40 CFR 63.1320 - PET and polystyrene affected sources-reporting provisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true PET and polystyrene affected sources... § 63.1320 PET and polystyrene affected sources—reporting provisions. (a) Except as specified in... PET Affected Sources Using a Dimethyl Terephthalate Process. Owners or operators complying with § 63...

40 CFR 63.1320 - PET and polystyrene affected sources-reporting provisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 11 2011-07-01 2011-07-01 false PET and polystyrene affected sources... § 63.1320 PET and polystyrene affected sources—reporting provisions. (a) Except as specified in... PET Affected Sources Using a Dimethyl Terephthalate Process. Owners or operators complying with § 63...

Influential sources affecting Bangkok adolescent body image perceptions.

PubMed

Thianthai, Chulanee

2006-01-01

The study of body image-related problems in non-Western countries is still very limited. Thus, this study aims to identify the main influential sources and show how they affect the body image perceptions of Bangkok adolescents. The researcher recruited 400 Thai male and female adolescents in Bangkok, attending high school to freshmen level, ranging from 16-19 years, to participate in this study. Survey questionnaires were distributed to every student and follow-up interviews conducted with 40 students. The findings showed that there are eight main influential sources respectively ranked from the most influential to the least influential: magazines, television, peer group, familial, fashion trend, the opposite gender, self-realization and health knowledge. Similar to those studies conducted in Western countries, more than half of the total percentage was the influence of mass media and peer groups. Bangkok adolescents also internalized Western ideal beauty through these mass media channels. Alike studies conducted in the West, there was similarities in the process of how these influential sources affect Bangkok adolescent body image perception, with the exception of familial source. In conclusion, taking the approach of identifying the main influential sources and understanding how they affect adolescent body image perceptions can help prevent adolescents from having unhealthy views and taking risky measures toward their bodies. More studies conducted in non-Western countries are needed in order to build a cultural sensitive program, catered to the body image problems occurring in adolescents within that particular society.

Cognitive Affective Engagement Model of Multiple Source Use

ERIC Educational Resources Information Center

List, Alexandra; Alexander, Patricia A.

2017-01-01

This article introduces the cognitive affective engagement model (CAEM) of multiple source use. The CAEM is presented as a way of unifying cognitive and behaviorally focused models of multiple text engagement with research on the role of affective factors (e.g., interest) in text processing. The CAEM proposes that students' engagement with…

Classifying Failing States

DTIC Science & Technology

2007-03-01

state failure, and Discriminant Analysis to classify states as Stable, Borderline, or Failing based on these indicators. Furthermore, each...nation’s discriminant function scores are used to determine their degree of instability. The methodology is applied to 200 countries for which open source...and go for a long walk. Finally, to my wonderful wife, who now knows more about Discriminant Analysis than any Legal Assistant on the planet, thank

40 CFR 63.1316 - PET and polystyrene affected sources-emissions control provisions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 12 2012-07-01 2011-07-01 true PET and polystyrene affected sources... and Resins § 63.1316 PET and polystyrene affected sources—emissions control provisions. (a) The owner or operator of an affected source producing PET using a continuous process shall comply with...

40 CFR 63.1316 - PET and polystyrene affected sources-emissions control provisions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false PET and polystyrene affected sources... Polymers and Resins § 63.1316 PET and polystyrene affected sources—emissions control provisions. (a) The owner or operator of an affected source producing PET using a continuous process shall comply with...

40 CFR 63.1316 - PET and polystyrene affected sources-emissions control provisions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 12 2014-07-01 2014-07-01 false PET and polystyrene affected sources... Polymers and Resins § 63.1316 PET and polystyrene affected sources—emissions control provisions. (a) The owner or operator of an affected source producing PET using a continuous process shall comply with...

Classifying Radio Galaxies with the Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Aniyan, A. K.; Thorat, K.

2017-06-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

Language abnormality in deaf people with schizophrenia: a problem with classifiers.

PubMed

Chatzidamianos, G; McCarthy, R A; Du Feu, M; Rosselló, J; McKenna, P J

2018-06-05

Although there is evidence for language abnormality in schizophrenia, few studies have examined sign language in deaf patients with the disorder. This is of potential interest because a hallmark of sign languages is their use of classifiers (semantic or entity classifiers), a reference-tracking device with few if any parallels in spoken languages. This study aimed to examine classifier production and comprehension in deaf signing adults with schizophrenia. Fourteen profoundly deaf signing adults with schizophrenia and 35 age- and IQ-matched deaf healthy controls completed a battery of tests assessing classifier and noun comprehension and production. The patients showed poorer performance than the healthy controls on comprehension and production of both nouns and entity classifiers, with the deficit being most marked in the production of classifiers. Classifier production errors affected handshape rather than other parameters such as movement and location. The findings suggest that schizophrenia affects language production in deaf patients with schizophrenia in a unique way not seen in hearing patients.

Classifying Radio Galaxies with the Convolutional Neural Network

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

Aniyan, A. K.; Thorat, K.

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categoriesmore » is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.« less

40 CFR 63.1340 - Applicability and designation of affected sources.

Code of Federal Regulations, 2010 CFR

2010-07-01

... CATEGORIES National Emission Standards for Hazardous Air Pollutants From the Portland Cement Manufacturing... portland cement plant which is a major source or an area source as defined in § 63.2. (b) The affected... portland cement plant which is a major source; (3) Each raw mill at any portland cement plant which is a...

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.

40 CFR Table 4 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the Source...

Code of Federal Regulations, 2010 CFR

2010-07-01

... Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 4 Table 4 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam Production Affected Sources... Foam Production Affected Sources Complying With the Source-Wide Emission Limitation 4 Table 4 to...

40 CFR Table 4 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the Source...

Code of Federal Regulations, 2011 CFR

2011-07-01

... Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 4 Table 4 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam Production Affected Sources... Foam Production Affected Sources Complying With the Source-Wide Emission Limitation 4 Table 4 to...

40 CFR Table 4 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the Source...

Code of Federal Regulations, 2012 CFR

2012-07-01

... Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 4 Table 4 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam Production Affected Sources... Foam Production Affected Sources Complying With the Source-Wide Emission Limitation 4 Table 4 to...

Classifier fusion for VoIP attacks classification

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Rezac, Filip

2017-05-01

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

HVAC SYSTEMS AS EMISSION SOURCES AFFECTING INDOOR AIR QUALITY: A CRITICAL REVIEW

EPA Science Inventory

The study evaluates heating, ventilating, and air-conditioning (HVAC) systems as contaminant emission sources that affect indoor air quality (IAQ). Various literature sources and methods for characterizing HVAC emission sources are reviewed. Available methods include in situ test...

HVAC SYSTEMS AS EMISSION SOURCES AFFECTING INDOOR AIR QUALITY: A CRITICAL REVIEW

EPA Science Inventory

The paper discusses results of an evaluation of literature on heating, ventilating, and air-conditioning (HVAC) systems as contaminant emission sources that affect indoor air quality (IAQ). The various literature sources and methods for characterizing HVAC emission sources are re...

40 CFR 63.1318 - PET and polystyrene affected sources-testing and compliance demonstration provisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 11 2011-07-01 2011-07-01 false PET and polystyrene affected sources...: Group IV Polymers and Resins § 63.1318 PET and polystyrene affected sources—testing and compliance... not apply and owners or operators are not required to comply with § 63.113. (b) PET affected sources...

40 CFR 63.1318 - PET and polystyrene affected sources-testing and compliance demonstration provisions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 12 2012-07-01 2011-07-01 true PET and polystyrene affected sources...: Group IV Polymers and Resins § 63.1318 PET and polystyrene affected sources—testing and compliance... not apply and owners or operators are not required to comply with § 63.113. (b) PET affected sources...

40 CFR 63.1318 - PET and polystyrene affected sources-testing and compliance demonstration provisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true PET and polystyrene affected sources...: Group IV Polymers and Resins § 63.1318 PET and polystyrene affected sources—testing and compliance... not apply and owners or operators are not required to comply with § 63.113. (b) PET affected sources...

40 CFR 63.54 - Preconstruction review procedures for new affected sources.

Code of Federal Regulations, 2010 CFR

2010-07-01

... new affected sources. 63.54 Section 63.54 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... CATEGORIES Requirements for Control Technology Determinations for Major Sources in Accordance With Clean Air... required. (2) The permitting authority will approve an applicant's proposed control technology, or the...

40 CFR 63.1317 - PET and polystyrene affected sources-monitoring provisions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false PET and polystyrene affected sources-monitoring provisions. 63.1317 Section 63.1317 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... and Resins § 63.1317 PET and polystyrene affected sources—monitoring provisions. Continuous process...

40 CFR 63.1317 - PET and polystyrene affected sources-monitoring provisions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 12 2012-07-01 2011-07-01 true PET and polystyrene affected sources-monitoring provisions. 63.1317 Section 63.1317 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... § 63.1317 PET and polystyrene affected sources—monitoring provisions. Continuous process vents using a...

40 CFR 63.1317 - PET and polystyrene affected sources-monitoring provisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 11 2011-07-01 2011-07-01 false PET and polystyrene affected sources-monitoring provisions. 63.1317 Section 63.1317 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... § 63.1317 PET and polystyrene affected sources—monitoring provisions. Continuous process vents using a...

40 CFR 63.1317 - PET and polystyrene affected sources-monitoring provisions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 12 2014-07-01 2014-07-01 false PET and polystyrene affected sources-monitoring provisions. 63.1317 Section 63.1317 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... and Resins § 63.1317 PET and polystyrene affected sources—monitoring provisions. Continuous process...

40 CFR 63.1317 - PET and polystyrene affected sources-monitoring provisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true PET and polystyrene affected sources-monitoring provisions. 63.1317 Section 63.1317 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... § 63.1317 PET and polystyrene affected sources—monitoring provisions. Continuous process vents using a...

40 CFR 63.1319 - PET and polystyrene affected sources-recordkeeping provisions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false PET and polystyrene affected sources... and Resins § 63.1319 PET and polystyrene affected sources—recordkeeping provisions. (a) Except as....113. (b) Records demonstrating compliance with the applicability determination procedure for PET...

40 CFR 63.5984 - What emission limits must I meet for tire production affected sources?

Code of Federal Regulations, 2010 CFR

2010-07-01

... tire production affected sources? 63.5984 Section 63.5984 Protection of Environment ENVIRONMENTAL... POLLUTANTS FOR SOURCE CATEGORIES National Emissions Standards for Hazardous Air Pollutants: Rubber Tire Manufacturing Emission Limits for Tire Production Affected Sources § 63.5984 What emission limits must I meet...

40 CFR 63.760 - Applicability and designation of affected source.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) of this section. (i) Each glycol dehydration unit; (ii) Each storage vessel with the potential for... affected source includes each triethylene glycol (TEG) dehydration unit located at a facility that meets...

40 CFR 63.760 - Applicability and designation of affected source.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) of this section. (i) Each glycol dehydration unit; (ii) Each storage vessel with the potential for... affected source includes each triethylene glycol (TEG) dehydration unit located at a facility that meets...

40 CFR 63.760 - Applicability and designation of affected source.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) of this section. (i) Each glycol dehydration unit; (ii) Each storage vessel with the potential for... affected source includes each triethylene glycol (TEG) dehydration unit located at a facility that meets...

Robust Combining of Disparate Classifiers Through Order Statistics

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2001-01-01

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

40 CFR 63.1940 - What is the affected source of this subpart?

Code of Federal Regulations, 2012 CFR

2012-07-01

... PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE CATEGORIES (CONTINUED) National Emission Standards for Hazardous Air Pollutants: Municipal Solid Waste Landfills What...). The affected source includes the entire disposal facility in a contiguous geographic space where...

40 CFR 63.1940 - What is the affected source of this subpart?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE CATEGORIES (CONTINUED) National Emission Standards for Hazardous Air Pollutants: Municipal Solid Waste Landfills What...). The affected source includes the entire disposal facility in a contiguous geographic space where...

40 CFR 63.7490 - What is the affected source of this subpart?

Code of Federal Regulations, 2010 CFR

2010-07-01

... PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE CATEGORIES (CONTINUED) National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters What This Subpart Covers § 63.7490 What is the affected source of this...

40 CFR 63.52 - Approval process for new and existing affected sources.

Code of Federal Regulations, 2010 CFR

2010-07-01

... affected sources. 63.52 Section 63.52 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... CATEGORIES Requirements for Control Technology Determinations for Major Sources in Accordance With Clean Air... emission reductions that can be achieved if the control technologies or work practices are installed...

40 CFR 63.5986 - What emission limits must I meet for tire cord production affected sources?

Code of Federal Regulations, 2010 CFR

2010-07-01

... tire cord production affected sources? 63.5986 Section 63.5986 Protection of Environment ENVIRONMENTAL... POLLUTANTS FOR SOURCE CATEGORIES National Emissions Standards for Hazardous Air Pollutants: Rubber Tire Manufacturing Emission Limits for Tire Cord Production Affected Sources § 63.5986 What emission limits must I...

Ship localization in Santa Barbara Channel using machine learning classifiers.

PubMed

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

Basic Emotions in the Nencki Affective Word List (NAWL BE): New Method of Classifying Emotional Stimuli.

