-01

The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object features are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. First, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.

Neural Dissociation in the Production of Lexical versus Classifier Signs in ASL: Distinct Patterns of Hemispheric Asymmetry

ERIC Educational Resources Information Center

Hickok, Gregory; Pickell, Herbert; Klima, Edward; Bellugi, Ursula

2009-01-01

We examine the hemispheric organization for the production of two classes of ASL signs, lexical signs and classifier signs. Previous work has found strong left hemisphere dominance for the production of lexical signs, but several authors have speculated that classifier signs may involve the right hemisphere to a greater degree because they can…

Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment

PubMed Central

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J.; Delp, Edward J.

2016-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier’s confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback. PMID:25561457

Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials

DOEpatents

Boucheron, Laura E

2013-07-16

Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.

Mysterious object He2-90

NASA Image and Video Library

1999-12-01

Astronomers using NASA Hubble Space Telescope stumbled upon a mysterious object that grudgingly yielded clues to its identity. The object is classified as a planetary nebula, the glowing remains of a dying, lightweight star.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

PubMed

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

2015-09-22

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

PubMed Central

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P.; Zelikowsky, Moriel; Navonne, Santiago G.; Perona, Pietro; Anderson, David J.

2015-01-01

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics. PMID:26354123

How Do Intermediate and Junior High School Students Conceptualize Living and Nonliving?

ERIC Educational Resources Information Center

Tamir, Pinchas; And Others

1981-01-01

The extent to which grades 3-7 students (N=424) hold animistic notions and the meanings of these notions were evaluated. A classification test composed of 16 pictures (8 living and 8 inanimate objects) and a questionnaire were used. Ninety-nine percent classified animals, 80 percent classified plants, and 56 percent classified embryos as living.…

System of technical vision for autonomous unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Bondarchuk, A. S.

2018-05-01

This paper is devoted to the implementation of image recognition algorithm using the LabVIEW software. The created virtual instrument is designed to detect the objects on the frames from the camera mounted on the UAV. The trained classifier is invariant to changes in rotation, as well as to small changes in the camera's viewing angle. Finding objects in the image using particle analysis, allows you to classify regions of different sizes. This method allows the system of technical vision to more accurately determine the location of the objects of interest and their movement relative to the camera.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

PubMed Central

Nagarajan, Mahesh B.; Huber, Markus B.; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2014-01-01

Objective While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. Methods and Materials We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Results Of the feature vectors investigated, the best performance was observed with Minkowski functional ’perimeter’ while comparable performance was observed with ’area’. Of the dimension reduction algorithms tested with ’perimeter’, the best performance was observed with Sammon’s mapping (0.84 ± 0.10) while comparable performance was achieved with exploratory observation machine (0.82 ± 0.09) and principal component analysis (0.80 ± 0.10). Conclusions The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction. PMID:24355697

Age-related deficits in inhibition in figure-ground assignment.

PubMed

Anderson, John A E; Healey, M Karl; Hasher, Lynn; Peterson, Mary A

2016-05-01

We assessed age differences in the ability to resolve competition for figural status in stationary displays using small, enclosed, symmetrical silhouettes that participants classified as depicting "novel" or "familiar" shapes. The silhouettes were biased such that the inside was perceived as the shaped figure, and the outside was perceived as a shapeless ground. The critical manipulation was whether a portion of a meaningful object was suggested on the outside of the border of some of the novel silhouettes but not others (M+Ground and M-Ground novel silhouettes, respectively). This manipulation was intended to induce greater inhibitory competition for figural status from the groundside in M+Ground silhouettes than M-Ground silhouettes. In previous studies, young adults classified M+Ground silhouettes as "novel" faster than M-Ground silhouettes (Trujillo, Allen, Schnyer, & Peterson, 2010), suggesting that young adults may recruit more inhibition to resolve figure-ground when there is more competition. We replicated this effect with young adults in the present study, but older adults showed the opposite pattern and were less accurate in classifying M+Ground than M-Ground silhouettes. These results extend the evidence for inhibitory deficits in older adults to figure assignment in stationary displays. The (M+Ground - M-Ground) RT differences were evident in observers' longest responses, consistent with the hypothesis that inhibitory deficits are evident when the need for inhibition is substantial.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

PubMed Central

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-01-01

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

PubMed

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-05-11

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

A fuzzy automated object classification by infrared laser camera

NASA Astrophysics Data System (ADS)

Kanazawa, Seigo; Taniguchi, Kazuhiko; Asari, Kazunari; Kuramoto, Kei; Kobashi, Syoji; Hata, Yutaka

2011-06-01

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.

Low mass companions to nearby stars: Spectral classification and its relation to the stellar/substellar break

NASA Technical Reports Server (NTRS)

Kirkpatrick, J. Davy; Mccarthy, Donald W., Jr.

1994-01-01

The relationship between mass and spectral class for main-sequence stars has never been obtained for dwarfs cooler than M6; currently, the true nature of objects classified as M7, M8, M9, or later (be they stellar or substellar) is not known. In this paper, spectral types for the components in five low mass binary systems are estimated based on previously published infrared speckle measurements, red/infrared photometry, and parallax data, together with newly acquired high signal-to-noise composite spectra of the systems and revised magnitude difference relations for M dwarfs. For two of these binaries, the secondary has a smaller mass (less than 0.09 solar mass) than any object having a dynamically measured mass and a known spectral type, thus extending the spectral class/mass relation to lower masses than has previously been possible. Data from the higher mass components (0.09 solar mass less than M less than 0.40 solar mass) are consistent with earlier results; the two lowest mass objects -- though having mass errors which could place them on either side of the M dwarf/brown dwarf dividing line (Mass is about 0.08 solar mass) -- are found to have spectral types no cooler than M6.5 V. An extrapolation of the updated spectral class/mass relation to the hydrogen-burning limit suggests that objects of type M7 and later may be substellar. Direct confirmation of this awaits the discovery of a close, very late-type binary for which dynamical masses can be measured.

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Monitoring industrial facilities using principles of integration of fiber classifier and local sensor networks

NASA Astrophysics Data System (ADS)

Korotaev, Valery V.; Denisov, Victor M.; Rodrigues, Joel J. P. C.; Serikova, Mariya G.; Timofeev, Andrey V.

2015-05-01

The paper deals with the creation of integrated monitoring systems. They combine fiber-optic classifiers and local sensor networks. These systems allow for the monitoring of complex industrial objects. Together with adjacent natural objects, they form the so-called geotechnical systems. An integrated monitoring system may include one or more spatially continuous fiber-optic classifiers based on optic fiber and one or more arrays of discrete measurement sensors, which are usually combined in sensor networks. Fiber-optic classifiers are already widely used for the control of hazardous extended objects (oil and gas pipelines, railways, high-rise buildings, etc.). To monitor local objects, discrete measurement sensors are generally used (temperature, pressure, inclinometers, strain gauges, accelerometers, sensors measuring the composition of impurities in the air, and many others). However, monitoring complex geotechnical systems require a simultaneous use of continuous spatially distributed sensors based on fiber-optic cable and connected local discrete sensors networks. In fact, we are talking about integration of the two monitoring methods. This combination provides an additional way to create intelligent monitoring systems. Modes of operation of intelligent systems can automatically adapt to changing environmental conditions. For this purpose, context data received from one sensor (e.g., optical channel) may be used to change modes of work of other sensors within the same monitoring system. This work also presents experimental results of the prototype of the integrated monitoring system.

Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010

USGS Publications Warehouse

Enwright, Nicholas M.; Hartley, Stephen B.; Couvillion, Brady R.; Michael G. Brasher,; Jenneke M. Visser,; Michael K. Mitchell,; Bart M. Ballard,; Mark W. Parr,; Barry C. Wilson,

2015-07-23

This study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

Development and evaluation of a hand tracker using depth images captured from an overhead perspective.

PubMed

Czarnuch, Stephen; Mihailidis, Alex

2015-03-27

We present the development and evaluation of a robust hand tracker based on single overhead depth images for use in the COACH, an assistive technology for people with dementia. The new hand tracker was designed to overcome limitations experienced by the COACH in previous clinical trials. We train a random decision forest classifier using ∼5000 manually labeled, unbalanced, training images. Hand positions from the classifier are translated into task actions based on proximity to environmental objects. Tracker performance is evaluated using a large set of ∼24 000 manually labeled images captured from 41 participants in a fully-functional washroom, and compared to the system's previous colour-based hand tracker. Precision and recall were 0.994 and 0.938 for the depth tracker compared to 0.981 and 0.822 for the colour tracker with the current data, and 0.989 and 0.466 in the previous study. The improved tracking performance supports integration of the depth-based tracker into the COACH toward unsupervised, real-world trials. Implications for Rehabilitation The COACH is an intelligent assistive technology that can enable people with cognitive disabilities to stay at home longer, supporting the concept of aging-in-place. Automated prompting systems, a type of intelligent assistive technology, can help to support the independent completion of activities of daily living, increasing the independence of people with cognitive disabilities while reducing the burden of care experienced by caregivers. Robust motion tracking using depth imaging supports the development of intelligent assistive technologies like the COACH. Robust motion tracking also has application to other forms of assistive technologies including gaming, human-computer interaction and automated assessments.

Factors determining the smooth flow and the non-operative time in a one-induction room to one-operating room setting

PubMed Central

Mulier, Jan P; De Boeck, Liesje; Meulders, Michel; Beliën, Jeroen; Colpaert, Jan; Sels, Annabel

2015-01-01

Rationale, aims and objectives What factors determine the use of an anaesthesia preparation room and shorten non-operative time? Methods A logistic regression is applied to 18 751 surgery records from AZ Sint-Jan Brugge AV, Belgium, where each operating room has its own anaesthesia preparation room. Surgeries, in which the patient's induction has already started when the preceding patient's surgery has ended, belong to a first group where the preparation room is used as an induction room. Surgeries not fulfilling this property belong to a second group. A logistic regression model tries to predict the probability that a surgery will be classified into a specific group. Non-operative time is calculated as the time between end of the previous surgery and incision of the next surgery. A log-linear regression of this non-operative time is performed. Results It was found that switches in surgeons, being a non-elective surgery as well as the previous surgery being non-elective, increase the probability of being classified into the second group. Only a few surgery types, anaesthesiologists and operating rooms can be found exclusively in one of the two groups. Analysis of variance demonstrates that the first group has significantly lower non-operative times. Switches in surgeons, anaesthesiologists and longer scheduled durations of the previous surgery increases the non-operative time. A switch in both surgeon and anaesthesiologist strengthens this negative effect. Only a few operating rooms and surgery types influence the non-operative time. Conclusion The use of the anaesthesia preparation room shortens the non-operative time and is determined by several human and structural factors. PMID:25496600

Program Evaluation: An Overview.

ERIC Educational Resources Information Center

McCluskey, Lawrence

1973-01-01

Various models of educational evaluation are presented. These include: (1) the classical type model, which contains the following guidelines: formulate objectives, classify objectives, define objectives in behavioral terms, suggest situations in which achievement of objectives will be shown, develop or select appraisal techniques, and gather and…

Sharing skills: using augmented reality for human-robot collaboration

NASA Astrophysics Data System (ADS)

Giesler, Bjorn; Steinhaus, Peter; Walther, Marcus; Dillmann, Ruediger

2004-05-01

Both stationary 'industrial' and autonomous mobile robots nowadays pervade many workplaces, but human-friendly interaction with them is still very much an experimental subject. One of the reasons for this is that computer and robotic systems are very bad at performing certain tasks well and robust. A prime example is classification of sensor readings: Which part of a 3D depth image is the cup, which the saucer, which the table? These are tasks that humans excel at. To alleviate this problem, we propose a team approah, wherein the robot records sensor data and uses an Augmented-Reality (AR) system to present the data to the user directly in the 3D environment. The user can then perform classification decisions directly on the data by pointing, gestures and speech commands. After the classification has been performed by the user, the robot takes the classified data and matches it to its environment model. As a demonstration of this approach, we present an initial system for creating objects on-the-fly in the environment model. A rotating laser scanner is used to capture a 3D snapshot of the environment. This snapshot is presented to the user as an overlay over his view of the scene. The user classifies unknown objects by pointing at them. The system segments the snapshot according to the user's indications and presents the results of segmentation back to the user, who can then inspect, correct and enhance them interactively. After a satisfying result has been reached, the laser-scanner can take more snapshots from other angles and use the previous segmentation hints to construct a 3D model of the object.

Looking for blazars in a sample of unidentified high-energy emitting Fermi sources

NASA Astrophysics Data System (ADS)

Marchesini, E. J.; Masetti, N.; Chavushyan, V.; Cellone, S. A.; Andruchow, I.; Bassani, L.; Bazzano, A.; Jiménez-Bailón, E.; Landi, R.; Malizia, A.; Palazzi, E.; Patiño-Álvarez, V.; Rodríguez-Castillo, G. A.; Stephen, J. B.; Ubertini, P.

2016-11-01

Context. Based on their overwhelming dominance among associated Fermi γ-ray catalogue sources, it is expected that a large fraction of the unidentified Fermi objects are blazars. Through crossmatching between the positions of unidentified γ-ray sources from the First Fermi Catalog of γ-ray sources emitting above 10 GeV (1FHL) and the ROSAT and Swift/XRT catalogues of X-ray objects and between pointed XRT observations, a sample of 36 potential associations was found in previous works with less than 15 arcsec of positional offset. One-third of them have recently been classified; the remainder, though believed to belong to the blazar class, still lack spectroscopic classifications. Aims: We study the optical spectrum of the putative counterparts of these unidentified gamma-ray sources in order to find their redshifts and to determine their nature and main spectral characteristics. Methods: An observational campaign was carried out on the putative counterparts of 13 1FHL sources using medium-resolution optical spectroscopy from the Osservatorio Astronomico di Bologna in Loiano, Italy; the Telescopio Nazionale Galileo and the Nordic Optical Telescope, both in the Canary Islands, Spain; and the Observatorio Astronómico Nacional San Pedro Mártir in Baja California, Mexico. Results: We were able to classify 14 new objects based on their continuum shapes and spectral features. Conclusions: Twelve new blazars were found, along with one new quasar and one new narrow line Seyfert 1 (NLS1) to be potentially associated with the 1FHL sources of our sample. Redshifts or lower limits were obtained when possible alongside central black hole mass and luminosity estimates for the NLS1 and the quasar.

A new algorithm for reducing the workload of experts in performing systematic reviews.

PubMed

Matwin, Stan; Kouznetsov, Alexandre; Inkpen, Diana; Frunza, Oana; O'Blenis, Peter

2010-01-01

To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment. The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance. Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning-based automation of systematic reviews of drug class efficacy for disease treatment.

Oral bisphosphonate-related osteonecrosis of the jaws: Clinical characteristics of a series of 20 cases in Spain

PubMed Central

López-Cedrún, José L.; Fernández-Sanromán, Jacinto; García-García, Abel; Fernández-Feijoo, Javier; Diz-Dios, Pedro

2012-01-01

Objective: The objective of this study was to define the clinical characteristics of osteonecrosis of the jaws (ONJ) induced by oral bisphosphonates in a series of patients from a circumscribed area in northwest Spain. Study Design:A retrospective multicentre study was undertaken in 3 hospitals in an area with a radius less than 100 km in the Autonomous Community of Galicia (Spain). The medical records were reviewed and an oral examination was performed of patients diagnosed with oral bisphosphonate-related ONJ in the previous 3 years. Results: We detected 20 cases of ONJ (24 lesions) related to oral bisphosphonates (alendronate [16 patients] and ibandronate [4 patients]), which were mainly administered as treatment for osteoporosis (17 patients). The mean interval between initiation of treatment and confirmation of a diagnosis of ONJ was 66±43 months (range, 6-132 months); in 7 patients (35%) the interval was less than 36 months. The past history revealed hypertension in 13 cases (65%) and diabetes in 4 (20%); 7 patients (35%) were on corticosteroid treatment. Oral surgery had been previously performed in 13 patients (65%) and the remaining 7 patients (35%) had removable dental prostheses. The lesions most frequently affected the posterior mandible (62.5%). The majority of the lesions (75%) were classified as stage 2, although lesions were identified in all established clinical stages (including 2 stage 0 lesions). Conclusion: In conclusion, in the present series, ONJ induced by oral bisphosphonates typically develops in women around 70 years of age, taking alendronate, that underwent oral surgery. Most lesions are located in the posterior mandible and are classified as stage 2 at diagnosis. Some patients presented no known risk factors, suggesting that there may be risk factors still to be identified. There are well-defined patterns of clinical presentation that can facilitate early diagnosis of ONJ. Key words:Oral bisphosphonates, osteonecrosis of the jaws, alendronate. PMID:22549688

Salaries and Fringe Benefits, Classified Personnel, 1974-1975. A Study of the Salary and Fringe Benefits for Selected Classified Personnel in Western New York.

ERIC Educational Resources Information Center

Western New York Regional Office for Educational Planning, Cheektowaga.

This survey is designed to provide comparative information for classified personnel in the six counties of western New York State. Data collection was more difficult this year than in previous years because of the increasing length of the negotiation process. This year 74 of 89 districts supplied data. This study includes two features that were…

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Comparison of Body Composition Assessed by Dual-Energy X-Ray Absorptiometry and BMI in Current and Former U.S. Navy Service Members

PubMed Central

Gasier, Heath G.; Hughes, Linda M.; Young, Colin R.; Richardson, Annely M.

2015-01-01

Background Little is known of the diagnostic accuracy of BMI in classifying obesity in active duty military personnel and those that previously served. Thus, the primary objectives were to determine the relationship between lean and fat mass, and body fat percentage (BF%) with BMI, and assess the agreement between BMI and BF% in defining obesity. Methods Body composition was measured by dual-energy X-ray absorptiometry in 462 males (20–91 years old) who currently or previously served in the U.S. Navy. A BMI of ≥ 30 kg/m2 and a BF% ≥ 25% were used for obesity classification. Results The mean BMI (± SD) and BF% were 28.8 ± 4.1 and 28.9 ± 6.6%, respectively, with BF% increasing with age. Lean mass, fat mass, and BF% were significantly correlated with BMI for all age groups. The exact agreement of obesity defined by BMI and BF% was fair (61%), however, 38% were misclassified by a BMI cut-off of 30 when obesity was defined by BF%. Conclusions From this data we determined that there is a good correlation between body composition and BMI, and fair agreement between BMI and BF% in classifying obesity in a group of current and former U.S. Navy service members. However, as observed in the general population, a significant proportion of individuals with excess fat are misclassified by BMI cutoffs. PMID:26197480

Comparison of Body Composition Assessed by Dual-Energy X-Ray Absorptiometry and BMI in Current and Former U.S. Navy Service Members.

PubMed

Gasier, Heath G; Hughes, Linda M; Young, Colin R; Richardson, Annely M

2015-01-01

Little is known of the diagnostic accuracy of BMI in classifying obesity in active duty military personnel and those that previously served. Thus, the primary objectives were to determine the relationship between lean and fat mass, and body fat percentage (BF%) with BMI, and assess the agreement between BMI and BF% in defining obesity. Body composition was measured by dual-energy X-ray absorptiometry in 462 males (20-91 years old) who currently or previously served in the U.S. Navy. A BMI of ≥ 30 kg/m2 and a BF% ≥ 25% were used for obesity classification. The mean BMI (± SD) and BF% were 28.8 ± 4.1 and 28.9 ± 6.6%, respectively, with BF% increasing with age. Lean mass, fat mass, and BF% were significantly correlated with BMI for all age groups. The exact agreement of obesity defined by BMI and BF% was fair (61%), however, 38% were misclassified by a BMI cut-off of 30 when obesity was defined by BF%. From this data we determined that there is a good correlation between body composition and BMI, and fair agreement between BMI and BF% in classifying obesity in a group of current and former U.S. Navy service members. However, as observed in the general population, a significant proportion of individuals with excess fat are misclassified by BMI cutoffs.

Searching for δ Scuti-type pulsation and characterising northern pre-main-sequence field stars

NASA Astrophysics Data System (ADS)

Díaz-Fraile, D.; Rodríguez, E.; Amado, P. J.

2014-08-01

Context. Pre-main-sequence (PMS) stars are objects evolving from the birthline to the zero-age main sequence (ZAMS). Given a mass range near the ZAMS, the temperatures and luminosities of PMS and main-sequence stars are very similar. Moreover, their evolutionary tracks intersect one another causing some ambiguity in the determination of their evolutionary status. In this context, the detection and study of pulsations in PMS stars is crucial for differentiating between both types of stars by obtaining information of their interiors via asteroseismic techniques. Aims: A photometric variability study of a sample of northern field stars, which previously classified as either PMS or Herbig Ae/Be objects, has been undertaken with the purpose of detecting δ Scuti-type pulsations. Determination of physical parameters for these stars has also been carried out to locate them on the Hertzsprung-Russell diagram and check the instability strip for this type of pulsators. Methods: Multichannel photomultiplier and CCD time series photometry in the uvby Strömgren and BVI Johnson bands were obtained during four consecutive years from 2007 to 2010. The light curves have been analysed, and a variability criterion has been established. Among the objects classified as variable stars, we have selected those which present periodicities above 4 d-1, which was established as the lowest limit for δ Scuti-type pulsations in this investigation. Finally, these variable stars have been placed in a colour-magnitude diagram using the physical parameters derived with the collected uvbyβ Strömgren-Crawford photometry. Results: Five PMS δ Scuti- and three probable β Cephei-type stars have been detected. Two additional PMS δ Scuti stars are also confirmed in this work. Moreover, three new δ Scuti- and two γ Doradus-type stars have been detected among the main-sequence objects used as comparison or check stars.

Real-time object-to-features vectorisation via Siamese neural networks

NASA Astrophysics Data System (ADS)

Fedorenko, Fedor; Usilin, Sergey

2017-03-01

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

Classification of vegetation types in military region

NASA Astrophysics Data System (ADS)

Gonçalves, Miguel; Silva, Jose Silvestre; Bioucas-Dias, Jose

2015-10-01

In decision-making process regarding planning and execution of military operations, the terrain is a determining factor. Aerial photographs are a source of vital information for the success of an operation in hostile region, namely when the cartographic information behind enemy lines is scarce or non-existent. The objective of present work is the development of a tool capable of processing aerial photos. The methodology implemented starts with feature extraction, followed by the application of an automatic selector of features. The next step, using the k-fold cross validation technique, estimates the input parameters for the following classifiers: Sparse Multinomial Logist Regression (SMLR), K Nearest Neighbor (KNN), Linear Classifier using Principal Component Expansion on the Joint Data (PCLDC) and Multi-Class Support Vector Machine (MSVM). These classifiers were used in two different studies with distinct objectives: discrimination of vegetation's density and identification of vegetation's main components. It was found that the best classifier on the first approach is the Sparse Logistic Multinomial Regression (SMLR). On the second approach, the implemented methodology applied to high resolution images showed that the better performance was achieved by KNN classifier and PCLDC. Comparing the two approaches there is a multiscale issue, in which for different resolutions, the best solution to the problem requires different classifiers and the extraction of different features.

ARE LARGE, COMETARY-SHAPED PROPLYDS REALLY (FREE-FLOATING) EVAPORATING GAS GLOBULES?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahai, R.; Guesten, R.; Morris, M. R., E-mail: raghvendra.sahai@jpl.nasa.gov

2012-12-20

We report the detection of strong and compact molecular line emission (in the CO J = 3-2, 4-3, 6-5, 7-6, {sup 13}CO J = 3-2, HCN, and HCO{sup +} J = 4-3 transitions) from a cometary-shaped object (Carina-frEGG1) in the Carina star-forming region (SFR) previously classified as a photoevaporating protoplanetary disk (proplyd). We derive a molecular mass of 0.35 M{sub Sun} for Carina-frEGG1, which shows that it is not a proplyd, but belongs to a class of free-floating evaporating gas globules (frEGGs) recently found in the Cygnus SFR by Sahai et al. Archival adaptive optics near-IR (Ks) images show amore » central hourglass-shaped nebula. The derived source luminosity (about 8-18 L{sub Sun }), the hourglass morphology, and the presence of collimated jets seen in Hubble Space Telescope images imply the presence of a jet-driving, young, low-mass star deeply embedded in the dust inside Carina-frEGG1. Our results suggest that the true nature of many or most such cometary-shaped objects seen in massive SFRs and previously labeled as proplyds has been misunderstood, and that these are really frEGGs.« less

Study on Differentiation Management of Grid Energy Metering Device under High Permeability by Distributed Energy and Smart Grid Technology

NASA Astrophysics Data System (ADS)

Wang, Haiyuan; Huang, Rui; Yang, Maotao; Chen, Hao

2017-12-01

At present, the electric energy metering device is classified according to the amount of electric energy and the degree of importance of the measurement object. The measuring device is also selected according to the characteristics of the traditional metering object.With the continuous development of smart grid, the diversification of measurement objects increasingly appear, the traditional measurement object classification has been unable to meet the new measurement object of personalized, differentiated needs.Withal, this paper constructs the subdivision model based on the object feature-system evaluation, classifies according to the characteristics of the measurement object, and carries on the empirical analysis with some kind of measurement object as the research object.The results show that the model works well and can be used to subdivide the metrological objects into different customer groups, which can be reasonably configured and managed for the metering devices. The research of this paper has effectively improved the economy and rationality of the energy metering device management, and improved the working efficiency.

Classification of different reaching movements from the same limb using EEG

NASA Astrophysics Data System (ADS)

Shiman, Farid; López-Larraz, Eduardo; Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; Spüler, Martin; Birbaumer, Niels; Ramos-Murguialday, Ander

2017-08-01

Objective. Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

A dictionary learning approach for human sperm heads classification.

PubMed

Shaker, Fariba; Monadjemi, S Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza

2017-12-01

To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

46 CFR 503.56 - General declassification and downgrading policy.

Code of Federal Regulations, 2011 CFR

2011-10-01

... INFORMATION Information Security Program § 503.56 General declassification and downgrading policy. (a) The... Order 13526, only over that information originally classified by the Commission under previous Executive... declassify information originally classified by other agencies. (b) The Commission does not now have original...

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

PubMed Central

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

2016-01-01

Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc. PMID:27662651

Deep learning decision fusion for the classification of urban remote sensing data

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Area under precision-recall curves for weighted and unweighted data.

PubMed

Keilwagen, Jens; Grosse, Ivo; Grau, Jan

2014-01-01

Precision-recall curves are highly informative about the performance of binary classifiers, and the area under these curves is a popular scalar performance measure for comparing different classifiers. However, for many applications class labels are not provided with absolute certainty, but with some degree of confidence, often reflected by weights or soft labels assigned to data points. Computing the area under the precision-recall curve requires interpolating between adjacent supporting points, but previous interpolation schemes are not directly applicable to weighted data. Hence, even in cases where weights were available, they had to be neglected for assessing classifiers using precision-recall curves. Here, we propose an interpolation for precision-recall curves that can also be used for weighted data, and we derive conditions for classification scores yielding the maximum and minimum area under the precision-recall curve. We investigate accordances and differences of the proposed interpolation and previous ones, and we demonstrate that taking into account existing weights of test data is important for the comparison of classifiers.

Area under Precision-Recall Curves for Weighted and Unweighted Data

PubMed Central

Grosse, Ivo

2014-01-01

Precision-recall curves are highly informative about the performance of binary classifiers, and the area under these curves is a popular scalar performance measure for comparing different classifiers. However, for many applications class labels are not provided with absolute certainty, but with some degree of confidence, often reflected by weights or soft labels assigned to data points. Computing the area under the precision-recall curve requires interpolating between adjacent supporting points, but previous interpolation schemes are not directly applicable to weighted data. Hence, even in cases where weights were available, they had to be neglected for assessing classifiers using precision-recall curves. Here, we propose an interpolation for precision-recall curves that can also be used for weighted data, and we derive conditions for classification scores yielding the maximum and minimum area under the precision-recall curve. We investigate accordances and differences of the proposed interpolation and previous ones, and we demonstrate that taking into account existing weights of test data is important for the comparison of classifiers. PMID:24651729

Prospective monitoring and self-report of previous falls among older women at high risk of falls and fractures: a study of comparison and agreement

PubMed Central

Garcia, Patrícia A.; Dias, João M. D.; Silva, Silvia L. A.; Dias, Rosângela C.

2015-01-01

Background: The identification of the occurrence of falls is an important step for screening and for rehabilitation processes for the elderly. The methods of monitoring these events are susceptible to recording biases, and the choice of the most accurate method remains challenging. Objectives: (i) To investigate the agreement between retrospective self-reporting and prospective monitoring of methods of recording falls, and (ii) to compare the retrospective self-reporting of falls and the prospective monitoring of falls and recurrent falls over a 12-month period among older women at high risk of falls and fractures. Method: A total of 118 community-dwelling older women with low bone density were recruited. The incidence of falls was monitored prospectively in 116 older women (2 losses) via monthly phone calls over the course of a year. At the end of this monitoring period, the older women were asked about their recall of falls in the same 12-month period. The agreement between the two methods was analyzed, and the sensitivity and specificity of self-reported previous falls in relation to the prospective monitoring were calculated. Results: There was moderate agreement between the prospective monitoring and the retrospective self-reporting of falls in classifying fallers (Kappa=0.595) and recurrent fallers (Kappa=0.589). The limits of agreement were 0.35±1.66 falls. The self-reporting of prior falls had a 67.2% sensitivity and a 94.2% specificity in classifying fallers among older women and a 50% sensitivity and a 98.9% specificity in classifying recurrent fallers. Conclusion: Self-reporting of falls over a 12-month period underestimated 32.8% of falls and 50% of recurrent falls. The findings recommend caution if one is considering replacing monthly monitoring with annual retrospective questioning. PMID:26083603

Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries

PubMed Central

Kang, Bumjoon; Moudon, Anne V.; Hurvitz, Philip M.; Reichley, Lucas; Saelens, Brian E.

2013-01-01

Purpose This study developed and tested an algorithm to classify accelerometer data as walking or non-walking using either GPS or travel diary data within a large sample of adults under free-living conditions. Methods Participants wore an accelerometer and a GPS unit, and concurrently completed a travel diary for 7 consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or non-walking based on a decision-tree algorithm consisting of 7 classification scenarios. Algorithm reliability was examined relative to two independent analysts’ classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts. Results The 706 participants’ (mean age 51 years, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4,702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified physical activity into 8,170 (58.5 %) walking bouts and 5,337 (38.2%) non-walking bouts; 464 (3.3%) bouts were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the non-walking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean duration of PA bouts classified as walking was 15.2 min (SD=12.9). On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day. Conclusions GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or non-walking behavior. PMID:23439414

New neural-networks-based 3D object recognition system

NASA Astrophysics Data System (ADS)

Abolmaesumi, Purang; Jahed, M.

1997-09-01

Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

The Impact of Quantitative Data Provided by a Multi-spectral Digital Skin Lesion Analysis Device on Dermatologists'Decisions to Biopsy Pigmented Lesions.