PubMed

Wierzba, Małgorzata; Riegel, Monika; Wypych, Marek; Jednoróg, Katarzyna; Turnau, Paweł; Grabowska, Anna; Marchewka, Artur

2015-01-01

The Nencki Affective Word List (NAWL) has recently been introduced as a standardized database of Polish words suitable for studying various aspects of language and emotions. Though the NAWL was originally based on the most commonly used dimensional approach, it is not the only way of studying emotions. Another framework is based on discrete emotional categories. Since the two perspectives are recognized as complementary, the aim of the present study was to supplement the NAWL database by the addition of categories corresponding to basic emotions. Thus, 2902 Polish words from the NAWL were presented to 265 subjects, who were instructed to rate them according to the intensity of each of the five basic emotions: happiness, anger, sadness, fear and disgust. The general characteristics of the present word database, as well as the relationships between the studied variables are shown to be consistent with typical patterns found in previous studies using similar databases for different languages. Here we present the Basic Emotions in the Nencki Affective Word List (NAWL BE) as a database of verbal material suitable for highly controlled experimental research. To make the NAWL more convenient to use, we introduce a comprehensive method of classifying stimuli to basic emotion categories. We discuss the advantages of our method in comparison to other methods of classification. Additionally, we provide an interactive online tool (http://exp.lobi.nencki.gov.pl/nawl-analysis) to help researchers browse and interactively generate classes of stimuli to meet their specific requirements.

Basic Emotions in the Nencki Affective Word List (NAWL BE): New Method of Classifying Emotional Stimuli

PubMed Central

Wierzba, Małgorzata; Riegel, Monika; Wypych, Marek; Jednoróg, Katarzyna; Turnau, Paweł; Grabowska, Anna; Marchewka, Artur

2015-01-01

The Nencki Affective Word List (NAWL) has recently been introduced as a standardized database of Polish words suitable for studying various aspects of language and emotions. Though the NAWL was originally based on the most commonly used dimensional approach, it is not the only way of studying emotions. Another framework is based on discrete emotional categories. Since the two perspectives are recognized as complementary, the aim of the present study was to supplement the NAWL database by the addition of categories corresponding to basic emotions. Thus, 2902 Polish words from the NAWL were presented to 265 subjects, who were instructed to rate them according to the intensity of each of the five basic emotions: happiness, anger, sadness, fear and disgust. The general characteristics of the present word database, as well as the relationships between the studied variables are shown to be consistent with typical patterns found in previous studies using similar databases for different languages. Here we present the Basic Emotions in the Nencki Affective Word List (NAWL BE) as a database of verbal material suitable for highly controlled experimental research. To make the NAWL more convenient to use, we introduce a comprehensive method of classifying stimuli to basic emotion categories. We discuss the advantages of our method in comparison to other methods of classification. Additionally, we provide an interactive online tool (http://exp.lobi.nencki.gov.pl/nawl-analysis) to help researchers browse and interactively generate classes of stimuli to meet their specific requirements. PMID:26148193

Histogram deconvolution - An aid to automated classifiers

NASA Technical Reports Server (NTRS)

Lorre, J. J.

1983-01-01

It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.

HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source.

PubMed

Wan, Shixiang; Duan, Yucong; Zou, Quan

2017-09-01

Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels. In reality, multiple locations of particular proteins imply that there are vital and unique biological significances that deserve special focus and cannot be ignored. Second, techniques for handling imbalanced data in multi-label classification problems are necessary, but never employed. For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source. For convenience, a user-friendly webserver has been established at http://server.malab.cn/HPSLPred. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

40 CFR 63.741 - Applicability and designation of affected sources.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 11 2013-07-01 2013-07-01 false Applicability and designation of affected sources. 63.741 Section 63.741 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... requirements of this subpart. (f) This subpart does not contain control requirements for use of specialty...

40 CFR 63.741 - Applicability and designation of affected sources.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 11 2014-07-01 2014-07-01 false Applicability and designation of affected sources. 63.741 Section 63.741 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... requirements of this subpart. (f) This subpart does not contain control requirements for use of specialty...

40 CFR 63.741 - Applicability and designation of affected sources.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 11 2012-07-01 2012-07-01 false Applicability and designation of affected sources. 63.741 Section 63.741 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... requirements of this subpart. (f) This subpart does not contain control requirements for use of specialty...

40 CFR 63.741 - Applicability and designation of affected sources.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 10 2011-07-01 2011-07-01 false Applicability and designation of affected sources. 63.741 Section 63.741 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... requirements of this subpart. (f) This subpart does not contain control requirements for use of specialty...

40 CFR 63.741 - Applicability and designation of affected sources.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 10 2010-07-01 2010-07-01 false Applicability and designation of affected sources. 63.741 Section 63.741 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... requirements of this subpart. (f) This subpart does not contain control requirements for use of specialty...

Classifying clinical notes with pain assessment using machine learning.

PubMed

Fodeh, Samah Jamal; Finch, Dezon; Bouayad, Lina; Luther, Stephen L; Ling, Han; Kerns, Robert D; Brandt, Cynthia

2017-12-26

Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ) including pain assessment, treatment, and reassessment. Currently, there is no intelligent and reliable approach to identify PCQ indicators inelectronic health records (EHR). Hereby, we used unstructured text narratives in the EHR to derive pain assessment in clinical notes for patients with chronic pain. Our dataset includes patients with documented pain intensity rating ratings > = 4 and initial musculoskeletal diagnoses (MSD) captured by (ICD-9-CM codes) in fiscal year 2011 and a minimal 1 year of follow-up (follow-up period is 3-yr maximum); with complete data on key demographic variables. A total of 92 patients with 1058 notes was used. First, we manually annotated qualifiers and descriptors of pain assessment using the annotation schema that we previously developed. Second, we developed a reliable classifier for indicators of pain assessment in clinical note. Based on our annotation schema, we found variations in documenting the subclasses of pain assessment. In positive notes, providers mostly documented assessment of pain site (67%) and intensity of pain (57%), followed by persistence (32%). In only 27% of positive notes, did providers document a presumed etiology for the pain complaint or diagnosis. Documentation of patients' reports of factors that aggravate pain was only present in 11% of positive notes. Random forest classifier achieved the best performance labeling clinical notes with pain assessment information, compared to other classifiers; 94, 95, 94, and 94% was observed in terms of accuracy, PPV, F1-score, and AUC, respectively. Despite the wide spectrum of research that utilizes machine learning in many clinical applications, none explored using these methods for pain assessment research. In addition, previous studies using large datasets to

A statistical approach to combining multisource information in one-class classifiers

DOE PAGES

Simonson, Katherine M.; Derek West, R.; Hansen, Ross L.; ...

2017-06-08

A new method is introduced in this paper for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher's technique for combining p-values, modified to handle nonindependent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorousmore » assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated in two challenge problems, one for skin segmentation and the other for terrain labeling. Finally, the method is seen to be particularly effective for relatively small training samples.« less

A statistical approach to combining multisource information in one-class classifiers

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

Simonson, Katherine M.; Derek West, R.; Hansen, Ross L.

A new method is introduced in this paper for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher's technique for combining p-values, modified to handle nonindependent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorousmore » assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated in two challenge problems, one for skin segmentation and the other for terrain labeling. Finally, the method is seen to be particularly effective for relatively small training samples.« less

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

Banerjee, Portia; Safdarnejad, Seyed; Udpa, Lalita; Udpa, Satish

2016-02-01

Ensemble of classifiers in general, aims to improve classification accuracy by combining results from multiple weak hypotheses into a single strong classifier through weighted majority voting. Improved versions of ensemble of classifiers generate self-rated confidence scores which estimate the reliability of each of its prediction and boost the classifier using these confidence-rated predictions. However, such a confidence metric is based only on the rate of correct classification. In existing works, although ensemble of classifiers has been widely used in computational intelligence, the effect of all factors of unreliability on the confidence of classification is highly overlooked. With relevance to NDE, classification results are affected by inherent ambiguity of classifica-tion, non-discriminative features, inadequate training samples and noise due to measurement. In this paper, we extend the existing ensemble classification by maximizing confidence of every classification decision in addition to minimizing the classification error. Initial results of the approach on data from eddy current inspection show improvement in classification performance of defect and non-defect indications.

40 CFR Table 4 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the Source...

Code of Federal Regulations, 2013 CFR

2013-07-01

...) National Emission Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 4 Table 4 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam... Foam Production Affected Sources Complying With the Source-Wide Emission Limitation 4 Table 4 to...

40 CFR Table 4 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the Source...

Code of Federal Regulations, 2014 CFR

2014-07-01

...) National Emission Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 4 Table 4 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam... Foam Production Affected Sources Complying With the Source-Wide Emission Limitation 4 Table 4 to...

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Affective attitudes to face images associated with intracerebral EEG source location before face viewing.

PubMed

Pizzagalli, D; Koenig, T; Regard, M; Lehmann, D

1999-01-01

We investigated whether different, personality-related affective attitudes are associated with different brain electric field (EEG) sources before any emotional challenge (stimulus exposure). A 27-channel EEG was recorded in 15 subjects during eyes-closed resting. After recording, subjects rated 32 images of human faces for affective appeal. The subjects in the first (i.e., most negative) and fourth (i.e., most positive) quartile of general affective attitude were further analyzed. The EEG data (mean=25+/-4. 8 s/subject) were subjected to frequency-domain model dipole source analysis (FFT-Dipole-Approximation), resulting in 3-dimensional intracerebral source locations and strengths for the delta-theta, alpha, and beta EEG frequency band, and for the full range (1.5-30 Hz) band. Subjects with negative attitude (compared to those with positive attitude) showed the following source locations: more inferior for all frequency bands, more anterior for the delta-theta band, more posterior and more right for the alpha, beta and 1.5-30 Hz bands. One year later, the subjects were asked to rate the face images again. The rating scores for the same face images were highly correlated for all subjects, and original and retest affective mean attitude was highly correlated across subjects. The present results show that subjects with different affective attitudes to face images had different active, cerebral, neural populations in a task-free condition prior to viewing the images. We conclude that the brain functional state which implements affective attitude towards face images as a personality feature exists without elicitors, as a continuously present, dynamic feature of brain functioning. Copyright 1999 Elsevier Science B.V.

40 CFR 63.5987 - What are my alternatives for meeting the emission limits for tire cord production affected sources?

Code of Federal Regulations, 2010 CFR

2010-07-01

... the emission limits for tire cord production affected sources? 63.5987 Section 63.5987 Protection of... Pollutants: Rubber Tire Manufacturing Emission Limits for Tire Cord Production Affected Sources § 63.5987 What are my alternatives for meeting the emission limits for tire cord production affected sources? You...

40 CFR 63.7884 - What are the general standards I must meet for each site remediation with affected sources?

Code of Federal Regulations, 2010 CFR

2010-07-01

... meet for each site remediation with affected sources? 63.7884 Section 63.7884 Protection of Environment... Pollutants: Site Remediation General Standards § 63.7884 What are the general standards I must meet for each site remediation with affected sources? (a) For each site remediation with an affected source...

Developing collaborative classifiers using an expert-based model

USGS Publications Warehouse

Mountrakis, G.; Watts, R.; Luo, L.; Wang, Jingyuan

2009-01-01

This paper presents a hierarchical, multi-stage adaptive strategy for image classification. We iteratively apply various classification methods (e.g., decision trees, neural networks), identify regions of parametric and geographic space where accuracy is low, and in these regions, test and apply alternate methods repeating the process until the entire image is classified. Currently, classifiers are evaluated through human input using an expert-based system; therefore, this paper acts as the proof of concept for collaborative classifiers. Because we decompose the problem into smaller, more manageable sub-tasks, our classification exhibits increased flexibility compared to existing methods since classification methods are tailored to the idiosyncrasies of specific regions. A major benefit of our approach is its scalability and collaborative support since selected low-accuracy classifiers can be easily replaced with others without affecting classification accuracy in high accuracy areas. At each stage, we develop spatially explicit accuracy metrics that provide straightforward assessment of results by non-experts and point to areas that need algorithmic improvement or ancillary data. Our approach is demonstrated in the task of detecting impervious surface areas, an important indicator for human-induced alterations to the environment, using a 2001 Landsat scene from Las Vegas, Nevada. ?? 2009 American Society for Photogrammetry and Remote Sensing.

MScanner: a classifier for retrieving Medline citations

PubMed Central

Poulter, Graham L; Rubin, Daniel L; Altman, Russ B; Seoighe, Cathal

2008-01-01

retrieving topics for which many features may indicate relevance. Its web interface simplifies the task of classifying Medline citations, compared to building a pre-filter and classifier specific to the topic. The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material, and the web interface may be accessed at . PMID:18284683

40 CFR 63.1318 - PET and polystyrene affected sources-testing and compliance demonstration provisions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false PET and polystyrene affected sources... Pollutant Emissions: Group IV Polymers and Resins § 63.1318 PET and polystyrene affected sources—testing and... effectiveness) do not apply and owners or operators are not required to comply with § 63.113. (b) PET affected...

Comparison of artificial intelligence classifiers for SIP attack data

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Slachta, Jiri

2016-05-01

Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

Categorizing sources of risk and the estimated magnitude of risk.

PubMed

Aragonés, Juan Ignacio; Moyano, Emilio; Talayero, Fernando

2008-05-01

The social perception of risk is considered a multidimensional task, yet little attention has been paid to the cognitive components that organize sources of risk, despite their having been discovered in various research studies. This study attempts to concretely analyze the cultural dimension involved in those processes. In the first phase, we tried to discover to what extent sources of risk are organized into the same categories by people from different countries. In order to do so, two groups of participants were formed: 60 Spanish psychology students and 60 Chilean psychology students classified 43 sources of risk into different groups according to the criteria they found appropriate. The two samples classified risk into identical groups: acts of violence, drugs, electricity and home appliances, household chemicals, chemicals in the environment, public construction projects, transportation, sports, and natural disasters. In a second study, 100 Spanish and 84 Chilean students were asked to evaluate the magnitude of the damage incurred by 17 sources of risk. In both groups, it was observed that the evaluation of damage resulting from each source of risk was affected by its category.

Ensemble stump classifiers and gene expression signatures in lung cancer.

PubMed

Frey, Lewis; Edgerton, Mary; Fisher, Douglas; Levy, Shawn

2007-01-01

Microarray data sets for cancer tumor tissue generally have very few samples, each sample having thousands of probes (i.e., continuous variables). The sparsity of samples makes it difficult for machine learning techniques to discover probes relevant to the classification of tumor tissue. By combining data from different platforms (i.e., data sources), data sparsity is reduced, but this typically requires normalizing data from the different platforms, which can be non-trivial. This paper proposes a variant on the idea of ensemble learners to circumvent the need for normalization. To facilitate comprehension we build ensembles of very simple classifiers known as decision stumps--decision trees of one test each. The Ensemble Stump Classifier (ESC) identifies an mRNA signature having three probes and high accuracy for distinguishing between adenocarcinoma and squamous cell carcinoma of the lung across four data sets. In terms of accuracy, ESC outperforms a decision tree classifier on all four data sets, outperforms ensemble decision trees on three data sets, and simple stump classifiers on two data sets.

The influence of category coherence on inference about cross-classified entities.

PubMed

Patalano, Andrea L; Wengrovitz, Steven M; Sharpes, Kirsten M

2009-01-01

A critical function of categories is their use in property inference (Heit, 2000). However, one challenge to using categories in inference is that most entities in the world belong to multiple categories (e.g., Fido could be a dog, a pet, a mammal, or a security system). Building on Patalano, Chin-Parker, and Ross (2006), we tested the hypothesis that category coherence (the extent to which category features go together in light of prior knowledge) influences the selection of categories for use in property inference about cross-classified entities. In two experiments, we directly contrasted coherent and incoherent categories, both of which included cross-classified entities as members, and we found that the coherent categories were used more readily as the source of both property transfer and property extension. We conclude that category coherence, which has been found to be a potent influence on strength of inference for singly classified entities (Rehder & Hastie, 2004), is also central to category use in reasoning about novel cross-classified ones.

40 CFR 63.11194 - What is the affected source of this subpart?

Code of Federal Regulations, 2011 CFR

2011-07-01

... commence construction. (d) A boiler is a new affected source if you commenced fuel switching from natural gas to solid fossil fuel, biomass, or liquid fuel after June 4, 2010. (e) If you are an owner or...

40 CFR 63.11194 - What is the affected source of this subpart?

Code of Federal Regulations, 2012 CFR

2012-07-01

... commence construction. (d) A boiler is a new affected source if you commenced fuel switching from natural gas to solid fossil fuel, biomass, or liquid fuel after June 4, 2010. (e) If you are an owner or...

How generation affects source memory.

PubMed

Geghman, Kindiya D; Multhaup, Kristi S

2004-07-01

Generation effects (better memory for self-produced items than for provided items) typically occur in item memory. Jurica and Shimamura (1999) reported a negative generation effect in source memory, but their procedure did not test participants on the items they had generated. In Experiment 1, participants answered questions and read statements made by a face on a computer screen. The target word was unscrambled, or letters were filled in. Generation effects were found for target recall and source recognition (which person did which task). Experiment 2 extended these findings to a condition in which the external sources were two different faces. Generation had a positive effect on source memory, supporting an overlap in the underlying mechanisms of item and source memory.

Separation of Powers in Classifying International Agreements

DTIC Science & Technology

1996-01-01

SEPARATION OF POWERS IN CLASSIFYING INTERNATIONAL AGREEMENTS CORE COURSE III ESSAY CDR James F Duffy, JAGC, USN, Class of 96 The National Secmty Policy Process SemmrH Faculty Semmar Instructor Dr John Rexhart Faculty Adviser CAPT J Kelso, USN Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing

ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease

PubMed Central

Apostolova, Liana G.; Hwang, Kristy S.; Kohannim, Omid; Avila, David; Elashoff, David; Jack, Clifford R.; Shaw, Leslie; Trojanowski, John Q.; Weiner, Michael W.; Thompson, Paul M.

2014-01-01

Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers

Predicting breast cancer using an expression values weighted clinical classifier.

PubMed

Thomas, Minta; De Brabanter, Kris; Suykens, Johan A K; De Moor, Bart

2014-12-31

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

Dynamic system classifier.

PubMed

Pumpe, Daniel; Greiner, Maksim; Müller, Ewald; Enßlin, Torsten A

2016-07-01

Stochastic differential equations describe well many physical, biological, and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time-dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of the DSC to oscillation processes with a time-dependent frequency ω(t) and damping factor γ(t). Although real systems might be more complex, this simple oscillator captures many characteristic features. The ω and γ time lines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiments show that such classifiers perform well even in the low signal-to-noise regime.

40 CFR Table 1 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2014 CFR

2014-07-01

... Subpart PPP Affected Sources 1 Table 1 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Emissions for Polyether Polyols Production Pt. 63, Subpt. PPP, Table 1 Table 1 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies tosubpart PPP Explanation...

40 CFR Table 1 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2013 CFR

2013-07-01

... Subpart PPP Affected Sources 1 Table 1 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Emissions for Polyether Polyols Production Pt. 63, Subpt. PPP, Table 1 Table 1 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies tosubpart PPP Explanation...

40 CFR Table 2 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... Subpart PPP Affected Sources 2 Table 2 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 2 Table 2 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies to subpart PPP Explanation Applicable...

40 CFR Table 2 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2012 CFR

2012-07-01

... Subpart PPP Affected Sources 2 Table 2 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 2 Table 2 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies to subpart PPP Explanation Applicable...

40 CFR Table 2 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... Subpart PPP Affected Sources 2 Table 2 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 2 Table 2 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies to subpart PPP Explanation Applicable...

40 CFR Table 1 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2012 CFR

2012-07-01

... Subpart PPP Affected Sources 1 Table 1 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 1 Table 1 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies tosubpart PPP Explanation 63.1(a)(1...

40 CFR Table 1 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... Subpart PPP Affected Sources 1 Table 1 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 1 Table 1 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies tosubpart PPP Explanation 63.1(a)(1...

40 CFR Table 2 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2013 CFR

2013-07-01

... Subpart PPP Affected Sources 2 Table 2 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Emissions for Polyether Polyols Production Pt. 63, Subpt. PPP, Table 2 Table 2 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies to subpart PPP Explanation...

40 CFR Table 2 of Subpart Ppp of... - Applicability of HON Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2014 CFR

2014-07-01

... Subpart PPP Affected Sources 2 Table 2 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Emissions for Polyether Polyols Production Pt. 63, Subpt. PPP, Table 2 Table 2 of Subpart PPP of Part 63—Applicability of HON Provisions to Subpart PPP Affected Sources Reference Applies to subpart PPP Explanation...

40 CFR Table 1 of Subpart Ppp of... - Applicability of General Provisions to Subpart PPP Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... Subpart PPP Affected Sources 1 Table 1 of Subpart PPP of Part 63 Protection of Environment ENVIRONMENTAL... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 1 Table 1 of Subpart PPP of Part 63—Applicability of General Provisions to Subpart PPP Affected Sources Reference Applies tosubpart PPP Explanation 63.1(a)(1...

Women in the Academy: The Impact of Culture, Climate and Policies on Female Classified Staff

ERIC Educational Resources Information Center

Costello, Carla A.

2012-01-01

The purpose of this study was to obtain an understanding of the impact of gendered organizations on female classified staff and their perception of climate and culture on advancement opportunities. The findings shed light on critical factors affecting the work experiences of female classified staff. The findings also offer a variety of ways…

Privacy-Preserving Classifier Learning

NASA Astrophysics Data System (ADS)

Brickell, Justin; Shmatikov, Vitaly

We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database held by a remote server without learning any additional information about the records held in the database. The server does not learn anything about the constructed classifier, not even the user’s choice of feature and class attributes.

A machine learned classifier for RR Lyrae in the VVV survey

NASA Astrophysics Data System (ADS)

Elorrieta, Felipe; Eyheramendy, Susana; Jordán, Andrés; Dékány, István; Catelan, Márcio; Angeloni, Rodolfo; Alonso-García, Javier; Contreras-Ramos, Rodrigo; Gran, Felipe; Hajdu, Gergely; Espinoza, Néstor; Saito, Roberto K.; Minniti, Dante

2016-11-01

Variable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and particularly those of the more easily recognized type ab (I.e., fundamental-mode pulsators), from the 106-107 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.

40 CFR 63.5795 - How do I know if my reinforced plastic composites production facility is a new affected source or...

Code of Federal Regulations, 2010 CFR

2010-07-01

... composites production facility is a new affected source or an existing affected source? 63.5795 Section 63... for Hazardous Air Pollutants: Reinforced Plastic Composites Production What This Subpart Covers § 63.5795 How do I know if my reinforced plastic composites production facility is a new affected source or...

Protein and starch concentrates of air-classified field pea and zero-tannin faba bean for weaned pigs.

PubMed

Gunawardena, C K; Zijlstra, R T; Goonewardene, L A; Beltranena, E

2010-08-01

Air-classified pulse (non-oilseed legume) protein and starch may replace specialty protein and starch feedstuffs in diets for weaned pigs. In Exp. 1, three specialty protein sources (5% soy protein concentrate, 5% corn gluten meal, and 5% menhaden meal in the control diet) were replaced with 16% zero-tannin hulled or dehulled faba bean, or 17.5% field pea protein concentrate. In total, 192 group-housed pigs (2 gilts and 2 barrows per pen; BW = 7.5 +/- 1.4 kg) were fed wheat-based diets (3.60 Mcal/kg of DE and 3.3 g of standardized ileal digestible Lys/Mcal DE) over 28 d for 12 pen observations per each of 4 diets. Overall, protein source did not affect ADFI, ADG, or G:F. Apparent total tract digestibility (ATTD) of DM, GE, and P was greater (P < 0.05) for dehulled faba bean and field pea protein concentrate diets than the diet with 3 specialty protein sources. In Exp. 2, faba bean and field pea starch concentrates were compared with corn, wheat, tapioca, and potato starch as dietary energy sources. In total, 36 individually housed barrows (BW = 8.0 +/- 1.5 kg) were fed 1 of 6 diets for 15 d. Feces and urine were collected from d 8 to 14, and jugular blood was sampled after overnight fast and refeeding on d 15. Starch source did not affect N retention as a percentage of N intake. For d 0 to 14, ADFI of pigs fed field pea starch was greater (P < 0.05) than pigs fed corn, wheat, potato, and faba bean starch. Pigs fed tapioca, field pea, wheat, or corn starch grew faster (P < 0.05) than those fed faba bean or potato starch. For d 0 to 14, pigs fed corn or wheat starch had a 0.1 greater (P < 0.05) G:F than pigs fed faba bean, field pea, or potato starch. The ATTD of DM, GE, CP, and starch and the DE value of potato starch were much less (P < 0.05) than those of other starch diets. Postprandial plasma glucose was 4.9, 6.3, and 9 mmol/L greater (P < 0.05) for pigs fed tapioca than for pigs fed faba bean, wheat, and potato starch, respectively. However, postprandial plasma

40 CFR Table 5 to Subpart III of... - Compliance Requirements for Molded and Rebond Foam Production Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 5 Table 5 to Subpart III of Part 63—Compliance Requirements for Molded and Rebond Foam Production Affected Sources Emission point Emission point... Rebond Foam Production Affected Sources 5 Table 5 to Subpart III of Part 63 Protection of Environment...

40 CFR Table 5 to Subpart III of... - Compliance Requirements for Molded and Rebond Foam Production Affected Sources

Code of Federal Regulations, 2014 CFR

2014-07-01

... Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 5 Table 5 to Subpart III of Part 63—Compliance Requirements for Molded and Rebond Foam Production Affected Sources Emission point... Rebond Foam Production Affected Sources 5 Table 5 to Subpart III of Part 63 Protection of Environment...

40 CFR Table 5 to Subpart III of... - Compliance Requirements for Molded and Rebond Foam Production Affected Sources

Code of Federal Regulations, 2013 CFR

2013-07-01

... Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 5 Table 5 to Subpart III of Part 63—Compliance Requirements for Molded and Rebond Foam Production Affected Sources Emission point... Rebond Foam Production Affected Sources 5 Table 5 to Subpart III of Part 63 Protection of Environment...

40 CFR Table 5 to Subpart III of... - Compliance Requirements for Molded and Rebond Foam Production Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 5 Table 5 to Subpart III of Part 63—Compliance Requirements for Molded and Rebond Foam Production Affected Sources Emission point Emission point... Rebond Foam Production Affected Sources 5 Table 5 to Subpart III of Part 63 Protection of Environment...

40 CFR Table 5 to Subpart III of... - Compliance Requirements for Molded and Rebond Foam Production Affected Sources

Code of Federal Regulations, 2012 CFR

2012-07-01

... Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 5 Table 5 to Subpart III of Part 63—Compliance Requirements for Molded and Rebond Foam Production Affected Sources Emission point Emission point... Rebond Foam Production Affected Sources 5 Table 5 to Subpart III of Part 63 Protection of Environment...

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 distributed approach for optimizing cascaded classifier topologies in real-time stream mining systems.

PubMed

Foo, Brian; van der Schaar, Mihaela

2010-11-01

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

Bayes classifiers for imbalanced traffic accidents datasets.

PubMed

Mujalli, Randa Oqab; López, Griselda; Garach, Laura

2016-03-01

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantum ensembles of quantum classifiers.

PubMed

Schuld, Maria; Petruccione, Francesco

2018-02-09

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

Air classifier technology (ACT) in dry powder inhalation Part 3. Design and development of an air classifier family for the Novolizer multi-dose dry powder inhaler.