PubMed

Farberg, Aaron S; Winkelmann, Richard R; Tucker, Natalie; White, Richard; Rigel, Darrell S

2017-09-01

BACKGROUND: Early diagnosis of melanoma is critical to survival. New technologies, such as a multi-spectral digital skin lesion analysis (MSDSLA) device [MelaFind, STRATA Skin Sciences, Horsham, Pennsylvania] may be useful to enhance clinician evaluation of concerning pigmented skin lesions. Previous studies evaluated the effect of only the binary output. OBJECTIVE: The objective of this study was to determine how decisions dermatologists make regarding pigmented lesion biopsies are impacted by providing both the underlying classifier score (CS) and associated probability risk provided by multi-spectral digital skin lesion analysis. This outcome was also compared against the improvement reported with the provision of only the binary output. METHODS: Dermatologists attending an educational conference evaluated 50 pigmented lesions (25 melanomas and 25 benign lesions). Participants were asked if they would biopsy the lesion based on clinical images, and were asked this question again after being shown multi-spectral digital skin lesion analysis data that included the probability graphs and classifier score. RESULTS: Data were analyzed from a total of 160 United States board-certified dermatologists. Biopsy sensitivity for melanoma improved from 76 percent following clinical evaluation to 92 percent after quantitative multi-spectral digital skin lesion analysis information was provided ( p <0.0001). Specificity improved from 52 percent to 79 percent ( p <0.0001). The positive predictive value increased from 61 percent to 81 percent ( p <0.01) when the quantitative data were provided. Negative predictive value also increased (68% vs. 91%, p<0.01), and overall biopsy accuracy was greater with multi-spectral digital skin lesion analysis (64% vs. 86%, p <0.001). Interrater reliability improved (intraclass correlation 0.466 before, 0.559 after). CONCLUSION: Incorporating the classifier score and probability data into physician evaluation of pigmented lesions led to both increased sensitivity and specificity, thereby resulting in more accurate biopsy decisions.

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas.

PubMed

Sha, Chulin; Barrans, Sharon; Care, Matthew A; Cunningham, David; Tooze, Reuben M; Jack, Andrew; Westhead, David R

2015-01-01

Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis. Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance. We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma. In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.

Phylogenetic classification of Aureobasidium pullulans strains for production of feruloyl esterase

USDA-ARS?s Scientific Manuscript database

The objective was to phylogenetically classify diverse strains of A. pullulans and determine their production of feruloyl esterase. Seventeen strains from the A. pullulans literature were phylogenetically classified. Phenotypic traits of color variation and endo-ß-1,4-xylanase overproduction were as...

Foreign object detection via texture recognition and a neural classifier

NASA Astrophysics Data System (ADS)

Patel, Devesh; Hannah, I.; Davies, E. R.

1993-10-01

It is rate to find pieces of stone, wood, metal, or glass in food packets, but when they occur, these foreign objects (FOs) cause distress to the consumer and concern to the manufacturer. Using x-ray imaging to detect FOs within food bags, hard contaminants such as stone or metal appear darker, whereas soft contaminants such as wood or rubber appear slightly lighter than the food substrate. In this paper we concentrate on the detection of soft contaminants such as small pieces of wood in bags of frozen corn kernels. Convolution masks are used to generate textural features which are then classified into corresponding homogeneous regions on the image using an artificial neural network (ANN) classifier. The separate ANN outputs are combined using a majority operator, and region discrepancies are removed by a median filter. Comparisons with classical classifiers showed the ANN approach to have the best overall combination of characteristics for our particular problem. The detected boundaries are in good agreement with the visually perceived segmentations.

Anomaly detection in forward looking infrared imaging using one-class classifiers

NASA Astrophysics Data System (ADS)

Popescu, Mihail; Stone, Kevin; Havens, Timothy; Ho, Dominic; Keller, James

2010-04-01

In this paper we describe a method for generating cues of possible abnormal objects present in the field of view of an infrared (IR) camera installed on a moving vehicle. The proposed method has two steps. In the first step, for each frame, we generate a set of possible points of interest using a corner detection algorithm. In the second step, the points related to the background are discarded from the point set using an one class classifier (OCC) trained on features extracted from a local neighborhood of each point. The advantage of using an OCC is that we do not need examples from the "abnormal object" class to train the classifier. Instead, OCC is trained using corner points from images known to be abnormal object free, i.e., that contain only background scenes. To further reduce the number of false alarms we use a temporal fusion procedure: a region has to be detected as "interesting" in m out of n, m

Yet Another Stream Search Among 2401 Photographic Meteors

NASA Technical Reports Server (NTRS)

Cook, A. F., II; Lindblad, B.; Marsden, B. G.; Mccrosky, R. E.; Posen, A.

1973-01-01

Two streams previously listed (one of them with a classification on Ceplecha's system in terms of beginning height) by Cook are shown probably not to exist, a possibility already pointed out by Cook. One stream that he questioned was revised as to membership and then classified. Four streams are added to the list and one of these is classified. Previous reports exist for three of these streams, while one is new. The two Piscid streams of Lindblad and his alpha Triangulid stream are regrouped into two streams, one already called the Andromedids by Cook and the other still called the Piscids; the alpha Triangulids are absorbed into the Andromedids. The Piscids are classified along with the iota Aquarids. The classifications of the Taurids and the Andromedids remain unchanged.

Mapping spatial patterns with morphological image processing

Treesearch

Peter Vogt; Kurt H. Riitters; Christine Estreguil; Jacek Kozak; Timothy G. Wade; James D. Wickham

2006-01-01

We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as 'perforated,' 'edge,' 'patch,' and 'core' with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the...

Uterine influences on conceptus development in fertility-classified heifers

USDA-ARS?s Scientific Manuscript database

A major unresolved issue is how the uterus influences infertility and subfertility in cattle. Serial embryo transfer was previously used to classify heifers as high fertile (HF), subfertile (SF), or infertile (IF). To assess pregnancy loss in those animals, two in vivo produced embryos were transfer...

Recognition of lesion correspondence on two mammographic views: a new method of false-positive reduction for computerized mass detection

NASA Astrophysics Data System (ADS)

Sahiner, Berkman; Petrick, Nicholas; Chan, Heang-Ping; Paquerault, Sophie; Helvie, Mark A.; Hadjiiski, Lubomir M.

2001-07-01

We used the correspondence of detected structures on two views of the same breast for false-positive (FP) reduction in computerized detection of mammographic masses. For each initially detected object on one view, we considered all possible pairings with objects on the other view that fell within a radial band defined by the nipple-to-object distances. We designed a 'correspondence classifier' to classify these pairs as either the same mass (a TP-TP pair) or a mismatch (a TP-FP, FP-TP or FP-FP pair). For each pair, similarity measures of morphological and texture features were derived and used as input features in the correspondence classifier. Two-view mammograms from 94 cases were used as a preliminary data set. Initial detection provided 6.3 FPs/image at 96% sensitivity. Further FP reduction in single view resulted in 1.9 FPs/image at 80% sensitivity and 1.1 FPs/image at 70% sensitivity. By combining single-view detection with the correspondence classifier, detection accuracy improved to 1.5 FPs/image at 80% sensitivity and 0.7 FPs/image at 70% sensitivity. Our preliminary results indicate that the correspondence of geometric, morphological, and textural features of a mass on two different views provides valuable additional information for reducing FPs.

Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data

NASA Astrophysics Data System (ADS)

Teodoro, Ana C.; Araujo, Ricardo

2016-01-01

The use of unmanned aerial vehicles (UAVs) for remote sensing applications is becoming more frequent. However, this type of information can result in several software problems related to the huge amount of data available. Object-based image analysis (OBIA) has proven to be superior to pixel-based analysis for very high-resolution images. The main objective of this work was to explore the potentialities of the OBIA methods available in two different open source software applications, Spring and OTB/Monteverdi, in order to generate an urban land cover map. An orthomosaic derived from UAVs was considered, 10 different regions of interest were selected, and two different approaches were followed. The first one (Spring) uses the region growing segmentation algorithm followed by the Bhattacharya classifier. The second approach (OTB/Monteverdi) uses the mean shift segmentation algorithm followed by the support vector machine (SVM) classifier. Two strategies were followed: four classes were considered using Spring and thereafter seven classes were considered for OTB/Monteverdi. The SVM classifier produces slightly better results and presents a shorter processing time. However, the poor spectral resolution of the data (only RGB bands) is an important factor that limits the performance of the classifiers applied.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

NASA Astrophysics Data System (ADS)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

Object integration requires attention: Visual search for Kanizsa figures in parietal extinction.

PubMed

Gögler, Nadine; Finke, Kathrin; Keller, Ingo; Müller, Hermann J; Conci, Markus

2016-11-01

The contribution of selective attention to object integration is a topic of debate: integration of parts into coherent wholes, such as in Kanizsa figures, is thought to arise either from pre-attentive, automatic coding processes or from higher-order processes involving selective attention. Previous studies have attempted to examine the role of selective attention in object integration either by employing visual search paradigms or by studying patients with unilateral deficits in selective attention. Here, we combined these two approaches to investigate object integration in visual search in a group of five patients with left-sided parietal extinction. Our search paradigm was designed to assess the effect of left- and right-grouped nontargets on detecting a Kanizsa target square. The results revealed comparable reaction time (RT) performance in patients and controls when they were presented with displays consisting of a single to-be-grouped item that had to be classified as target vs. nontarget. However, when display size increased to two items, patients showed an extinction-specific pattern of enhanced RT costs for nontargets that induced a partial shape grouping on the right, i.e., in the attended hemifield (relative to the ungrouped baseline). Together, these findings demonstrate a competitive advantage for right-grouped objects, which in turn indicates that in parietal extinction, attentional competition between objects particularly limits integration processes in the contralesional, i.e., left hemifield. These findings imply a crucial contribution of selective attentional resources to visual object integration. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

NASA Astrophysics Data System (ADS)

Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

2011-12-01

Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because individual classes differed in scales at which they were best discriminated from others. Main classification challenges included a) presence of C3 grasses in C4-grass areas, particularly following harvesting of C4 reeds and b) mixtures of emergent, floating and submerged aquatic plants at sub-object and sub-pixel scales. We conclude that OBIA with advanced statistical classifiers offers useful instruments for landscape vegetation analyses, and that spatial scale considerations are critical in mapping PFTs, while multi-scale comparisons can be used to guide class selection. Future work will further apply fuzzy classification and field-collected spectral data for PFT analysis and compare results with MODIS PFT products.

Combination of dynamic Bayesian network classifiers for the recognition of degraded characters

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2009-01-01

We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.

VizieR Online Data Catalog: Overlooked wide companions of nearby F stars (Scholz, 2016)

NASA Astrophysics Data System (ADS)

Scholz, R.-D.

2016-02-01

We checked a sample of 545 F stars within 50pc for wide companions using existing near-infrared and optical sky surveys. Applying the common proper motion (CPM) criterion, we detected wide companion candidates with 6-120arcsec angular separations by visual inspection of multi-epoch finder charts and by searching in proper motion catalogues. Final proper motions were measured by involving positional measurements from up to eleven surveys. Spectral types of red CPM companions were estimated from their absolute J-band magnitudes based on the Hipparcos distances of the primaries. In addition to about 100 known CPM objects, we found 19 new CPM companions and confirmed 31 previously known candidates. A few CPM objects are still considered as candidates according to their level of proper motion agreement. Among the new objects there are nine M0-M4, eight M5-M6, one ~L3.5 dwarf (HD 3861B), and one white dwarf (WD) (HD 2726B), whereas we confirmed two K, 19 M0-M4, six M5-M6, two early-L dwarfs, and two DA WDs as CPM companions. In a few cases, previous spectral types were available that all agree well with our estimates. Two companions (HD 22879B and HD 49933B) are associated with moderately metal-poor Gaia benchmark stars. One doubtful CPM companion, spectroscopically classified as WD but found to be very bright (J=11.1) by others, should either be a very nearby foreground WD or a different kind of object associated with HD 165670. The main results of this research note, data on new, confirmed, and rejected CPM pairs, are listed in tablea1.dat, tableb1.dat, and tablec1.dat, respectively. (4 data files).

User-customized brain computer interfaces using Bayesian optimization

NASA Astrophysics Data System (ADS)

Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

2016-04-01

Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Photometric brown-dwarf classification. I. A method to identify and accurately classify large samples of brown dwarfs without spectroscopy

NASA Astrophysics Data System (ADS)

Skrzypek, N.; Warren, S. J.; Faherty, J. K.; Mortlock, D. J.; Burgasser, A. J.; Hewett, P. C.

2015-02-01

Aims: We present a method, named photo-type, to identify and accurately classify L and T dwarfs onto the standard spectral classification system using photometry alone. This enables the creation of large and deep homogeneous samples of these objects efficiently, without the need for spectroscopy. Methods: We created a catalogue of point sources with photometry in 8 bands, ranging from 0.75 to 4.6 μm, selected from an area of 3344 deg2, by combining SDSS, UKIDSS LAS, and WISE data. Sources with 13.0 0.8, were then classified by comparison against template colours of quasars, stars, and brown dwarfs. The L and T templates, spectral types L0 to T8, were created by identifying previously known sources with spectroscopic classifications, and fitting polynomial relations between colour and spectral type. Results: Of the 192 known L and T dwarfs with reliable photometry in the surveyed area and magnitude range, 189 are recovered by our selection and classification method. We have quantified the accuracy of the classification method both externally, with spectroscopy, and internally, by creating synthetic catalogues and accounting for the uncertainties. We find that, brighter than J = 17.5, photo-type classifications are accurate to one spectral sub-type, and are therefore competitive with spectroscopic classifications. The resultant catalogue of 1157 L and T dwarfs will be presented in a companion paper.

Classifier transfer with data selection strategies for online support vector machine classification with class imbalance

NASA Astrophysics Data System (ADS)

Krell, Mario Michael; Wilshusen, Nils; Seeland, Anett; Kim, Su Kyoung

2017-04-01

Objective. Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM). Approach. We focus on data selection strategies which limit the size of the stored training data by different inclusion, exclusion, and further dataset manipulation criteria like handling class imbalance with two new approaches. We provide a comparison of the strategies with linear SVMs on several synthetic datasets with different data shifts as well as on different transfer settings with electroencephalographic (EEG) data. Main results. For the synthetic data, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add samples near the decision boundary of the SVM which reduces processing resources. Significance. For brain-computer interfaces based on EEG data, models trained on data from a calibration session, a previous recording session, or even from a recording session with another subject are used. We show, that by using the right combination of data selection criteria, it is possible to adapt the SVM classifier to overcome the performance drop from the transfer.

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2013 CFR

2013-07-01

...); 211112Natural Gas Liquid Extraction Limited to facilities that recover sulfur from natural gas (previously classified under SIC 2819, Industrial Inorganic chemicals, NEC (recovering sulfur from natural gas... engaged in providing combinations of electric, gas, and other services, not elsewhere classified (N.E.C...

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2011 CFR

2011-07-01

...); 211112Natural Gas Liquid Extraction Limited to facilities that recover sulfur from natural gas (previously classified under SIC 2819, Industrial Inorganic chemicals, NEC (recovering sulfur from natural gas... engaged in providing combinations of electric, gas, and other services, not elsewhere classified (N.E.C...

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2012 CFR

2012-07-01

...); 211112Natural Gas Liquid Extraction Limited to facilities that recover sulfur from natural gas (previously classified under SIC 2819, Industrial Inorganic chemicals, NEC (recovering sulfur from natural gas... engaged in providing combinations of electric, gas, and other services, not elsewhere classified (N.E.C...

Classifying and Finding Nearby Compact Stellar Systems

NASA Astrophysics Data System (ADS)

Colebaugh, Alexander; Cunningham, Devin; Dixon, Christopher; Romanowsky, Aaron; Striegel, Stephanie

2018-01-01

Compact stellar systems (CSSs) such as compact ellipticals (cEs) and ultracompact dwarfs (UCDs) are relatively rare and poorly understood types of galaxies. To build a more complete picture of these objects, we create search queries using the Sloan Digital Sky Survey, to inventory CSSs in the nearby universe and to explore their properties. We develop an objective set of criteria for classifying cEs, and use these to construct a large, novel catalog of cEs both during and after formation. We also investigate the numbers of cEs and UCDs around nearby giant galaxies.

Diagnosis and characterization of mania: Quantifying increased energy and activity in the human behavioral pattern monitor

PubMed Central

Perry, William; McIlwain, Meghan; Kloezeman, Karen; Henry, Brook L.; Minassian, Arpi

2016-01-01

Increased energy or activity is now an essential feature of the mania of Bipolar Disorder (BD) according to DSM-5. This study examined whether objective measures of increased energy can differentiate manic BD individuals and provide greater diagnostic accuracy compared to rating scales, extending the work of previous studies with smaller samples. We also tested the relationship between objective measures of energy and rating scales. 50 hospitalized manic BD patients were compared to healthy subjects (HCS, n=39) in the human Behavioral Pattern Monitor (hBPM) which quantifies motor activity and goal-directed behavior in an environment containing novel stimuli. Archival hBPM data from 17 schizophrenia patients were used in sensitivity and specificity analyses. Manic BD patients exhibited higher motor activity than HCS and higher novel object interactions. hBPM activity measures were not correlated with observer-rated symptoms, and hBPM activity was more sensitive in accurately classifying hospitalized BD subjects than observer ratings. Although the findings can only be generalized to inpatient populations, they suggest that increased energy, particularly specific and goal-directed exploration, is a distinguishing feature of BD mania and is best quantified by objective measures of motor activity. A better understanding is needed of the biological underpinnings of this cardinal feature. PMID:27138818

Proper motions of young stars in Chamaeleon. II. New kinematical candidate members of Chamaeleon I and II

NASA Astrophysics Data System (ADS)

López Martí, Belén; Jiménez-Esteban, Francisco; Bayo, Amelia; Barrado, David; Solano, Enrique; Bouy, Hervé; Rodrigo, Carlos

2013-08-01

Context. The Chamaeleon star-forming region has been extensively studied in the last decades. However, most studies have been confined to the densest parts of the clouds. In a previous paper, we analysed the kinematical properties of the spectroscopically confirmed population of the Chamaeleon I and II clouds. Aims: We want to search for new kinematical candidate members to the Chamaeleon I and II moving groups, extending the studied area beyond the clouds, and to characterize these new populations using available information from public databases and catalogues. We also want to check if the populations of the moving groups are confined to the present dark clouds. Methods: Kinematic candidate members were initially selected on the basis of proper motions and colours using the Fourth US Naval Observatory CCD Astrograph Catalog (UCAC4). The SEDs of the objects were constructed using photometry retrieved from the Virtual Observatory and other resources, and fitted to models of stellar photospheres to derive effective temperatures, gravity values, and luminosities. Masses and ages were estimated by comparison with theoretical evolutionary tracks in a Hertzprung-Russell diagram. Objects with ages ≲20 Myr were selected as probable members of the moving groups. Results: We have identified 51 and 14 candidate members to the Chamaeleon I and II moving groups, respectively, of which 17 and 1, respectively, are classified as probable young stars according to the SED analysis. Another object in Chamaeleon I located slightly above the 1 Myr isochrone is classified as a possible young star. All these objects are diskless stars with masses in the range 0.3 ≲ M/M⊙ ≲ 1.4 and ages consistent with those reported for the corresponding confirmed members. They tend to be located at the boundaries of or outside the dark clouds, preferably to the north-east and south-east in the case of Chamaeleon I, and to the north-east in the case of Chamaeleon II. Conclusions: The kinematical population of Chamaeleon I and II could be larger and spread over a larger area of the sky than suggested by previous studies. However, the results of this study should be confirmed with spectroscopic data and more precise kinematic information. Tables 2, 3, 4, 6 and 8 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/556/A144

The Nature of Object Marking in American Sign Language

ERIC Educational Resources Information Center

Gokgoz, Kadir

2013-01-01

In this dissertation, I examine the nature of object marking in American Sign Language (ASL). I investigate object marking by means of directionality (the movement of the verb towards a certain location in signing space) and by means of handling classifiers (certain handshapes accompanying the verb). I propose that object marking in ASL is…

14 CFR 1203.402 - Classifying material other than documentation.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Classifying material other than documentation. 1203.402 Section 1203.402 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION... that there is at least one aspect of the item or object which requires protection. If mere knowledge of...

14 CFR 1203.402 - Classifying material other than documentation.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 5 2011-01-01 2010-01-01 true Classifying material other than documentation. 1203.402 Section 1203.402 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION... that there is at least one aspect of the item or object which requires protection. If mere knowledge of...

Simulated Real-Life Experiences Using Classified Ads in the Classroom.

ERIC Educational Resources Information Center

Hechler, Ellen

This guide contains activities to help teachers give middle school students experience in practical life skills. Techniques include role playing and using classified advertisements from newspapers. The five lessons include teacher tips on conducting the activities. Lessons contain objectives, materials needed, discussion, and suggested dialogue.…

The Clinical Utility of Personality Subtypes in Patients with Anorexia Nervosa

ERIC Educational Resources Information Center

Wildes, Jennifer E.; Marcus, Marsha D.; Crosby, Ross D.; Ringham, Rebecca M.; Dapelo, Marcela Marin; Gaskill, Jill A.; Forbush, Kelsie T.

2011-01-01

Objective: Elucidation of clinically relevant subtypes has been proposed as a means of advancing treatment research, but classifying anorexia nervosa (AN) patients into restricting and binge-eating/purging types has demonstrated limited predictive validity. This study aimed to evaluate whether an approach to classifying eating disorder patients on…

Technical support for creating an artificial intelligence system for feature extraction and experimental design

NASA Technical Reports Server (NTRS)

Glick, B. J.

1985-01-01

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

Automated detection of tuberculosis on sputum smeared slides using stepwise classification

NASA Astrophysics Data System (ADS)

Divekar, Ajay; Pangilinan, Corina; Coetzee, Gerrit; Sondh, Tarlochan; Lure, Fleming Y. M.; Kennedy, Sean

2012-03-01

Routine visual slide screening for identification of tuberculosis (TB) bacilli in stained sputum slides under microscope system is a tedious labor-intensive task and can miss up to 50% of TB. Based on the Shannon cofactor expansion on Boolean function for classification, a stepwise classification (SWC) algorithm is developed to remove different types of false positives, one type at a time, and to increase the detection of TB bacilli at different concentrations. Both bacilli and non-bacilli objects are first analyzed and classified into several different categories including scanty positive, high concentration positive, and several non-bacilli categories: small bright objects, beaded, dim elongated objects, etc. The morphological and contrast features are extracted based on aprior clinical knowledge. The SWC is composed of several individual classifiers. Individual classifier to increase the bacilli counts utilizes an adaptive algorithm based on a microbiologist's statistical heuristic decision process. Individual classifier to reduce false positive is developed through minimization from a binary decision tree to classify different types of true and false positive based on feature vectors. Finally, the detection algorithm is was tested on 102 independent confirmed negative and 74 positive cases. A multi-class task analysis shows high accordance rate for negative, scanty, and high-concentration as 88.24%, 56.00%, and 97.96%, respectively. A binary-class task analysis using a receiver operating characteristics method with the area under the curve (Az) is also utilized to analyze the performance of this detection algorithm, showing the superior detection performance on the high-concentration cases (Az=0.913) and cases mixed with high-concentration and scanty cases (Az=0.878).

Physiological Differences in the Follicular, Luteal, and Menstrual Phases in Healthy Women Determined by Electrovestibulography: Depression, Anxiety, or Other Associations?

PubMed

Lithgow, Brian J; Moussavi, Zahra

2018-06-05

Electrovestibulography (EVestG) recordings have been previously applied toward classifying and/or measuring the severity of several neurological disorders including depression with and without anxiety. This study's objectives were to: (1) extract EVestG features representing physiological differences of healthy women during their menses, and follicular and luteal phases of their menstrual cycle, and (2) compare these features to those observed in previous studies for depression with and without anxiety. Three EVestG recordings were made on 15 young healthy menstruating females during menses, and follicular and luteal phases. Three features were extracted, using the shape and timing of the detected spontaneously evoked vestibulo-acoustic field potentials. Using these features, a 3-way separation of the 3 phases was achieved, with a leave-one-out cross-validation, resulting in accuracy of > 72%. Using an EVestG shape feature, separation of the follicular and luteal phases was achieved with a leave-one-out cross-validation accuracy of > 93%. The mechanism of separation was not like that in previous depression analyses, and is postulated to be more akin to a form of anxiety and/or progesterone sensitivity. © 2018 S. Karger AG, Basel.

Local classifier weighting by quadratic programming.

PubMed

Cevikalp, Hakan; Polikar, Robi

2008-10-01

It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple classifiers with various (potentially conflicting) decisions is still an open problem. A rich collection of classifier combination procedures -- many of which are heuristic in nature -- have been developed for this goal. In this brief, we describe a dynamic approach to combine classifiers that have expertise in different regions of the input space. To this end, we use local classifier accuracy estimates to weight classifier outputs. Specifically, we estimate local recognition accuracies of classifiers near a query sample by utilizing its nearest neighbors, and then use these estimates to find the best weights of classifiers to label the query. The problem is formulated as a convex quadratic optimization problem, which returns optimal nonnegative classifier weights with respect to the chosen objective function, and the weights ensure that locally most accurate classifiers are weighted more heavily for labeling the query sample. Experimental results on several data sets indicate that the proposed weighting scheme outperforms other popular classifier combination schemes, particularly on problems with complex decision boundaries. Hence, the results indicate that local classification-accuracy-based combination techniques are well suited for decision making when the classifiers are trained by focusing on different regions of the input space.

a Point Cloud Classification Approach Based on Vertical Structures of Ground Objects

NASA Astrophysics Data System (ADS)

Zhao, Y.; Hu, Q.; Hu, W.

2018-04-01

This paper proposes a novel method for point cloud classification using vertical structural characteristics of ground objects. Since urbanization develops rapidly nowadays, urban ground objects also change frequently. Conventional photogrammetric methods cannot satisfy the requirements of updating the ground objects' information efficiently, so LiDAR (Light Detection and Ranging) technology is employed to accomplish this task. LiDAR data, namely point cloud data, can obtain detailed three-dimensional coordinates of ground objects, but this kind of data is discrete and unorganized. To accomplish ground objects classification with point cloud, we first construct horizontal grids and vertical layers to organize point cloud data, and then calculate vertical characteristics, including density and measures of dispersion, and form characteristic curves for each grids. With the help of PCA processing and K-means algorithm, we analyze the similarities and differences of characteristic curves. Curves that have similar features will be classified into the same class and point cloud correspond to these curves will be classified as well. The whole process is simple but effective, and this approach does not need assistance of other data sources. In this study, point cloud data are classified into three classes, which are vegetation, buildings, and roads. When horizontal grid spacing and vertical layer spacing are 3 m and 1 m respectively, vertical characteristic is set as density, and the number of dimensions after PCA processing is 11, the overall precision of classification result is about 86.31 %. The result can help us quickly understand the distribution of various ground objects.

An evaluation of several different classification schemes - Their parameters and performance. [maximum likelihood decision for crop identification

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.

1979-01-01

The overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

Mid-level perceptual features contain early cues to animacy.

PubMed

Long, Bria; Störmer, Viola S; Alvarez, George A

2017-06-01

While substantial work has focused on how the visual system achieves basic-level recognition, less work has asked about how it supports large-scale distinctions between objects, such as animacy and real-world size. Previous work has shown that these dimensions are reflected in our neural object representations (Konkle & Caramazza, 2013), and that objects of different real-world sizes have different mid-level perceptual features (Long, Konkle, Cohen, & Alvarez, 2016). Here, we test the hypothesis that animates and manmade objects also differ in mid-level perceptual features. To do so, we generated synthetic images of animals and objects that preserve some texture and form information ("texforms"), but are not identifiable at the basic level. We used visual search efficiency as an index of perceptual similarity, as search is slower when targets are perceptually similar to distractors. Across three experiments, we find that observers can find animals faster among objects than among other animals, and vice versa, and that these results hold when stimuli are reduced to unrecognizable texforms. Electrophysiological evidence revealed that this mixed-animacy search advantage emerges during early stages of target individuation, and not during later stages associated with semantic processing. Lastly, we find that perceived curvature explains part of the mixed-animacy search advantage and that observers use perceived curvature to classify texforms as animate/inanimate. Taken together, these findings suggest that mid-level perceptual features, including curvature, contain cues to whether an object may be animate versus manmade. We propose that the visual system capitalizes on these early cues to facilitate object detection, recognition, and classification.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

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

2014-01-01

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

Automated Analysis of CT Images for the Inspection of Hardwood Logs

Treesearch

Harbin Li; A. Lynn Abbott; Daniel L. Schmoldt

1996-01-01

This paper investigates several classifiers for labeling internal features of hardwood logs using computed tomography (CT) images. A primary motivation is to locate and classify internal defects so that an optimal cutting strategy can be chosen. Previous work has relied on combinations of low-level processing, image segmentation, autoregressive texture modeling, and...

Adversarial Feature Selection Against Evasion Attacks.

PubMed

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

2016-03-01

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

Change in physiological signals during mindfulness meditation

PubMed Central

Ahani, Asieh; Wahbeh, Helane; Miller, Meghan; Nezamfar, Hooman; Erdogmus, Deniz; Oken, Barry

2014-01-01

Mindfulness meditation (MM) is an inward mental practice, in which a resting but alert state of mind is maintained. MM intervention was performed for a population of older people with high stress levels. This study assessed signal processing methodologies of electroencephalographic (EEG) and respiration signals during meditation and control condition to aid in quantification of the meditative state. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis and support vector machine classification to evaluate an objective marker for meditation. We observed meditation and control condition differences in the alpha, beta and theta frequency bands. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy at discriminating between meditation and control conditions than one using the EEG signal only. EEG and respiration based classifier is a viable objective marker for meditation ability. Future studies should quantify different levels of meditation depth and meditation experience using this classifier. Development of an objective physiological meditation marker will allow the mind-body medicine field to advance by strengthening rigor of methods. PMID:24748422

Deep Stromvil Photometry for Star Formation in the Head of the Pelican Nebula

NASA Astrophysics Data System (ADS)

Boyle, Richard P.; J., S.; Stott, J.; J., S.; Janusz, R.; J., S.; Straizys, V.

2010-01-01

The North America and Pelican Nebulae, and specifically the dark cloud L935 contain regions of active star formation (Herbig, G. H. 1958, ApJ, 128,259). Previously we reported on Vatican telescope observations by Stromvil intermediate-band filters in a 12-arcmin field in the "Gulf of Mexico" region of L935. There we classify A, F, and G-type stars. However, the many faint K and M-type dwarf stars remain somewhat ambiguous in calibration and classification. But attaining reasonable progress, we turn to another part of L935 located near the Pelican head. This area includes the "bright rim" which is formed by dust and gas condensed by the light pressure of an unseen O-type star hidden behind the dense dark cloud. Straizys and Laugalys (2008 Baltic Astronomy, 17, 143 ) have identified this star to be one of the 2MASS objects with Av=23 mag. A few concentrations of faint stars, V 13 to 14 mag. are immersed in this dark region. Among these stars are a few known emission-line objects (T-Tauri or post T-Tauri stars). A half degree nearby are some photometric Vilnius standards we use to calibrate our new field. We call on 2MASS data for correlative information. Also the Stromvil photometry offers candidate stars for spectral observations. The aim of this study in the Vilnius and Stromvil photometric systems is to classify stars down to V = 18 mag., to confirm the existence of the young star clusters, and to determine the distance of the cloud covering the suspected hidden ionizing star.