PubMed

de Boer, A H; Hagedoorn, P; Gjaltema, D; Goede, J; Frijlink, H W

2006-03-09

In this study, the design of a multifarious classifier family for different applications is described. The main design and development steps are presented as well as some special techniques that have been applied to achieve preset objectives. It is shown by increasing the number of air supply channels to the classifier chamber (from 2 to 8), that the fine particle losses from adhesion onto the classifier walls can be reduced from 75% to less than 5% of the real dose for soft (spherical) agglomerates. By applying a bypass flow that is arranged as a co-axial sheath of clean air around the aerosol cloud from the classifier, the airflow resistance of the classifier can be controlled over a relatively wide range of values (0.023-0.041 kPa(0.5) min l(-1)). This, without affecting the fine particle dose or increasing the fine particle losses in the inhaler. Moreover, the sheath flow can be modelled to reduce the depositions in the induction port to the cascade impactor or in the patient's mouth, which are the result of back flows in these regions. The principle of powder induced pressure drop reduction across a classifier enables assessment of the amount of powder in the classifier at any moment during inhalation, from which classifier loading (from the dose system) and discharge rates can be derived. This principle has been applied to study the residence time of a dose in the classifier as function of the carrier size fraction and the flow rate. It has been found that this residence time can be controlled in order to obtain an optimal balance between the generated fine particle fraction and the inhalation manoeuvre of the patient. A residence time between 0.5 and 2 s at 60 l/min is considered favourable, as this yields a high fine particle dose (depending on the type of formulation used) and leaves sufficient inhaled volume for particle transport into the deep lung.

The diabolo classifier

PubMed

Schwenk

1998-11-15

We present a new classification architecture based on autoassociative neural networks that are used to learn discriminant models of each class. The proposed architecture has several interesting properties with respect to other model-based classifiers like nearest-neighbors or radial basis functions: it has a low computational complexity and uses a compact distributed representation of the models. The classifier is also well suited for the incorporation of a priori knowledge by means of a problem-specific distance measure. In particular, we will show that tangent distance (Simard, Le Cun, & Denker, 1993) can be used to achieve transformation invariance during learning and recognition. We demonstrate the application of this classifier to optical character recognition, where it has achieved state-of-the-art results on several reference databases. Relations to other models, in particular those based on principal component analysis, are also discussed.

40 CFR 63.7882 - What site remediation sources at my facility does this subpart affect?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 13 2010-07-01 2010-07-01 false What site remediation sources at my... Remediation What This Subpart Covers § 63.7882 What site remediation sources at my facility does this subpart... remediation as designated by paragraphs (a)(1) through (3) of this section. (1) Process vents. The affected...

28 CFR 701.14 - Classified information.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 2 2013-07-01 2013-07-01 false Classified information. 701.14 Section... UNDER THE FREEDOM OF INFORMATION ACT § 701.14 Classified information. In processing a request for information that is classified or classifiable under Executive Order 12356 or any other Executive Order...

Classifying elephant behaviour through seismic vibrations.

PubMed

Mortimer, Beth; Rees, William Lake; Koelemeijer, Paula; Nissen-Meyer, Tarje

2018-05-07

Seismic waves - vibrations within and along the Earth's surface - are ubiquitous sources of information. During propagation, physical factors can obscure information transfer via vibrations and influence propagation range [1]. Here, we explore how terrain type and background seismic noise influence the propagation of seismic vibrations generated by African elephants. In Kenya, we recorded the ground-based vibrations of different wild elephant behaviours, such as locomotion and infrasonic vocalisations [2], as well as natural and anthropogenic seismic noise. We employed techniques from seismology to transform the geophone recordings into source functions - the time-varying seismic signature generated at the source. We used computer modelling to constrain the propagation ranges of elephant seismic vibrations for different terrains and noise levels. Behaviours that generate a high force on a sandy terrain with low noise propagate the furthest, over the kilometre scale. Our modelling also predicts that specific elephant behaviours can be distinguished and monitored over a range of propagation distances and noise levels. We conclude that seismic cues have considerable potential for both behavioural classification and remote monitoring of wildlife. In particular, classifying the seismic signatures of specific behaviours of large mammals remotely in real time, such as elephant running, could inform on poaching threats. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

NASA Astrophysics Data System (ADS)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

NASA Astrophysics Data System (ADS)

Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad

2018-04-01

There are many ways of implementing the use of sentiments often found in documents; one of which is the sentiments found on the product or service reviews. It is so important to be able to process and extract textual data from the documents. Therefore, we propose a system that is able to classify sentiments from review documents into two classes: positive sentiment and negative sentiment. We use Naive Bayes Classifier method in this document classification system that we build. We choose Movienthusiast, a movie reviews in Bahasa Indonesia website as the source of our review documents. From there, we were able to collect 1201 movie reviews: 783 positive reviews and 418 negative reviews that we use as the dataset for this machine learning classifier. The classifying accuracy yields an average of 88.37% from five times of accuracy measuring attempts using aforementioned dataset.

40 CFR 63.55 - Maximum achievable control technology (MACT) determinations for affected sources subject to case...

Code of Federal Regulations, 2010 CFR

2010-07-01

... (MACT) determinations for affected sources subject to case-by-case determination of equivalent emission... sources subject to case-by-case determination of equivalent emission limitations. (a) Requirements for... hazardous air pollutant emissions limitations equivalent to the limitations that would apply if an emission...

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

40 CFR Table 3 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the...

Code of Federal Regulations, 2012 CFR

2012-07-01

... Foam Production Affected Sources Complying With the Emission Point Specific Limitations 3 Table 3 to... Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 3 Table 3 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam Production Affected Sources...

40 CFR Table 3 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the...

Code of Federal Regulations, 2011 CFR

2011-07-01

... Foam Production Affected Sources Complying With the Emission Point Specific Limitations 3 Table 3 to... Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 3 Table 3 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam Production Affected Sources...

40 CFR Table 3 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the...

Code of Federal Regulations, 2010 CFR

2010-07-01

... Foam Production Affected Sources Complying With the Emission Point Specific Limitations 3 Table 3 to... Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 3 Table 3 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam Production Affected Sources...

40 CFR 63.55 - Maximum achievable control technology (MACT) determinations for affected sources subject to case...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 10 2013-07-01 2013-07-01 false Maximum achievable control technology (MACT) determinations for affected sources subject to case-by-case determination of equivalent emission... Requirements for Control Technology Determinations for Major Sources in Accordance With Clean Air Act Sections...

40 CFR 63.55 - Maximum achievable control technology (MACT) determinations for affected sources subject to case...

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 10 2012-07-01 2012-07-01 false Maximum achievable control technology (MACT) determinations for affected sources subject to case-by-case determination of equivalent emission... Requirements for Control Technology Determinations for Major Sources in Accordance With Clean Air Act Sections...

40 CFR 63.55 - Maximum achievable control technology (MACT) determinations for affected sources subject to case...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 9 2011-07-01 2011-07-01 false Maximum achievable control technology (MACT) determinations for affected sources subject to case-by-case determination of equivalent emission... Requirements for Control Technology Determinations for Major Sources in Accordance With Clean Air Act Sections...

40 CFR 63.55 - Maximum achievable control technology (MACT) determinations for affected sources subject to case...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 10 2014-07-01 2014-07-01 false Maximum achievable control technology (MACT) determinations for affected sources subject to case-by-case determination of equivalent emission... Requirements for Control Technology Determinations for Major Sources in Accordance With Clean Air Act Sections...

Classifying Motion.

ERIC Educational Resources Information Center

Duzen, Carl; And Others

1992-01-01

Presents a series of activities that utilizes a leveling device to classify constant and accelerated motion. Applies this classification system to uniform circular motion and motion produced by gravitational force. (MDH)

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

NASA Astrophysics Data System (ADS)

da Silva, Flávio Altinier Maximiano; Pedrini, Helio

2015-03-01

Facial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well

A selection of giant radio sources from NVSS

DOE PAGES

Proctor, D. D.

2016-06-01

Results of the application of pattern-recognition techniques to the problem of identifying giant radio sources (GRSs) from the data in the NVSS catalog are presented, and issues affecting the process are explored. Decision-tree pattern-recognition software was applied to training-set source pairs developed from known NVSS large-angular-size radio galaxies. The full training set consisted of 51,195 source pairs, 48 of which were known GRSs for which each lobe was primarily represented by a single catalog component. The source pairs had a maximum separation ofmore » $$20^{\\prime} $$ and a minimum component area of 1.87 square arcmin at the 1.4 mJy level. The importance of comparing the resulting probability distributions of the training and application sets for cases of unknown class ratio is demonstrated. The probability of correctly ranking a randomly selected (GRS, non-GRS) pair from the best of the tested classifiers was determined to be 97.8 ± 1.5%. The best classifiers were applied to the over 870,000 candidate pairs from the entire catalog. Images of higher-ranked sources were visually screened, and a table of over 1600 candidates, including morphological annotation, is presented. These systems include doubles and triples, wide-angle tail and narrow-angle tail, S- or Z-shaped systems, and core-jets and resolved cores. In conclusion, while some resolved-lobe systems are recovered with this technique, generally it is expected that such systems would require a different approach.« less

Probabilistic classifiers with high-dimensional data

PubMed Central

Kim, Kyung In; Simon, Richard

2011-01-01

For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probabilistic classifiers and developed 2 extensions of the Bayesian compound covariate classifier. Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. It is important to ensure that a probabilistic classifier is well calibrated or at least not “anticonservative” using the methods developed here. We provide this evaluation for several probabilistic classifiers and also evaluate their refinement as a function of sample size under weak and strong signal conditions. We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set. PMID:21087946

14 CFR 1216.317 - Classified information.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Classified information. 1216.317 Section 1216.317 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION ENVIRONMENTAL QUALITY... Classified information. Environmental assessments and impact statements which contain classified information...

14 CFR 1216.317 - Classified information.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 5 2011-01-01 2010-01-01 true Classified information. 1216.317 Section 1216.317 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION ENVIRONMENTAL QUALITY... Classified information. Environmental assessments and impact statements which contain classified information...

14 CFR 1216.317 - Classified information.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 5 2012-01-01 2012-01-01 false Classified information. 1216.317 Section 1216.317 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION ENVIRONMENTAL QUALITY... Classified information. Environmental assessments and impact statements which contain classified information...

Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

Treesearch

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

1998-01-01

Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

Use of machine-learning classifiers to predict requests for preoperative acute pain service consultation.

PubMed

Tighe, Patrick J; Lucas, Stephen D; Edwards, David A; Boezaart, André P; Aytug, Haldun; Bihorac, Azra

2012-10-01

  The purpose of this project was to determine whether machine-learning classifiers could predict which patients would require a preoperative acute pain service (APS) consultation.   Retrospective cohort.   University teaching hospital.   The records of 9,860 surgical patients posted between January 1 and June 30, 2010 were reviewed.   Request for APS consultation. A cohort of machine-learning classifiers was compared according to its ability or inability to classify surgical cases as requiring a request for a preoperative APS consultation. Classifiers were then optimized utilizing ensemble techniques. Computational efficiency was measured with the central processing unit processing times required for model training. Classifiers were tested using the full feature set, as well as the reduced feature set that was optimized using a merit-based dimensional reduction strategy.   Machine-learning classifiers correctly predicted preoperative requests for APS consultations in 92.3% (95% confidence intervals [CI], 91.8-92.8) of all surgical cases. Bayesian methods yielded the highest area under the receiver operating curve (0.87, 95% CI 0.84-0.89) and lowest training times (0.0018 seconds, 95% CI, 0.0017-0.0019 for the NaiveBayesUpdateable algorithm). An ensemble of high-performing machine-learning classifiers did not yield a higher area under the receiver operating curve than its component classifiers. Dimensional reduction decreased the computational requirements for multiple classifiers, but did not adversely affect classification performance.   Using historical data, machine-learning classifiers can predict which surgical cases should prompt a preoperative request for an APS consultation. Dimensional reduction improved computational efficiency and preserved predictive performance. Wiley Periodicals, Inc.

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Information Security Program § 503.59 Safeguarding classified information. (a) All classified information... security; (2) Takes appropriate steps to protect classified information from unauthorized disclosure or... security check; (2) To protect the classified information in accordance with the provisions of Executive...

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.

40 CFR Table 2 to Subpart Xxxx of... - Emission Limits for Tire Cord Production Affected Sources

Code of Federal Regulations, 2013 CFR

2013-07-01

... source Emissions must not exceed 280 grams HAP per megagram (0.56 pounds per ton) of fabric processed at... tire cord production affected source Emissions must not exceed 220 grams HAP per megagram (0.43 pounds... Table 16 to this subpart must not exceed 1,000 grams HAP per megagram (2 pounds per ton) of total...

40 CFR Table 2 to Subpart Xxxx of... - Emission Limits for Tire Cord Production Affected Sources

Code of Federal Regulations, 2014 CFR

2014-07-01

... source Emissions must not exceed 280 grams HAP per megagram (0.56 pounds per ton) of fabric processed at... tire cord production affected source Emissions must not exceed 220 grams HAP per megagram (0.43 pounds... Table 16 to this subpart must not exceed 1,000 grams HAP per megagram (2 pounds per ton) of total...

Effect of Inverter Power Source Characteristics on Welding Stability and Heat Affected Zone Dimensions

NASA Astrophysics Data System (ADS)

Il'yaschenko, D. P.; Chinakhov, D. A.; Mamadaliev, R. A.

2018-01-01

The paper presents results the research in the effect of power sources dynamic characteristics on stability of melting and electrode metal transfer to the weld pool shielded metal arc welding. It is proved that when applying inverter-type welding power sources, heat and mass transfer characteristics change, arc gap short-circuit time and drop generation time are reduced. This leads to reduction of weld pool heat content and contraction of the heat-affected zone by 36% in comparison the same parameters obtained using a diode rectifier.

15 CFR 4.8 - Classified Information.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily the...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Classified actions. 651.13 Section 651.13... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information...

Comparison of seven protocols to identify fecal contamination sources using Escherichia coli

USGS Publications Warehouse

Stoeckel, D.M.; Mathes, M.V.; Hyer, K.E.; Hagedorn, C.; Kator, H.; Lukasik, J.; O'Brien, T. L.; Fenger, T.W.; Samadpour, M.; Strickler, K.M.; Wiggins, B.A.

2004-01-01

Microbial source tracking (MST) uses various approaches to classify fecal-indicator microorganisms to source hosts. Reproducibility, accuracy, and robustness of seven phenotypic and genotypic MST protocols were evaluated by use of Escherichia coli from an eight-host library of known-source isolates and a separate, blinded challenge library. In reproducibility tests, measuring each protocol's ability to reclassify blinded replicates, only one (pulsed-field gel electrophoresis; PFGE) correctly classified all test replicates to host species; three protocols classified 48-62% correctly, and the remaining three classified fewer than 25% correctly. In accuracy tests, measuring each protocol's ability to correctly classify new isolates, ribotyping with EcoRI and PvuII approached 100% correct classification but only 6% of isolates were classified; four of the other six protocols (antibiotic resistance analysis, PFGE, and two repetitive-element PCR protocols) achieved better than random accuracy rates when 30-100% of challenge isolates were classified. In robustness tests, measuring each protocol's ability to recognize isolates from nonlibrary hosts, three protocols correctly classified 33-100% of isolates as "unknown origin," whereas four protocols classified all isolates to a source category. A relevance test, summarizing interpretations for a hypothetical water sample containing 30 challenge isolates, indicated that false-positive classifications would hinder interpretations for most protocols. Study results indicate that more representation in known-source libraries and better classification accuracy would be needed before field application. Thorough reliability assessment of classification results is crucial before and during application of MST protocols.

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

PubMed

Ozcift, Akin

2012-08-01

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Classifying Microorganisms.

ERIC Educational Resources Information Center

Baker, William P.; Leyva, Kathryn J.; Lang, Michael; Goodmanis, Ben

2002-01-01

Focuses on an activity in which students sample air at school and generate ideas about how to classify the microorganisms they observe. The results are used to compare air quality among schools via the Internet. Supports the development of scientific inquiry and technology skills. (DDR)

Integrating multiple data sources for malware classification

DOEpatents

Anderson, Blake Harrell; Storlie, Curtis B; Lane, Terran

2015-04-28

Disclosed herein are representative embodiments of tools and techniques for classifying programs. According to one exemplary technique, at least one graph representation of at least one dynamic data source of at least one program is generated. Also, at least one graph representation of at least one static data source of the at least one program is generated. Additionally, at least using the at least one graph representation of the at least one dynamic data source and the at least one graph representation of the at least one static data source, the at least one program is classified.

Multicategory Composite Least Squares Classifiers

PubMed Central

Park, Seo Young; Liu, Yufeng; Liu, Dacheng; Scholl, Paul

2010-01-01

Classification is a very useful statistical tool for information extraction. In particular, multicategory classification is commonly seen in various applications. Although binary classification problems are heavily studied, extensions to the multicategory case are much less so. In view of the increased complexity and volume of modern statistical problems, it is desirable to have multicategory classifiers that are able to handle problems with high dimensions and with a large number of classes. Moreover, it is necessary to have sound theoretical properties for the multicategory classifiers. In the literature, there exist several different versions of simultaneous multicategory Support Vector Machines (SVMs). However, the computation of the SVM can be difficult for large scale problems, especially for problems with large number of classes. Furthermore, the SVM cannot produce class probability estimation directly. In this article, we propose a novel efficient multicategory composite least squares classifier (CLS classifier), which utilizes a new composite squared loss function. The proposed CLS classifier has several important merits: efficient computation for problems with large number of classes, asymptotic consistency, ability to handle high dimensional data, and simple conditional class probability estimation. Our simulated and real examples demonstrate competitive performance of the proposed approach. PMID:21218128

Characteristics of the internal and external sources of the Mediterranean synoptic cyclones for the period 1956-2013

NASA Astrophysics Data System (ADS)

Almazroui, Mansour; Awad, Adel M.; Nazrul Islam, M.

2017-07-01

This paper investigates the main sources and features of the Mediterranean synoptic cyclones affecting the basin, using the cyclone tracks. The cyclones' tracks are identified using sea level pressure (SLP) from the NCEP/NCAR reanalysis data for the period 1956-2013. The identified cyclones are classified into two categories: basin affected and basin non-affected. Most of the basin-affected (non-affected) cyclones are internal (external), i.e., generated inside (outside) the Mediterranean basin. This study reveals four (five) main sources of internal (external) cyclones. These four (five) main sources generated about 63.76% (57.25%) of the internal (external) cyclones. Seasonal analysis shows that most of the basin-affected internal (external) cyclones were generated in the winter (spring) season. The lowest number of cyclones were found in the summer. Moreover, the synoptic study of the atmospheric systems accompanied the highest- and lowest-generated years demonstrates that the deepening of the north Europe cyclones and the relative positions of Azores- and Siberian-high systems represent the important factors that influence the number of internal cyclones. Essential factors influencing the external cyclones are the strength of the maximum upper wind, Azores high, Siberian high, and orientations of their ridges.

40 CFR Table 3 to Subpart Kkkk of... - Emission Limits for Affected Sources Using the Control Efficiency/Outlet Concentration Compliance...

Code of Federal Regulations, 2013 CFR

2013-07-01

... reconstructed affected source a. reduce emissions of total HAP, measured as THC (as carbon), a by 97 percent; orb. limit emissions of total HAP, measured as THC (as carbon), a to 20 ppmvd at the control device outlet and use a PTE. 2. in an existing affected source a. reduce emissions of total HAP, measured as THC...

40 CFR Table 3 to Subpart Kkkk of... - Emission Limits for Affected Sources Using the Control Efficiency/Outlet Concentration Compliance...

Code of Federal Regulations, 2012 CFR

2012-07-01

... reconstructed affected source a. reduce emissions of total HAP, measured as THC (as carbon), a by 97 percent; orb. limit emissions of total HAP, measured as THC (as carbon), a to 20 ppmvd at the control device outlet and use a PTE. 2. in an existing affected source a. reduce emissions of total HAP, measured as THC...

40 CFR Table 3 to Subpart Kkkk of... - Emission Limits for Affected Sources Using the Control Efficiency/Outlet Concentration Compliance...

Code of Federal Regulations, 2014 CFR

2014-07-01

... reconstructed affected source a. reduce emissions of total HAP, measured as THC (as carbon), a by 97 percent; orb. limit emissions of total HAP, measured as THC (as carbon), a to 20 ppmvd at the control device outlet and use a PTE. 2. in an existing affected source a. reduce emissions of total HAP, measured as THC...

40 CFR Table 3 to Subpart Kkkk of... - Emission Limits for Affected Sources Using the Control Efficiency/Outlet Concentration Compliance...

Code of Federal Regulations, 2011 CFR

2011-07-01

... affected source a. reduce emissions of total HAP, measured as THC (as carbon), a by 97 percent; orb. limit emissions of total HAP, measured as THC (as carbon), a to 20 ppmvd at the control device outlet and use a PTE. 2. in an existing affected source a. reduce emissions of total HAP, measured as THC (as carbon...

40 CFR Table 3 to Subpart Kkkk of... - Emission Limits for Affected Sources Using the Control Efficiency/Outlet Concentration Compliance...

Code of Federal Regulations, 2010 CFR

2010-07-01

... affected source a. reduce emissions of total HAP, measured as THC (as carbon), a by 97 percent; orb. limit emissions of total HAP, measured as THC (as carbon), a to 20 ppmvd at the control device outlet and use a PTE. 2. in an existing affected source a. reduce emissions of total HAP, measured as THC (as carbon...

32 CFR 775.5 - Classified actions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Air Act (42 U.S.C. 7609 et seq.). (b) It should be noted that a classified EA/EIS serves the same “informed decisionmaking” purpose as does a published unclassified EA/EIS. Even though the classified EA/EIS... be considered by the decisionmaker for the proposed action. The content of a classified EA/EIS (or...

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

Karr, C. L.; Phillips, J. C.

1994-01-01

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

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

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K., E-mail: s.farrell@physics.usyd.edu.au

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of amore » random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.« less

Feature Selection and Effective Classifiers.

ERIC Educational Resources Information Center

Deogun, Jitender S.; Choubey, Suresh K.; Raghavan, Vijay V.; Sever, Hayri

1998-01-01

Develops and analyzes four algorithms for feature selection in the context of rough set methodology. Experimental results confirm the expected relationship between the time complexity of these algorithms and the classification accuracy of the resulting upper classifiers. When compared, results of upper classifiers perform better than lower…

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

40 CFR 63.3300 - Which of my emission sources are affected by this subpart?

Code of Federal Regulations, 2014 CFR

2014-07-01

... CATEGORIES (CONTINUED) National Emission Standards for Hazardous Air Pollutants: Paper and Other Web Coating... affected source subject to this subpart is the collection of all web coating lines at your facility. This includes web coating lines engaged in the coating of metal webs that are used in flexible packaging, and...

40 CFR 63.3300 - Which of my emission sources are affected by this subpart?

Code of Federal Regulations, 2012 CFR

2012-07-01

... CATEGORIES (CONTINUED) National Emission Standards for Hazardous Air Pollutants: Paper and Other Web Coating... affected source subject to this subpart is the collection of all web coating lines at your facility. This includes web coating lines engaged in the coating of metal webs that are used in flexible packaging, and...

40 CFR 63.3300 - Which of my emission sources are affected by this subpart?

Code of Federal Regulations, 2013 CFR

2013-07-01

... CATEGORIES (CONTINUED) National Emission Standards for Hazardous Air Pollutants: Paper and Other Web Coating... affected source subject to this subpart is the collection of all web coating lines at your facility. This includes web coating lines engaged in the coating of metal webs that are used in flexible packaging, and...

40 CFR Table 6 to Subpart Xxxx of... - Initial Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... if . . . 1. Sources complying with the purchase compliance alternative in § 63.5985(a) The HAP... cements and solvents were purchased and used at the affected source containing HAP in amounts above the...) and (b)(1). 2. Sources complying with the monthly average compliance alternative without using a...

40 CFR Table 6 to Subpart Xxxx of... - Initial Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... if . . . 1. Sources complying with the purchase compliance alternative in § 63.5985(a) The HAP... cements and solvents were purchased and used at the affected source containing HAP in amounts above the...) and (b)(1). 2. Sources complying with the monthly average compliance alternative without using a...

Ensemble of classifiers for ontology enrichment

NASA Astrophysics Data System (ADS)

Semenova, A. V.; Kureichik, V. M.

2018-05-01

A classifier is a basis of ontology learning systems. Classification of text documents is used in many applications, such as information retrieval, information extraction, definition of spam. A new ensemble of classifiers based on SVM (a method of support vectors), LSTM (neural network) and word embedding are suggested. An experiment was conducted on open data, which allows us to conclude that the proposed classification method is promising. The implementation of the proposed classifier is performed in the Matlab using the functions of the Text Analytics Toolbox. The principal difference between the proposed ensembles of classifiers is the high quality of classification of data at acceptable time costs.

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

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

PubMed Central

Arshad, Sannia; Rho, Seungmin

2014-01-01

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes. PMID:25295302

Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level.

PubMed

Khalid, Shehzad; Arshad, Sannia; Jabbar, Sohail; Rho, Seungmin

2014-01-01

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

Semantic trouble sources and their repair in conversations affected by Parkinson's disease

PubMed Central

Saldert, Charlotta; Ferm, Ulrika; Bloch, Steven

2014-01-01

Background It is known that dysarthria arising from Parkinson's disease may affect intelligibility in conversational interaction. Research has also shown that Parkinson's disease may affect cognition and cause word-retrieval difficulties and pragmatic problems in the use of language. However, it is not known whether or how these problems become manifest in everyday conversations or how conversation partners handle such problems. Aims To describe the pragmatic problems related to the use of words that occur in everyday conversational interaction in dyads including an individual with Parkinson's disease, and to explore how interactants in conversation handle the problems to re-establish mutual understanding. Methods & Procedures Twelve video-recorded everyday conversations involving three couples where one of the individuals had Parkinson's disease were included in the study. All instances of other-initiated repair following a contribution from the people with Parkinson's disease were analysed. Those instances involving a trouble source relating to the use of words were analysed with a qualitative interaction analysis based on the principles of conversation analysis. Outcomes & Results In 70% of the instances of other-initiated repair the trouble source could be related to the semantic content produced by the individual with Parkinson's disease. The problematic contributions were typically characterized by more or less explicit symptoms of word search or use of atypical wording. The conversation partners completed the repair work collaboratively, but typically the non-impaired individual made a rephrasing or provided a suggestion for what the intended meaning had been. Conclusions & Implications In clinical work with people with Parkinson's disease and their conversation partners it is important to establish what type of trouble sources occur in conversations in a specific dyad. It may often be necessary to look beyond intelligibility and into aspects of pragmatics

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.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

PubMed

Pandey, Gaurav; Pandey, Om P; Rogers, Angela J; Ahsen, Mehmet E; Hoffman, Gabriel E; Raby, Benjamin A; Weiss, Scott T; Schadt, Eric E; Bunyavanich, Supinda

2018-06-11

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

An Ocular Protein Triad Can Classify Four Complex Retinal Diseases

NASA Astrophysics Data System (ADS)

Kuiper, J. J. W.; Beretta, L.; Nierkens, S.; van Leeuwen, R.; Ten Dam-van Loon, N. H.; Ossewaarde-van Norel, J.; Bartels, M. C.; de Groot-Mijnes, J. D. F.; Schellekens, P.; de Boer, J. H.; Radstake, T. R. D. J.

2017-01-01

Retinal diseases generally are vision-threatening conditions that warrant appropriate clinical decision-making which currently solely dependents upon extensive clinical screening by specialized ophthalmologists. In the era where molecular assessment has improved dramatically, we aimed at the identification of biomarkers in 175 ocular fluids to classify four archetypical ocular conditions affecting the retina (age-related macular degeneration, idiopathic non-infectious uveitis, primary vitreoretinal lymphoma, and rhegmatogenous retinal detachment) with one single test. Unsupervised clustering of ocular proteins revealed a classification strikingly similar to the clinical phenotypes of each disease group studied. We developed and independently validated a parsimonious model based merely on three proteins; interleukin (IL)-10, IL-21, and angiotensin converting enzyme (ACE) that could correctly classify patients with an overall accuracy, sensitivity and specificity of respectively, 86.7%, 79.4% and 92.5%. Here, we provide proof-of-concept for molecular profiling as a diagnostic aid for ophthalmologists in the care for patients with retinal conditions.