On the use of variability time-scales as an early classifier of radio transients and variables

NASA Astrophysics Data System (ADS)

Pietka, M.; Staley, T. D.; Pretorius, M. L.; Fender, R. P.

2017-11-01

We have shown previously that a broad correlation between the peak radio luminosity and the variability time-scales, approximately L ∝ τ5, exists for variable synchrotron emitting sources and that different classes of astrophysical sources occupy different regions of luminosity and time-scale space. Based on those results, we investigate whether the most basic information available for a newly discovered radio variable or transient - their rise and/or decline rate - can be used to set initial constraints on the class of events from which they originate. We have analysed a sample of ≈800 synchrotron flares, selected from light curves of ≈90 sources observed at 5-8 GHz, representing a wide range of astrophysical phenomena, from flare stars to supermassive black holes. Selection of outbursts from the noisy radio light curves has been done automatically in order to ensure reproducibility of results. The distribution of rise/decline rates for the selected flares is modelled as a Gaussian probability distribution for each class of object, and further convolved with estimated areal density of that class in order to correct for the strong bias in our sample. We show in this way that comparing the measured variability time-scale of a radio transient/variable of unknown origin can provide an early, albeit approximate, classification of the object, and could form part of a suite of measurements used to provide early categorization of such events. Finally, we also discuss the effect scintillating sources will have on our ability to classify events based on their variability time-scales.

Selection of Norway spruce somatic embryos by computer vision

NASA Astrophysics Data System (ADS)

Hamalainen, Jari J.; Jokinen, Kari J.

1993-05-01

A computer vision system was developed for the classification of plant somatic embryos. The embryos are in a Petri dish that is transferred with constant speed and they are recognized as they pass a line scan camera. A classification algorithm needs to be installed for every plant species. This paper describes an algorithm for the recognition of Norway spruce (Picea abies) embryos. A short review of conifer micropropagation by somatic embryogenesis is also given. The recognition algorithm is based on features calculated from the boundary of the object. Only part of the boundary corresponding to the developing cotyledons (2 - 15) and the straight sides of the embryo are used for recognition. An index of the length of the cotyledons describes the developmental stage of the embryo. The testing set for classifier performance consisted of 118 embryos and 478 nonembryos. With the classification tolerances chosen 69% of the objects classified as embryos by a human classifier were selected and 31$% rejected. Less than 1% of the nonembryos were classified as embryos. The basic features developed can probably be easily adapted for the recognition of other conifer somatic embryos.

Quantitative change of EEG and respiration signals during mindfulness meditation.

PubMed

Ahani, Asieh; Wahbeh, Helane; Nezamfar, Hooman; Miller, Meghan; Erdogmus, Deniz; Oken, Barry

2014-05-14

This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies.

Quantitative change of EEG and respiration signals during mindfulness meditation

PubMed Central

2014-01-01

Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. PMID:24939519

Persistence of the Intuitive Conception of Living Things in Adolescence

ERIC Educational Resources Information Center

Babai, Reuven; Sekal, Rachel; Stavy, Ruth

2010-01-01

This study investigated whether intuitive, naive conceptions of "living things" based on objects' mobility (movement = alive) persist into adolescence and affect 10th graders' accuracy of responses and reaction times during object classification. Most of the 58 students classified the test objects correctly as living/nonliving, yet they…

Building an automated SOAP classifier for emergency department reports.

PubMed

Mowery, Danielle; Wiebe, Janyce; Visweswaran, Shyam; Harkema, Henk; Chapman, Wendy W

2012-02-01

Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined. The first step to evaluating the SOAP framework's usefulness for clinical information extraction is to apply the model to clinical narratives and develop an automated SOAP classifier that classifies sentences from clinical reports. In this quantitative study, we applied the SOAP framework to sentences from emergency department reports, and trained and evaluated SOAP classifiers built with various linguistic features. We found the SOAP framework can be applied manually to emergency department reports with high agreement (Cohen's kappa coefficients over 0.70). Using a variety of features, we found classifiers for each SOAP class can be created with moderate to outstanding performance with F(1) scores of 93.9 (subjective), 94.5 (objective), 75.7 (assessment), and 77.0 (plan). We look forward to expanding the framework and applying the SOAP classification to clinical information extraction tasks. Copyright © 2011. Published by Elsevier Inc.

BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

PubMed

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan

2015-06-01

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.

Selective Transfer Machine for Personalized Facial Expression Analysis

PubMed Central

Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.

2017-01-01

Automatic facial action unit (AU) and expression detection from videos is a long-standing problem. The problem is challenging in part because classifiers must generalize to previously unknown subjects that differ markedly in behavior and facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) from those on which the classifiers are trained. While some progress has been achieved through improvements in choices of features and classifiers, the challenge occasioned by individual differences among people remains. Person-specific classifiers would be a possible solution but for a paucity of training data. Sufficient training data for person-specific classifiers typically is unavailable. This paper addresses the problem of how to personalize a generic classifier without additional labels from the test subject. We propose a transductive learning method, which we refer as a Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific mismatches. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. We compared STM to both generic classifiers and cross-domain learning methods on four benchmarks: CK+ [44], GEMEP-FERA [67], RU-FACS [4] and GFT [57]. STM outperformed generic classifiers in all. PMID:28113267

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention are Coordinated Using Surface-Based Attentional Shrouds

ERIC Educational Resources Information Center

Fazl, Arash; Grossberg, Stephen; Mingolla, Ennio

2009-01-01

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified…

Classification of Aerial Photogrammetric 3d Point Clouds

NASA Astrophysics Data System (ADS)

Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

2017-05-01

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

Local morphologic scale: application to segmenting tumor infiltrating lymphocytes in ovarian cancer TMAs

NASA Astrophysics Data System (ADS)

Janowczyk, Andrew; Chandran, Sharat; Feldman, Michael; Madabhushi, Anant

2011-03-01

In this paper we present the concept and associated methodological framework for a novel locally adaptive scale notion called local morphological scale (LMS). Broadly speaking, the LMS at every spatial location is defined as the set of spatial locations, with associated morphological descriptors, which characterize the local structure or heterogeneity for the location under consideration. More specifically, the LMS is obtained as the union of all pixels in the polygon obtained by linking the final location of trajectories of particles emanating from the location under consideration, where the path traveled by originating particles is a function of the local gradients and heterogeneity that they encounter along the way. As these particles proceed on their trajectory away from the location under consideration, the velocity of each particle (i.e. do the particles stop, slow down, or simply continue around the object) is modeled using a physics based system. At some time point the particle velocity goes to zero (potentially on account of encountering (a) repeated obstructions, (b) an insurmountable image gradient, or (c) timing out) and comes to a halt. By using a Monte-Carlo sampling technique, LMS is efficiently determined through parallelized computations. LMS is different from previous local scale related formulations in that it is (a) not a locally connected sets of pixels satisfying some pre-defined intensity homogeneity criterion (generalized-scale), nor is it (b) constrained by any prior shape criterion (ball-scale, tensor-scale). Shape descriptors quantifying the morphology of the particle paths are used to define a tensor LMS signature associated with every spatial image location. These features include the number of object collisions per particle, average velocity of a particle, and the length of the individual particle paths. These features can be used in conjunction with a supervised classifier to correctly differentiate between two different object classes based on local structural properties. In this paper, we apply LMS to the specific problem of classifying regions of interest in Ovarian Cancer (OCa) histology images as either tumor or stroma. This approach is used to classify lymphocytes as either tumor infiltrating lymphocytes (TILs) or non-TILs; the presence of TILs having been identified as an important prognostic indicator for disease outcome in patients with OCa. We present preliminary results on the tumor/stroma classification of 11,000 randomly selected locations of interest, across 11 images obtained from 6 patient studies. Using a Probabilistic Boosting Tree (PBT), our supervised classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.8341 +/-0.0059 over 5 runs of randomized cross validation. The average LMS computation time at every spatial location for an image patch comprising 2000 pixels with 24 particles at every location was only 18s.

The Potential of AutoClass as an Asteroidal Data Mining Tool

NASA Astrophysics Data System (ADS)

Walker, Matthew; Ziffer, J.; Harvell, T.; Fernandez, Y. R.; Campins, H.

2011-05-01

AutoClass-C, an artificial intelligence program designed to classify large data sets, was developed by NASA to classify stars based upon their infrared colors. Wanting to investigate its ability to classify asteroidal data, we conducted a preliminary test to determine if it could accurately reproduce the Tholen taxonomy using the data from the Eight Color Asteroid Survey (ECAS). For our initial test, we limited ourselves to those asteroids belonging to S, C, or X classes, and to asteroids with a color difference error of less than +/- 0.05 magnitudes. Of those 406 asteroids, AutoClass was able to confidently classify 85%: identifying the remaining asteroids as belonging to more than one class. Of the 346 asteroids that AutoClass classified, all but 3 (<1%) were classified as they had been in the Tholen classification scheme. Inspired by our initial success, we reran AutoClass, this time including IRAS albedos and limiting the asteroids to those that had also been observed and classified in the Bus taxonomy. Of those 258 objects, AutoClass was able to classify 248 with greater than 75% certainty, and ranked albedo, not color, as the most influential factor. Interestingly, AutoClass consistently put P type objects in with the C class (there were 19 P types and 7 X types mixed in with the other 154 C types), and omitted P types from the group associated with the other X types (which had only one rogue B type in with its other 49 X-types). Autoclass classified the remaining classes with a high accuracy: placing one A and one CU type in with an otherwise perfect S group; placing three P type and one T type in an otherwise perfect D group; and placing the four remaining asteroids (V, A, R, and Q) into a class together.

A proposal to classify happiness as a psychiatric disorder.

PubMed Central

Bentall, R P

1992-01-01

It is proposed that happiness be classified as a psychiatric disorder and be included in future editions of the major diagnostic manuals under the new name: major affective disorder, pleasant type. In a review of the relevant literature it is shown that happiness is statistically abnormal, consists of a discrete cluster of symptoms, is associated with a range of cognitive abnormalities, and probably reflects the abnormal functioning of the central nervous system. One possible objection to this proposal remains--that happiness is not negatively valued. However, this objection is dismissed as scientifically irrelevant. PMID:1619629

ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

PubMed

Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

2017-10-01

Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography

PubMed Central

Liu, George S.; Zhu, Michael H.; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E.; Oghalai, John S.

2017-01-01

Detection of endolymphatic hydrops is important for diagnosing Meniere’s disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification. PMID:29082086

Automatic detection and classification of obstacles with applications in autonomous mobile robots

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

2016-04-01

Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

Optical microvariability of selected PG QSOs

NASA Astrophysics Data System (ADS)

Jang, Minhwan

2005-02-01

This is a preliminary report of the continuing search for significant optical microvariability in a selected sample of radio-quiet and radio-loud quasi-stellar objects (QSOs). Total of 24 Paloma-Green (PG) quasars are observed. Among them, 17 objects are classified as radio-quiet and seven objects are classified radio-loud. In this classification, the ratio R, radio to optical flux density, is required to be less than 1 in order to assure the objects are reasonably radio-quiet. Two out of 17 radio-quiet QSOs and six out of eight radio-loud QSOs were microvariable. This apparent contrast in microvariations between radio-quiet and radio-loud QSOs does not provide firm support for the theoretical model utilizing discrete events in accretion disks and implies substantial support for models based on propagating shocks on relativistic jets as the likely process responsible for producing most of the microvariations are observed in AGNs.

[High voltage objects and radiocommunication investments in view of requirements of the environmental protection act].

PubMed

Szuba, Marek

2006-01-01

This paper presents the most important elements of the localization procedure of high voltage overhead lines and substations and radiocommunication objects which are the source of electromagnetic fields. These fields are perceived as a major threat to human health. The point of departure to make a choice of investments is the special classification of technical installations described in one of the executive directive issued by virtue of the Environmental Protection Act. This special executive directive enumerates a lot of technical objects (installation), classified in the group of investments which have significant impact on the environment and some objects which could be classified in this group. For all this technical installations (e.g., overhead high voltage power lines) the provisions of the Environmental Protection Act impose an obligation to take particular steps to assure transparency of the environmental protection procedures, transborder procedures and the protection of areas included in the Natura 2000 network.

Learn ++.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes.

PubMed

Muhlbaier, Michael D; Topalis, Apostolos; Polikar, Robi

2009-01-01

We have previously introduced an incremental learning algorithm Learn(++), which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn(++) suffers from an inherent "outvoting" problem when asked to learn a new class omega(new) introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omega(new) instances. The collective votes of earlier classifiers, for an inevitably incorrect decision, then outweigh the votes of the new classifiers' correct decision on omega(new) instances--until there are enough new classifiers to counteract the unfair outvoting. This forces Learn(++) to generate an unnecessarily large number of classifiers. This paper describes Learn(++).NC, specifically designed for efficient incremental learning of multiple new classes using significantly fewer classifiers. To do so, Learn (++).NC introduces dynamically weighted consult and vote (DW-CAV), a novel voting mechanism for combining classifiers: individual classifiers consult with each other to determine which ones are most qualified to classify a given instance, and decide how much weight, if any, each classifier's decision should carry. Experiments on real-world problems indicate that the new algorithm performs remarkably well with substantially fewer classifiers, not only as compared to its predecessor Learn(++), but also as compared to several other algorithms recently proposed for similar problems.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Integrating visual learning within a model-based ATR system

NASA Astrophysics Data System (ADS)

Carlotto, Mark; Nebrich, Mark

2017-05-01

Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual learning in an ATR context requires the ability to recognize objects independent of location, scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate target locations. A bootstrap learning method effectively extends the operation of the classifier beyond the training set and provides a measure of confidence. We show how the classifier can be used to learn other features that are difficult to compute from imagery such as target direction, and to assess the performance of the visual learning process itself.

Identifying thematic roles from neural representations measured by functional magnetic resonance imaging.

PubMed

Wang, Jing; Cherkassky, Vladimir L; Yang, Ying; Chang, Kai-Min Kevin; Vargas, Robert; Diana, Nicholas; Just, Marcel Adam

2016-01-01

The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions. When tested on a held-out video, the classifiers were able to reliably identify the thematic role of an object from its associated fMRI activation pattern. Moreover, when trained on one subset of the study participants, classifiers reliably identified the thematic roles in the data of a left-out participant (mean accuracy = .66), indicating that the neural representations of thematic roles were common across individuals.

Slit identification for a uranium slab using a binary classifier based on cosmic-ray muon scattering

NASA Astrophysics Data System (ADS)

Xiao, S.; He, W.; Chen, Y.; Dang, X.; Wu, L.; Shuai, M.

2017-12-01

Traditional muon tomographic method has been fraught with difficulty when it is applied to identify some defective high-Z objects or other complicated structures, since it usually gets into trouble when attempting to produce a precise three-dimensional image for such objects. In this paper, we present a binary classifier based on cosmic-ray muon scattering to identify the slit potentially located in a uranium slab. The superiority of this classifier is established by steering clear of the stubborn imaging procedure necessary for the conventional methods. Simulation results demonstrate its capability to spot a horizontal or vertical slit with a reasonable exposure time. The minimum width of a spotted slit is on the level of millimeters or even sub-millimeters. Therefore, this technique will be prospective in terms of monitoring the long-term status of nuclear storage and facilities in real life.

Infant Temperament and the Brainstem Auditory Evoked Response in Later Childhood.

ERIC Educational Resources Information Center

Woodward, Sue A.; McManis, Mark H.; Kagan, Jerome; Deldin, Patricia; Snidman, Nancy; Lewis, Melissa; Kahn, Vali

2001-01-01

Evaluated brainstem auditory evoked responses (BAERs) on 10- to 12-year-olds who had been classified as high or low reactive to unfamiliar stimuli at 4 months of age. Found that children previously classified as high reactive at 4 months had larger wave V components than did low reactive children, possibly suggesting greater excitability in…

31 CFR 2.2 - Access to classified information by historical researchers, former Treasury Presidential and Vice...

Code of Federal Regulations, 2013 CFR

2013-07-01

... and Vice Presidents. 2.2 Section 2.2 Money and Finance: Treasury Office of the Secretary of the Treasury NATIONAL SECURITY INFORMATION § 2.2 Access to classified information by historical researchers... research projects; (2) Previously occupied a position in the Treasury to which they were appointed by the...

31 CFR 2.2 - Access to classified information by historical researchers, former Treasury Presidential and Vice...

Code of Federal Regulations, 2014 CFR

2014-07-01

... and Vice Presidents. 2.2 Section 2.2 Money and Finance: Treasury Office of the Secretary of the Treasury NATIONAL SECURITY INFORMATION § 2.2 Access to classified information by historical researchers... research projects; (2) Previously occupied a position in the Treasury to which they were appointed by the...

31 CFR 2.2 - Access to classified information by historical researchers, former Treasury Presidential and Vice...

Code of Federal Regulations, 2012 CFR

2012-07-01

... and Vice Presidents. 2.2 Section 2.2 Money and Finance: Treasury Office of the Secretary of the Treasury NATIONAL SECURITY INFORMATION § 2.2 Access to classified information by historical researchers... research projects; (2) Previously occupied a position in the Treasury to which they were appointed by the...

31 CFR 2.2 - Access to classified information by historical researchers, former Treasury Presidential and Vice...

Code of Federal Regulations, 2011 CFR

2011-07-01

... and Vice Presidents. 2.2 Section 2.2 Money and Finance: Treasury Office of the Secretary of the Treasury NATIONAL SECURITY INFORMATION § 2.2 Access to classified information by historical researchers... research projects; (2) Previously occupied a position in the Treasury to which they were appointed by the...

From sixty-two interviews on 'the worst and the best episode of your life'. Relationships between internal working models and a grammatical scale of subject-object affective connections.

PubMed

Seganti, A; Carnevale, G; Mucelli, R; Solano, L; Target, M

2000-06-01

The authors address the issue of inferring unconscious internal working models of interaction through language. After reviewing Main's seminal work of linguistic assessment through the 'adult attachment interview', they stress the idea of adults' internal working models (IWMs) as information-processing devices, which give moment-to-moment sensory orientation in the face of any past or present, animate or inanimate object. They propose that a selective perception of the objects could match expected with actual influence of objects on the subject's self, through very simple 'parallel-processed' categories of internal objects. They further hypothesise that the isomorphism between internal working models of interaction and grammatical connections between subjects and objects within a clause could be a key to tracking positive and negative images of self and other during discourse. An experiment is reported applying the authors' 'scale of subject/object affective connection' to the narratives of sixty-two subjects asked to write about the 'worst' and 'best' episodes of their lives. Participants had previously been classified using Hazan & Shaver's self-reported 'attachment types' (avoidant, anxious and secure) categorising individuals' general expectations in relation to others. The findings were that the subject/object distribution of positive and negative experience, through verbs defined for this purpose as either performative or state verbs, did significantly differ between groups. In addition, different groups tended, during the best episodes, significantly to invert the trend of positive/negative subject/object distribution shown during the worst episode. Results are discussed in terms of a psychoanalytic theory of improvement through co-operative elaboration of negative relational issues.

United Kingdom Infrared Telescope's Spectrograph Observations of Human-Made Space Objects

NASA Technical Reports Server (NTRS)

Buckalew, Brent; Abercromby, Kira; Lederer, Susan; Frith, James; Cowardin, Heather

2017-01-01

Presented here are the results of the United Kingdom Infrared Telescope (UKIRT) spectral observations of human-made space objects taken from 2014 to 2015. The data collected using the UIST infrared spectrograph cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 18 different orbiting objects at or near the geosynchronous (GEO) regime. Thirteen of the objects are spacecraft, one is a rocket body, and four are cataloged as debris pieces. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials; thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons and silicon. The spacecraft show distinct features due to the presence of solar panels. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give excellent indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include more materials, noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. A comparison conducted between objects observed previously with the NASA Infrared Telescope Facility (IRTF) shows similar materials and trends from the two telescopes and from the two distinct data sets. However, based on the current state of the model, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.

Adapting a tourism crime typology: classifying outdoor recreation crime

Treesearch

Joanne F. Tynon; Deborah J. Chavez

2006-01-01

Using a qualitative aproach, the authors tested a crime typology developed for tourism destinations in a U.S. National Forest recreation setting. Specific objectives were to classify the attributes of crime and violence, examine the effects of crime and violence on visitor demand, and suggest methods of prevention and recovery. A key modification to the crime typology...

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Layered classification techniques for remote sensing applications

NASA Technical Reports Server (NTRS)

Swain, P. H.; Wu, C. L.; Landgrebe, D. A.; Hauska, H.

1975-01-01

The single-stage method of pattern classification utilizes all available features in a single test which assigns the unknown to a category according to a specific decision strategy (such as the maximum likelihood strategy). The layered classifier classifies the unknown through a sequence of tests, each of which may be dependent on the outcome of previous tests. Although the layered classifier was originally investigated as a means of improving classification accuracy and efficiency, it was found that in the context of remote sensing data analysis, other advantages also accrue due to many of the special characteristics of both the data and the applications pursued. The layered classifier method and several of the diverse applications of this approach are discussed.

Knowledge and Cognitive Process Dimensions of Technology Teachers' Lesson Objectives

ERIC Educational Resources Information Center

Mathumbu, David; Rauscher, Willem; Braun, Max

2014-01-01

A clearly stated lesson objective is considered an essential component of a well-planned lesson. Many teachers of Technology, a relatively new subject in South African schools, teach Technology with rather limited training both in content and methodological approaches. This study sought to investigate and classify lesson objectives framed or…

Childhood interstitial lung diseases: an 18-year retrospective analysis.

PubMed

Soares, Jennifer J; Deutsch, Gail H; Moore, Paul E; Fazili, Mohammad F; Austin, Eric D; Brown, Rebekah F; Sokolow, Andrew G; Hilmes, Melissa A; Young, Lisa R

2013-10-01

Childhood interstitial lung diseases (ILD) occur in a variety of clinical contexts. Advances in the understanding of disease pathogenesis and use of standardized terminology have facilitated increased case ascertainment. However, as all studies have been performed at specialized referral centers, the applicability of these findings to general pulmonary practice has been uncertain. The objective of this study was to determine the historical occurrence of childhood ILD to provide information reflecting general pediatric pulmonary practice patterns. Childhood ILD cases seen at Vanderbilt Children's Hospital from 1994 to 2011 were retrospectively reviewed and classified according to the current pediatric diffuse lung disease histopathologic classification system. A total of 93 cases were identified, of which 91.4% were classifiable. A total of 68.8% (64/93) of subjects underwent lung biopsy in their evaluations. The largest classification categories were disorders related to systemic disease processes (24.7%), disorders of the immunocompromised host (24.7%), and disorders more prevalent in infancy (22.6%). Eight cases of neuroendocrine cell hyperplasia of infancy (NEHI) were identified, including 5 that were previously unrecognized before this review. Our findings demonstrate the general scope of childhood ILD and that these cases present within a variety of pediatric subspecialties. Retrospective review was valuable in recognizing more recently described forms of childhood ILD. As a significant portion of cases were classifiable based on clinical, genetic, and/or radiographic criteria, we urge greater consideration to noninvasive diagnostic approaches and suggest modification to the current childhood ILD classification scheme to accommodate the increasing number of cases diagnosed without lung biopsy.

Development and Validation of a Semiquantitative, Multitarget PCR Assay for Diagnosis of Bacterial Vaginosis

PubMed Central

Lembke, Bryndon D.; Ramachandran, Kalpana; Body, Barbara A.; Nye, Melinda B.; Rivers, Charles A.; Schwebke, Jane R.

2012-01-01

Quantitative PCR assays were developed for 4 organisms reported previously to be useful positive indicators for the diagnosis of bacterial vaginosis (BV)—Atopobium vaginae, Bacterial Vaginosis-Associated Bacterium 2 (BVAB-2), Gardnerella vaginalis, and Megasphaera-1—and a single organism (Lactobacillus crispatus) that has been implicated as a negative indicator for BV. Vaginal samples (n = 169), classified as positive (n = 108) or negative (n = 61) for BV based on a combination of the Nugent Gram stain score and Amsel clinical criteria, were analyzed for the presence and quantity of each of the marker organisms, and the results were used to construct a semiquantitative, multiplex PCR assay for BV based on detection of 3 positive indicator organisms (A. vaginae, BVAB-2, and Megasphaera-1) and classification of samples using a combinatorial scoring system. The prototype BV PCR assay was then used to analyze the 169-member developmental sample set and, in a prospective, blinded manner, an additional 227 BV-classified vaginal samples (110 BV-positive samples and 117 BV-negative samples). The BV PCR assay demonstrated a sensitivity of 96.7% (202/209), a specificity of 92.2% (153/166), a positive predictive value of 94.0%, and a negative predictive value of 95.6%, with 21 samples (5.3%) classified as indeterminate for BV. This assay provides a reproducible and objective means of evaluating critical components of the vaginal microflora in women with signs and symptoms of vaginitis and is comparable in diagnostic accuracy to the conventional gold standard for diagnosis of BV. PMID:22535982

DISCOVERY OF FOUR HIGH PROPER MOTION L DWARFS, INCLUDING A 10 pc L DWARF AT THE L/T TRANSITION {sup ,}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castro, Philip J.; Gizis, John E.; Harris, Hugh C.

2013-10-20

We discover four high proper motion L dwarfs by comparing the Wide-field Infrared Survey Explorer (WISE) to the Two Micron All Sky Survey. WISE J140533.32+835030.5 is an L dwarf at the L/T transition with a proper motion of 0.85 ± 0.''02 yr{sup –1}, previously overlooked due to its proximity to a bright star (V ≈ 12 mag). From optical spectroscopy we find a spectral type of L8, and from moderate-resolution J band spectroscopy we find a near-infrared spectral type of L9. We find WISE J140533.32+835030.5 to have a distance of 9.7 ± 1.7 pc, bringing the number of L dwarfsmore » at the L/T transition within 10 pc from six to seven. WISE J040137.21+284951.7, WISE J040418.01+412735.6, and WISE J062442.37+662625.6 are all early L dwarfs within 25 pc, and were classified using optical and low-resolution near-infrared spectra. WISE J040418.01+412735.6 is an L2 pec (red) dwarf, a member of the class of unusually red L dwarfs. We use follow-up optical and low-resolution near-infrared spectroscopy to classify a previously discovered fifth object WISEP J060738.65+242953.4 as an (L8 Opt/L9 NIR), confirming it as an L dwarf at the L/T transition within 10 pc. WISEP J060738.65+242953.4 shows tentative CH{sub 4} in the H band, possibly the result of unresolved binarity with an early T dwarf, a scenario not supported by binary spectral template fitting. If WISEP J060738.65+242953.4 is a single object, it represents the earliest onset of CH{sub 4} in the H band of an L/T transition dwarf in the SpeX Library. As very late L dwarfs within 10 pc, WISE J140533.32+835030.5 and WISEP J060738.65+242953.4 will play a vital role in resolving outstanding issues at the L/T transition.« less

Multiobjective GAs, quantitative indices, and pattern classification.

PubMed

Bandyopadhyay, Sanghamitra; Pal, Sankar K; Aruna, B

2004-10-01

The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.

Earthquake damage mapping by using remotely sensed data: the Haiti case study

NASA Astrophysics Data System (ADS)

Romaniello, Vito; Piscini, Alessandro; Bignami, Christian; Anniballe, Roberta; Stramondo, Salvatore

2017-01-01

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

Galaxy Zoo: Infrared and Optical Morphology

NASA Astrophysics Data System (ADS)

Carla Shanahan, Jesse; Lintott, Chris; Zoo, Galaxy

2018-01-01

We present the detailed, visual morphologies of approximately 60,000 galaxies observed by the UKIRT Infrared Deep Sky Survey and then classified by participants in the Galaxy Zoo project. Our sample is composed entirely of nearby objects with redshifts of z ≤ 0.3, which enables us to robustly analyze their morphological characteristics including smoothness, bulge properties, spiral structure, and evidence of bars or rings. The determination of these features is made via a consensus-based analysis of the Galaxy Zoo project data in which inconsistent and outlying classifications are statistically down-weighted. We then compare these classifications of infrared morphology to the objects’ optical classifications in the Galaxy Zoo 2 release (Willett et al. 2013). It is already known that morphology is an effective tool for uncovering a galaxy’s dynamical past, and previous studies have shown significant correlations with physical characteristics such as stellar mass distribution and star formation history. We show that majority of the sample has agreement or expected differences between the optical and infrared classifications, but also present a preliminary analysis of a subsample of objects with striking discrepancies.

The composite nature of the peculiar star HR 6560 (HD 159870)

NASA Technical Reports Server (NTRS)

Wegner, Gary; Cowley, Charles R.

1992-01-01

Ground-based high-dispersion photographic spectra and ultraviolet spectra obtained with the IUE satellite are described and employed to determine the nature of the peculiar star HR 6560 (HD 159870). Previously this object had been described as both a composite system and as a strong Fm star. The UBVRI, Stromgren, and ultraviolet colors of HR 6560 are compared with objects classified composite from the Bright Star Catalogue and normal dwarfs and giants. The colors of HR 6560 are not unusual for a composite and are consistent with a late-A dwarf, combined with a late-G or early-K giant. The ultraviolet satellite clearly shows the presence of an A component, but its precise spectral type is difficult to assign. The IUE and TD-1 data suggest that the ultraviolet is dominated by light from an A5 V secondary and the visual from a GO III primary. This does not agree well with the most plausible model that fits the visual photometry. The peculiar nature of HR 6560's spectrum is most likely due to its composite nature.

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

PubMed

Massé, Fabien; Gonzenbach, Roman R; Arami, Arash; Paraschiv-Ionescu, Anisoara; Luft, Andreas R; Aminian, Kamiar

2015-08-25

Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients' mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

NASA Astrophysics Data System (ADS)

Liu, Tao; Abd-Elrahman, Amr

2018-05-01

Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework.

Diagnosis and characterization of mania: Quantifying increased energy and activity in the human behavioral pattern monitor.

PubMed

Perry, William; McIlwain, Meghan; Kloezeman, Karen; Henry, Brook L; Minassian, Arpi

2016-06-30

Increased energy or activity is now an essential feature of the mania of Bipolar Disorder (BD) according to DSM-5. This study examined whether objective measures of increased energy can differentiate manic BD individuals and provide greater diagnostic accuracy compared to rating scales, extending the work of previous studies with smaller samples. We also tested the relationship between objective measures of energy and rating scales. 50 hospitalized manic BD patients were compared to healthy subjects (HCS, n=39) in the human Behavioral Pattern Monitor (hBPM) which quantifies motor activity and goal-directed behavior in an environment containing novel stimuli. Archival hBPM data from 17 schizophrenia patients were used in sensitivity and specificity analyses. Manic BD patients exhibited higher motor activity than HCS and higher novel object interactions. hBPM activity measures were not correlated with observer-rated symptoms, and hBPM activity was more sensitive in accurately classifying hospitalized BD subjects than observer ratings. Although the findings can only be generalized to inpatient populations, they suggest that increased energy, particularly specific and goal-directed exploration, is a distinguishing feature of BD mania and is best quantified by objective measures of motor activity. A better understanding is needed of the biological underpinnings of this cardinal feature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Exploratory Spectroscopy of Magnetic Cataclysmic Variables Candidates and Other Variable Objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oliveira, A. S.; Palhares, M. S.; Rodrigues, C. V.