Error minimizing algorithms for nearest eighbor classifiers

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

Porter, Reid B; Hush, Don; Zimmer, G. Beate

2011-01-03

Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. Wemore » use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.« less

49 CFR 1280.6 - Storage of classified documents.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 9 2012-10-01 2012-10-01 false Storage of classified documents. 1280.6 Section 1280.6 Transportation Other Regulations Relating to Transportation (Continued) SURFACE TRANSPORTATION... SECURITY INFORMATION AND CLASSIFIED MATERIAL § 1280.6 Storage of classified documents. All classified...

49 CFR 1280.6 - Storage of classified documents.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 9 2011-10-01 2011-10-01 false Storage of classified documents. 1280.6 Section 1280.6 Transportation Other Regulations Relating to Transportation (Continued) SURFACE TRANSPORTATION... SECURITY INFORMATION AND CLASSIFIED MATERIAL § 1280.6 Storage of classified documents. All classified...

49 CFR 1280.6 - Storage of classified documents.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 9 2010-10-01 2010-10-01 false Storage of classified documents. 1280.6 Section 1280.6 Transportation Other Regulations Relating to Transportation (Continued) SURFACE TRANSPORTATION... SECURITY INFORMATION AND CLASSIFIED MATERIAL § 1280.6 Storage of classified documents. All classified...

49 CFR 1280.6 - Storage of classified documents.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 9 2014-10-01 2014-10-01 false Storage of classified documents. 1280.6 Section 1280.6 Transportation Other Regulations Relating to Transportation (Continued) SURFACE TRANSPORTATION... SECURITY INFORMATION AND CLASSIFIED MATERIAL § 1280.6 Storage of classified documents. All classified...

48 CFR 3.908-8 - Classified information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 48 Federal Acquisition Regulations System 1 2013-10-01 2013-10-01 false Classified information. 3... Employees 3.908-8 Classified information. 41 U.S.C. 4712 does not provide any right to disclose classified information not otherwise provided by law. [78 FR 60171, Sept. 30, 2013] ...

40 CFR 74.46 - Opt-in source permanent shutdown, reconstruction, or change in affected status.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 16 2011-07-01 2011-07-01 false Opt-in source permanent shutdown, reconstruction, or change in affected status. 74.46 Section 74.46 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) SULFUR DIOXIDE OPT-INS Allowance Tracking and Transfer...

40 CFR 74.46 - Opt-in source permanent shutdown, reconstruction, or change in affected status.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 16 2010-07-01 2010-07-01 false Opt-in source permanent shutdown, reconstruction, or change in affected status. 74.46 Section 74.46 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) SULFUR DIOXIDE OPT-INS Allowance Tracking and Transfer...

Toward generalized human factors taxonomy for classifying ASAP incident reports, AQP performance ratings, and FOQA output

DOT National Transportation Integrated Search

2003-01-01

Over the years, the FAA has partnered with industry to develop a number of programs for reporting, classifying, and analyzing safety-related data. Despite their successes, none of these programs has been able to integrate data from multiple sources. ...

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

PubMed Central

Bush, Keith A.; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

2017-01-01

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective

Mycofier: a new machine learning-based classifier for fungal ITS sequences.

PubMed

Delgado-Serrano, Luisa; Restrepo, Silvia; Bustos, Jose Ricardo; Zambrano, Maria Mercedes; Anzola, Juan Manuel

2016-08-11

The taxonomic and phylogenetic classification based on sequence analysis of the ITS1 genomic region has become a crucial component of fungal ecology and diversity studies. Nowadays, there is no accurate alignment-free classification tool for fungal ITS1 sequences for large environmental surveys. This study describes the development of a machine learning-based classifier for the taxonomical assignment of fungal ITS1 sequences at the genus level. A fungal ITS1 sequence database was built using curated data. Training and test sets were generated from it. A Naïve Bayesian classifier was built using features from the primary sequence with an accuracy of 87 % in the classification at the genus level. The final model was based on a Naïve Bayes algorithm using ITS1 sequences from 510 fungal genera. This classifier, denoted as Mycofier, provides similar classification accuracy compared to BLASTN, but the database used for the classification contains curated data and the tool, independent of alignment, is more efficient and contributes to the field, given the lack of an accurate classification tool for large data from fungal ITS1 sequences. The software and source code for Mycofier are freely available at https://github.com/ldelgado-serrano/mycofier.git .

A scaling transformation for classifier output based on likelihood ratio: Applications to a CAD workstation for diagnosis of breast cancer

PubMed Central

Horsch, Karla; Pesce, Lorenzo L.; Giger, Maryellen L.; Metz, Charles E.; Jiang, Yulei

2012-01-01

Purpose: The authors developed scaling methods that monotonically transform the output of one classifier to the “scale” of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers. Methods: Using both simulated and radiologists’ rating data of breast imaging cases, the authors investigated a likelihood-ratio-scaling transformation, based on “matching” classifier likelihood ratios. For comparison, three other scaling transformations were investigated that were based on matching classifier true positive fraction, false positive fraction, or cumulative distribution function, respectively. The authors explored modifying the computer output to reflect the scale of the radiologist, as well as modifying the radiologist’s ratings to reflect the scale of the computer. They also evaluated how dataset size affects the transformations. Results: When ROC curves of two classifiers differed substantially, the four transformations were found to be quite different. The likelihood-ratio scaling transformation was found to vary widely from radiologist to radiologist. Similar results were found for the other transformations. Our simulations explored the effect of database sizes on the accuracy of the estimation of our scaling transformations. Conclusions: The likelihood-ratio-scaling transformation that the authors have developed and evaluated was shown to be capable of transforming computer and radiologist outputs to a common scale reliably, thereby allowing the comparison of the computer and radiologist outputs on the basis of a clinically relevant statistic. PMID:22559651

Sources of practice knowledge among Australian fitness trainers.

PubMed

Bennie, Jason A; Wiesner, Glen H; van Uffelen, Jannique G Z; Harvey, Jack T; Biddle, Stuart J H

2017-12-01

Few studies have examined the sources of practice knowledge fitness trainers use to inform their training methods and update knowledge. This study aims to describe sources of practice knowledge among Australian fitness trainers. In July 2014, 9100 Australian fitness trainers were invited to complete an online survey. Respondents reported the frequency of use of eight sources of practice knowledge (e.g. fitness magazines, academic texts). In a separate survey, exercise science experts (n = 27) ranked each source as either (1) 'high-quality' or (2) 'low-quality'. Proportions of users of 'high-quality' sources were calculated across demographic (age, sex) and fitness industry-related characteristics (qualification, setting, role). A multivariate logistic regression analysis assessed the odds of being classified as a user of high-quality sources, adjusting for demographic and fitness industry-related factors. Out of 1185 fitness trainers (response rate = 13.0%), aged 17-72 years, 47.6% (95% CI, 44.7-50.4%) were classified as frequent users of high-quality sources of practice knowledge. In the adjusted analysis, compared to trainers aged 17-26 years, those aged ≥61 years (OR, 2.15; 95% CI, 1.05-4.38) and 40-50 years (OR, 1.54; 95% CI, 1.02-2.31) were more likely to be classified as a user of high-quality sources. When compared to trainers working in large centres, those working in outdoor settings (OR, 1.81; 95% CI, 1.23-2.65) and medium centres (OR, 1.59; 95% CI, 1.12-2.29) were more likely to be classified as users of high-quality sources. Our findings suggest that efforts should be made to improve the quality of knowledge acquisition among Australian fitness trainers.

36 CFR 1256.46 - National security-classified information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false National security-classified... Restrictions § 1256.46 National security-classified information. In accordance with 5 U.S.C. 552(b)(1), NARA... properly classified under the provisions of the pertinent Executive Order on Classified National Security...

Gender Performativity in the Community College: A Case Study of Female Backline Classified Staff

ERIC Educational Resources Information Center

Powers, Samantha Rose

2012-01-01

This case study explored the gendered performances of five female backline classified staff members who work in non-traditional fields within a community college. More specifically, this study defined gendered behaviors at a community college, and explored how these behaviors have affected the identities of women working in non-traditional fields…

Class-specific Error Bounds for Ensemble Classifiers

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

Prenger, R; Lemmond, T; Varshney, K

2009-10-06

The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missedmore » detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.« less

Gulls identified as major source of fecal pollution in coastal waters: a microbial source tracking study.

PubMed

Araújo, Susana; Henriques, Isabel S; Leandro, Sérgio Miguel; Alves, Artur; Pereira, Anabela; Correia, António

2014-02-01

Gulls were reported as sources of fecal pollution in coastal environments and potential vectors of human infections. Microbial source tracking (MST) methods were rarely tested to identify this pollution origin. This study was conducted to ascertain the source of water fecal contamination in the Berlenga Island, Portugal. A total of 169 Escherichia coli isolates from human sewage, 423 isolates from gull feces and 334 water isolates were analyzed by BOX-PCR. An average correct classification of 79.3% was achieved. When an 85% similarity cutoff was applied 24% of water isolates were present in gull feces against 2.7% detected in sewage. Jackknifing resulted in 29.3% of water isolates classified as gull, and 10.8% classified as human. Results indicate that gulls constitute a major source of water contamination in the Berlenga Island. This study validated a methodology to differentiate human and gull fecal pollution sources in a real case of a contaminated beach. © 2013.

Consensus Classification Using Non-Optimized Classifiers.

PubMed

Brownfield, Brett; Lemos, Tony; Kalivas, John H

2018-04-03

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

The fusion of large scale classified side-scan sonar image mosaics.

PubMed

Reed, Scott; Tena, Ruiz Ioseba; Capus, Chris; Petillot, Yvan

2006-07-01

This paper presents a unified framework for the creation of classified maps of the seafloor from sonar imagery. Significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed. The techniques described are directly applicable to a range of remote sensing problems. Recent advances in side-scan data correction are incorporated to compensate for the sonar beam pattern and motion of the acquisition platform. The corrected images are segmented using pixel-based textural features and standard classifiers. In parallel, the navigation of the sonar device is processed using Kalman filtering techniques. A simultaneous localization and mapping framework is adopted to improve the navigation accuracy and produce georeferenced mosaics of the segmented side-scan data. These are fused within a Markovian framework and two fusion models are presented. The first uses a voting scheme regularized by an isotropic Markov random field and is applicable when the reliability of each information source is unknown. The Markov model is also used to inpaint regions where no final classification decision can be reached using pixel level fusion. The second model formally introduces the reliability of each information source into a probabilistic model. Evaluation of the two models using both synthetic images and real data from a large scale survey shows significant quantitative and qualitative improvement using the fusion approach.

Just-in-time classifiers for recurrent concepts.

PubMed

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

2013-04-01

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.

Deconvolution When Classifying Noisy Data Involving Transformations.

PubMed

Carroll, Raymond; Delaigle, Aurore; Hall, Peter

2012-09-01

In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

76 FR 19707 - Classified Information: Classification/Declassification/Access; Authority To Classify Information

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-08

... SECRET or CONFIDENTIAL to the Administrator of the Federal Aviation Administration, and to the Assistant... authority to originally classify information as SECRET or CONFIDENTIAL, with further authorization to...

Milk protein composition and stability changes affected by iron in water sources.

PubMed

Wang, Aili; Duncan, Susan E; Knowlton, Katharine F; Ray, William K; Dietrich, Andrea M

2016-06-01

Water makes up more than 80% of the total weight of milk. However, the influence of water chemistry on the milk proteome has not been extensively studied. The objective was to evaluate interaction of water-sourced iron (low, medium, and high levels) on milk proteome and implications on milk oxidative state and mineral content. Protein composition, oxidative stability, and mineral composition of milk were investigated under conditions of iron ingestion through bovine drinking water (infused) as well as direct iron addition to commercial milk in 2 studies. Four ruminally cannulated cows each received aqueous infusions (based on water consumption of 100L) of 0, 2, 5, and 12.5mg/L Fe(2+) as ferrous lactate, resulting in doses of 0, 200, 500 or 1,250mg of Fe/d, in a 4×4Latin square design for a 14-d period. For comparison, ferrous sulfate solution was directly added into commercial retail milk at the same concentrations: control (0mg of Fe/L), low (2mg of Fe/L), medium (5mg of Fe/L), and high (12.5mg of Fe/L). Two-dimensional electrophoresis coupled with matrix-assisted laser desorption/ionization-tandem time-of-flight (MALDI-TOF/TOF) high-resolution tandem mass spectrometry analysis was applied to characterize milk protein composition. Oxidative stability of milk was evaluated by the thiobarbituric acid reactive substances (TBARS) assay for malondialdehyde, and mineral content was measured by inductively coupled plasma mass spectrometry. For milk from both abomasal infusion of ferrous lactate and direct addition of ferrous sulfate, an iron concentration as low as 2mg of Fe/L was able to cause oxidative stress in dairy cattle and infused milk, respectively. Abomasal infusion affected both caseins and whey proteins in the milk, whereas direct addition mainly influenced caseins. Although abomasal iron infusion did not significantly affect oxidation state and mineral balance (except iron), it induced oxidized off-flavor and partial degradation of whey proteins. Direct

Automatic Classification of Time-variable X-Ray Sources

NASA Astrophysics Data System (ADS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Automatic classification of time-variable X-ray sources

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

Lo, Kitty K.; Farrell, Sean; Murphy, Tara

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

Classifying Cereal Data

Cancer.gov

The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.