2017-04-01

The increasing number of synoptic surveys made by small robotic telescopes, such as the photometric Catalina Real-Time Transient Survey (CRTS), provides a unique opportunity to discover variable sources and improves the statistical samples of such classes of objects. Our goal is the discovery of magnetic Cataclysmic Variables (mCVs). These are rare objects that probe interesting accretion scenarios controlled by the white-dwarf magnetic field. In particular, improved statistics of mCVs would help to address open questions on their formation and evolution. We performed an optical spectroscopy survey to search for signatures of magnetic accretion in 45 variable objects selected mostly from themore » CRTS. In this sample, we found 32 CVs, 22 being mCV candidates, 13 of which were previously unreported as such. If the proposed classifications are confirmed, it would represent an increase of 4% in the number of known polars and 12% in the number of known IPs. A fraction of our initial sample was classified as extragalactic sources or other types of variable stars by the inspection of the identification spectra. Despite the inherent complexity in identifying a source as an mCV, variability-based selection, followed by spectroscopic snapshot observations, has proved to be an efficient strategy for their discoveries, being a relatively inexpensive approach in terms of telescope time.« less

Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Sun, Yujie; Wang, Qiao

2018-07-01

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region-line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region-line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques.

A Learning Outcome-Oriented Approach towards Classifying Pervasive Games for Learning Using Game Design Patterns and Contextual Information

ERIC Educational Resources Information Center

Schmitz, Birgit; Klemke, Roland; Specht, Marcus

2013-01-01

Mobile and in particular pervasive games are a strong component of future scenarios for teaching and learning. Based on results from a previous review of practical papers, this work explores the educational potential of pervasive games for learning by analysing underlying game mechanisms. In order to determine and classify cognitive and affective…

Wide-field spectral imaging of human ovary autofluorescence and oncologic diagnosis via previously collected probe data

NASA Astrophysics Data System (ADS)

Renkoski, Timothy E.; Hatch, Kenneth D.; Utzinger, Urs

2012-03-01

With no sufficient screening test for ovarian cancer, a method to evaluate the ovarian disease state quickly and nondestructively is needed. The authors have applied a wide-field spectral imager to freshly resected ovaries of 30 human patients in a study believed to be the first of its magnitude. Endogenous fluorescence was excited with 365-nm light and imaged in eight emission bands collectively covering the 400- to 640-nm range. Linear discriminant analysis was used to classify all image pixels and generate diagnostic maps of the ovaries. Training the classifier with previously collected single-point autofluorescence measurements of a spectroscopic probe enabled this novel classification. The process by which probe-collected spectra were transformed for comparison with imager spectra is described. Sensitivity of 100% and specificity of 51% were obtained in classifying normal and cancerous ovaries using autofluorescence data alone. Specificity increased to 69% when autofluorescence data were divided by green reflectance data to correct for spatial variation in tissue absorption properties. Benign neoplasm ovaries were also found to classify as nonmalignant using the same algorithm. Although applied ex vivo, the method described here appears useful for quick assessment of cancer presence in the human ovary.

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

NASA Astrophysics Data System (ADS)

Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene

2016-07-01

Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Foliage discrimination using a rotating ladar

NASA Technical Reports Server (NTRS)

Castano, A.; Matthies, L.

2003-01-01

We present a real time algorithm that detects foliage using range from a rotating laser. Objects not classified as foliage are conservatively labeled as non-driving obstacles. In contrast to related work that uses range statistics to classify objects, we exploit the expected localities and continuities of an obstacle, in both space and time. Also, instead of attempting to find a single accurate discriminating factor for every ladar return, we hypothesize the class of some few returns and then spread the confidence (and classification) to other returns using the locality constraints. The Urbie robot is presently using this algorithm to descriminate drivable grass from obstacles during outdoor autonomous navigation tasks.

Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

NASA Astrophysics Data System (ADS)

Iabchoon, Sanwit; Wongsai, Sangdao; Chankon, Kanoksuk

2017-10-01

Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

Recognition of pornographic web pages by classifying texts and images.

PubMed

Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

2007-06-01

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Use of joint two-view information for computerized lesion detection on mammograms: improvement of microcalcification detection accuracy

NASA Astrophysics Data System (ADS)

Sahiner, Berkman; Gurcan, Metin N.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.

2002-05-01

We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Object pairs were classified with a joint two-view classifier that used the similarity of objects in a pair. Each cluster candidate was also classified as a true microcalcification cluster or a false-positive (FP) using its single-view features. The outputs of these two classifiers were fused. A data set of 38 pairs of mammograms from our database was used to train the new detection technique. The independent test set consisted of 77 pairs of mammograms from the University of South Florida public database. At a per-film sensitivity of 70%, the FP rates were 0.17 and 0.27 with the fusion and single-view detection methods, respectively. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing false from true microcalcification clusters.

Leveraging anatomical information to improve transfer learning in brain-computer interfaces

NASA Astrophysics Data System (ADS)

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian K. C.

2015-08-01

Objective. Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main results. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.

Semantic Shot Classification in Sports Video

NASA Astrophysics Data System (ADS)

Duan, Ling-Yu; Xu, Min; Tian, Qi

2003-01-01

In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of a specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervised learning and classification of the given sports video with low-level and middle-level features extracted from the sports video. It is observed that for each sport we can predefine a small number of semantic shot classes, about 5~10, which covers 90~95% of sports broadcasting video. With the supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot classes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 4 types of sports videos: tennis, basketball, volleyball and soccer. Good classification accuracy of 85~95% has been achieved. With correctly classified sports video shots, further structural and temporal analysis, such as event detection, video skimming, table of content, etc, will be greatly facilitated.

Prediction of antiepileptic drug treatment outcomes using machine learning

NASA Astrophysics Data System (ADS)

Colic, Sinisa; Wither, Robert G.; Lang, Min; Zhang, Liang; Eubanks, James H.; Bardakjian, Berj L.

2017-02-01

Objective. Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Approach. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. Main results. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Significance. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Leveraging anatomical information to improve transfer learning in brain-computer interfaces

PubMed Central

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian KC

2015-01-01

Objective Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main Results Based on simulated and measured EEG activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time. PMID:26169961

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Remembering Left–Right Orientation of Pictures

PubMed Central

Bartlett, James C.; Gernsbacher, Morton Ann; Till, Robert E.

2015-01-01

In a study of recognition memory for pictures, we observed an asymmetry in classifying test items as “same” versus “different” in left–right orientation: Identical copies of previously viewed items were classified more accurately than left–right reversals of those items. Response bias could not explain this asymmetry, and, moreover, correct “same” and “different” classifications were independently manipulable: Whereas repetition of input pictures (one vs. two presentations) affected primarily correct “same” classifications, retention interval (3 hr vs. 1 week) affected primarily correct “different” classifications. In addition, repetition but not retention interval affected judgments that previously seen pictures (both identical and reversed) were “old”. These and additional findings supported a dual-process hypothesis that links “same” classifications to high familiarity, and “different” classifications to conscious sampling of images of previously viewed pictures. PMID:2949051

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

Object classification for obstacle avoidance

NASA Astrophysics Data System (ADS)

Regensburger, Uwe; Graefe, Volker

1991-03-01

Object recognition is necessary for any mobile robot operating autonomously in the real world. This paper discusses an object classifier based on a 2-D object model. Obstacle candidates are tracked and analyzed false alarms generated by the object detector are recognized and rejected. The methods have been implemented on a multi-processor system and tested in real-world experiments. They work reliably under favorable conditions but sometimes problems occur e. g. when objects contain many features (edges) or move in front of structured background.

Neural dissociation in the production of lexical versus classifier signs in ASL: distinct patterns of hemispheric asymmetry.

PubMed

Hickok, Gregory; Pickell, Herbert; Klima, Edward; Bellugi, Ursula

2009-01-01

We examine the hemispheric organization for the production of two classes of ASL signs, lexical signs and classifier signs. Previous work has found strong left hemisphere dominance for the production of lexical signs, but several authors have speculated that classifier signs may involve the right hemisphere to a greater degree because they can represent spatial information in a topographic, non-categorical manner. Twenty-one unilaterally brain damaged signers (13 left hemisphere damaged, 8 right hemisphere damaged) were presented with a story narration task designed to elicit both lexical and classifier signs. Relative frequencies of the two types of errors were tabulated. Left hemisphere damaged signers produced significantly more lexical errors than did right hemisphere damaged signers, whereas the reverse pattern held for classifier signs. Our findings argue for different patterns of hemispheric asymmetry for these two classes of ASL signs. We suggest that the requirement to encode analogue spatial information in the production of classifier signs results in the increased involvement of the right hemisphere systems.

Recognition of Simple 3D Geometrical Objects under Partial Occlusion

NASA Astrophysics Data System (ADS)

Barchunova, Alexandra; Sommer, Gerald

In this paper we present a novel procedure for contour-based recognition of partially occluded three-dimensional objects. In our approach we use images of real and rendered objects whose contours have been deformed by a restricted change of the viewpoint. The preparatory part consists of contour extraction, preprocessing, local structure analysis and feature extraction. The main part deals with an extended construction and functionality of the classifier ensemble Adaptive Occlusion Classifier (AOC). It relies on a hierarchical fragmenting algorithm to perform a local structure analysis which is essential when dealing with occlusions. In the experimental part of this paper we present classification results for five classes of simple geometrical figures: prism, cylinder, half cylinder, a cube, and a bridge. We compare classification results for three classical feature extractors: Fourier descriptors, pseudo Zernike and Zernike moments.

Selective Transfer Machine for Personalized Facial Action Unit Detection

PubMed Central

Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffery F.

2014-01-01

Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. The alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2]. STM outperformed generic classifiers in all. PMID:25242877

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

PubMed

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

The phase-space structure of nearby dark matter as constrained by the SDSS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leclercq, Florent; Percival, Will; Jasche, Jens

Previous studies using numerical simulations have demonstrated that the shape of the cosmic web can be described by studying the Lagrangian displacement field. We extend these analyses, showing that it is now possible to perform a Lagrangian description of cosmic structure in the nearby Universe based on large-scale structure observations. Building upon recent Bayesian large-scale inference of initial conditions, we present a cosmographic analysis of the dark matter distribution and its evolution, referred to as the dark matter phase-space sheet, in the nearby universe as probed by the Sloan Digital Sky Survey main galaxy sample. We consider its stretchings andmore » foldings using a tetrahedral tessellation of the Lagrangian lattice. The method provides extremely accurate estimates of nearby density and velocity fields, even in regions of low galaxy density. It also measures the number of matter streams, and the deformation and parity reversals of fluid elements, which were previously thought inaccessible using observations. We illustrate the approach by showing the phase-space structure of known objects of the nearby Universe such as the Sloan Great Wall, the Coma cluster and the Boötes void. We dissect cosmic structures into four distinct components (voids, sheets, filaments, and clusters), using the Lagrangian classifiers DIVA, ORIGAMI, and a new scheme which we introduce and call LICH. Because these classifiers use information other than the sheer local density, identified structures explicitly carry physical information about their formation history. Accessing the phase-space structure of dark matter in galaxy surveys opens the way for new confrontations of observational data and theoretical models. We have made our data products publicly available.« less

The phase-space structure of nearby dark matter as constrained by the SDSS

NASA Astrophysics Data System (ADS)

Leclercq, Florent; Jasche, Jens; Lavaux, Guilhem; Wandelt, Benjamin; Percival, Will

2017-06-01

Previous studies using numerical simulations have demonstrated that the shape of the cosmic web can be described by studying the Lagrangian displacement field. We extend these analyses, showing that it is now possible to perform a Lagrangian description of cosmic structure in the nearby Universe based on large-scale structure observations. Building upon recent Bayesian large-scale inference of initial conditions, we present a cosmographic analysis of the dark matter distribution and its evolution, referred to as the dark matter phase-space sheet, in the nearby universe as probed by the Sloan Digital Sky Survey main galaxy sample. We consider its stretchings and foldings using a tetrahedral tessellation of the Lagrangian lattice. The method provides extremely accurate estimates of nearby density and velocity fields, even in regions of low galaxy density. It also measures the number of matter streams, and the deformation and parity reversals of fluid elements, which were previously thought inaccessible using observations. We illustrate the approach by showing the phase-space structure of known objects of the nearby Universe such as the Sloan Great Wall, the Coma cluster and the Boötes void. We dissect cosmic structures into four distinct components (voids, sheets, filaments, and clusters), using the Lagrangian classifiers DIVA, ORIGAMI, and a new scheme which we introduce and call LICH. Because these classifiers use information other than the sheer local density, identified structures explicitly carry physical information about their formation history. Accessing the phase-space structure of dark matter in galaxy surveys opens the way for new confrontations of observational data and theoretical models. We have made our data products publicly available.

SVM based colon polyps classifier in a wireless active stereo endoscope.

PubMed

Ayoub, J; Granado, B; Mhanna, Y; Romain, O

2010-01-01

This work focuses on the recognition of three-dimensional colon polyps captured by an active stereo vision sensor. The detection algorithm consists of SVM classifier trained on robust feature descriptors. The study is related to Cyclope, this prototype sensor allows real time 3D object reconstruction and continues to be optimized technically to improve its classification task by differentiation between hyperplastic and adenomatous polyps. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains detailed statistics about the detection rate and the computing complexity. Inspired by intensity histogram, the work shows a new approach that extracts a set of features based on depth histogram and combines stereo measurement with SVM classifiers to correctly classify benign and malignant polyps.

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2010-01-01

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

published in the leading data mining journal, Data Mining and Knowledge Discovery (Webb et. al., 2016)1. We have shown that the previous qualitative...learner Low-bias learner Aggregated classifier Figure 7: Architecture for learning fr m streaming data in th co text of variable or unknown...Learning limited dependence Bayesian classifiers, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD

Object instance recognition using motion cues and instance specific appearance models

NASA Astrophysics Data System (ADS)

Schumann, Arne

2014-03-01

In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

New data clustering for RBF classifier of agriculture products from x-ray images

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

1999-08-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a subsystem for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering and how it is vital to achieve useful classification. New clustering methods using class identify and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.

Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

NASA Astrophysics Data System (ADS)

Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

Learning for VMM + WTA Embedded Classifiers

DTIC Science & Technology

2016-03-31

enabling correct classification of each novel acoustic signal (generator, idle car , and idle truck). The classification structure requires, after...measured on our SoC FPAA IC. The test input is composed of signals from urban environment for 3 objects (generator, idle car , and idle truck...classifier results from a rural truck data set, an urban generator set, and urban idle car dataset. Solid lines represent our extracted background

Can We Talk to Robots? Ten-Month-Old Infants Expected Interactive Humanoid Robots to Be Talked to by Persons

ERIC Educational Resources Information Center

Arita, A.; Hiraki, K.; Kanda, T.; Ishiguro, H.

2005-01-01

As technology advances, many human-like robots are being developed. Although these humanoid robots should be classified as objects, they share many properties with human beings. This raises the question of how infants classify them. Based on the looking-time paradigm used by [Legerstee, M., Barna, J., & DiAdamo, C., (2000). Precursors to the…

Optimizing classification performance in an object-based very-high-resolution land use-land cover urban application

NASA Astrophysics Data System (ADS)

Georganos, Stefanos; Grippa, Tais; Vanhuysse, Sabine; Lennert, Moritz; Shimoni, Michal; Wolff, Eléonore

2017-10-01

This study evaluates the impact of three Feature Selection (FS) algorithms in an Object Based Image Analysis (OBIA) framework for Very-High-Resolution (VHR) Land Use-Land Cover (LULC) classification. The three selected FS algorithms, Correlation Based Selection (CFS), Mean Decrease in Accuracy (MDA) and Random Forest (RF) based Recursive Feature Elimination (RFE), were tested on Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest (RF) classifiers. The results demonstrate that the accuracy of SVM and KNN classifiers are the most sensitive to FS. The RF appeared to be more robust to high dimensionality, although a significant increase in accuracy was found by using the RFE method. In terms of classification accuracy, SVM performed the best using FS, followed by RF and KNN. Finally, only a small number of features is needed to achieve the highest performance using each classifier. This study emphasizes the benefits of rigorous FS for maximizing performance, as well as for minimizing model complexity and interpretation.

Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koch, Mark William; Steinbach, Ryan Matthew; Moya, Mary M

2015-10-01

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithmsmore » using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.« less

Recent Advances in the Classification of Low-grade Papillary-like Thyroid Neoplasms and Aggressive Papillary Thyroid Carcinomas: Evolution of Diagnostic Criteria.

PubMed

Guo, Zhenying; Ge, Minghua; Chu, Ying-Hsia; Asioli, Sofia; Lloyd, Ricardo V

2018-07-01

Papillary thyroid carcinomas account for ∼80% of well-differentiated thyroid tumors. During the past decade, several new variants of papillary-like thyroid neoplasms and papillary thyroid carcinomas have been recognized. Some of these neoplasms that were previously classified as malignant have been reclassified as low-grade neoplasms, as the diagnostic criteria have evolved. Similarly, some of the papillary thyroid carcinomas that were previously classified as conventional or classic papillary thyroid carcinomas have now been recognized as more aggressive variants of papillary thyroid carcinomas. Recognizing these differences becomes more important for the proper medical, surgical, and radiotherapeutic management of patients with these neoplasms.

Analysis of the Effects of the Commander’s Battle Positioning on Unit Combat Performance

DTIC Science & Technology

1991-03-01

Analysis ......... .. 58 Logistic Regression Analysis ......... .. 61 Canonical Correlation Analysis ........ .. 62 Descriminant Analysis...entails classifying objects into two or more distinct groups, or responses. Dillon defines descriminant analysis as "deriving linear combinations of the...object given it’s predictor variables. The second objective is, through analysis of the parameters of the descriminant functions, determine those

Invariant 2D object recognition using the wavelet transform and structured neural networks

NASA Astrophysics Data System (ADS)

Khalil, Mahmoud I.; Bayoumi, Mohamed M.

1999-03-01

This paper applies the dyadic wavelet transform and the structured neural networks approach to recognize 2D objects under translation, rotation, and scale transformation. Experimental results are presented and compared with traditional methods. The experimental results showed that this refined technique successfully classified the objects and outperformed some traditional methods especially in the presence of noise.

A New 3D Object Pose Detection Method Using LIDAR Shape Set

PubMed Central

Kim, Jung-Un

2018-01-01

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird’s eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets. PMID:29547551

A New 3D Object Pose Detection Method Using LIDAR Shape Set.

PubMed

Kim, Jung-Un; Kang, Hang-Bong

2018-03-16

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird's eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.

Overlooked wide companions of nearby F stars

NASA Astrophysics Data System (ADS)

Scholz, R.-D.

2016-03-01

Aims: We checked a sample of 545 F stars within 50 pc for wide companions using existing near-infrared and optical sky surveys. Methods: Applying the common proper motion (CPM) criterion, we detected wide companion candidates with 6-120 arcsec angular separations by visual inspection of multi-epoch finder charts and by searching in proper motion catalogues. Final proper motions were measured by involving positional measurements from up to eleven surveys. Spectral types of red CPM companions were estimated from their absolute J-band magnitudes based on the Hipparcos distances of the primaries. Results: In addition to about 100 known CPM objects, we found 19 new CPM companions and confirmed 31 previously known candidates. A few CPM objects are still considered as candidates according to their level of proper motion agreement. Among the new objects there are nine M0-M4, eight M5-M6, one ≈L3.5 dwarf (HD 3861B), and one white dwarf (WD) (HD 2726B), whereas we confirmed two K, 19 M0-M4, six M5-M6, two early-L dwarfs, and two DA WDs as CPM companions. In a few cases, previous spectral types were available that all agree well with our estimates. Two companions (HD 22879B and HD 49933B) are associated with moderately metal-poor Gaia benchmark stars. One doubtful CPM companion, spectroscopically classified as WD but found to be very bright (J = 11.1) by others, should either be a very nearby foreground WD or a different kind of object associated with HD 165670. Tables A.1, B.1, and C.1 are also available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/587/A51

Classification of burn wounds using support vector machines

NASA Astrophysics Data System (ADS)

Acha, Begona; Serrano, Carmen; Palencia, Sergio; Murillo, Juan Jose

2004-05-01

The purpose of this work is to improve a previous method developed by the authors for the classification of burn wounds into their depths. The inputs of the system are color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. Our previous work consisted in segmenting the burn wound from the rest of the image and classifying the burn into its depth. In this paper we focus on the classification problem only. We already proposed to use a Fuzzy-ARTMAP neural network (NN). However, we may take advantage of new powerful classification tools such as Support Vector Machines (SVM). We apply the five-folded cross validation scheme to divide the database into training and validating sets. Then, we apply a feature selection method for each classifier, which will give us the set of features that yields the smallest classification error for each classifier. Features used to classify are first-order statistical parameters extracted from the L*, u* and v* color components of the image. The feature selection algorithms used are the Sequential Forward Selection (SFS) and the Sequential Backward Selection (SBS) methods. As data of the problem faced here are not linearly separable, the SVM was trained using some different kernels. The validating process shows that the SVM method, when using a Gaussian kernel of variance 1, outperforms classification results obtained with the rest of the classifiers, yielding an error classification rate of 0.7% whereas the Fuzzy-ARTMAP NN attained 1.6 %.

Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning.

PubMed

Mjahad, A; Rosado-Muñoz, A; Bataller-Mompeán, M; Francés-Víllora, J V; Guerrero-Martínez, J F

2017-04-01

To safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an erroneous detection might lead to serious injuries to the patient or even cause Ventricular Fibrillation (VF). The main novelty of this paper is the use of time-frequency (t-f) representation images as the direct input to the classifier. We hypothesize that this method allow to improve classification results as it allows to eliminate the typical feature selection and extraction stage, and its corresponding loss of information. The standard AHA and MIT-BIH databases were used for evaluation and comparison with other authors. Previous to t-f Pseudo Wigner-Ville (PWV) calculation, only a basic preprocessing for denoising and signal alignment is necessary. In order to check the validity of the method independently of the classifier, four different classifiers are used: Logistic Regression with L2 Regularization (L2 RLR), Adaptive Neural Network Classifier (ANNC), Support Vector Machine (SSVM), and Bagging classifier (BAGG). The main classification results for VF detection (including flutter episodes) are 95.56% sensitivity and 98.8% specificity, 88.80% sensitivity and 99.5% specificity for ventricular tachycardia (VT), 98.98% sensitivity and 97.7% specificity for normal sinus, and 96.87% sensitivity and 99.55% specificity for other rhythms. Results shows that using t-f data representations to feed classifiers provide superior performance values than the feature selection strategies used in previous works. It opens the door to be used in any other detection applications. Copyright © 2017 Elsevier B.V. All rights reserved.

An automatic device for detection and classification of malaria parasite species in thick blood film.

PubMed

Kaewkamnerd, Saowaluck; Uthaipibull, Chairat; Intarapanich, Apichart; Pannarut, Montri; Chaotheing, Sastra; Tongsima, Sissades

2012-01-01

Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation. The prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick blood films (40 blood films containing infected red blood cells and 20 control blood films of normal red blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative blood films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick blood films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%. This work presents an automatic device for both detection and classification of malaria parasite species on thick blood film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

Diagnosis of the OCD Patients using Drawing Features of the Bender Gestalt Shapes

PubMed Central

Boostani, R.; Asadi, F.; Mohammadi, N.

2017-01-01

Background: Since psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the psychologists cannot assess their patients while running the tests. One of the famous drawing tests to measure the degree of Obsession Compulsion Disorder (OCD) is the Bender Gestalt, though its reliability is not promising. Objective: The main objective of this study is to make the Bender Gestalt test quantitative; therefore, an optical pen along with a digital tablet is utilized to preserve the key drawing features of OCD patients during the test. Materials and Methods: Among a large population of patients who referred to a special clinic of OCD, 50 under therapy subjects voluntarily took part in this study. In contrast, 50 subjects with no sign of OCD performed the test as a control group. This test contains 9 shapes and the participants were not constraint to draw the shapes in a certain interval of time; consequently, to classify the stream of feature vectors (samples through drawing) Hidden Markov Model (HMM) is employed and its flexibility increased by incorporating the fuzzy technique into its learning scheme. Results: Applying fuzzy HMM classifier to the data stream of subjects could classify two groups up to 95.2% accuracy, whereas the results by applying the standard HMM resulted in 94.5%. In addition, multi-layer perceptron (MLP), as a strong static classifier, is applied to the features and resulted in 86.6% accuracy. Conclusion: Applying the pair of T-test to the results implies a significant supremacy of the fuzzy HMM to the standard HMM and MLP classifiers. PMID:28462208

Objective Classification of Radar Profile Types, and Their Relationship to Lightning Occurrence

NASA Technical Reports Server (NTRS)

Boccippio, Dennis

2003-01-01

A cluster analysis technique is used to identify 16 "archetypal" vertical radar profile types from a large, globally representative sample of profiles from the TRMM Precipitation Radar. These include nine convective types (7 of these deep convective) and seven stratiform types (5 of these clearly glaciated). Radar profile classification provides an alternative to conventional deep convective storm metrics, such as 30 dBZ echo height, maximum reflectivity or VIL. As expected, the global frequency of occurrence of deep convective profile types matches satellite-observed total lightning production, including to very small scall local features. Each location's "mix" of profile types provides an objective description of the local convective spectrum, and in turn, is a first step in objectively classifying convective regimes. These classifiers are tested as inputs to a neural network which attempts to predict lightning occurrence based on radar-only storm observations, and performance is compared with networks using traditional radar metrics as inputs.

Color distributions in E-S0 galaxies. I. Frequency and importance of dust patterns for various brands of E classified galaxies

NASA Astrophysics Data System (ADS)

Michard, R.

1998-06-01

From the consideration of a sample of color distributions in 67 E classified objects of the Local Supercluster, it is found that local dust features are much more frequent and important in disky E's than boxy E's. The subclass of undeterminate objects, those which cannot be assigned to the diE or boE groups, is intermediate. Subsets of objects of common properties are considered from the point of view of local dust features occurrence: giant boxy E's; minor boxy E's with rotational support; compact dwarfs; SB0-like E's. It is noted that the detection of dust features is more than twice less frequent in Virgo cluster ellipticals than in the full sample, but the significance of this result is not clear. Based on observations collected at the Canada-France-Hawaii Telescope and at the Observatoire du Pic du Midi

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2011-09-30

Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR Systems Center Pacific...APPROACH Odontocete click detection and classification. A multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier

The Utility of the SASSI-3 in Early Detection of Substance Use Disorders in Not Guilty by Reason of Insanity Acquittees: An Exploratory Study

ERIC Educational Resources Information Center

Wright, Ervin E., II; Piazza, Nick J.; Laux, John M.

2008-01-01

Previous studies have shown the Substance Abuse Subtle Screening Inventory-3 (G. Miller, 1999) to be valid in classifying substance use disorders in forensic and mentally ill populations. The authors found that it also correctly classified substance use disorders in the understudied not guilty by reason of insanity population. (Contains 3 tables.)

Wide-field spectral imaging of human ovary autofluorescence and oncologic diagnosis via previously collected probe data

PubMed Central

Hatch, Kenneth D.

2012-01-01

Abstract. With no sufficient screening test for ovarian cancer, a method to evaluate the ovarian disease state quickly and nondestructively is needed. The authors have applied a wide-field spectral imager to freshly resected ovaries of 30 human patients in a study believed to be the first of its magnitude. Endogenous fluorescence was excited with 365-nm light and imaged in eight emission bands collectively covering the 400- to 640-nm range. Linear discriminant analysis was used to classify all image pixels and generate diagnostic maps of the ovaries. Training the classifier with previously collected single-point autofluorescence measurements of a spectroscopic probe enabled this novel classification. The process by which probe-collected spectra were transformed for comparison with imager spectra is described. Sensitivity of 100% and specificity of 51% were obtained in classifying normal and cancerous ovaries using autofluorescence data alone. Specificity increased to 69% when autofluorescence data were divided by green reflectance data to correct for spatial variation in tissue absorption properties. Benign neoplasm ovaries were also found to classify as nonmalignant using the same algorithm. Although applied ex vivo, the method described here appears useful for quick assessment of cancer presence in the human ovary. PMID:22502561

X-shooter spectroscopy of young stellar objects. VI. H I line decrements

NASA Astrophysics Data System (ADS)

Antoniucci, S.; Nisini, B.; Giannini, T.; Rigliaco, E.; Alcalá, J. M.; Natta, A.; Stelzer, B.

2017-03-01

Context. Hydrogen recombination emission lines commonly observed in accreting young stellar objects represent a powerful tracer for the gas conditions in the circumstellar structures (accretion columns, and winds or jets). Aims: Here we perform a study of the H I decrements and line profiles, from the Balmer and Paschen H I lines detected in the X-shooter spectra of a homogeneous sample of 36 T Tauri objects in Lupus, the accretion and stellar properties of which were already derived in a previous work. We aim to obtain information on the H I gas physical conditions to delineate a consistent picture of the H I emission mechanisms in pre-main sequence low-mass stars (M∗< 2 M⊙). Methods: We have empirically classified the sources based on their H I line profiles and decrements. We identified four Balmer decrement types (which we classified as 1, 2, 3, and 4) and three Paschen decrement types (A, B, and C), characterised by different shapes. We first discussed the connection between the decrement types and the source properties and then compared the observed decrements with predictions from recently published local line excitation models. Results: We identify a few groups of sources that display similar H I properties. One third of the objects show lines with narrow symmetric profiles, and present similar Balmer and Paschen decrements (straight decrements, types 2 and A). Lines in these sources are consistent with optically thin emission from gas with hydrogen densities of order 109 cm-3 and 5000 < T < 15 000 K. These objects are associated with low mass accretion rates. Type 4 (L-shaped) Balmer and type B Paschen decrements are found in conjunction with very wide line profiles and are characteristic of strong accretors, with optically thick emission from high-density gas (log nH > 11 cm-3). Type 1 (curved) Balmer decrements are observed only in three sub-luminous sources viewed edge-on, so we speculate that these are actually type 2 decrements that are reddened because of neglecting a residual amount of extinction in the line emission region. About 20% of the objects present type 3 Balmer decrements (bumpy), which, however, cannot be reproduced with current models. Based on observations collected at the European Southern Observatory at Paranal, Chile, under programmes 084.C-0269(A), 085.C-238(A), 086.C-0173(A), 087.C-0244(A), and 089.C-0143(A).

Clinical evaluation of a new noninvasive ankle arthrometer.