PubMed

Whiffin, Nicola; Walsh, Roddy; Govind, Risha; Edwards, Matthew; Ahmad, Mian; Zhang, Xiaolei; Tayal, Upasana; Buchan, Rachel; Midwinter, William; Wilk, Alicja E; Najgebauer, Hanna; Francis, Catherine; Wilkinson, Sam; Monk, Thomas; Brett, Laura; O'Regan, Declan P; Prasad, Sanjay K; Morris-Rosendahl, Deborah J; Barton, Paul J R; Edwards, Elizabeth; Ware, James S; Cook, Stuart A

2018-01-25

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher's P = 1.1  ×  10 -18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.GENETICS in MEDICINE advance online publication, 25 January 2018; doi:10.1038/gim.2017.258.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of

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.

28 CFR 16.7 - Classified information.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 1 2013-07-01 2013-07-01 false Classified information. 16.7 Section 16.7 Judicial Administration DEPARTMENT OF JUSTICE PRODUCTION OR DISCLOSURE OF MATERIAL OR INFORMATION Procedures for Disclosure of Records Under the Freedom of Information Act § 16.7 Classified information. In...

28 CFR 16.44 - Classified information.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 1 2013-07-01 2013-07-01 false Classified information. 16.44 Section 16.44 Judicial Administration DEPARTMENT OF JUSTICE PRODUCTION OR DISCLOSURE OF MATERIAL OR INFORMATION... information. In processing a request for access to a record containing information that is classified under...

28 CFR 16.7 - Classified information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Classified information. 16.7 Section 16.7 Judicial Administration DEPARTMENT OF JUSTICE PRODUCTION OR DISCLOSURE OF MATERIAL OR INFORMATION Procedures for Disclosure of Records Under the Freedom of Information Act § 16.7 Classified information. In...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

Radio Galaxy Zoo: compact and extended radio source classification with deep learning

NASA Astrophysics Data System (ADS)

Lukic, V.; Brüggen, M.; Banfield, J. K.; Wong, O. I.; Rudnick, L.; Norris, R. P.; Simmons, B.

2018-05-01

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. In the context of radio-interferometric imaging in astronomy, we looked for ways to identify multiple components of individual sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers, and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple-component extended sources. We found that a three-convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4 per cent on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test accuracy of 93.5 per cent. The best-performing convolutional neural network set-up has been verified against RGZ Data Release 1 where a final test accuracy of 94.8 per cent was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.

How Source Affects Response to Public Service Advertising.

ERIC Educational Resources Information Center

Lynn, Jerry R.; And Others

1978-01-01

Reports that public service advertising attributed to the Advertising Council elicited higher message ratings than did public service advertising attributed to a commercial source, a noncommercial source, or no source; however, it produced the lowest behavioral responses. (GT)

6 CFR 5.24 - Classified information.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Classified information. 5.24 Section 5.24 Domestic Security DEPARTMENT OF HOMELAND SECURITY, OFFICE OF THE SECRETARY DISCLOSURE OF RECORDS AND INFORMATION Privacy Act § 5.24 Classified information. In processing a request for access to a record...

28 CFR 16.44 - Classified information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Classified information. 16.44 Section 16.44 Judicial Administration DEPARTMENT OF JUSTICE PRODUCTION OR DISCLOSURE OF MATERIAL OR INFORMATION Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 16.44 Classified...

40 CFR Table 10 to Subpart Xxxx of... - Continuous Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 13 2014-07-01 2014-07-01 false Continuous Compliance With the Emission Limits for Tire Production Affected Sources 10 Table 10 to Subpart XXXX of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 10 to Subpart Xxxx of... - Continuous Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 13 2013-07-01 2012-07-01 true Continuous Compliance With the Emission Limits for Tire Production Affected Sources 10 Table 10 to Subpart XXXX of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 10 to Subpart Xxxx of... - Continuous Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 12 2011-07-01 2009-07-01 true Continuous Compliance With the Emission Limits for Tire Production Affected Sources 10 Table 10 to Subpart XXXX of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 10 to Subpart Xxxx of... - Continuous Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 12 2010-07-01 2010-07-01 true Continuous Compliance With the Emission Limits for Tire Production Affected Sources 10 Table 10 to Subpart XXXX of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 10 to Subpart Xxxx of... - Continuous Compliance With the Emission Limits for Tire Production Affected Sources

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 13 2012-07-01 2012-07-01 false Continuous Compliance With the Emission Limits for Tire Production Affected Sources 10 Table 10 to Subpart XXXX of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.

PubMed

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; McDonald, Elizabeth S; Rosen, Mark; Mies, Carolyn; Feldman, Michael; Kontos, Despina

2015-06-01

Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer. Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay. Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94). The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information. HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.

6 CFR 5.7 - Classified information.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Classified information. 5.7 Section 5.7 Domestic Security DEPARTMENT OF HOMELAND SECURITY, OFFICE OF THE SECRETARY DISCLOSURE OF RECORDS AND INFORMATION Freedom of Information Act § 5.7 Classified information. In processing a request for information that is...

28 CFR 700.14 - Classified information.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 2 2013-07-01 2013-07-01 false Classified information. 700.14 Section... INFORMATION OF THE OFFICE OF INDEPENDENT COUNSEL Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 700.14 Classified information. In processing a request for access to a...

[Spatial heterogeneity and classified control of agricultural non-point source pollution in Huaihe River Basin].

PubMed

Zhou, Liang; Xu, Jian-Gang; Sun, Dong-Qi; Ni, Tian-Hua

2013-02-01

Agricultural non-point source pollution is of importance in river deterioration. Thus identifying and concentrated controlling the key source-areas are the most effective approaches for non-point source pollution control. This study adopts inventory method to analysis four kinds of pollution sources and their emissions intensity of the chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in 173 counties (cities, districts) in Huaihe River Basin. The four pollution sources include livestock breeding, rural life, farmland cultivation, aquacultures. The paper mainly addresses identification of non-point polluted sensitivity areas, key pollution sources and its spatial distribution characteristics through cluster, sensitivity evaluation and spatial analysis. A geographic information system (GIS) and SPSS were used to carry out this study. The results show that: the COD, TN and TP emissions of agricultural non-point sources were 206.74 x 10(4) t, 66.49 x 10(4) t, 8.74 x 10(4) t separately in Huaihe River Basin in 2009; the emission intensity were 7.69, 2.47, 0.32 t.hm-2; the proportions of COD, TN, TP emissions were 73%, 24%, 3%. The paper achieves that: the major pollution source of COD, TN and TP was livestock breeding and rural life; the sensitivity areas and priority pollution control areas among the river basin of non-point source pollution are some sub-basins of the upper branches in Huaihe River, such as Shahe River, Yinghe River, Beiru River, Jialu River and Qingyi River; livestock breeding is the key pollution source in the priority pollution control areas. Finally, the paper concludes that pollution type of rural life has the highest pollution contribution rate, while comprehensive pollution is one type which is hard to control.

49 CFR 1.65 - Authority to classify information.

Code of Federal Regulations, 2010 CFR

2010-10-01

... originally classify information as Top Secret.) (b) The following delegations of this authority, which may... Transportation authority to originally classify information as Secret and Confidential with further authorization... National Security Plans (Confidential only). (c) Authority to originally classify information as Secret or...

Affective modulation of external misattribution bias in source monitoring in schizophrenia.

PubMed

Costafreda, S G; Brébion, G; Allen, P; McGuire, P K; Fu, C H Y

2008-06-01

Schizophrenic patients tend to attribute internal events to external agents, a bias that may be linked to positive symptoms. We investigated the effect of emotional valence on the cognitive bias. Male schizophrenic subjects (n=30) and an experimenter alternatively produced neutral and negative words. The subject then decided whether he or the experimenter had generated the item. External misattributions were more common than self-misattributions, and the bias was greater for patients with active hallucinations and delusions relative to patients in remission. Actively psychotic patients but not patients in remission were more likely to generate external misattributions with negative relative to neutral words. Affective modulation of the externalizing cognitive bias in source monitoring is evident in patients with hallucinations and delusions.

19 CFR 10.532 - Integrated Sourcing Initiative.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Integrated Sourcing Initiative. 10.532 Section 10... Trade Agreement Rules of Origin § 10.532 Integrated Sourcing Initiative. (a) For purposes of General... Sourcing Initiative if: (1) The good, in its condition as imported, is both classified in a tariff...

Evaluation of classifier topologies for the real-time classification of simultaneous limb motions.

PubMed

Ortiz-Catalan, Max; Branemark, Rickard; Hakansson, Bo

2013-01-01

The prediction of motion intent through the decoding of myoelectric signals has the potential to improve the functionally of limb prostheses. Considerable research on individual motion classifiers has been done to exploit this idea. A drawback with the individual prediction approach, however, is its limitation to serial control, which is slow, cumbersome, and unnatural. In this work, different classifier topologies suitable for the decoding of mixed classes, and thus capable of predicting simultaneous motions, were investigated in real-time. These topologies resulted in higher offline accuracies than previously achieved, but more importantly, positive indications of their suitability for real-time systems were found. Furthermore, in order to facilitate further development, benchmarking, and cooperation, the algorithms and data generated in this study are freely available as part of BioPatRec, an open source framework for the development of advanced prosthetic control strategies.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

Local point sources that affect ground-water quality in the East Meadow area, Long Island, New York

USGS Publications Warehouse

Heisig, Paul M.

1994-01-01

The extent and chemical characteristics of ground water affected by three local point sources--a stormwater basin, uncovered road-salt-storage piles, and an abandoned sewage-treatment plant--were delineated during a 3-year study of the chemical characteristics and migration of a body of reclaimed wastewater that was applied to the watertable aquifer during recharge experiments from October 1982 through January 1984 in East Meadow. The timing, magnitude, and chemical quality of recharge from these point sources is highly variable, and all sources have the potential to skew determinations of the quality of ambient ground-water and of the reclaimed-wastewater plume if they are not taken into account. Ground water affected by recharge from the stormwater basin is characterized by low concentrations of nitrate + nitrite (less than 5 mg/L [milligrams per liter] as N) and sulfate (less than 40 mg/L) and is almost entirely within the upper glacial aquifer. The plume derived from road-salt piles is narrow, has high concentrations of chloride (greater than 50 mg/L) and sodium (greater than 75 mg/L), and also is limited to the upper glacial aquifer. The sodium, in high concentrations, could react with aquifer material and exchange for sorbed cations such as calcium, potassium, and magnesium. Water affected by secondary-treated sewage from the abandoned treatment plant extends 152 feet below land surface into the upper part of the Magothy aquifer and longitudinally beyond the southern edge of the study area, 7,750 feet south of the recharge site. Ground water affected by secondary-treated sewage within the study area typically contains elevated concentrations of reactive chemical constituents, such as potassium and ammonium, and low concentrations of dissolved oxygen. Conservative or minimally reactive constituents such as chloride and sodium have been transported out of the study area in the upper glacial aquifer and the intermediate (transitional) zone but remain in the less

Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Seo, Joon Beom; Kang, Bokyoung; Kim, Dongil; Lee, June Goo; Kim, Song Soo; Kim, Namkug; Kang, Suk Ho

2007-03-01

The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naÃve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.

40 CFR Table 3 to Subpart Xxxx of... - Emission Limits for Puncture Sealant Application Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 12 2010-07-01 2010-07-01 true Emission Limits for Puncture Sealant Application Affected Sources 3 Table 3 to Subpart XXXX of Part 63 Protection of Environment ENVIRONMENTAL... Manufacturing Pt. 63, Subpt. XXXX, Table 3 Table 3 to Subpart XXXX of Part 63—Emission Limits for Puncture...

Classifying Drivers' Cognitive Load Using EEG Signals.

PubMed

Barua, Shaibal; Ahmed, Mobyen Uddin; Begum, Shahina

2017-01-01

A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.

Characteristics of streams and aquifers and processes affecting the salinity of water in the upper Colorado River basin, Texas

USGS Publications Warehouse

Slade, R.M.; Buszka, P.M.

1994-01-01

The chemical characteristics of the saline water in streams and shallow aquifers in the study area were compared to characteristics of water that would result from the probable processes affecting the salinity of water, such as evapotranspiration, mineral dissolution, and mixing of water from streams and shallow-aquifer water with brines from deep aquifers. Dissolution of halite or mixing with deep-aquifer water was the most common cause of increased salinity in 48.0 percent of 77 water samples from shallow aquifers, as classified using salt-norm analysis; the second most common cause was the weathering and dissolution of sulfur-bearing minerals. Mixing with water from soil-mineral dissolution was classified as the principal source of chloride in 28.4 percent of 67 water samples from shallow aquifers with nitrate determinations. Trace-species/chloride ratios indicated that mixing with water from deep aquifers in rocks of the Pennsylvanian System was the principal source of chloride in 24.4 percent of 45 shallow-aquifer samples lacking nitrate determinations.

How well Can We Classify SWOT-derived Water Surface Profiles?

NASA Astrophysics Data System (ADS)

Frasson, R. P. M.; Wei, R.; Picamilh, C.; Durand, M. T.

2015-12-01

The upcoming Surface Water Ocean Topography (SWOT) mission will detect water bodies and measure water surface elevation throughout the globe. Within its continental high resolution mask, SWOT is expected to deliver measurements of river width, water elevation and slope of rivers wider than ~50 m. The definition of river reaches is an integral step of the computation of discharge based on SWOT's observables. As poorly defined reaches can negatively affect the accuracy of discharge estimations, we seek strategies to break up rivers into physically meaningful sections. In the present work, we investigate how accurately we can classify water surface profiles based on simulated SWOT observations. We assume that most river sections can be classified as either M1 (mild slope, with depth larger than the normal depth), or A1 (adverse slope with depth larger than the critical depth). This assumption allows the classification to be based solely on the second derivative of water surface profiles, with convex profiles being classified as A1 and concave profiles as M1. We consider a HEC-RAS model of the Sacramento River as a representation of the true state of the river. We employ the SWOT instrument simulator to generate a synthetic pass of the river, which includes our best estimates of height measurement noise and geolocation errors. We process the resulting point cloud of water surface heights with the RiverObs package, which delineates the river center line and draws the water surface profile. Next, we identify inflection points in the water surface profile and classify the sections between the inflection points. Finally, we compare our limited classification of simulated SWOT-derived water surface profile to the "exact" classification of the modeled Sacramento River. With this exercise, we expect to determine if SWOT observations can be used to find inflection points in water surface profiles, which would bring knowledge of flow regimes into the definition of river reaches.