PubMed

Nauck, Tanja; Lohrer, Heinz; Gollhofer, Albert

2010-06-01

A nonradiographic arthrometer was developed to objectively quantify anterior talar drawer instability in stable and unstable ankles. Diagnostic validity of this device was previously demonstrated in a cadaver study. The aim of the present study was to validate the ankle arthrometer in an in vivo setting. Twenty-three subjects participated in the study. An orthopedic surgeon first performed a manual anterior talar drawer test to classify the subjects' ankles as stable or unstable. The subjects were then evaluated using the ankle arthrometer, and filled out a validated self-reported questionnaire (German version of the Foot and Ankle Ability Measure [FAAM-G]). Ankle stiffness was calculated from the low linear region (40-60 N) of the load deformation curves obtained from the ankle arthrometer. Reliability testing of these stiffness values was done based on load deformation curves, with 150 and 200 N maximum anterior drawer loads applied in the ankle arthrometer. Using the manual anterior drawer test, 16 ankles were classified as stable and 7 were classified as unstable. Arthrometer stiffness analysis differentiated stable from unstable ankles (P = 0.00 and P = 0.01, respectively). Test-retest demonstrated an accurate reliability (intraclass correlation coefficient = 0.80). A significant correlation was found between both FAAM-G subscales and the arthrometer stiffness values (r = 0.43 and 0.54; P = 0.04 and 0.01). Discussion Subjects with and without mechanical ankle instability could be differentiated by ankle arthrometer stiffness analysis and the FAAM-G questionnaire results. This nonradiographic device may be relevant for screening athletes at risk for ankle injuries, for clinical follow-up studies, and implementing preventive strategies. Validity and reliability of the new ankle arthrometer is demonstrated in a small cohort in an in vivo setting.

Performance of the fourth-generation Bio-Rad GS HIV Combo Ag/Ab enzyme immunoassay for diagnosis of HIV infection in Southern Africa

PubMed Central

Piwowar-Manning, Estelle; Fogel, Jessica M.; Richardson, Paul; Wolf, Shauna; Clarke, William; Marzinke, Mark A.; Fiamma, Agnès; Donnell, Deborah; Kulich, Michal; Mbwambo, Jessie K.K.; Richter, Linda; Gray, Glenda; Sweat, Michael; Coates, Thomas J.; Eshleman, Susan H.

2015-01-01

Background Fourth-generation HIV assays detect both antigen and antibody, facilitating detection of acute/early HIV infection. The Bio-Rad GS HIV Combo Ag/Ab assay (Bio-Rad Combo) is an enzyme immunoassay that simultaneously detects HIV p24 antigen and antibodies to HIV-1 and HIV-2 in serum or plasma. Objective To evaluate the performance of the Bio-Rad Combo assay for detection of HIV infection in adults from Southern Africa. Study design Samples were obtained from adults in Soweto and Vulindlela, South Africa and Dar es Salaam, Tanzania (300 HIV-positive samples; 300 HIV-negative samples; 12 samples from individuals previously classified as having acute/early HIV infection). The samples were tested with the Bio-Rad Combo assay. Additional testing was performed to characterize the 12 acute/early samples. Results All 300 HIV-positive samples were reactive using the Bio-Rad Combo assay; false positive test results were obtained for 10 (3.3%) of the HIV-negative samples (sensitivity: 100%, 95% confidence interval [CI]: 98.8–100%); specificity: 96.7%, 95% CI: 94.0–98.4%). The assay detected 10 of the 12 infections classified as acute/early. The two infections that were not detected had viral loads < 400 copies/mL; one of those samples contained antiretroviral drugs consistent with antiretroviral therapy. Conclusions The Bio-Rad Combo assay correctly classified the majority of study specimens. The specificity reported here may be higher than that seen in other settings, since HIV-negative samples were pre-screened using a different fourth-generation test. The assay also had high sensitivity for detection of acute/early infection. False-negative test results may be obtained in individuals who are virally suppressed. PMID:25542477

Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

PubMed Central

Guerra, Luis; McGarry, Laura M; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2011-01-01

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011 PMID:21154911

Fibromyalgia is associated to receiving chronic medications beyond appropriateness: a cross-sectional study.

PubMed

Rivera, Javier; Vallejo, Miguel A

2016-12-01

The objectives of this study are to describe appropriateness and drug treatment of comorbidities in fibromyalgia (FM). Cross-sectional study of a group of patients. Number of drugs, indication, duration and appropriateness of prescriptions were evaluated. Patients were classified as: group 1, (FM/FM) previous FM diagnosis and fulfilling criteria; group 2, (noFM/noFM) other diagnosis and not fulfilling criteria; and group 3, (noFM/FM) other diagnosis but fulfilling criteria. Drugs were classified into drugs for nervous system, analgesics/NSAID and drugs for other comorbidities. Appropriateness was evaluated following clinical therapeutic guidelines. A total of 159 patients were included in the study and classified into group 1, with 59 patients; group 2, with 67 patients; and group 3, with 33 patients. Group 1 received a greater number of different drugs and for a longer period of time, there were less severe comorbidities and more unjustified treatments. No difference was found between the other two groups. Major opioids were only consumed in group 1. Also, in group 1, 45.8 % of patients were attended in psychiatry versus 15.6 % in group 3 and 3 % in group 2. The number of somatic symptoms correlated significantly with the number of drugs. Nervous system treatments were of shorter duration than other drug treatments. There was no difference in severe comorbidities. Comorbidities in FM are similar to those of other patients, but they receive more drugs and for a longer period of time. Drugs for nervous system comorbidities are introduced later, when other somatic symptoms are already treated. In patients with FM the treatments for mild comorbidities are not well justified.

Multimodal manifold-regularized transfer learning for MCI conversion prediction.

PubMed

Cheng, Bo; Liu, Mingxia; Suk, Heung-Il; Shen, Dinggang; Zhang, Daoqiang

2015-12-01

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery

NASA Astrophysics Data System (ADS)

Sharghi, Elan

The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER®) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.

Opening the Black Box: Toward Classifying Care and Treatment for Children and Adolescents with Behavioral and Emotional Problems within and across Care Organizations

ERIC Educational Resources Information Center

Evenboer, K. E.; Huyghen, A. M. N.; Tuinstra, J.; Reijneveld, S. A.; Knorth, E. J.

2016-01-01

Objective: The Taxonomy of Care for Youth was developed to gather information about the care offered to children and adolescents with behavioral and emotional problems in various care settings. The aim was to determine similarities and differences in the content of care and thereby to classify the care offered to these children and youth within…

Proposal for Classifying the Severity of Speech Disorder Using a Fuzzy Model in Accordance with the Implicational Model of Feature Complexity

ERIC Educational Resources Information Center

Brancalioni, Ana Rita; Magnago, Karine Faverzani; Keske-Soares, Marcia

2012-01-01

The objective of this study is to create a new proposal for classifying the severity of speech disorders using a fuzzy model in accordance with a linguistic model that represents the speech acquisition of Brazilian Portuguese. The fuzzy linguistic model was run in the MATLAB software fuzzy toolbox from a set of fuzzy rules, and it encompassed…

A Novel Locally Linear KNN Method With Applications to Visual Recognition.

PubMed

Liu, Qingfeng; Liu, Chengjun

2017-09-01

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients' truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.

Language and thought in Bilinguals: The Case of Grammatical Number and Nonverbal Classification Preferences

ERIC Educational Resources Information Center

Athanasopoulos, Panos; Kasai, Chise

2008-01-01

Recent research shows that speakers of languages with obligatory plural marking (English) preferentially categorize objects based on common shape, whereas speakers of nonplural-marking classifier languages (Yucatec and Japanese) preferentially categorize objects based on common material. The current study extends that investigation to the domain…

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

USDA-ARS?s Scientific Manuscript database

Ultra high resolution digital aerial photography has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands...

The Independent Living Behavior Checklist: Experimental Edition.

ERIC Educational Resources Information Center

Walls, Richard T.; And Others

The document describes independent living skills, and provides information on how they can be measured. It is explained in an introductory chapter that the checklist is an extensive list of 343 independent living skill objectives specified in terms of conditions (antecedents or givens), behaviors, and standards. Objectives are classified and…

When does a system become phonological? Handshape production in gesturers, signers, and homesigners

PubMed Central

Coppola, Marie; Mazzoni, Laura; Goldin-Meadow, Susan

2013-01-01

Sign languages display remarkable crosslinguistic consistencies in the use of handshapes. In particular, handshapes used in classifier predicates display a consistent pattern in finger complexity: classifier handshapes representing objects display more finger complexity than those representing how objects are handled. Here we explore the conditions under which this morphophonological phenomenon arises. In Study 1, we ask whether hearing individuals in Italy and the United States, asked to communicate using only their hands, show the same pattern of finger complexity found in the classifier handshapes of two sign languages: Italian Sign Language (LIS) and American Sign Language (ASL). We find that they do not: gesturers display more finger complexity in handling handshapes than in object handshapes. The morphophonological pattern found in conventional sign languages is therefore not a codified version of the pattern invented by hearing individuals on the spot. In Study 2, we ask whether continued use of gesture as a primary communication system results in a pattern that is more similar to the morphophonological pattern found in conventional sign languages or to the pattern found in gesturers. Homesigners have not acquired a signed or spoken language and instead use a self-generated gesture system to communicate with their hearing family members and friends. We find that homesigners pattern more like signers than like gesturers: their finger complexity in object handshapes is higher than that of gesturers (indeed as high as signers); and their finger complexity in handling handshapes is lower than that of gesturers (but not quite as low as signers). Generally, our findings indicate two markers of the phonologization of handshape in sign languages: increasing finger complexity in object handshapes, and decreasing finger complexity in handling handshapes. These first indicators of phonology appear to be present in individuals developing a gesture system without benefit of a linguistic community. Finally, we propose that iconicity, morphology and phonology each play an important role in the system of sign language classifiers to create the earliest markers of phonology at the morphophonological interface. PMID:23723534

Local-global classifier fusion for screening chest radiographs

NASA Astrophysics Data System (ADS)

Ding, Meng; Antani, Sameer; Jaeger, Stefan; Xue, Zhiyun; Candemir, Sema; Kohli, Marc; Thoma, George

2017-03-01

Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is critical for population screening, especially in medical resource constrained developing regions. In this article, we describe steps that improve previously reported performance of NLM's CXR screening algorithms and help advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary classification systems are combined. The local classifier focuses on subtle and partial presentation of the disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition, the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve, sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global fusion method over any single classifier.

Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harmer, Paul K; Temple, Michael A; Buckner, Mark A

2011-01-01

Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identicalmore » classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.« less

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

NASA Astrophysics Data System (ADS)

Sopharak, Akara; Dailey, Matthew N.; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom; Thet Nwe, Khine; Aye Moe, Yin

2010-01-01

Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients' chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training errors) and γ (radial basis function width). We compare the best naive Bayes and SVM classifiers to a baseline nearest neighbour (NN) classifier using the best feature sets from both classifiers. We find that the naive Bayes and SVM classifiers perform better than the NN classifier. The overall best sensitivity, specificity, precision, and accuracy are 92.28%, 98.52%, 53.05%, and 98.41%, respectively.

Determining root correspondence between previously and newly detected objects

DOEpatents

Paglieroni, David W.; Beer, N Reginald

2014-06-17

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

Subsurface classification of objects under turbid waters by means of regularization techniques applied to real hyperspectral data

NASA Astrophysics Data System (ADS)

Carpena, Emmanuel; Jiménez, Luis O.; Arzuaga, Emmanuel; Fonseca, Sujeily; Reyes, Ernesto; Figueroa, Juan

2017-05-01

Improved benthic habitat mapping is needed to monitor coral reefs around the world and to assist coastal zones management programs. A fundamental challenge to remotely sensed mapping of coastal shallow waters is due to the significant disparity in the optical properties of the water column caused by the interaction between the coast and the sea. The objects to be classified have weak signals that interact with turbid waters that include sediments. In real scenarios, the absorption and backscattering coefficients are unknown with different sources of variability (river discharges and coastal interactions). Under normal circumstances, another unknown variable is the depth of shallow waters. This paper presents the development of algorithms for retrieving information and its application to the classification and mapping of objects under coastal shallow waters with different unknown concentrations of sediments. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and classification of hyperspectral data. The algorithms developed were applied to one set of real hyperspectral imagery taken in a tank filled with water and TiO2 that emulates turbid coastal shallow waters. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the water tank using a priori information in the form of stored spectral signatures, previously measured, of objects of interest.

Point pattern match-based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.

2016-06-07

A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

PubMed Central

Hramov, Alexander E.; Maksimenko, Vladimir A.; Pchelintseva, Svetlana V.; Runnova, Anastasiya E.; Grubov, Vadim V.; Musatov, Vyacheslav Yu.; Zhuravlev, Maksim O.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2017-01-01

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces. PMID:29255403

Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime Antarctica.

PubMed

Pina, Pedro; Vieira, Gonçalo; Bandeira, Lourenço; Mora, Carla

2016-12-15

The ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock, together with various vegetation communities dominated by lichens and mosses. The microscale variability is difficult to characterize and quantify, but is essential for ground-truthing and for defining classifiers for large areas using, for example high resolution satellite imagery, or even ultra-high resolution unmanned aerial vehicle (UAV) imagery. The main objective of this paper is to verify the ability and robustness of an automated approach to discriminate the variety of surface types in digital photographs acquired at ground level in ice-free regions of Maritime Antarctica. The proposed method is based on an object-based classification procedure built in two main steps: first, on the automated delineation of homogeneous regions (the objects) of the images through the watershed transform with adequate filtering to avoid an over-segmentation, and second, on labelling each identified object with a supervised decision classifier trained with samples of representative objects of ice-free surface types (bare rock, bare soil, moss and lichen formations). The method is evaluated with images acquired in summer campaigns in Fildes and Barton peninsulas (King George Island, South Shetlands). The best performances for the datasets of the two peninsulas are achieved with a SVM classifier with overall accuracies of about 92% and kappa values around 0.89. The excellent performances allow validating the adequacy of the approach for obtaining accurate surface reference data at the complete pixel scale (sub-metric) of current very high resolution (VHR) satellite images, instead of a common single point sampling. Copyright © 2016 Elsevier B.V. All rights reserved.

Hijazi Architectural Object Library (haol)

NASA Astrophysics Data System (ADS)

Baik, A.; Boehm, J.

2017-02-01

As with many historical buildings around the world, building façades are of special interest; moreover, the details of such windows, stonework, and ornaments give each historic building its individual character. Each object of these buildings must be classified in an architectural object library. Recently, a number of researches have been focusing on this topic in Europe and Canada. From this standpoint, the Hijazi Architectural Objects Library (HAOL) has reproduced Hijazi elements as 3D computer models, which are modelled using a Revit Family (RFA). The HAOL will be dependent on the image survey and point cloud data. The Hijazi Object such as Roshan and Mashrabiyah, become as vocabulary of many Islamic cities in the Hijazi region such as Jeddah in Saudi Arabia, and even for a number of Islamic historic cities such as Istanbul and Cairo. These architectural vocabularies are the main cause of the beauty of these heritage. However, there is a big gap in both the Islamic architectural library and the Hijazi architectural library to provide these unique elements. Besides, both Islamic and Hijazi architecture contains a huge amount of information which has not yet been digitally classified according to period and styles. Due to this issue, this paper will be focusing on developing of Heritage BIM (HBIM) standards and the HAOL library to reduce the cost and the delivering time for heritage and new projects that involve in Hijazi architectural styles. Through this paper, the fundamentals of Hijazi architecture informatics will be provided via developing framework for HBIM models and standards. This framework will provide schema and critical information, for example, classifying the different shapes, models, and forms of structure, construction, and ornamentation of Hijazi architecture in order to digitalize parametric building identity.

Classification of parotidectomies: a proposal of the European Salivary Gland Society.

PubMed

Quer, M; Guntinas-Lichius, O; Marchal, F; Vander Poorten, V; Chevalier, D; León, X; Eisele, D; Dulguerov, P

2016-10-01

The objective of this study is to provide a comprehensive classification system for parotidectomy operations. Data sources include Medline publications, author's experience, and consensus round table at the Third European Salivary Gland Society (ESGS) Meeting. The Medline database was searched with the term "parotidectomy" and "definition". The various definitions of parotidectomy procedures and parotid gland subdivisions extracted. Previous classification systems re-examined and a new classification proposed by a consensus. The ESGS proposes to subdivide the parotid parenchyma in five levels: I (lateral superior), II (lateral inferior), III (deep inferior), IV (deep superior), V (accessory). A new classification is proposed where the type of resection is divided into formal parotidectomy with facial nerve dissection and extracapsular dissection. Parotidectomies are further classified according to the levels removed, as well as the extra-parotid structures ablated. A new classification of parotidectomy procedures is proposed.

Portable XRF and principal component analysis for bill characterization in forensic science.

PubMed

Appoloni, C R; Melquiades, F L

2014-02-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.

A statistically derived index for classifying East Coast fever reactions in cattle challenged with Theileria parva under experimental conditions.

PubMed

Rowlands, G J; Musoke, A J; Morzaria, S P; Nagda, S M; Ballingall, K T; McKeever, D J

2000-04-01

A statistically derived disease reaction index based on parasitological, clinical and haematological measurements observed in 309 5 to 8-month-old Boran cattle following laboratory challenge with Theileria parva is described. Principal component analysis was applied to 13 measures including first appearance of schizonts, first appearance of piroplasms and first occurrence of pyrexia, together with the duration and severity of these symptoms, and white blood cell count. The first principal component, which was based on approximately equal contributions of the 13 variables, provided the definition for the disease reaction index, defined on a scale of 0-10. As well as providing a more objective measure of the severity of the reaction, the continuous nature of the index score enables more powerful statistical analysis of the data compared with that which has been previously possible through clinically derived categories of non-, mild, moderate and severe reactions.

Multi-scale Imaging of Cellular and Sub-cellular Structures using Scanning Probe Recognition Microscopy.

NASA Astrophysics Data System (ADS)

Chen, Q.; Rice, A. F.

2005-03-01

Scanning Probe Recognition Microscopy is a new scanning probe capability under development within our group to reliably return to and directly interact with a specific nanobiological feature of interest. In previous work, we have successfully recognized and classified tubular versus globular biological objects from experimental atomic force microscope images using a method based on normalized central moments [ref. 1]. In this paper we extend this work to include recognition schemes appropriate for cellular and sub-cellular structures. Globular cells containing tubular actin filaments are under investigation. Thus there are differences in external/internal shapes and scales. Continuous Wavelet Transform with a differential Gaussian mother wavelet is employed for multi- scale analysis. [ref. 1] Q. Chen, V. Ayres and L. Udpa, ``Biological Investigation Using Scanning Probe Recognition Microscopy,'' Proceedings 3rd IEEE Conference on Nanotechnology, vol. 2, p 863-865 (2003).

A New Supernova Remnant Coincident with the Slow X-Ray Pulsar AX J1845-0258.

PubMed

Gaensler; Gotthelf; Vasisht

1999-11-20

We report on Very Large Array observations in the direction of the recently discovered slow X-ray pulsar AX J1845-0258. In the resulting images, we find a 5&arcmin; shell of radio emission; the shell is linearly polarized with a nonthermal spectral index. We classify this source as a previously unidentified, young (<8000 yr) supernova remnant (SNR), G29.6+0.1, which we propose is physically associated with AX J1845-0258. The young age of G29.6+0.1 is then consistent with the interpretation that anomalous X-ray pulsars (AXPs) are isolated, highly magnetized neutron stars ("magnetars"). Three of the six known AXPs can now be associated with SNRs; we conclude that AXPs are young ( less, similar10,000 yr) objects and that they are produced in at least 5% of core-collapse supernovae.

LANDSAT-4 MSS and Thematic Mapper data quality and information content analysis

NASA Technical Reports Server (NTRS)

Anuta, P.; Bartolucci, L.; Dean, E.; Lozano, F.; Malaret, E.; Mcgillem, C. D.; Valdes, J.; Valenzuela, C.

1984-01-01

LANDSAT-4 thematic mapper (TM) and multispectral scanner (MSS) data were analyzed to obtain information on data quality and information content. Geometric evaluations were performed to test band-to-band registration accuracy. Thematic mapper overall system resolution was evaluated using scene objects which demonstrated sharp high contrast edge responses. Radiometric evaluation included detector relative calibration, effects of resampling, and coherent noise effects. Information content evaluation was carried out using clustering, principal components, transformed divergence separability measure, and supervised classifiers on test data. A detailed spectral class analysis (multispectral classification) was carried out to compare the information content of the MSS and TM for a large number of scene classes. A temperature-mapping experiment was carried out for a cooling pond to test the quality of thermal-band calibration. Overall TM data quality is very good. The MSS data are noisier than previous LANDSAT results.

C.E.T.A. in Balance-of-State Areas. Workshop Report of the Rural Manpower Policy Research Consortium, June 6-7, 1974. Special Paper No. 24.

ERIC Educational Resources Information Center

Moser, Collette H., Comp.

The workshop on the Comprehensive Employment and Training Act (CETA) in Balance-of-State areas explored the implications for rural areas of various manpower policies. In a previous workshop, it was found that a close correlation existed between counties classified as Balance-of-State and those classified as "rural" by the U.S. Department…

ExSTraCS 2.0: Description and Evaluation of a Scalable Learning Classifier System.

PubMed

Urbanowicz, Ryan J; Moore, Jason H

2015-09-01

Algorithmic scalability is a major concern for any machine learning strategy in this age of 'big data'. A large number of potentially predictive attributes is emblematic of problems in bioinformatics, genetic epidemiology, and many other fields. Previously, ExS-TraCS was introduced as an extended Michigan-style supervised learning classifier system that combined a set of powerful heuristics to successfully tackle the challenges of classification, prediction, and knowledge discovery in complex, noisy, and heterogeneous problem domains. While Michigan-style learning classifier systems are powerful and flexible learners, they are not considered to be particularly scalable. For the first time, this paper presents a complete description of the ExS-TraCS algorithm and introduces an effective strategy to dramatically improve learning classifier system scalability. ExSTraCS 2.0 addresses scalability with (1) a rule specificity limit, (2) new approaches to expert knowledge guided covering and mutation mechanisms, and (3) the implementation and utilization of the TuRF algorithm for improving the quality of expert knowledge discovery in larger datasets. Performance over a complex spectrum of simulated genetic datasets demonstrated that these new mechanisms dramatically improve nearly every performance metric on datasets with 20 attributes and made it possible for ExSTraCS to reliably scale up to perform on related 200 and 2000-attribute datasets. ExSTraCS 2.0 was also able to reliably solve the 6, 11, 20, 37, 70, and 135 multiplexer problems, and did so in similar or fewer learning iterations than previously reported, with smaller finite training sets, and without using building blocks discovered from simpler multiplexer problems. Furthermore, ExS-TraCS usability was made simpler through the elimination of previously critical run parameters.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Repliscan: a tool for classifying replication timing regions.

PubMed

Zynda, Gregory J; Song, Jawon; Concia, Lorenzo; Wear, Emily E; Hanley-Bowdoin, Linda; Thompson, William F; Vaughn, Matthew W

2017-08-07

Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage.

Frequency and morphology of tropical tropopause layer cirrus from CALIPSO observations: Are isolated cirrus different from those connected to deep convection?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riihimaki, Laura D.; McFarlane, Sally A.

2010-09-16

Tropical Tropopause Layer cirrus (TTLC) profiles identified from CALIPSO LIDAR measurements are grouped into cloud objects and classified according to whether or not they are connected to deep convection. TTLC objects connected to deep convection are optically and physically thicker than isolated objects, consistent with what would be expected if connected objects were formed from convective detrainment and isolated objects formed in situ. In the tropics (±20 Latitude), 36% of TTLC profiles are classified as connected to deep convection, 43% as isolated, and the remaining 21% are part of lower, thicker cirrus clouds. Regions with higher occurence of deep convectionmore » also have higher occurrence of TTLC, and a greater percentage of those TTLC are connected to deep convection. Cloud top heights of both isolated and connected clouds are distributed similarly with respect to the height of the cold point tropopause. No difference in thickness or optical depth was found between TTLC above deep convection or above clear sky, though both cloud base and top heights are higher over deep convection than over clear sky.« less

Attribute and topology based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

PubMed

Yu, Hualong; Ni, Jun

2014-01-01

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

NASA Astrophysics Data System (ADS)

Assaleh, Khaled; Al-Rousan, M.

2005-12-01

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Prediction of chemical biodegradability using support vector classifier optimized with differential evolution.

PubMed

Cao, Qi; Leung, K M

2014-09-22

Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

Modification of the Integrated Sasang Constitutional Diagnostic Model

PubMed Central

Nam, Jiho

2017-01-01

In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine. PMID:29317897

Classification of ephemeral, intermittent, and perennial stream reaches using a TOPMODEL-based approach

USGS Publications Warehouse

Williamson, Tanja N.; Agouridis, Carmen T.; Barton, Christopher D.; Villines, Jonathan A.; Lant, Jeremiah G.

2015-01-01

Whether a waterway is temporary or permanent influences regulatory protection guidelines, however, classification can be subjective due to a combination of factors, including time of year, antecedent moisture conditions, and previous experience of the field investigator. Our objective was to develop a standardized protocol using publically available spatial information to classify ephemeral, intermittent, and perennial streams. Our hypothesis was that field observations of flow along the stream channel could be compared to results from a hydrologic model, providing an objective method of how these stream reaches can be identified. Flow-state sensors were placed at ephemeral, intermittent, and perennial stream reaches from May to December 2011 in the Appalachian coal basin of eastern Kentucky. This observed flow record was then used to calibrate the simulated saturation deficit in each channel reach based on the topographic wetness index used by TOPMODEL. Saturation deficit values were categorized as flow or no-flow days, and the simulated record of streamflow was compared to the observed record. The hydrologic model was more accurate for simulating flow during the spring and fall seasons. However, the model effectively identified stream reaches as intermittent and perennial in each of the two basins.

Transgenic mice reveal unexpected diversity of On-Off direction selective retinal ganglion cell subtypes and brain structures involved in motion processing

PubMed Central

Rivlin-Etzion, Michal; Zhou, Kaili; Wei, Wei; Elstrott, Justin; Nguyen, Phong L.; Barres, Ben; Huberman, Andrew D.; Feller, Marla B.

2011-01-01

On-Off direction selective retinal ganglion cells (DSGCs) encode the axis of visual motion. They respond strongly to an object moving in a preferred direction and weakly to an object moving in the opposite, ‘null’, direction. Historically, On-Off DSGCs were classified into 4 subtypes according to their directional preference (anterior, posterior, superior or inferior). Here, we compare two genetically identified populations of On-Off DSGCs: DRD4-DSGCs and TRHR-DSGCs. We find that although both populations are tuned for posterior motion, they can be distinguished by a variety of physiological and anatomical criteria. First, the directional tuning of TRHR-DSGCs is broader than that of DRD4-DSGCs. Second, whereas both populations project similarly to the dorsal lateral geniculate nucleus, they project differently to the ventral lateral geniculate nucleus and the superior colliculus. Moreover, TRHR-DSGCs, but not DRD4-DSGCs, also project to the zona incerta, a thalamic area not previously known to receive direction-tuned visual information. Our findings reveal unexpected diversity among mouse On-Off DSGC subtypes that uniquely process and convey image motion to the brain. PMID:21677160

United Kingdom Infrared Telescope's Spectrograph Observations of Human-Made Space Objects

NASA Technical Reports Server (NTRS)

Buckalew, Brent; Abercromby, Kira; Lederer, Susan; Cowardin, Heather; Frith, James

2017-01-01

Presented here are the results of the United Kingdom Infrared Telescope (UKIRT) spectral observations of human-made space objects taken from 2014 to 2015. The data collected using the UKIRT 1-5 micron Imager Spectrometer (UIST) cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 18 different orbiting objects at or near geosynchronous orbit (GEO). Two of the objects are controlled spacecraft, twelve are non-controlled spacecraft, one is a rocket body, and three are cataloged as debris. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials; thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons and silicon. The spacecraft, both the controlled and non-controlled, show distinct features due to the presence of solar panels whereas the rocket bodies do not. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give well-correlated indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. A comparison conducted between objects observed previously with the NASA Infrared Telescope Facility (IRTF) shows similar materials and trends from the two telescopes and different times. However, based on the current state of the model, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.

Approach to the problem of the parameters optimization of the shooting system

NASA Astrophysics Data System (ADS)

Demidova, L. A.; Sablina, V. A.; Sokolova, Y. S.

2018-02-01

The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers’ ensembles, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

Automatic morphological classification of galaxy images

PubMed Central

Shamir, Lior

2009-01-01

We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects. PMID:20161594

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

PubMed

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

New approach to information fusion for Lipschitz classifiers ensembles: Application in multi-channel C-OTDR-monitoring systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Timofeev, Andrey V.; Egorov, Dmitry V.

This paper presents new results concerning selection of an optimal information fusion formula for an ensemble of Lipschitz classifiers. The goal of information fusion is to create an integral classificatory which could provide better generalization ability of the ensemble while achieving a practically acceptable level of effectiveness. The problem of information fusion is very relevant for data processing in multi-channel C-OTDR-monitoring systems. In this case we have to effectively classify targeted events which appear in the vicinity of the monitored object. Solution of this problem is based on usage of an ensemble of Lipschitz classifiers each of which corresponds tomore » a respective channel. We suggest a brand new method for information fusion in case of ensemble of Lipschitz classifiers. This method is called “The Weighing of Inversely as Lipschitz Constants” (WILC). Results of WILC-method practical usage in multichannel C-OTDR monitoring systems are presented.« less

Study design in high-dimensional classification analysis.

PubMed

Sánchez, Brisa N; Wu, Meihua; Song, Peter X K; Wang, Wen

2016-10-01

Advances in high throughput technology have accelerated the use of hundreds to millions of biomarkers to construct classifiers that partition patients into different clinical conditions. Prior to classifier development in actual studies, a critical need is to determine the sample size required to reach a specified classification precision. We develop a systematic approach for sample size determination in high-dimensional (large [Formula: see text] small [Formula: see text]) classification analysis. Our method utilizes the probability of correct classification (PCC) as the optimization objective function and incorporates the higher criticism thresholding procedure for classifier development. Further, we derive the theoretical bound of maximal PCC gain from feature augmentation (e.g. when molecular and clinical predictors are combined in classifier development). Our methods are motivated and illustrated by a study using proteomics markers to classify post-kidney transplantation patients into stable and rejecting classes. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Sink or Float. Modified Primary. Revised. Anchorage School District Elementary Science Program.

ERIC Educational Resources Information Center

Defendorf, Jean, Ed.

This publication provides information and activities for teaching about water, whether certain objects will sink or float, and process skills including observing, classifying, inferring, measuring, predicting, and collecting and interpreting data. There are 14 lessons in the unit. The first four lessons deal with the classification of objects and…

English Speakers Attend More Strongly than Spanish Speakers to Manner of Motion when Classifying Novel Objects and Events

ERIC Educational Resources Information Center

Kersten, Alan W.; Meissner, Christian A.; Lechuga, Julia; Schwartz, Bennett L.; Albrechtsen, Justin S.; Iglesias, Adam

2010-01-01

Three experiments provide evidence that the conceptualization of moving objects and events is influenced by one's native language, consistent with linguistic relativity theory. Monolingual English speakers and bilingual Spanish/English speakers tested in an English-speaking context performed better than monolingual Spanish speakers and bilingual…

Primary and Secondary Control among Children Undergoing Medical Procedures: Adjustment as a Function of Coping Style.

ERIC Educational Resources Information Center

Weisz, John R.; And Others

1994-01-01

Obtained reports of coping and goals from 33 children being treated for leukemia. Coping strategies were classified as primary control coping (attempts to alter objective conditions), secondary control coping (attempts to adjust to objective conditions), or relinquished control (no attempt to cope). Secondary control coping was positively…

A Classification of Developmental Activities of Academic Family Medicine Supported by Federal Grants.

ERIC Educational Resources Information Center

Davis, Junius A.; And Others

1991-01-01

Analysis of 61 successful grant applications for the federal Establishment of Departments of Family Medicine grants program identified three dimensions for classifying supported developmental activities: (1) the functional area of the activity; (2) the objectives of the activity; and (3) the strategies to be used to attain the objectives.…

Semantic Integration Processes at Different Levels of Syntactic Hierarchy during Sentence Comprehension: An ERP Study

ERIC Educational Resources Information Center

Zhou, Xiaolin; Jiang, Xiaoming; Ye, Zheng; Zhang, Yaxu; Lou, Kaiyang; Zhan, Weidong

2010-01-01

An event-related potential (ERP) study was conducted to investigate the temporal neural dynamics of semantic integration processes at different levels of syntactic hierarchy during Chinese sentence reading. In a hierarchical structure, "subject noun" + "verb" + "numeral" + "classifier" + "object noun," the object noun is constrained by selectional…

"Primärerfahrung" with Living Animals in Contrast to Educational Videos: A Comparative Intervention Study

ERIC Educational Resources Information Center

Klingenberg, Konstantin

2014-01-01

"Primärerfahrung" (PE) describes direct contact with learning objects; in biological education with plants or animals in particular. Visualising these objects using videos, books, etc, can be classified as "Sekundärerfahrung" (SE) because the information is not presented in the "original" way. In this intervention…

Fuzzy recognition of noncompact musical objects

NASA Astrophysics Data System (ADS)

Cristobal Salas, Alfredo; Tchernykh, Andrei

1997-03-01

This article describes and compares some techniques to extract attributes from black and white images which contain musical objects. The inertia moment, the central moments and the wavelet transform methods are used to describe the images. Two supervised neural networks are applied to classify the images: backpropagation and fuzzy backpropagation. The results are compared.

Rotation And Scale Invariant Object Recognition Using A Distributed Associative Memory

NASA Astrophysics Data System (ADS)

Wechsler, Harry; Zimmerman, George Lee

1988-04-01

This paper describes an approach to 2-dimensional object recognition. Complex-log conformal mapping is combined with a distributed associative memory to create a system which recognizes objects regardless of changes in rotation or scale. Recalled information from the memorized database is used to classify an object, reconstruct the memorized version of the object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments, using real, gray scale images, are presented to show the feasibility of our approach.

Land use change analysis using spectral similarity and vegetation indices and its effect on runoff and sediment yield in tropical environment

NASA Astrophysics Data System (ADS)

Christanto, N.; Sartohadi, J.; Setiawan, M. A.; Shrestha, D. B. P.; Jetten, V. G.

2018-04-01

Land use change influences the hydrological as well as landscape processes such as runoff and sediment yields. The main objectives of this study are to assess the land use change and its impact on the runoff and sediment yield of the upper Serayu Catchment. Land use changes of 1991 to 2014 have been analyzed. Spectral similarity and vegetation indices were used to classify the old image. Therefore, the present and the past images are comparable. The influence of the past and present land use on runoff and sediment yield has been compared with field measurement. The effect of land use changes shows the increased surface runoff which is the result of change in the curve number (CN) values. The study shows that it is possible to classify previously obtained image based on spectral characteristics and indices of major land cover types derived from recently obtained image. This avoids the necessity of having training samples which will be difficult to obtain. On the other hand, it also demonstrates that it is possible to link land cover changes with land degradation processes and finally to sedimentation in the reservoir. The only condition is the requirement for having the comparable dataset which should not be difficult to generate. Any variation inherent in the data which are other than surface reflectance has to be corrected.

Sporadic pediatric meningiomas: a neuroradiological and neuropathological study of 15 cases.

PubMed

Huntoon, Kristin; Pluto, Charles P; Ruess, Lynne; Boué, Daniel R; Pierson, Christopher R; Rusin, Jerome A; Leonard, Jeffrey

2017-08-01

OBJECTIVE Sporadic meningiomas have been classified in many different ways. Radiographically, these lesions can be described as occurring in either typical or atypical locations. The purpose of this study was to determine if there are any histopathological differences between sporadic meningiomas that arise in these varying locations in children. METHODS The neuroimaging, histopathological findings, and clinical records in patients with sporadic pediatric meningiomas not associated with neurofibromatosis Type 2 or prior radiation therapy were retrospectively reviewed. Tumors were classified by radiological findings as either typical or atypical, and they were categorized histopathologically by using the latest WHO nomenclature and grading criteria. RESULTS Fifteen sporadic meningiomas in pediatric patients were biopsied or resected at the authors' institution between 1989 and 2013. Five (33%) were typical in radiographic appearance and/or location and 10 (67%) were atypical. Four (80%) typical meningiomas were WHO Grade I tumors. Most (60%) of the atypical meningiomas were WHO Grade II or III. CONCLUSIONS This study is the largest series of sporadic pediatric meningiomas in atypical locations to date. Although sporadic meningiomas are relatively infrequent in children, those with atypical imaging, specifically those with apparently intraparenchymal and intraosseous locations, may be more common than previously recognized. In this study, pediatric sporadic meningiomas arising in atypical locations, in particular intraparenchymal meningiomas, may be of higher histopathological grade. The authors' findings should alert clinicians to the potential for more aggressive clinical behavior in these tumors.

From "overweight" to "about right": evidence of a generational shift in body weight norms.

PubMed

Burke, Mary A; Heiland, Frank W; Nadler, Carl M

2010-06-01

In this article, we describe differences in the self-perception of weight status in the United States between the two most recent National Health and Nutrition Examination Survey (NHANES) periods (1988-1994 and 1999-2004), and test the hypothesis that secular increases in adult mean BMI, adult obesity, and childhood obesity contributed to changes over time in weight perceptions. We find that the probability of self-classifying as overweight is significantly lower on average in the more recent survey, for both women and men, controlling for objective weight status and other factors. Among women, the decline in the tendency to self-classify as overweight is concentrated in the 17-35 age range, and is more pronounced among women with normal BMI than those with overweight BMI. Among men, the shift away from feeling overweight is roughly equal across age groups. Overweight men exhibit a sharper decline in feeling overweight than normal weight men. Despite the declines in feeling overweight between surveys, weight misperception did not increase significantly for men and decreased by a sizable margin among women. We interpret the findings as evidence of a generational shift in social norms related to body weight. As a result, people may be less likely to desire weight loss than previously, limiting the effectiveness of public health campaigns aimed at weight reduction. On the other hand, there may be health benefits associated with improved body image.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Response to treatment of myasthenia gravis according to clinical subtype.

PubMed

Akaishi, Tetsuya; Suzuki, Yasushi; Imai, Tomihiro; Tsuda, Emiko; Minami, Naoya; Nagane, Yuriko; Uzawa, Akiyuki; Kawaguchi, Naoki; Masuda, Masayuki; Konno, Shingo; Suzuki, Hidekazu; Murai, Hiroyuki; Aoki, Masashi; Utsugisawa, Kimiaki

2016-11-17

We have previously reported using two-step cluster analysis to classify myasthenia gravis (MG) patients into the following five subtypes: ocular MG; thymoma-associated MG; MG with thymic hyperplasia; anti-acetylcholine receptor antibody (AChR-Ab)-negative MG; and AChR-Ab-positive MG without thymic abnormalities. The objectives of the present study were to examine the reproducibility of this five-subtype classification using a new data set of MG patients and to identify additional characteristics of these subtypes, particularly in regard to response to treatment. A total of 923 consecutive MG patients underwent two-step cluster analysis for the classification of subtypes. The variables used for classification were sex, age of onset, disease duration, presence of thymoma or thymic hyperplasia, positivity for AChR-Ab or anti-muscle-specific tyrosine kinase antibody, positivity for other concurrent autoantibodies, and disease condition at worst and current. The period from the start of treatment until the achievement of minimal manifestation status (early-stage response) was determined and then compared between subtypes using Kaplan-Meier analysis and the log-rank test. In addition, between subtypes, the rate of the number of patients who maintained minimal manifestations during the study period/that of patients who only achieved the status once (stability of improved status) was compared. As a result of two-step cluster analysis, 923 MG patients were classified into five subtypes as follows: ocular MG (AChR-Ab-positivity, 77%; histogram of onset age, skewed to older age); thymoma-associated MG (100%; normal distribution); MG with thymic hyperplasia (89%; skewed to younger age); AChR-Ab-negative MG (0%; normal distribution); and AChR-Ab-positive MG without thymic abnormalities (100%, skewed to older age). Furthermore, patients classified as ocular MG showed the best early-stage response to treatment and stability of improved status, followed by those classified as thymoma-associated MG and AChR-Ab-positive MG without thymic abnormalities; by contrast, those classified as AChR-Ab-negative MG showed the worst early-stage response to treatment and stability of improved status. Differences were seen between the five subtypes in demographic characteristics, clinical severity, and therapeutic response. Our five-subtype classification approach would be beneficial not only to elucidate disease subtypes, but also to plan treatment strategies for individual MG patients.

Supervised Detection of Anomalous Light Curves in Massive Astronomical Catalogs

NASA Astrophysics Data System (ADS)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

2014-09-01

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each ofmore » the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.« less

Applying machine learning classification techniques to automate sky object cataloguing

NASA Astrophysics Data System (ADS)

Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

1993-08-01

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is consistency of classification. The classification rules which are the product of the inductive learning techniques will form an objective, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically catalogued data.

Cancer cell classification with coherent diffraction imaging using an extreme ultraviolet radiation source

PubMed Central

Zürch, Michael; Foertsch, Stefan; Matzas, Mark; Pachmann, Katharina; Kuth, Rainer; Spielmann, Christian

2014-01-01

Abstract. In cancer treatment, it is highly desirable to classify single cancer cells in real time. The standard method is polymerase chain reaction requiring a substantial amount of resources and time. Here, we present an innovative approach for rapidly classifying different cell types: we measure the diffraction pattern of a single cell illuminated with coherent extreme ultraviolet (XUV) laser-generated radiation. These patterns allow distinguishing different breast cancer cell types in a subsequent step. Moreover, the morphology of the object can be retrieved from the diffraction pattern with submicron resolution. In a proof-of-principle experiment, we prepared single MCF7 and SKBR3 breast cancer cells on gold-coated silica slides. The output of a laser-driven XUV light source is focused onto a single unstained and unlabeled cancer cell. With the resulting diffraction pattern, we could clearly identify the different cell types. With an improved setup, it will not only be feasible to classify circulating tumor cells with a high throughput, but also to identify smaller objects such as bacteria or even viruses. PMID:26158049

Rich in vitamin C or just a convenient snack? Multiple-category reasoning with cross-classified foods.

PubMed

Hayes, Brett K; Kurniawan, Hendy; Newell, Ben R

2011-01-01

Two studies examined multiple category reasoning in property induction with cross-classified foods. Pilot tests identified foods that were more typical of a taxonomic category (e.g., "fruit"; termed 'taxonomic primary') or a script based category (e.g., "snack foods"; termed 'script primary'). They also confirmed that taxonomic categories were perceived as more coherent than script categories. In Experiment 1 participants completed an induction task in which information from multiple categories could be searched and combined to generate a property prediction about a target food. Multiple categories were more often consulted and used in prediction for script primary than for taxonomic primary foods. Experiment 2 replicated this finding across a range of property types but found that multiple category reasoning was reduced in the presence of a concurrent cognitive load. Property type affected which categories were consulted first and how information from multiple categories was weighted. The results show that multiple categories are more likely to be used for property predictions about cross-classified objects when an object is primarily associated with a category that has low coherence.

Artificial neural network EMG classifier for functional hand grasp movements prediction.

PubMed

Gandolla, Marta; Ferrante, Simona; Ferrigno, Giancarlo; Baldassini, Davide; Molteni, Franco; Guanziroli, Eleonora; Cotti Cottini, Michele; Seneci, Carlo; Pedrocchi, Alessandra

2017-12-01

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-freedom hand grasp movements (i.e. pinching, grasp an object, grasping) were predicted by means of surface EMG signals, recorded from 10 bipolar EMG electrodes arranged in a circular configuration around the forearm 2-3 cm from the elbow. Two cascaded artificial neural networks were then exploited to detect the patient's motion intention from the EMG signal window starting from the electrical activity onset to movement onset (i.e. electromechanical delay). Results The proposed approach was tested on eight healthy control subjects (4 females; age range 25-26 years) and it demonstrated a mean ± SD testing performance of 76% ± 14% for correctly predicting healthy users' motion intention. Two post-stroke patients tested the controller and obtained 79% and 100% of correctly classified movements under testing conditions. Conclusion A task-selection controller was developed to estimate the intended movement from the EMG measured during the electromechanical delay.

Cancer cell classification with coherent diffraction imaging using an extreme ultraviolet radiation source.

PubMed

Zürch, Michael; Foertsch, Stefan; Matzas, Mark; Pachmann, Katharina; Kuth, Rainer; Spielmann, Christian

2014-10-01

In cancer treatment, it is highly desirable to classify single cancer cells in real time. The standard method is polymerase chain reaction requiring a substantial amount of resources and time. Here, we present an innovative approach for rapidly classifying different cell types: we measure the diffraction pattern of a single cell illuminated with coherent extreme ultraviolet (XUV) laser-generated radiation. These patterns allow distinguishing different breast cancer cell types in a subsequent step. Moreover, the morphology of the object can be retrieved from the diffraction pattern with submicron resolution. In a proof-of-principle experiment, we prepared single MCF7 and SKBR3 breast cancer cells on gold-coated silica slides. The output of a laser-driven XUV light source is focused onto a single unstained and unlabeled cancer cell. With the resulting diffraction pattern, we could clearly identify the different cell types. With an improved setup, it will not only be feasible to classify circulating tumor cells with a high throughput, but also to identify smaller objects such as bacteria or even viruses.

The health of people classified as lesbian, gay and bisexual attending family practitioners in London: a controlled study

PubMed Central

King, Michael; Nazareth, Irwin

2006-01-01

Background The morbidity of gay, lesbian or bisexual people attending family practice has not been previously assessed. We compared health measures of family practice attendees classified as lesbian, gay and bisexual. Methods We conducted a cross-sectional, controlled study conducted in 13 London family practices and compared the responses of 26 lesbian and 85 bisexual classified women, with that of 934 heterosexual classified women and 38 gay and 23 bisexual classified men with that of 373 heterosexual classified men. Our outcomes of interest were: General health questionnaire; CAGE questionnaire; short form12; smoking status; sexual experiences during childhood; number of sexual partners and sexual function and satisfaction. Results In comparison to people classified as heterosexuals: men classified as gay reported higher levels of psychological symptoms (OR 2.48, CI 1.05–5.90); women classified as bisexual were more likely to misuse alcohol (OR 2.73, 1.70–4.40); women classified as bisexual (OR 2.53, 1.60–4.00) and lesbian (OR 3.13, 1.41–6.97) and men classified as bisexual (OR 2.48, 1,04, 5.86) were more likely to be smokers and women classified as bisexual (OR 3.27, 1.97–5.43) and men classified as gay (OR 4.86, 2.28–10.34) were much more likely to report childhood sexual experiences in childhood. Psychological distress was associated with reporting sexual experiences in childhood in men classified as gay and bisexual and women classified as heterosexual. Men classified as bisexual (OR 5.00, 1.73–14.51) and women classified as bisexual (OR 2.88, 1.24- 6.56) were more likely than heterosexuals to report more than one sexual partner in the preceding four weeks. Lesbian, gay and bisexual classified people encountered no more sexual function problems than heterosexuals but men classified as bisexual (OR 2.74, 1.12–6.70) were more dissatisfied with their sex lives. Conclusion Bisexual and lesbian classified people attending London general practices were more likely to be smokers and gay classified men were at increased risk of psychological distress in comparison to heterosexual classified people. Increased awareness of the sexuality of people seen in primary care can provide opportunities for health promotion. PMID:16681849

StarCAT: A Catalog of Space Telescope Imaging Spectrograph Ultraviolet Echelle Spectra of Stars

NASA Astrophysics Data System (ADS)

Ayres, Thomas R.

2010-03-01

StarCAT is a catalog of high resolution ultraviolet spectra of objects classified as "stars," recorded by Space Telescope Imaging Spectrograph (STIS) during its initial seven years of operations (1997-2004). StarCAT is based on 3184 echelle observations of 545 distinct targets, with a total exposure duration of 5.2 Ms. For many of the objects, broad ultraviolet coverage has been achieved by splicing echellegrams taken in two or more FUV (1150-1700 Å) and/or NUV (1600-3100 Å) settings. In cases of multiple pointings on conspicuously variable sources, spectra were separated into independent epochs. Otherwise, different epochs were combined to enhance the signal-to-noise ratio (S/N). A post-facto correction to the calstis pipeline data sets compensated for subtle wavelength distortions identified in a previous study of the STIS calibration lamps. An internal "fluxing" procedure yielded coherent spectral energy distributions (SEDs) for objects with broadly overlapping wavelength coverage. The best StarCAT material achieves 300 m s-1 internal velocity precision; absolute accuracy at the 1 km s-1 level; photometric accuracy of order 4%; and relative flux precision several times better (limited mainly by knowledge of SEDs of UV standard stars). While StarCAT represents a milestone in the large-scale post-processing of STIS echellegrams, a number of potential improvements in the underlying "final" pipeline are identified.

Unsupervised classification of variable stars

NASA Astrophysics Data System (ADS)

Valenzuela, Lucas; Pichara, Karim

2018-03-01

During the past 10 years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric data sets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data come from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query-based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire data set of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.

Incrementally learning objects by touch: online discriminative and generative models for tactile-based recognition.

PubMed

Soh, Harold; Demiris, Yiannis

2014-01-01

Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate "early" classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches.

Novel maximum-margin training algorithms for supervised neural networks.

PubMed

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by MICI, MMGDX, and Levenberg-Marquard (LM), respectively. The resulting neural network was named assembled neural network (ASNN). Benchmark data sets of real-world problems have been used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and balanced error rate.

Photometry and taxonomy of trans-Neptunian objects and Centaurs in support of a Herschel key program

NASA Astrophysics Data System (ADS)

Perna, D.; Dotto, E.; Barucci, M. A.; Mazzotta Epifani, E.; Vilenius, E.; Dall'Ora, M.; Fornasier, S.; Müller, T. G.

2013-06-01

Context. The investigation of Centaurs and trans-Neptunian objects (TNOs) provides essential information about the early conditions and evolution of the outer solar system. The radiometric technique combines measurements in the visible and thermal infrared; with these one can estimate the size and albedo of Centaurs and TNOs. Aims: Our aim is to obtain visible photometry of a sample of Centaurs and TNOs, a subset of the targets of the "TNOs are cool" key program at the Herschel Space Observatory. Methods: We carried out visible photometry of Centaurs and TNOs, making use of the DOLORES instrument at the Telescopio Nazionale Galileo (TNG, La Palma, Spain). Results: We report photometric observations of 20 objects and present the computed absolute magnitudes. We derive the taxonomy of our targets (nine are classified for the first time, the results for five objects agree with the literature, the other targets are tentatively classified based on incomplete datasets) and combine the results with the literature, searching for correlations between taxonomy and dynamics. We look for comet-like activity in our Centaur sample, including (248835) 2006 SX368, which was previously described as active. Conclusions: We provide an accurate determination of the absolute magnitude and of the relative error for each of our targets. These values can be readily used in combination with thermal infrared data. The surface of TNO (65489) Ceto seems to be heterogeneous. Our results seem to support an evolutionary origin for the color dichotomy of Centaurs, and the occurrence of a strong mixing after the TNO formation. No evident cometary activity is detected around the five Centaurs in our sample; assuming that an unresolved coma is present around (248835) 2006 SX368, we use the "photometric model" to derive the possible dust production rate, finding that Qdust is in the range 1-31 kg/s. Based on observations made with the Italian Telescopio Nazionale Galileo (TNG) operated on the island of La Palma by the Fundacion Galileo Galilei of the INAF (Istituto Nazionale di Astrofisica) at the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias (programs AOT19/TAC33, AOT20/TAC45, and AOT21/TAC56).

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

HIGH-RESOLUTION FOURIER TRANSFORM SPECTROSCOPY OF Nb i IN THE NEAR-INFRARED

DOE Office of Scientific and Technical Information (OSTI.GOV)

Er, A.; Güzelçimen, F.; Başar, Gö.

In this study, a Fourier Transform spectrum of Niobium (Nb) is investigated in the near-infrared spectral range from 6000 to 12,000 cm{sup −1} (830–1660 nm). The Nb spectrum is produced using a hollow cathode discharge lamp in an argon atmosphere. Both Nb and Ar spectral lines are visible in the spectrum. A total of 110 spectral lines are assigned to the element Nb. Of these lines, 90 could be classified as transitions between known levels of atomic Nb. From these classified Nb i transitions, 27 have not been listed in literature previously. Additionally, 8 lines are classified for the firstmore » time.« less

TRAFIC: fiber tract classification using deep learning

NASA Astrophysics Data System (ADS)

Ngattai Lam, Prince D.; Belhomme, Gaetan; Ferrall, Jessica; Patterson, Billie; Styner, Martin; Prieto, Juan C.

2018-03-01

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data.

PubMed

Terrill, Philip I; Wilson, Stephen J; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2012-08-01

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.

Decoding Information for Grasping from the Macaque Dorsomedial Visual Stream.

PubMed

Filippini, Matteo; Breveglieri, Rossella; Akhras, M Ali; Bosco, Annalisa; Chinellato, Eris; Fattori, Patrizia

2017-04-19

Neurodecoders have been developed by researchers mostly to control neuroprosthetic devices, but also to shed new light on neural functions. In this study, we show that signals representing grip configurations can be reliably decoded from neural data acquired from area V6A of the monkey medial posterior parietal cortex. Two Macaca fascicularis monkeys were trained to perform an instructed-delay reach-to-grasp task in the dark and in the light toward objects of different shapes. Population neural activity was extracted at various time intervals on vision of the objects, the delay before movement, and grasp execution. This activity was used to train and validate a Bayes classifier used for decoding objects and grip types. Recognition rates were well over chance level for all the epochs analyzed in this study. Furthermore, we detected slightly different decoding accuracies, depending on the task's visual condition. Generalization analysis was performed by training and testing the system during different time intervals. This analysis demonstrated that a change of code occurred during the course of the task. Our classifier was able to discriminate grasp types fairly well in advance with respect to grasping onset. This feature might be important when the timing is critical to send signals to external devices before the movement start. Our results suggest that the neural signals from the dorsomedial visual pathway can be a good substrate to feed neural prostheses for prehensile actions. SIGNIFICANCE STATEMENT Recordings of neural activity from nonhuman primate frontal and parietal cortex have led to the development of methods of decoding movement information to restore coordinated arm actions in paralyzed human beings. Our results show that the signals measured from the monkey medial posterior parietal cortex are valid for correctly decoding information relevant for grasping. Together with previous studies on decoding reach trajectories from the medial posterior parietal cortex, this highlights the medial parietal cortex as a target site for transforming neural activity into control signals to command prostheses to allow human patients to dexterously perform grasping actions. Copyright © 2017 the authors 0270-6474/17/374311-12$15.00/0.

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN).

PubMed

Keenan, S J; Diamond, J; McCluggage, W G; Bharucha, H; Thompson, D; Bartels, P H; Hamilton, P W

2000-11-01

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n=30), koilocytosis (n=46), CIN 1 (n=52), CIN 2 (n=56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright 2000 John Wiley & Sons, Ltd.

Local decision makers’ awareness of the social determinants of health in Turkey: a cross-sectional study

PubMed Central

2012-01-01

Background Social determinants have been described as having a greater influence than other determinants of health status. The major social determinants of health and the necessary policy objectives have been defined; it is now necessary to evaluate the effectiveness of these policies. Previous studies have shown that descriptions of the awareness level of citizens and local decision makers, practice-based research and evidence, and intersectoral studies are the best options for investigating the social determinants of health at the community level. The objective of the present study was to define local decision makers’ awareness of the social determinants of health in the Aydin province of Turkey. Methods A total of 53 mayors serve the Aydin city center, districts and towns. Aydin city center has 22 neighborhoods and 22 headmen responsible for them. The present study targeted all mayors and headmen in Aydin - a total of 75 possible participants. A questionnaire was used to collect the data. The questionnaire was faxed to the mayors and administered face-to-face with the headmen. Results Headmen identified the three most important determinants of public health as environmental issues, addictions (smoking, alcohol) and malnutrition. According to the mayors, the major determinant of public health is stress, followed by malnutrition, environmental issues, an inactive lifestyle, and the social and economic conditions of the country. Both groups expressed that the Turkish Ministry of Health, municipalities and universities are the institutions responsible for developing health policy. Headmen were found to be unaware and mayors were aware of the social determinants of health as classified by the World Health Organisation. Both groups were classified as unaware with regard to their awareness of the Marmot Review policy objectives. Conclusions Studies such as the present study provide important additional information on the social determinants of health, and help to increase the awareness levels of both local decision-makers and the community. Such studies must be considered a vital first step in future public health research on health determinants and their impact on national and international policies. PMID:22703525

Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

USGS Publications Warehouse

Mitchell, Michael; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

2016-01-01

The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

An Ensemble Method with Integration of Feature Selection and Classifier Selection to Detect the Landslides

NASA Astrophysics Data System (ADS)

Zhongqin, G.; Chen, Y.

2017-12-01

Abstract Quickly identify the spatial distribution of landslides automatically is essential for the prevention, mitigation and assessment of the landslide hazard. It's still a challenging job owing to the complicated characteristics and vague boundary of the landslide areas on the image. The high resolution remote sensing image has multi-scales, complex spatial distribution and abundant features, the object-oriented image classification methods can make full use of the above information and thus effectively detect the landslides after the hazard happened. In this research we present a new semi-supervised workflow, taking advantages of recent object-oriented image analysis and machine learning algorithms to quick locate the different origins of landslides of some areas on the southwest part of China. Besides a sequence of image segmentation, feature selection, object classification and error test, this workflow ensemble the feature selection and classifier selection. The feature this study utilized were normalized difference vegetation index (NDVI) change, textural feature derived from the gray level co-occurrence matrices (GLCM), spectral feature and etc. The improvement of this study shows this algorithm significantly removes some redundant feature and the classifiers get fully used. All these improvements lead to a higher accuracy on the determination of the shape of landslides on the high resolution remote sensing image, in particular the flexibility aimed at different kinds of landslides.

Object-based locust habitat mapping using high-resolution multispectral satellite data in the southern Aral Sea basin

NASA Astrophysics Data System (ADS)

Navratil, Peter; Wilps, Hans

2013-01-01

Three different object-based image classification techniques are applied to high-resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l'Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was compared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides.

Security authentication with a three-dimensional optical phase code using random forest classifier: an overview

NASA Astrophysics Data System (ADS)

Markman, Adam; Carnicer, Artur; Javidi, Bahram

2017-05-01

We overview our recent work [1] on utilizing three-dimensional (3D) optical phase codes for object authentication using the random forest classifier. A simple 3D optical phase code (OPC) is generated by combining multiple diffusers and glass slides. This tag is then placed on a quick-response (QR) code, which is a barcode capable of storing information and can be scanned under non-uniform illumination conditions, rotation, and slight degradation. A coherent light source illuminates the OPC and the transmitted light is captured by a CCD to record the unique signature. Feature extraction on the signature is performed and inputted into a pre-trained random-forest classifier for authentication.

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

NASA Astrophysics Data System (ADS)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

Thermal bioaerosol cloud tracking with Bayesian classification

NASA Astrophysics Data System (ADS)

Smith, Christian W.; Dupuis, Julia R.; Schundler, Elizabeth C.; Marinelli, William J.

2017-05-01

The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.

Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data

NASA Astrophysics Data System (ADS)

Dementev, A. O.; Dmitriev, E. V.; Kozoderov, V. V.; Egorov, V. D.

2017-10-01

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

Three-dimensional passive sensing photon counting for object classification

NASA Astrophysics Data System (ADS)

Yeom, Seokwon; Javidi, Bahram; Watson, Edward

2007-04-01

In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of II. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.

The Upper and Lower Bounds of the Prediction Accuracies of Ensemble Methods for Binary Classification

PubMed Central

Wang, Xueyi; Davidson, Nicholas J.

2011-01-01

Ensemble methods have been widely used to improve prediction accuracy over individual classifiers. In this paper, we achieve a few results about the prediction accuracies of ensemble methods for binary classification that are missed or misinterpreted in previous literature. First we show the upper and lower bounds of the prediction accuracies (i.e. the best and worst possible prediction accuracies) of ensemble methods. Next we show that an ensemble method can achieve > 0.5 prediction accuracy, while individual classifiers have < 0.5 prediction accuracies. Furthermore, for individual classifiers with different prediction accuracies, the average of the individual accuracies determines the upper and lower bounds. We perform two experiments to verify the results and show that it is hard to achieve the upper and lower bounds accuracies by random individual classifiers and better algorithms need to be developed. PMID:21853162

Machine learning in infrared object classification - an all-sky selection of YSO candidates

NASA Astrophysics Data System (ADS)

Marton, Gabor; Zahorecz, Sarolta; Toth, L. Viktor; Magnus McGehee, Peregrine; Kun, Maria

2015-08-01

Object classification is a fundamental and challenging problem in the era of big data. I will discuss up-to-date methods and their application to classify infrared point sources.We analysed the ALLWISE catalogue, the most recent public source catalogue of the Wide-field Infrared Survey Explorer (WISE) to compile a reliable list of Young Stellar Object (YSO) candidates. We tested and compared classical and up-to-date statistical methods as well, to discriminate source types like extragalactic objects, evolved stars, main sequence stars, objects related to the interstellar medium and YSO candidates by using their mid-IR WISE properties and associated near-IR 2MASS data.In the particular classification problem the Support Vector Machines (SVM), a class of supervised learning algorithm turned out to be the best tool. As a result we classify Class I and II YSOs with >90% accuracy while the fraction of contaminating extragalactic objects remains well below 1%, based on the number of known objects listed in the SIMBAD and VizieR databases. We compare our results to other classification schemes from the literature and show that the SVM outperforms methods that apply linear cuts on the colour-colour and colour-magnitude space. Our homogenous YSO candidate catalog can serve as an excellent pathfinder for future detailed observations of individual objects and a starting point of statistical studies that aim to add pieces to the big picture of star formation theory.

Stellar Classification Online - Public Exploration

NASA Astrophysics Data System (ADS)

Castelaz, Michael W.; Bedell, W.; Barker, T.; Cline, J.; Owen, L.

2009-01-01

The Michigan Objective Prism Blue Survey (e.g. Sowell et al 2007, AJ, 134, 1089) photographic plates located in the Astronomical Photographic Data Archive at the Pisgah Astronomical Research Institute hold hundreds of thousands of stellar spectra, many of which have not been classified before. The public is invited to participate in a distributed computing online environment to classify the stars on the objective prism plates. The online environment is called Stellar Classification Online - Public Exploration (SCOPE). Through a website, SCOPE participants are given a tutorial on stellar spectra and their classification, and given the chance to practice their skills at classification. After practice, participants register, login, and select stars for classification from scans of the objective prism plates. Their classifications are recorded in a database where the accumulation of classifications of the same star by many users will be statistically analyzed. The project includes stars with known spectral types to help test the reliability of classifications. The SCOPE webpage and the use of results will be described.

Impact of Design Trade Studies on System Human Resources.

ERIC Educational Resources Information Center

Whalen, Gary V.; Askren, William B.

This study focused on two objectives. The first objective was to identify and classify the characteristics of conceptual design trade studies that have high potential impact on human resource requirements of Air Force weapon systems. The approach used was a case history review and analysis of 129 F-15 aircraft design trade studies. The analysis…

Latent Profile Analysis to Determine the Typology of Disinhibited Eating Behaviors in Children and Adolescents

ERIC Educational Resources Information Center

Vannucci, Anna; Tanofsky-Kraff, Marian; Crosby, Ross D.; Ranzenhofer, Lisa M.; Shomaker, Lauren B.; Field, Sara E.; Mooreville, Mira; Reina, Samantha A.; Kozlosky, Merel; Yanovski, Susan Z.; Yanovski, Jack A.

2013-01-01

Objective: We used latent profile analysis (LPA) to classify children and adolescents into subtypes based on the overlap of disinhibited eating behaviors--eating in the absence of hunger, emotional eating, and subjective and objective binge eating. Method: Participants were 411 youths (8-18 years) from the community who reported on their…

Fusion of ultrasonic and infrared signatures for personnel detection by a mobile robot

NASA Astrophysics Data System (ADS)

Carroll, Matthew S.; Meng, Min; Cadwallender, William K.

1992-04-01

Passive Infrared sensors used for intrusion detection, especially those used on mobile robots, are vulnerable to false alarms caused by clutter objects such as radiators, steam pipes, windows, etc., as well as deliberately caused false alarms caused by decoy objects. To overcome these sources of false alarms, we are now combining thermal and ultrasonic signals, the results being a more robust system for detecting personnel. Our paper will discuss the fusion strategies used for combining sensor information. Our first strategy uses a statistical classifier using features such as the sonar cross-section, the received thermal energy, and ultrasonic range. Our second strategy uses s 3-layered neural classifier trained by backpropagation. The probability of correct classification and the false alarm rate for both strategies will be presented in the paper.

Optimization of a Multi-Stage ATR System for Small Target Identification

NASA Technical Reports Server (NTRS)

Lin, Tsung-Han; Lu, Thomas; Braun, Henry; Edens, Western; Zhang, Yuhan; Chao, Tien- Hsin; Assad, Christopher; Huntsberger, Terrance

2010-01-01

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

Multi-view L2-SVM and its multi-view core vector machine.

PubMed

Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

2016-03-01

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Food and Drug Administration criteria for the diagnosis of drug-induced valvular heart disease in patients previously exposed to benfluorex: a prospective multicentre study.

PubMed

Maréchaux, Sylvestre; Rusinaru, Dan; Jobic, Yannick; Ederhy, Stéphane; Donal, Erwan; Réant, Patricia; Arnalsteen, Elise; Boulanger, Jacques; Garban, Thierry; Ennezat, Pierre-Vladimir; Jeu, Antoine; Szymanski, Catherine; Tribouilloy, Christophe

2015-02-01

The Food and Drug Administration (FDA) criteria for diagnosis of drug-induced valvular heart disease (DIVHD) are only based on the observation of aortic regurgitation ≥ mild and/or mitral regurgitation ≥ moderate. We sought to evaluate the diagnostic value of FDA criteria in a cohort of control patients and in a cohort of patients exposed to a drug (benfluorex) known to induce VHD. This prospective, multicentre study included 376 diabetic control patients not exposed to valvulopathic drugs and 1000 subjects previously exposed to benfluorex. Diagnosis of mitral or aortic DIVHD was based on a combined functional and morphological echocardiographic analysis of cardiac valves. Patients were classified according to the FDA criteria [mitral or aortic-FDA(+) and mitral or aortic-FDA(-)]. Among the 376 control patients, 2 were wrongly classified as mitral-FDA(+) and 17 as aortic-FDA(+) (0.53 and 4.5% of false positives, respectively). Of those exposed to benfluorex, 48 of 58 with a diagnosis of mitral DIVHD (83%) were classified as mitral-FDA(-), and 901 of the 910 patients (99%) without a diagnosis of the mitral DIVHD group were classified as mitral-FDA(-). All 40 patients with a diagnosis of aortic DIVHD were classified as aortic-FDA(+), and 105 of the 910 patients without a diagnosis of aortic DIVHD (12%) were classified aortic-FDA(+). Older age and lower BMI were independent predictors of disagreement between FDA criteria and the diagnosis of DIVHD in patients exposed to benfluorex (both P ≤ 0.001). FDA criteria solely based on the Doppler detection of cardiac valve regurgitation underestimate for the mitral valve and overestimate for the aortic valve the frequency of DIVHD. Therefore, the diagnosis of DIVHD must be based on a combined echocardiographic and Doppler morphological and functional analysis of cardiac valves. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

Comparison of transcriptomic signature of post-Chernobyl and postradiotherapy thyroid tumors.

PubMed

Ory, Catherine; Ugolin, Nicolas; Hofman, Paul; Schlumberger, Martin; Likhtarev, Illya A; Chevillard, Sylvie

2013-11-01

We previously identified two highly discriminating and predictive radiation-induced transcriptomic signatures by comparing series of sporadic and postradiotherapy thyroid tumors (322-gene signature), and by reanalyzing a previously published data set of sporadic and post-Chernobyl thyroid tumors (106-gene signature). The aim of the present work was (i) to compare the two signatures in terms of gene expression deregulations and molecular features/pathways, and (ii) to test the capacity of the postradiotherapy signature in classifying the post-Chernobyl series of tumors and reciprocally of the post-Chernobyl signature in classifying the postradiotherapy-induced tumors. We now explored if postradiotherapy and post-Chernobyl papillary thyroid carcinomas (PTC) display common molecular features by comparing molecular pathways deregulated in the two tumor series, and tested the potential of gene subsets of the postradiotherapy signature to classify the post-Chernobyl series (14 sporadic and 12 post-Chernobyl PTC), and reciprocally of gene subsets of the post-Chernobyl signature to classify the postradiotherapy series (15 sporadic and 12 postradiotherapy PTC), by using conventional principal component analysis. We found that the five genes common to the two signatures classified the learning/training tumors (used to search these signatures) of both the postradiotherapy (seven PTC) and the post-Chernobyl (six PTC) thyroid tumor series as compared with the sporadic tumors (seven sporadic PTC in each series). Importantly, these five genes were also effective for classifying independent series of postradiotherapy (five PTC) and post-Chernobyl (six PTC) tumors compared to independent series of sporadic tumors (eight PTC and six PTC respectively; testing tumors). Moreover, part of each postradiotherapy (32 genes) and post-Chernobyl signature (16 genes) cross-classified the respective series of thyroid tumors. Finally, several molecular pathways deregulated in post-Chernobyl tumors matched those found to be deregulated in postradiotherapy tumors. Overall, our data suggest that thyroid tumors that developed following either external exposure or internal (131)I contamination shared common molecular features, related to DNA repair, oxidative and endoplasmic reticulum stresses, allowing their classification as radiation-induced tumors in comparison with sporadic counterparts, independently of doses and dose rates, which suggests there may be a "general" radiation-induced signature of thyroid tumors.

Recognising promoter sequences using an artificial immune system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cooke, D.E.; Hunt, J.E.

1995-12-31

We have developed an artificial immune system (AIS) which is based on the human immune system. The AIS possesses an adaptive learning mechanism which enables antibodies to emerge which can be used for classification tasks. In this paper, we describe how the AIS has been used to evolve antibodies which can classify promoter containing and promoter negative DNA sequences. The DNA sequences used for teaching were 57 nucleotides in length and contained procaryotic promoters. The system classified previously unseen DNA sequences with an accuracy of approximately 90%.

The Hpp Rule with Memory and the Density Classification Task

NASA Astrophysics Data System (ADS)

Alonso-Sanz, Ramón

This article considers an extension to the standard framework of cellular automata by implementing memory capability in cells. It is shown that the important block HPP rule behaves as an excellent classifier of the density in the initial configuration when applied to cells endowed with pondered memory of their previous states. If the weighing is made so that the most recent state values are assigning the highest weights, the HPP rule surpasses the performance of the best two-dimensional density classifiers reported in the literature.

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy

PubMed Central

2017-01-01

Background Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or innovative and those who were not rated at all (P>.05). Conclusions Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance. PMID:28298265

Mineralogical characterization of asteroid (1951) Lick

NASA Astrophysics Data System (ADS)

de Leon, J.; Duffard, R.; Licandro, J.; Lazzaro, D.

A-type asteroids are usually found in the main asteroid belt and their spectra are very similar to spectra of the silicate mineral olivine (Cruikshank and Hartmann 1984). The existence of olivine-rich asteroids is a result of differentiation, those being the pieces of the mantle of a larger parent body. Extraterrestrial sources of such material must exist because we have meteorites that are nearly pure olivine (dunites). There is a limited number of observed asteroids classified as A-type, all of them belonging to the Main Belt and the study of such objects is crucial to better understand their origin and formation and their relation with dunites. We have obtained visible and near infrared reflectance spectra of asteroid (1951) Lick using the telescopes located at Observatorio del Roque de los Muchachos (Canary Islands, Spain). According to its spectral characteristics in the visible region, this object has been classified as an A-type asteroid by Bus and Binzel (2002). Although considered an Amor object by several authors, according to its orbital parameters (a = 1.390 AU, e = 0.061, i = 39.093 deg, q = 1.304) this object is just in the limit that separates Amors from Mars Crossers (q = 1.3). Whether it is classified as an Amor or a Mars Crosser, (1951) Lick is the first object with such orbital characteristics classified as an A-type asteroid. Here we present a mineralogical analysis of the reflectance spectra obtained for (1951) Lick. We calculate several parameters that are extracted from the spectrum of the asteroid and that give relevant information about its mineralogical composition, using the method defined by Gaffey et al. (1993). We also present results obtained by a preliminary fit to the absorption band associated to the presence of the olivine mineral using the Modified Gaussian Model method (MGM) developed by Sunshine et al.(1990). References Bus, J. S. and Binzel, R. P. 2002. Icarus, 158, 146 Cuikshank, D. P. and Hartmann, W. K. 1984. Science, 223, 281 Gaffey, M. J., Bell, J. F., Brown, R. H., Burbine, T. H., Piatek, J. L., Reed, K. L. and Chaky, D. A. 1993. Icarus, 106, 573 Sunshine, J. M., Pieters, C. M. and Pratt, S. F. 1990. JGR, 95, B5, 6955

VLBA Observations of Low Luminosity Flat Spectrum Radio Galaxies and BL Lac Objects: Polarisation Properties

NASA Astrophysics Data System (ADS)

Bondi, M.; Dallacasa, D.; Stanghellini, C.; Marchã, M. J. M.

We obtained two-epoch VLBA observations at 5 GHz of a list of radio galaxies drawn from the 200 mJy sample (Marcha et al. 1996). The objects selected for milli-arcsecond scale observations are classified, on the basis of their optical spectroscopic and polarimetric properties, as BL Lac objects, normal weak line radio galaxies, broad line radio galaxies, and transition objects (those with intermediate properties). We present preliminary results on the radio polarization properties, on the milli-arcsecond scale, of objects with different optical properties and discuss structural variations detected from the two epochs.

Modeling guidance and recognition in categorical search: bridging human and computer object detection.

PubMed

Zelinsky, Gregory J; Peng, Yifan; Berg, Alexander C; Samaras, Dimitris

2013-10-08

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

Modeling guidance and recognition in categorical search: Bridging human and computer object detection

PubMed Central

Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

2013-01-01

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

A Spitzer View of Star Formation in the Cyngus X North Complex

DTIC Science & Technology

2009-11-10

Sky Survey ( 2MASS ) data are used to identify and classify young stellar objects. Of the 8,231 sources detected exhibiting infrared excess in Cygnus X...Telescope. A combination of IRAC, MIPS, UKIRT Deep Infrared Sky Survey (UKIDSS), and Two Micron All Sky Survey ( 2MASS ) data are used to identify and classify...MIPS, Two-Micron All-Sky Survey ( 2MASS , Skrutskie et al. 2006) and UKIRT Deep Sky Survey DR4 (UKIDSS, Lawrence et al. 2007; Lucas et al. 2008

Deferred discrimination algorithm (nibbling) for target filter management

NASA Astrophysics Data System (ADS)

Caulfield, H. John; Johnson, John L.

1999-07-01

A new method of classifying objects is presented. Rather than trying to form the classifier in one step or in one training algorithm, it is done in a series of small steps, or nibbles. This leads to an efficient and versatile system that is trained in series with single one-shot examples but applied in parallel, is implemented with single layer perceptrons, yet maintains its fully sequential hierarchical structure. Based on the nibbling algorithm, a basic new method of target reference filter management is described.

Multiple-object tracking as a tool for parametrically modulating memory reactivation

PubMed Central

Poppenk, J.; Norman, K.A.

2017-01-01

Converging evidence supports the “non-monotonic plasticity” hypothesis that although complete retrieval may strengthen memories, partial retrieval weakens them. Yet, the classic experimental paradigms used to study effects of partial retrieval are not ideally suited to doing so, because they lack the parametric control needed to ensure that the memory is activated to the appropriate degree (i.e., that there is some retrieval, but not enough to cause memory strengthening). Here we present a novel procedure designed to accommodate this need. After participants learned a list of word-scene associates, they completed a cued mental visualization task that was combined with a multiple-object tracking (MOT) procedure, which we selected for its ability to interfere with mental visualization in a parametrically adjustable way (by varying the number of MOT targets). We also used fMRI data to successfully train an “associative recall” classifier for use in this task: this classifier revealed greater memory reactivation during trials in which associative memories were cued while participants tracked one, rather than five MOT targets. However, the classifier was insensitive to task difficulty when recall was not taking place, suggesting it had indeed tracked memory reactivation rather than task difficulty per se. Consistent with the classifier findings, participants’ introspective ratings of visualization vividness were modulated by MOT task difficulty. In addition, we observed reduced classifier output and slowing of responses in a post-reactivation memory test, consistent with the hypothesis that partial reactivation, induced by MOT, weakened memory. These results serve as a “proof of concept” that MOT can be used to parametrically modulate memory retrieval – a property that may prove useful in future investigation of partial retrieval effects, e.g., in closed-loop experiments. PMID:28387587

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

PubMed

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery

NASA Astrophysics Data System (ADS)

Piragnolo, Marco; Masiero, Andrea; Pirotti, Francesco

2017-04-01

Since recent years surveying with unmanned aerial vehicles (UAV) is getting a great amount of attention due to decreasing costs, higher precision and flexibility of usage. UAVs have been applied for geomorphological investigations, forestry, precision agriculture, cultural heritage assessment and for archaeological purposes. It can be used for land use and land cover classification (LULC). In literature, there are two main types of approaches for classification of remote sensing imagery: pixel-based and object-based. On one hand, pixel-based approach mostly uses training areas to define classes and respective spectral signatures. On the other hand, object-based classification considers pixels, scale, spatial information and texture information for creating homogeneous objects. Machine learning methods have been applied successfully for classification, and their use is increasing due to the availability of faster computing capabilities. The methods learn and train the model from previous computation. Two machine learning methods which have given good results in previous investigations are Random Forest (RF) and Support Vector Machine (SVM). The goal of this work is to compare RF and SVM methods for classifying LULC using images collected with a fixed wing UAV. The processing chain regarding classification uses packages in R, an open source scripting language for data analysis, which provides all necessary algorithms. The imagery was acquired and processed in November 2015 with cameras providing information over the red, blue, green and near infrared wavelength reflectivity over a testing area in the campus of Agripolis, in Italy. Images were elaborated and ortho-rectified through Agisoft Photoscan. The ortho-rectified image is the full data set, and the test set is derived from partial sub-setting of the full data set. Different tests have been carried out, using a percentage from 2 % to 20 % of the total. Ten training sets and ten validation sets are obtained from each test set. The control dataset consist of an independent visual classification done by an expert over the whole area. The classes are (i) broadleaf, (ii) building, (iii) grass, (iv) headland access path, (v) road, (vi) sowed land, (vii) vegetable. The RF and SVM are applied to the test set. The performances of the methods are evaluated using the three following accuracy metrics: Kappa index, Classification accuracy and Classification Error. All three are calculated in three different ways: with K-fold cross validation, using the validation test set and using the full test set. The analysis indicates that SVM gets better results in terms of good scores using K-fold cross or validation test set. Using the full test set, RF achieves a better result in comparison to SVM. It also seems that SVM performs better with smaller training sets, whereas RF performs better as training sets get larger.

Automatic Adviser on Mobile Objects Status Identification and Classification

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Saryan, A. S.

2018-05-01

A mobile object status identification task is defined within the image discrimination theory. It is proposed to classify objects into three classes: object operation status; its maintenance is required and object should be removed from the production process. Two methods were developed to construct the separating boundaries between the designated classes: a) using statistical information on the research objects executed movement, b) basing on regulatory documents and expert commentary. Automatic Adviser operation simulation and the operation results analysis complex were synthesized. Research results are commented using a specific example of cuts rolling from the hump yard. The work was supported by Russian Fundamental Research Fund, project No. 17-20-01040.

Guidebook for Developing Criterion-Referenced Tests

DTIC Science & Technology

1975-08-01

struction (each student learning at his own pace ), and then tested when he felt prepared to take the test. Note that an NRT is des 4 grea to spread people out...be able to build a CRT. A properly constructed CRT will allow you to classify the people who take it into two groups: , Masters--those who you are...Instructioial Objectives * Instructional Goals e Learning Objectives 9 Terminal Training Objectives This level describes work activities which can stand by

Interservice Procedures for Instructional Systems Development. Phase 3. Develop

DTIC Science & Technology

1975-08-01

Occur at wide intervals to be learned *Reads about the actions to *Occur at the end, but before be learned tests or on-the-job performance *Watches a...the particular sub-category. Use the learning objective action statement, conditions, standards, and the test item to help select which guidelines to...objective. EXAMPLE If you have a CLASSIFYING objective like "identifying poisonous plants,’ when you get to guideline 16. "To test learning, require the

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: A review.

PubMed

de los Reyes-Guzmán, Ana; Dimbwadyo-Terrer, Iris; Trincado-Alonso, Fernando; Monasterio-Huelin, Félix; Torricelli, Diego; Gil-Agudo, Angel

2014-08-01

Quantitative measures of human movement quality are important for discriminating healthy and pathological conditions and for expressing the outcomes and clinically important changes in subjects' functional state. However the most frequently used instruments for the upper extremity functional assessment are clinical scales, that previously have been standardized and validated, but have a high subjective component depending on the observer who scores the test. But they are not enough to assess motor strategies used during movements, and their use in combination with other more objective measures is necessary. The objective of the present review is to provide an overview on objective metrics found in literature with the aim of quantifying the upper extremity performance during functional tasks, regardless of the equipment or system used for registering kinematic data. A search in Medline, Google Scholar and IEEE Xplore databases was performed following a combination of a series of keywords. The full scientific papers that fulfilled the inclusion criteria were included in the review. A set of kinematic metrics was found in literature in relation to joint displacements, analysis of hand trajectories and velocity profiles. These metrics were classified into different categories according to the movement characteristic that was being measured. These kinematic metrics provide the starting point for a proposed objective metrics for the functional assessment of the upper extremity in people with movement disorders as a consequence of neurological injuries. Potential areas of future and further research are presented in the Discussion section. Copyright © 2014 Elsevier Ltd. All rights reserved.

Machine learning to classify and predict objective and subjective assessments of vehicle dynamics: the case of steering feel

NASA Astrophysics Data System (ADS)

Gil Gómez, Gaspar L.; Nybacka, Mikael; Drugge, Lars; Bakker, Egbert

2018-01-01

Objective measurements and computer-aided engineering simulations cannot be exploited to their full potential because of the high importance of driver feel in vehicle development. Furthermore, despite many studies, it is not easy to identify the relationship between objective metrics (OM) and subjective assessments (SA), a task further complicated by the fact that SA change between drivers and geographical locations or with time. This paper presents a method which uses two artificial neural networks built on top of each other that helps to close this gap. The first network, based solely on OM, generates a map that groups together similar vehicles, thus allowing a classification of measured vehicles to be visualised. This map objectively demonstrates that there exist brand and vehicle class identities. It also foresees the subjective characteristics of a new vehicle, based on its requirements, simulations and measurements. These characteristics are described by the neighbourhood of the new vehicle in the map, which is made up of known vehicles that are accompanied by word-clouds that enhance this description. This forecast is also extended to perform a sensitivity analysis of the tolerances in the requirements, as well as to validate previously published preferred range of steering feel metrics. The results suggest a few new modifications. Finally, the qualitative information given by this measurement-based classification is complemented with a second superimposed network. This network describes a regression surface that enables quantitative predictions, for example the SA of the steering feel of a new vehicle from its OM.

A bio-inspired system for spatio-temporal recognition in static and video imagery

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Moore, Christopher K.; Chelian, Suhas

2007-04-01

This paper presents a bio-inspired method for spatio-temporal recognition in static and video imagery. It builds upon and extends our previous work on a bio-inspired Visual Attention and object Recognition System (VARS). The VARS approach locates and recognizes objects in a single frame. This work presents two extensions of VARS. The first extension is a Scene Recognition Engine (SCE) that learns to recognize spatial relationships between objects that compose a particular scene category in static imagery. This could be used for recognizing the category of a scene, e.g., office vs. kitchen scene. The second extension is the Event Recognition Engine (ERE) that recognizes spatio-temporal sequences or events in sequences. This extension uses a working memory model to recognize events and behaviors in video imagery by maintaining and recognizing ordered spatio-temporal sequences. The working memory model is based on an ARTSTORE1 neural network that combines an ART-based neural network with a cascade of sustained temporal order recurrent (STORE)1 neural networks. A series of Default ARTMAP classifiers ascribes event labels to these sequences. Our preliminary studies have shown that this extension is robust to variations in an object's motion profile. We evaluated the performance of the SCE and ERE on real datasets. The SCE module was tested on a visual scene classification task using the LabelMe2 dataset. The ERE was tested on real world video footage of vehicles and pedestrians in a street scene. Our system is able to recognize the events in this footage involving vehicles and pedestrians.

Classifying four-category visual objects using multiple ERP components in single-trial ERP.

PubMed

Qin, Yu; Zhan, Yu; Wang, Changming; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan; Wu, Xia; Hu, Bin

2016-08-01

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

A liver cirrhosis classification on B-mode ultrasound images by the use of higher order local autocorrelation features

NASA Astrophysics Data System (ADS)

Sasaki, Kenya; Mitani, Yoshihiro; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

2017-02-01

In this paper, in order to classify liver cirrhosis on regions of interest (ROIs) images from B-mode ultrasound images, we have proposed to use the higher order local autocorrelation (HLAC) features. In a previous study, we tried to classify liver cirrhosis by using a Gabor filter based approach. However, the classification performance of the Gabor feature was poor from our preliminary experimental results. In order accurately to classify liver cirrhosis, we examined to use the HLAC features for liver cirrhosis classification. The experimental results show the effectiveness of HLAC features compared with the Gabor feature. Furthermore, by using a binary image made by an adaptive thresholding method, the classification performance of HLAC features has improved.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

NASA Astrophysics Data System (ADS)

Mafanya, Madodomzi; Tsele, Philemon; Botai, Joel; Manyama, Phetole; Swart, Barend; Monate, Thabang

2017-07-01

Invasive alien plants (IAPs) not only pose a serious threat to biodiversity and water resources but also have impacts on human and animal wellbeing. To support decision making in IAPs monitoring, semi-automated image classifiers which are capable of extracting valuable information in remotely sensed data are vital. This study evaluated the mapping accuracies of supervised and unsupervised image classifiers for mapping Harrisia pomanensis (a cactus plant commonly known as the Midnight Lady) using two interlinked evaluation strategies i.e. point and area based accuracy assessment. Results of the point-based accuracy assessment show that with reference to 219 ground control points, the supervised image classifiers (i.e. Maxver and Bhattacharya) mapped H. pomanensis better than the unsupervised image classifiers (i.e. K-mediuns, Euclidian Length and Isoseg). In this regard, user and producer accuracies were 82.4% and 84% respectively for the Maxver classifier. The user and producer accuracies for the Bhattacharya classifier were 90% and 95.7%, respectively. Though the Maxver produced a higher overall accuracy and Kappa estimate than the Bhattacharya classifier, the Maxver Kappa estimate of 0.8305 is not significantly (statistically) greater than the Bhattacharya Kappa estimate of 0.8088 at a 95% confidence interval. The area based accuracy assessment results show that the Bhattacharya classifier estimated the spatial extent of H. pomanensis with an average mapping accuracy of 86.1% whereas the Maxver classifier only gave an average mapping accuracy of 65.2%. Based on these results, the Bhattacharya classifier is therefore recommended for mapping H. pomanensis. These findings will aid in the algorithm choice making for the development of a semi-automated image classification system for mapping IAPs.

Small Schools Science Curriculum, K-3: Reading, Language Arts, Mathematics, Science, Social Studies. Scope, Objectives, Activities, Resources, Monitoring Procedures.

ERIC Educational Resources Information Center

Hartl, David, Ed.; And Others

Learning objectives and suggested activities, monitoring procedures and resources for the Washington K-3 Small Schools Science Curriculum are based on the rationale that "young children need the opportunity to observe, classify, predict, test ideas again and again in a variety of contexts, ask questions, explain, discuss ideas, fail, and succeed.…

Applying Objective Diagnostic Criteria to Students in a College Support Program for Learning Disabilities

ERIC Educational Resources Information Center

Sparks, Richard L.; Lovett, Benjamin J.

2013-01-01

This study examined whether a large group of postsecondary students participating in a support program for students classified as having learning disabilities (LD) met criteria for five objective diagnostic models for LD: IQ-achievement discrepancy (1.0 SD, 1.5 SD, and greater than 2.0 SD) models, a "Diagnostic and Statistical Manual of…

Rural People and Their Resources: North-Central New Mexico.

ERIC Educational Resources Information Center

Taylor, Marlowe M.

The general objective of a study conducted in the 1950's was to describe the physical and human resources of the rural areas in north-central New Mexico. The specific objectives were (1) to inventory and classify the land, labor, and capital resources, (2) to determine levels and sources of incomes of these households, and (3) to appraise some of…

Characterization of coarse woody debris across a 100 year chronosequence of upland oak-hickory forest

Treesearch

Travis W. Idol; Phillip E. Pope; Rebecca A. Figler; Felix Ponder Jr.

1999-01-01

Coarse woody debris is an important component influencing forest nutrient cycling and contributes to long-term soil productivity. The common practice of classifying coarse woody debris into different decomposition classes has seldom been related to the chemistry/biochemistry of the litter, which is the long term objective of our research. The objective of this...

Spinal-Exercise Prescription in Sport: Classifying Physical Training and Rehabilitation by Intention and Outcome

PubMed Central

Spencer, Simon; Wolf, Alex; Rushton, Alison

2016-01-01

Context: Identification of strategies to prevent spinal injury, optimize rehabilitation, and enhance performance is a priority for practitioners. Different exercises produce different effects on neuromuscular performance. Clarity of the purpose of a prescribed exercise is central to a successful outcome. Spinal exercises need to be classified according to the objective of the exercise and planned physical outcome. Objective: To define the modifiable spinal abilities that underpin optimal function during skilled athletic performance, clarify the effect of spinal pain and pathologic conditions, and classify spinal exercises according to the objective of the exercise and intended physical outcomes to inform training and rehabilitation. Design: Qualitative study. Data Collection and Analysis: We conducted a qualitative consensus method of 4 iterative phases. An exploratory panel carried out an extended review of the English-language literature using CINAHL, EMBASE, MEDLINE, and PubMed to identify key themes and subthemes to inform the definitions of exercise categories, physical abilities, and physical outcomes. An expert project group reviewed panel findings. A draft classification was discussed with physiotherapists (n = 49) and international experts. Lead physiotherapy and strength and conditioning teams (n = 17) reviewed a revised classification. Consensus was defined as unanimous agreement. Results: After the literature review and subsequent analysis, we defined spinal abilities in 4 categories: mobility, motor control, work capacity, and strength. Exercises were subclassified by functionality as nonfunctional or functional and by spinal displacement as either static (neutral spinal posture with no segmental displacement) or dynamic (dynamic segmental movement). The proposed terminology and classification support commonality of language for practitioners. Conclusions: The spinal-exercise classification will support clinical reasoning through a framework of spinal-exercise objectives that clearly define the nature of the exercise prescription required to deliver intended physical outcomes. PMID:27661792

First results of the seven-color asteroid survey

NASA Astrophysics Data System (ADS)

Clark, Beth E.; Bell, Jeffrey F.; Fanale, Fraser P.; Lucey, Paul G.

1993-03-01

The new Seven-Color infrared filter system (SCAS), designed specifically to capture the essential mineralogical information present in asteroid spectra, is composed of seven broad-band filters which allow for IR observations of objects as faint as 17th magnitude. The first test of the SCAS system occurred in Jul. 1992. In four nights at the IRTF on Mauna Kea, Hawaii, over 67 objects were observed. Five of the observations were to test the new system for accuracy relative to previous observations with the high-resolution 52 Color Infrared Survey and with the Eight-Color Asteroid Survey (ECAS). In three cases, the match with previous data was good. In two cases, the match to previous observations was not as good. In addition, sixty S-Type asteroids were measured with the SCAS system. Forty of those asteroids were also observed with the ECAS system. Among the new observations is infrared data of 371 Bohemia, a main belt asteroid which was classified 'QSV' according to its UBV colors in the taxonomic system of D.J. Tholen. There are no corresponding ECAS data for 371. Q-type asteroids are of special interest as they are proposed to be the elusive parent bodies of the ordinary chondrite meteorites. Most Q-types are Earth-crossing asteroids and have not yet been observed in the infrared (except, perhaps, 371). Positive identification of a large main belt Q-type would be of major importance in the scheme of the geological structure of the asteroid belt. Without visible wavelength data, however, the classification of 371 Bohemia remains ambiguous. An attempt to conjoin Bohemia SCAS data with ECAS data of both a typical Q-Type asteroid and an average S-Type asteroid is shown. This figure thus illustrates the importance of visible wavelength data to the SCAS system. In other words, without ECAS data of 371 Bohemia we cannot use its spectral characteristics to identify it as a possible parent body of ordinary chondrite meteorites.

Orthostatic hypotension

NASA Technical Reports Server (NTRS)

Grimm, J. J.

1980-01-01

Following a brief physiopathological review, orthostatic hypotension is classified into three groups: organic, functional and medication-dependent. The importance of etiological diagnosis, the use of objective tests and appropriate therapy, especially concerning the organic forms, is stressed.

Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

An 8-year longitudinal study of mirror self-recognition in chimpanzees (Pan troglodytes).

PubMed

de Veer, Monique W; Gallup, Gordon G; Theall, Laura A; van den Bos, Ruud; Povinelli, Daniel J

2003-01-01

In a previous cross-sectional study of mirror self-recognition involving 92 chimpanzees, Povinelli et al. [Journal of Comparative Psychology 107 (1993) 347] reported a peak in the proportion of animals exhibiting self-recognition in the adolescent/young adult sample (8-15 years), with 75% being classified as positive. In contrast, only 26% of the older animals (16-39 years) were classified as positive, suggesting a marked decline in self-recognition in middle to late adulthood. In the present study, all of the chimpanzees from the 8-15-year-old group in the Povinelli et al. study (n=12) were again tested for self-recognition, 8 years later. Using the same criteria, 67% of the animals were classified the same. Although a higher proportion of the adult animals in this study (50%) exhibited self-recognition than would be inferred on the basis of the previous study (25%), all changes in self-recognition status were in the negative direction. These results show that mirror self-recognition is a highly stable trait in many chimpanzees, but may be subject to decline with age. Connections with human research are briefly discussed.

Automated classification of periodic variable stars detected by the wide-field infrared survey explorer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Masci, Frank J.; Grillmair, Carl J.; Cutri, Roc M.

2014-07-01

We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodicmore » light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately ±2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.« less

Past speculations of future health technologies: a description of technologies predicted in 15 forecasting studies published between 1986 and 2010

PubMed Central

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2017-01-01

Objective To describe and classify health technologies predicted in forecasting studies. Design and methods A portrait describing health technologies predicted in 15 forecasting studies published between 1986 and 2010 that were identified in a previous systematic review. Health technologies are classified according to their type, purpose and clinical use; relating these to the original purpose and timing of the forecasting studies. Data sources All health-related technologies predicted in 15 forecasting studies identified in a previously published systematic review. Main outcome measure Outcomes related to (1) each forecasting study including country, year, intention and forecasting methods used and (2) the predicted technologies including technology type, purpose, targeted clinical area and forecast timeframe. Results Of the 896 identified health-related technologies, 685 (76.5%) were health technologies with an explicit or implied health application and included in our study. Of these, 19.1% were diagnostic or imaging tests, 14.3% devices or biomaterials, 12.6% information technology systems, eHealth or mHealth and 12% drugs. The majority of the technologies were intended to treat or manage disease (38.1%) or diagnose or monitor disease (26.1%). The most frequent targeted clinical areas were infectious diseases followed by cancer, circulatory and nervous system disorders. The most frequent technology types were for: infectious diseases—prophylactic vaccines (45.8%), cancer—drugs (40%), circulatory disease—devices and biomaterials (26.3%), and diseases of the nervous system—equally devices and biomaterials (25%) and regenerative medicine (25%). The mean timeframe for forecasting was 11.6 years (range 0–33 years, median=10, SD=6.6). The forecasting timeframe significantly differed by technology type (p=0.002), the intent of the forecasting group (p<0.001) and the methods used (p<001). Conclusion While description and classification of predicted health-related technologies is crucial in preparing healthcare systems for adopting new innovations, further work is needed to test the accuracy of predictions made. PMID:28760796

Identification of DNA-Binding Proteins Using Structural, Electrostatic and Evolutionary Features

PubMed Central

Nimrod, Guy; Szilágyi, András; Leslie, Christina; Ben-Tal, Nir

2009-01-01

Summary DNA binding proteins (DBPs) often take part in various crucial processes of the cell's life cycle. Therefore, the identification and characterization of these proteins are of great importance. We present here a random forests classifier for identifying DBPs among proteins with known three-dimensional structures. First, clusters of evolutionarily conserved regions (patches) on the protein's surface are detected using the PatchFinder algorithm; previous studies showed that these regions are typically the proteins' functionally important regions. Next, we train a classifier using features like the electrostatic potential, cluster-based amino acid conservation patterns and the secondary structure content of the patches, as well as features of the whole protein including its dipole moment. Using 10-fold cross validation on a dataset of 138 DNA-binding proteins and 110 proteins which do not bind DNA, the classifier achieved a sensitivity and a specificity of 0.90, which is overall better than the performance of previously published methods. Furthermore, when we tested 5 different methods on 11 new DBPs which did not appear in the original dataset, only our method annotated all correctly. The resulting classifier was applied to a collection of 757 proteins of known structure and unknown function. Of these proteins, 218 were predicted to bind DNA, and we anticipate that some of them interact with DNA using new structural motifs. The use of complementary computational tools supports the notion that at least some of them do bind DNA. PMID:19233205

Statistics of high-level scene context.

PubMed

Greene, Michelle R

2013-01-01

CONTEXT IS CRITICAL FOR RECOGNIZING ENVIRONMENTS AND FOR SEARCHING FOR OBJECTS WITHIN THEM: contextual associations have been shown to modulate reaction time and object recognition accuracy, as well as influence the distribution of eye movements and patterns of brain activations. However, we have not yet systematically quantified the relationships between objects and their scene environments. Here I seek to fill this gap by providing descriptive statistics of object-scene relationships. A total of 48, 167 objects were hand-labeled in 3499 scenes using the LabelMe tool (Russell et al., 2008). From these data, I computed a variety of descriptive statistics at three different levels of analysis: the ensemble statistics that describe the density and spatial distribution of unnamed "things" in the scene; the bag of words level where scenes are described by the list of objects contained within them; and the structural level where the spatial distribution and relationships between the objects are measured. The utility of each level of description for scene categorization was assessed through the use of linear classifiers, and the plausibility of each level for modeling human scene categorization is discussed. Of the three levels, ensemble statistics were found to be the most informative (per feature), and also best explained human patterns of categorization errors. Although a bag of words classifier had similar performance to human observers, it had a markedly different pattern of errors. However, certain objects are more useful than others, and ceiling classification performance could be achieved using only the 64 most informative objects. As object location tends not to vary as a function of category, structural information provided little additional information. Additionally, these data provide valuable information on natural scene redundancy that can be exploited for machine vision, and can help the visual cognition community to design experiments guided by statistics rather than intuition.

Stochastic control approaches for sensor management in search and exploitation

NASA Astrophysics Data System (ADS)

Hitchings, Darin Chester

Recent improvements in the capabilities of autonomous vehicles have motivated their increased use in such applications as defense, homeland security, environmental monitoring, and surveillance. To enhance performance in these applications, new algorithms are required to control teams of robots autonomously and through limited interactions with human operators. In this dissertation we develop new algorithms for control of robots performing information-seeking missions in unknown environments. These missions require robots to control their sensors in order to discover the presence of objects, keep track of the objects, and learn what these objects are, given a fixed sensing budget. Initially, we investigate control of multiple sensors, with a finite set of sensing options and finite-valued measurements, to locate and classify objects given a limited resource budget. The control problem is formulated as a Partially Observed Markov Decision Problem (POMDP), but its exact solution requires excessive computation. Under the assumption that sensor error statistics are independent and time-invariant, we develop a class of algorithms using Lagrangian Relaxation techniques to obtain optimal mixed strategies using performance bounds developed in previous research. We investigate alternative Receding Horizon (RH) controllers to convert the mixed strategies to feasible adaptive-sensing strategies and evaluate the relative performance of these controllers in simulation. The resulting controllers provide superior performance to alternative algorithms proposed in the literature and obtain solutions to large-scale POMDP problems several orders of magnitude faster than optimal Dynamic Programming (DP) approaches with comparable performance quality. We extend our results for finite action, finite measurement sensor control to scenarios with moving objects. We use Hidden Markov Models (HMMs) for the evolution of objects, according to the dynamics of a birth-death process. We develop a new lower bound on the performance of adaptive controllers in these scenarios, develop algorithms for computing solutions to this lower bound, and use these algorithms as part of a RH controller for sensor allocation in the presence of moving objects We also consider an adaptive Search problem where sensing actions are continuous and the underlying measurement space is also continuous. We extend our previous hierarchical decomposition approach based on performance bounds to this problem and develop novel implementations of Stochastic Dynamic Programming (SDP) techniques to solve this problem. Our algorithms are nearly two orders of magnitude faster than previously proposed approaches and yield solutions of comparable quality. For supervisory control, we discuss how human operators can work with and augment robotic teams performing these tasks. Our focus is on how tasks are partitioned among teams of robots and how a human operator can make intelligent decisions for task partitioning. We explore these questions through the design of a game that involves robot automata controlled by our algorithms and a human supervisor that partitions tasks based on different levels of support information. This game can be used with human subject experiments to explore the effect of information on quality of supervisory control.

Partially supervised P300 speller adaptation for eventual stimulus timing optimization: target confidence is superior to error-related potential score as an uncertain label

NASA Astrophysics Data System (ADS)

Zeyl, Timothy; Yin, Erwei; Keightley, Michelle; Chau, Tom

2016-04-01

Objective. Error-related potentials (ErrPs) have the potential to guide classifier adaptation in BCI spellers, for addressing non-stationary performance as well as for online optimization of system parameters, by providing imperfect or partial labels. However, the usefulness of ErrP-based labels for BCI adaptation has not been established in comparison to other partially supervised methods. Our objective is to make this comparison by retraining a two-step P300 speller on a subset of confident online trials using naïve labels taken from speller output, where confidence is determined either by (i) ErrP scores, (ii) posterior target scores derived from the P300 potential, or (iii) a hybrid of these scores. We further wish to evaluate the ability of partially supervised adaptation and retraining methods to adjust to a new stimulus-onset asynchrony (SOA), a necessary step towards online SOA optimization. Approach. Eleven consenting able-bodied adults attended three online spelling sessions on separate days with feedback in which SOAs were set at 160 ms (sessions 1 and 2) and 80 ms (session 3). A post hoc offline analysis and a simulated online analysis were performed on sessions two and three to compare multiple adaptation methods. Area under the curve (AUC) and symbols spelled per minute (SPM) were the primary outcome measures. Main results. Retraining using supervised labels confirmed improvements of 0.9 percentage points (session 2, p < 0.01) and 1.9 percentage points (session 3, p < 0.05) in AUC using same-day training data over using data from a previous day, which supports classifier adaptation in general. Significance. Using posterior target score alone as a confidence measure resulted in the highest SPM of the partially supervised methods, indicating that ErrPs are not necessary to boost the performance of partially supervised adaptive classification. Partial supervision significantly improved SPM at a novel SOA, showing promise for eventual online SOA optimization.

Artificial neural network EMG classifier for functional hand grasp movements prediction

PubMed Central

Ferrante, Simona; Ferrigno, Giancarlo; Baldassini, Davide; Molteni, Franco; Guanziroli, Eleonora; Cotti Cottini, Michele; Seneci, Carlo; Pedrocchi, Alessandra

2016-01-01

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-freedom hand grasp movements (i.e. pinching, grasp an object, grasping) were predicted by means of surface EMG signals, recorded from 10 bipolar EMG electrodes arranged in a circular configuration around the forearm 2–3 cm from the elbow. Two cascaded artificial neural networks were then exploited to detect the patient's motion intention from the EMG signal window starting from the electrical activity onset to movement onset (i.e. electromechanical delay). Results The proposed approach was tested on eight healthy control subjects (4 females; age range 25–26 years) and it demonstrated a mean ± SD testing performance of 76% ± 14% for correctly predicting healthy users' motion intention. Two post-stroke patients tested the controller and obtained 79% and 100% of correctly classified movements under testing conditions. Conclusion A task-selection controller was developed to estimate the intended movement from the EMG measured during the electromechanical delay. PMID:27677300

Explicit area-based accuracy assessment for mangrove tree crown delineation using Geographic Object-Based Image Analysis (GEOBIA)

NASA Astrophysics Data System (ADS)

Kamal, Muhammad; Johansen, Kasper

2017-10-01

Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user's accuracy (UA), producer's accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.

PubMed

Dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E; Dos Santos Filho, Plinio B

2008-01-01

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

Deploying a quantum annealing processor to detect tree cover in aerial imagery of California

PubMed Central

Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Mukhopadhyay, Supratik; Nemani, Ramakrishna R.

2017-01-01

Quantum annealing is an experimental and potentially breakthrough computational technology for handling hard optimization problems, including problems of computer vision. We present a case study in training a production-scale classifier of tree cover in remote sensing imagery, using early-generation quantum annealing hardware built by D-wave Systems, Inc. Beginning within a known boosting framework, we train decision stumps on texture features and vegetation indices extracted from four-band, one-meter-resolution aerial imagery from the state of California. We then impose a regulated quadratic training objective to select an optimal voting subset from among these stumps. The votes of the subset define the classifier. For optimization, the logical variables in the objective function map to quantum bits in the hardware device, while quadratic couplings encode as the strength of physical interactions between the quantum bits. Hardware design limits the number of couplings between these basic physical entities to five or six. To account for this limitation in mapping large problems to the hardware architecture, we propose a truncation and rescaling of the training objective through a trainable metaparameter. The boosting process on our basic 108- and 508-variable problems, thus constituted, returns classifiers that incorporate a diverse range of color- and texture-based metrics and discriminate tree cover with accuracies as high as 92% in validation and 90% on a test scene encompassing the open space preserves and dense suburban build of Mill Valley, CA. PMID:28241028

Subjective and Objective Measures of Dryness Symptoms in Primary Sjögren's Syndrome: Capturing the Discrepancy.

PubMed

Bezzina, Oriana M; Gallagher, Peter; Mitchell, Sheryl; Bowman, Simon J; Griffiths, Bridget; Hindmarsh, Victoria; Hargreaves, Ben; Price, Elizabeth J; Pease, Colin T; Emery, Paul; Lanyon, Peter; Bombardieri, Michele; Sutcliffe, Nurhan; Pitzalis, Costantino; Hunter, John; Gupta, Monica; McLaren, John; Cooper, Anne M; Regan, Marian; Giles, Ian P; Isenberg, David A; Saravanan, Vadivelu; Coady, David; Dasgupta, Bhaskar; McHugh, Neil J; Young-Min, Steven A; Moots, Robert J; Gendi, Nagui; Akil, Mohammed; MacKay, Kirsten; Ng, W Fai; Robinson, Lucy J

2017-11-01

To develop a novel method for capturing the discrepancy between objective tests and subjective dryness symptoms (a sensitivity scale) and to explore predictors of dryness sensitivity. Archive data from the UK Primary Sjögren's Syndrome Registry (n = 688) were used. Patients were classified on a scale from -5 (stoical) to +5 (sensitive) depending on the degree of discrepancy between their objective and subjective symptoms classes. Sensitivity scores were correlated with demographic variables, disease-related factors, and symptoms of pain, fatigue, anxiety, and depression. Patients were on average relatively stoical for both types of dryness symptoms (mean ± SD ocular dryness -0.42 ± 2.2 and -1.24 ± 1.6 oral dryness). Twenty-seven percent of patients were classified as sensitive to ocular dryness and 9% to oral dryness. Hierarchical regression analyses identified the strongest predictor of ocular dryness sensitivity to be self-reported pain and that of oral dryness sensitivity to be self-reported fatigue. Ocular and oral dryness sensitivity can be classified on a continuous scale. The 2 symptom types are predicted by different variables. A large number of factors remain to be explored that may impact symptom sensitivity in primary Sjögren's syndrome, and the proposed method could be used to identify relatively sensitive and stoical patients for future studies. © 2016, The Authors. Arthritis Care & Research published by Wiley Periodicals, Inc. on behalf of American College of Rheumatology.

Spectroscopic classification of X-ray sources in the Galactic Bulge Survey

NASA Astrophysics Data System (ADS)

Wevers, T.; Torres, M. A. P.; Jonker, P. G.; Nelemans, G.; Heinke, C.; Mata Sánchez, D.; Johnson, C. B.; Gazer, R.; Steeghs, D. T. H.; Maccarone, T. J.; Hynes, R. I.; Casares, J.; Udalski, A.; Wetuski, J.; Britt, C. T.; Kostrzewa-Rutkowska, Z.; Wyrzykowski, Ł.

2017-10-01

We present the classification of 26 optical counterparts to X-ray sources discovered in the Galactic Bulge Survey. We use (time-resolved) photometric and spectroscopic observations to classify the X-ray sources based on their multiwavelength properties. We find a variety of source classes, spanning different phases of stellar/binary evolution. We classify CX21 as a quiescent cataclysmic variable (CV) below the period gap, and CX118 as a high accretion rate (nova-like) CV. CXB12 displays excess UV emission, and could contain a compact object with a giant star companion, making it a candidate symbiotic binary or quiescent low-mass X-ray binary (although other scenarios cannot be ruled out). CXB34 is a magnetic CV (polar) that shows photometric evidence for a change in accretion state. The magnetic classification is based on the detection of X-ray pulsations with a period of 81 ± 2 min. CXB42 is identified as a young stellar object, namely a weak-lined T Tauri star exhibiting (to date unexplained) UX Ori-like photometric variability. The optical spectrum of CXB43 contains two (resolved) unidentified double-peaked emission lines. No known scenario, such as an active galactic nucleus or symbiotic binary, can easily explain its characteristics. We additionally classify 20 objects as likely active stars based on optical spectroscopy, their X-ray to optical flux ratios and photometric variability. In four cases we identify the sources as binary stars.

Cognitive representations of flight-deck information attributes

NASA Technical Reports Server (NTRS)

Ricks, Wendell R.; Jonsson, Jon E.; Rogers, William H.

1993-01-01

The experiment described in this paper had two ojectives. The first objective was to empirically identify how pilots organize flight-deck information attributes. Such an organization should provide a useful nomenclature for classifying Information Management (IM) issues and problems. The second objective of this study was to empirically assess pilots' estimate of the relative importance of each attribute on managing information. Results from addressing this latter objective were intended to suggest areas on which flight-deck researchers and designers will want to focus their attention.

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbedAllah, Loai

2016-12-22

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai

2016-12-01

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Evaluation of the internal oblique, external oblique, and transversus abdominalis muscles in patients with ankylosing spondylitis: an ultrasonographic study.

PubMed

Üşen, Ahmet; Kuran, Banu; Yılmaz, Figen; Aksu, Neşe; Erçalık, Cem

2017-11-01

The objectives of the study are to compare abdominal muscle thickness in ankylosing spondylitis (AS) patients with healthy subjects and determine the factors affecting these muscle thickness. Thirty-five male patients with a previous diagnosis of AS according to the Modified New York criteria and a control group consisting of 35 healthy male individuals were included in this cross-sectional and case-control study. Thicknesses of the internal oblique (IO), external oblique (EO), and transversus abdominalis (TrA) muscles were measured with ultrasound (US). AS patients were classified according to the International Physical Activity Questionnaire (IPAQ). There were 35 AS patients with a mean age of 35.17 ± 8.05 years and 35 healthy subjects with a mean age 32.57 ± 7.05 years. No significant difference was observed between the groups in terms of abdominal muscle thicknesses (p > 0.005). When the AS patients were classified according to the IPAQ scores, thicknesses of the IO and TrA muscles were significantly lower in patients who had the low level of IPAQ scores (p < 0.05). In the light of our first and preliminary results, muscle thickness of the IO, EO, and TrA muscles were similar in AS patients to healthy subjects. However, AS patients who had lower level of physical activity have also reduced thickness of IO and TrA muscles.

pROC: an open-source package for R and S+ to analyze and compare ROC curves.

PubMed

Robin, Xavier; Turck, Natacha; Hainard, Alexandre; Tiberti, Natalia; Lisacek, Frédérique; Sanchez, Jean-Charles; Müller, Markus

2011-03-17

Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Risk Factors for Treatment Default among Re-Treatment Tuberculosis Patients in India, 2006

PubMed Central

Jha, Ugra Mohan; Satyanarayana, Srinath; Dewan, Puneet K.; Chadha, Sarabjit; Wares, Fraser; Sahu, Suvanand; Gupta, Devesh; Chauhan, L. S.

2010-01-01

Setting Under India's Revised National Tuberculosis Control Programme (RNTCP), >15% of previously-treated patients in the reported 2006 patient cohort defaulted from anti-tuberculosis treatment. Objective To assess the timing, characteristics, and risk factors for default amongst re-treatment TB patients. Methodology For this case-control study, in 90 randomly-selected programme units treatment records were abstracted from all 2006 defaulters from the RNTCP re-treatment regimen (cases), with one consecutively-selected non-defaulter per case. Patients who interrupted anti-tuberculosis treatment for >2 months were classified as defaulters. Results 1,141 defaulters and 1,189 non-defaulters were included. The median duration of treatment prior to default was 81 days (25%–75% interquartile range 44–117 days) and documented retrieval efforts after treatment interruption were inadequate. Defaulters were more likely to have been male (adjusted odds ratio [aOR] 1.4, 95% confidence interval [CI] 1.2–1.7), have previously defaulted anti-tuberculosis treatment (aOR 1.3 95%CI 1.1–1.6], have previous treatment from non-RNTCP providers (AOR 1.3, 95%CI 1.0–1.6], or have public health facility-based treatment observation (aOR 1.3, 95%CI 1.1–1.6). Conclusions Amongst the large number of re-treatment patients in India, default occurs early and often. Improved pre-treatment counseling and community-based treatment provision may reduce default rates. Efforts to retrieve treatment interrupters prior to default require strengthening. PMID:20111727

Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio)

PubMed Central

2012-01-01

Background Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. PMID:22849515

Recollection is a continuous process: Evidence from plurality memory receiver operating characteristics.

PubMed

Slotnick, Scott D; Jeye, Brittany M; Dodson, Chad S

2016-01-01

Is recollection a continuous/graded process or a threshold/all-or-none process? Receiver operating characteristic (ROC) analysis can answer this question as the continuous model and the threshold model predict curved and linear recollection ROCs, respectively. As memory for plurality, an item's previous singular or plural form, is assumed to rely on recollection, the nature of recollection can be investigated by evaluating plurality memory ROCs. The present study consisted of four experiments. During encoding, words (singular or plural) or objects (single/singular or duplicate/plural) were presented. During retrieval, old items with the same plurality or different plurality were presented. For each item, participants made a confidence rating ranging from "very sure old", which was correct for same plurality items, to "very sure new", which was correct for different plurality items. Each plurality memory ROC was the proportion of same versus different plurality items classified as "old" (i.e., hits versus false alarms). Chi-squared analysis revealed that all of the plurality memory ROCs were adequately fit by the continuous unequal variance model, whereas none of the ROCs were adequately fit by the two-high threshold model. These plurality memory ROC results indicate recollection is a continuous process, which complements previous source memory and associative memory ROC findings.

A Puzzling Object - V348-SAGITTARIUS

NASA Astrophysics Data System (ADS)

Houziaux, L.

1983-09-01

V 348 Sagitarii is one of the many variables discovered by Miss Ida Woods on Harvard plates (Harvard Bulletin 838, 1926). Later, in the 1950s, 500 patrol plates have been scrutinized by Mrs. Jean Haies Anderson (Hoffleit, Astronomical Journal, 63, 78, 1958) at Maria Mitchell Observatory. This material covered the period 1906-1939 and the derived light Curve suggested a semi-regular variability with cycles of 200 to 400 days. Because of the resemblance of its light curve with the ones of stars undergoing at irregular intervals sudden drops of brightness before recovering more slowly their previous magnitude, that star was once classified as belonging to the R Coronae Borealis type by Schjan, who rediscovered the star in 1929 and gave it the number 3976 in the H(amburg) V(ariables) catalogue (Astronomische Nachrichten, 235, 417, 1929). V 348 Sgr, however, is seen more often at minimum light than at maximum, contrarily to the R Coronae Borealis stars. These objects are known to be carbon rich and this is indeed the case for V 348 Sgr; however, as shown first by George Herbig (Astrophysical Journal, 127, 312, 1958) the spectrum, unlike the other stars of the type reveals the presence of a hot diluted atmosphere around the star where many lines of ionized atoms of carbon are seen instead of absorption bands due to molecular compounds. So both by its light curve and by its spectrum V 348 Sgr is indeed a peculiar object.

Classification of ligand molecules in PDB with graph match-based structural superposition.

PubMed

Shionyu-Mitsuyama, Clara; Hijikata, Atsushi; Tsuji, Toshiyuki; Shirai, Tsuyoshi

2016-12-01

The fast heuristic graph match algorithm for small molecules, COMPLIG, was improved by adding a structural superposition process to verify the atom-atom matching. The modified method was used to classify the small molecule ligands in the Protein Data Bank (PDB) by their three-dimensional structures, and 16,660 types of ligands in the PDB were classified into 7561 clusters. In contrast, a classification by a previous method (without structure superposition) generated 3371 clusters from the same ligand set. The characteristic feature in the current classification system is the increased number of singleton clusters, which contained only one ligand molecule in a cluster. Inspections of the singletons in the current classification system but not in the previous one implied that the major factors for the isolation were differences in chirality, cyclic conformations, separation of substructures, and bond length. Comparisons between current and previous classification systems revealed that the superposition-based classification was effective in clustering functionally related ligands, such as drugs targeted to specific biological processes, owing to the strictness of the atom-atom matching.

Outcome of Endodontic Treatments Made by Postgraduate Students in the Dental Clinic of Bretonneau Hospital

PubMed Central

Touboul, Virginie; Germa, Alice; Lasfargues, Jean-Jacques; Bonte, Eric

2014-01-01

Objective. The objective of this retrospective study is double: (1) to assess the 1–4 years of outcome of endodontic treatment performed by postgraduate students in endodontics in the Dental Clinic of Bretonneau Hospital and (2) to examine outcome predictors. Method. 363 teeth in 296 patients were treated between 2007 and 2011. 183 patients (224 teeth) were lost during the followup. 113 patients were included in the study (recall: 38%), corresponding to 139 teeth of which 8 were extracted. 131 remaining teeth (36%) were examined clinically and radiographically. Apical periodontitis (AP) was absent (PAI = 1) or present (PAI ≥ 2). Outcome was classified as “healed,” “healing,” or “diseased”. Results. The success rate was 92%. No failure was observed among the 23 initial endodontic treatments. Among the 108 retreated teeth, 80% were “healed” and 11% were “healing.” An association was found between success rate and preoperative signs or symptoms (absent 95% versus present 83%), preoperative root filling density (inadequate 93% versus adequate 57%), but not between preoperative AP status and success. Conclusion. Outcomes in this retrospective study were similar to those previously reported. However, a larger sample size is needed to assess outcome predictors more precisely. PMID:24778652

Primeval very low-mass stars and brown dwarfs - III. The halo transitional brown dwarfs

NASA Astrophysics Data System (ADS)

Zhang, Z. H.; Pinfield, D. J.; Gálvez-Ortiz, M. C.; Homeier, D.; Burgasser, A. J.; Lodieu, N.; Martín, E. L.; Osorio, M. R. Zapatero; Allard, F.; Jones, H. R. A.; Smart, R. L.; Martí, B. López; Burningham, B.; Rebolo, R.

2018-05-01

We report the discovery of an esdL3 subdwarf, ULAS J020858.62+020657.0 and a usdL4.5 subdwarf, ULAS J230711.01+014447.1. They were identified as L subdwarfs by optical spectra obtained with the Gran Telescopio Canarias, and followed up by optical to near infrared spectroscopy with the Very Large Telescope. We also obtained an optical to near infrared spectrum of a previously known L subdwarf, ULAS J135058.85+081506.8, and re-classified it as a usdL3 subdwarf. These three objects all have typical halo kinematics. They have Teff around 2050-2250 K, -1.8 ≤ [Fe/H] ≤-1.5, and mass around 0.0822-0.0833 M⊙, according to model spectral fitting and evolutionary models. These sources are likely halo transitional brown dwarfs with unsteady hydrogen fusions, as their masses are just below the hydrogen-burning minimum mass, which is ˜ 0.0845 M⊙ at [Fe/H] = -1.6 and ˜ 0.0855 M⊙ at [Fe/H] = -1.8. Including these, there are now nine objects in the `halo brown dwarf transition zone', which is a `substellar subdwarf gap' spans a wide temperature range within a narrow mass range of the substellar population.

A Rapid Approach to Modeling Species-Habitat Relationships

NASA Technical Reports Server (NTRS)

Carter, Geoffrey M.; Breinger, David R.; Stolen, Eric D.

2005-01-01

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Objective Structured Clinical Examination as an Assessment Tool for Clinical Skills in Dermatology.

PubMed

Saceda-Corralo, D; Fonda-Pascual, P; Moreno-Arrones, Ó M; Alegre-Sánchez, A; Hermosa-Gelbard, Á; Jiménez-Gómez, N; Vañó-Galván, S; Jaén-Olasolo, P

2017-04-01

Objective Structured Clinical Evaluation (OSCE) is an excellent method to evaluate student's abilities, but there are no previous reports implementing it in dermatology. To determine the feasibility of implementation of a dermatology OSCE in the medical school. Five stations with standardized patients and image-based assessment were designed. A specific checklist was elaborated in each station with different items which evaluated one competency and were classified into five groups (medical history, physical examination, technical skills, case management and prevention). A total of 28 students were tested. Twenty-five of them (83.3%) passed the exam globally. Concerning each group of items tested: medical interrogation had a mean score of 71.0; physical examination had a mean score of 63.0; management had a mean score of 58.0; and prevention had a mean score of 58.0 points. The highest results were obtained in interpersonal skills items with 91.8 points. Testing a small sample of voluntary students may hinder generalization of our study. OSCE is an useful tool for assessing clinical skills in dermatology and it is possible to carry it out. Our experience enhances that medical school curriculum needs to establish OSCE as an assessment tool in dermatology. Copyright © 2016 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.

Target recognition of log-polar ladar range images using moment invariants

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jie; Yu, Haoyong

2017-01-01

The ladar range image has received considerable attentions in the automatic target recognition field. However, previous research does not cover target recognition using log-polar ladar range images. Therefore, we construct a target recognition system based on log-polar ladar range images in this paper. In this system combined moment invariants and backpropagation neural network are selected as shape descriptor and shape classifier, respectively. In order to fully analyze the effect of log-polar sampling pattern on recognition result, several comparative experiments based on simulated and real range images are carried out. Eventually, several important conclusions are drawn: (i) if combined moments are computed directly by log-polar range images, translation, rotation and scaling invariant properties of combined moments will be invalid (ii) when object is located in the center of field of view, recognition rate of log-polar range images is less sensitive to the changing of field of view (iii) as object position changes from center to edge of field of view, recognition performance of log-polar range images will decline dramatically (iv) log-polar range images has a better noise robustness than Cartesian range images. Finally, we give a suggestion that it is better to divide field of view into recognition area and searching area in the real application.

Ability Grouping, Good for Children or Not? Consequences of Ability Grouping: Ethnic and Socio-Economic Separation of Children. NCRIEEO Tipsheet, Number 4.

ERIC Educational Resources Information Center

Roye, Wendell J.

If the major educational objective of classifying children into restricted range classroom environments is greater provision for individual differences--and given that there is no clear-cut evidence indicating that this object has been realized--then one is compelled to entertain the conclusion that ability grouping, as presently implemented, has…