Novel layered clustering-based approach for generating ensemble of classifiers.

PubMed

Rahman, Ashfaqur; Verma, Brijesh

2011-05-01

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

40 CFR Table 5 to Subpart Jjj of... - Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 12 2014-07-01 2014-07-01 false Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics 5 Table 5 to Subpart JJJ of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 5 to Subpart Jjj of... - Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics 5 Table 5 to Subpart JJJ of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 5 to Subpart Jjj of... - Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 12 2012-07-01 2011-07-01 true Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics 5 Table 5 to Subpart JJJ of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 5 to Subpart Jjj of... - Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics 5 Table 5 to Subpart JJJ of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

40 CFR Table 5 to Subpart Jjj of... - Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 11 2011-07-01 2011-07-01 false Group 1 Storage Vessels at New Affected Sources Producing the Listed Thermoplastics 5 Table 5 to Subpart JJJ of Part 63 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE...

5 CFR 1312.5 - Authority to classify.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., DOWNGRADING, DECLASSIFICATION AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION Classification and Declassification of National Security Information § 1312.5 Authority to classify. (a) The authority to originally classify information or material under this part shall be limited to those officials concerned with matters...

Artificial neural network classifier predicts neuroblastoma patients' outcome.

PubMed

Cangelosi, Davide; Pelassa, Simone; Morini, Martina; Conte, Massimo; Bosco, Maria Carla; Eva, Alessandra; Sementa, Angela Rita; Varesio, Luigi

2016-11-08

More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients' outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients' outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with "Poor" or "Good" outcome. We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients' outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors' set. The NB-hypo classifier predicted the patients' outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile

40 CFR Table 2 to Subpart Xxxx of... - Emission Limits for Tire Cord Production Affected Sources

Code of Federal Regulations, 2011 CFR

2011-07-01

... must not exceed 280 grams HAP per megagram (0.56 pounds per ton) of fabric processed at the tire cord... affected source Emissions must not exceed 220 grams HAP per megagram (0.43 pounds per ton) of fabric... exceed 1,000 grams HAP per megagram (2 pounds per ton) of total coatings used at the tire cord production...

40 CFR Table 2 to Subpart Xxxx of... - Emission Limits for Tire Cord Production Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... must not exceed 280 grams HAP per megagram (0.56 pounds per ton) of fabric processed at the tire cord... affected source Emissions must not exceed 220 grams HAP per megagram (0.43 pounds per ton) of fabric... exceed 1,000 grams HAP per megagram (2 pounds per ton) of total coatings used at the tire cord production...

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.

Leveraging Wikipedia knowledge to classify multilingual biomedical documents.

PubMed

Antonio Mouriño García, Marcos; Pérez Rodríguez, Roberto; Anido Rifón, Luis

2018-05-02

This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with English documents. We propose the cross-language concept matching technique, which relies on Wikipedia interlanguage links to convert concept vectors between languages. The performance of the classifier is compared to a classifier based on machine translation, and two classifiers based on MetaMap. To perform the experiments, we created two multilingual corpus. The first one, Multi-Lingual UVigoMED (ML-UVigoMED) is composed of 23,647 Wikipedia documents about biomedical topics written in English, German, French, Spanish, Italian, Galician, Romanian, and Icelandic. The second one, English-French-Spanish-German UVigoMED (EFSG-UVigoMED) is composed of 19,210 biomedical abstract extracted from MEDLINE written in English, French, Spanish, and German. The performance of the approach proposed is superior to any of the state-of-the art classifier in the benchmark. We conclude that leveraging Wikipedia knowledge is of great advantage in tasks of multilingual classification of biomedical documents. Copyright © 2018 Elsevier B.V. All rights reserved.

Generating compact classifier systems using a simple artificial immune system.

PubMed

Leung, Kevin; Cheong, France; Cheong, Christopher

2007-10-01

Current artificial immune system (AIS) classifiers have two major problems: 1) their populations of B-cells can grow to huge proportions, and 2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, the design of a new AIS algorithm and classifier system called simple AIS is described. It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system, and in addition, no population control mechanism is needed. The classifier was tested on seven benchmark data sets using different classification techniques and was found to be very competitive when compared to other classifiers.

On Algorithms for Generating Computationally Simple Piecewise Linear Classifiers

DTIC Science & Technology

1989-05-01

suffers. - Waveform classification, e.g. speech recognition, seismic analysis (i.e. discrimination between earthquakes and nuclear explosions), target...assuming Gaussian distributions (B-G) d) Bayes classifier with probability densities estimated with the k-N-N method (B- kNN ) e) The -arest neighbour...range of classifiers are chosen including a fast, easy computable and often used classifier (B-G), reliable and complex classifiers (B- kNN and NNR

Cellular phone enabled non-invasive tissue classifier.

PubMed

Laufer, Shlomi; Rubinsky, Boris

2009-01-01

Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

Cellular Phone Enabled Non-Invasive Tissue Classifier

PubMed Central

Laufer, Shlomi; Rubinsky, Boris

2009-01-01

Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro. PMID:19365554

Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier

NASA Astrophysics Data System (ADS)

Pablo Fernández, Juan; Shubitidze, Fridon; Shamatava, Irma; Barrowes, Benjamin E.; O'Neill, Kevin

2010-12-01

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

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.

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

Palmprint authentication using multiple classifiers

NASA Astrophysics Data System (ADS)

Kumar, Ajay; Zhang, David

2004-08-01

This paper investigates the performance improvement for palmprint authentication using multiple classifiers. The proposed methods on personal authentication using palmprints can be divided into three categories; appearance- , line -, and texture-based. A combination of these approaches can be used to achieve higher performance. We propose to simultaneously extract palmprint features from PCA, Line detectors and Gabor-filters and combine their corresponding matching scores. This paper also investigates the comparative performance of simple combination rules and the hybrid fusion strategy to achieve performance improvement. Our experimental results on the database of 100 users demonstrate the usefulness of such approach over those based on individual classifiers.

Secondary science teachers' use of the affective domain in science education

NASA Astrophysics Data System (ADS)

Grauer, Bette L.

The purpose of this qualitative case study was to explore (a) the types of student affective responses that secondary science teachers reported emerged in science classes, (b) how those teachers worked with student affective responses, and (c) what interactions were present in the classroom when they worked with student affective responses. The study was motivated by research indicating that student interest and motivation for learning science is low. Eight secondary science teachers participated in the case study. The participants were selected from a pool of teachers who graduated from the same teacher education program at a large Midwest university. The primary sources of data were individual semi-structured interviews with the participants. Krathwohl's Taxonomy of the Affective Domain served as the research framework for the study. Student affective behavior reported by participants was classified within the five levels of Krathwohl's Affective Taxonomy: receiving, responding, valuing, organization, and characterization. Participants in the study reported student behavior representing all levels of the Affective Taxonomy. The types of behavior most frequently reported by participants were identified with the receiving and responding levels of the Affective Taxonomy. Organization behavior emerged during the study of perceived controversial science topics such as evolution. Participants in the study used student affective behavior to provide feedback on their lesson activities and instructional practices. Classroom interactions identified as collaboration and conversation contributed to the development of responding behavior. The researcher identified a process of affective progression in which teachers encouraged and developed student affective behavior changes from receiving to responding levels of the Affective Taxonomy.

New low-resolution spectrometer spectra for IRAS sources

NASA Astrophysics Data System (ADS)

Volk, Kevin; Kwok, Sun; Stencel, R. E.; Brugel, E.

1991-12-01

Low-resolution spectra of 486 IRAS point sources with Fnu(12 microns) in the range 20-40 Jy are presented. This is part of an effort to extract and classify spectra that were not included in the Atlas of Low-Resolution Spectra and represents an extension of the earlier work by Volk and Cohen which covers sources with Fnu(12 microns) greater than 40 Jy. The spectra have been examined by eye and classified into nine groups based on the spectral morphology. This new classification scheme is compared with the mechanical classification of the Atlas, and the differences are noted. Oxygen-rich stars of the asymptotic giant branch make up 33 percent of the sample. Solid state features dominate the spectra of most sources. It is found that the nature of the sources as implied by the present spectral classification is consistent with the classifications based on broad-band colors of the sources.

Health of children classified as underweight by CDC reference but normal by WHO standard.

PubMed

Meyers, Alan; Joyce, Katherine; Coleman, Sharon M; Cook, John T; Cutts, Diana; Ettinger de Cuba, Stephanie; Heeren, Timothy C; Rose-Jacobs, Ruth; Black, Maureen M; Casey, Patrick H; Chilton, Mariana; Sandel, Megan; Frank, Deborah A

2013-06-01

To ascertain measures of health status among 6- to 24-month-old children classified as below normal weight-for-age (underweight) by the Centers for Disease Control and Prevention (CDC) 2000 growth reference but as normal weight-for-age by the World Health Organization (WHO) 2006 standard. Data were gathered from children and primary caregivers at emergency departments and primary care clinics in 7 US cities. Outcome measures included caregiver rating of child health, parental evaluation of developmental status, history of hospitalizations, and admission to hospital at the time of visit. Children were classified as (1) not underweight by either CDC 2000 or WHO 2006 criteria, (2) underweight by CDC 2000 but not by WHO 2006 criteria, or (3) underweight by both criteria. Associations between these categories and health outcome measures were assessed by using multiple logistic regression analysis. Data were available for 18 420 children. For each health outcome measure, children classified as underweight by CDC 2000 but normal by WHO 2006 had higher adjusted odds ratios (aORs) of adverse health outcomes than children not classified as underweight by either; children classified as underweight by both had the highest aORs of adverse outcomes. For example, compared with children not underweight by either criteria, the aORs for fair/poor health rating were 2.54 (95% confidence interval: 2.20-2.93) among children underweight by CDC but not WHO and 3.76 (3.13-4.51) among children underweight by both. Children who are reclassified from underweight to normal weight in changing from CDC 2000 to WHO 2006 growth charts may still be affected by morbidities associated with underweight.

6 CFR 7.12 - Violations of classified information requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Violations of classified information requirements. 7.12 Section 7.12 Domestic Security DEPARTMENT OF HOMELAND SECURITY, OFFICE OF THE SECRETARY CLASSIFIED NATIONAL SECURITY INFORMATION Administration § 7.12 Violations of classified information...

Energy-Efficient Neuromorphic Classifiers.

PubMed

Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

2016-10-01

Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

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

Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

2014-08-01

We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together withmore » a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.« less

The role of local populations within a landscape context: Defining and classifying sources and sinks

USGS Publications Warehouse

Runge, J.P.; Runge, M.C.; Nichols, J.D.

2006-01-01

The interaction of local populations has been the focus of an increasing number of studies in the past 30 years. The study of source-sink dynamics has especially generated much interest. Many of the criteria used to distinguish sources and sinks incorporate the process of apparent survival (i.e., the combined probability of true survival and site fidelity) but not emigration. These criteria implicitly treat emigration as mortality, thus biasing the classification of sources and sinks in a manner that could lead to flawed habitat management. Some of the same criteria require rather restrictive assumptions about population equilibrium that, when violated, can also generate misleading inference. Here, we expand on a criterion (denoted ?contribution? or Cr) that incorporates successful emigration in differentiating sources and sinks and that makes no restrictive assumptions about dispersal or equilibrium processes in populations of interest. The metric Cr is rooted in the theory of matrix population models, yet it also contains clearly specified parameters that have been estimated in previous empirical research. We suggest that estimates of emigration are important for delineating sources and sinks and, more generally, for evaluating how local populations interact to generate overall system dynamics. This suggestion has direct implications for issues such as species conservation and habitat management.

Mental Representation and Cognitive Consequences of Chinese Individual Classifiers

ERIC Educational Resources Information Center

Gao, Ming Y.; Malt, Barbara C.

2009-01-01

Classifier languages are spoken by a large portion of the world's population, but psychologists have only recently begun to investigate the psychological reality of classifier categories and their potential for influencing non-linguistic thought. The current work evaluates both the mental representation of classifiers and potential cognitive…

Deep Learning to Classify Radiology Free-Text Reports.

PubMed

Chen, Matthew C; Ball, Robyn L; Yang, Lingyao; Moradzadeh, Nathaniel; Chapman, Brian E; Larson, David B; Langlotz, Curtis P; Amrhein, Timothy J; Lungren, Matthew P

2018-03-01

Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

40 CFR Table 3 to Subpart Ppp of... - Group 1 Storage Vessels at Existing and New Affected Sources

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 12 2014-07-01 2014-07-01 false Group 1 Storage Vessels at Existing...—Group 1 Storage Vessels at Existing and New Affected Sources Vessel capacity(cubic meters) Vapor Pressure a (kilopascals) 75 ≤capacity pressure of total...

40 CFR Table 3 to Subpart Ppp of... - Group 1 Storage Vessels at Existing and New Affected Sources

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false Group 1 Storage Vessels at Existing...—Group 1 Storage Vessels at Existing and New Affected Sources Vessel capacity(cubic meters) Vapor Pressure a (kilopascals) 75 ≤ capacity pressure of...

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target

Pharmaceutical Residues Affecting the UNESCO Biosphere Reserve Kristianstads Vattenrike Wetlands: Sources and Sinks.

PubMed

Björklund, Erland; Svahn, Ola; Bak, Søren; Bekoe, Samuel Oppong; Hansen, Martin

2016-10-01

This study is the first to investigate the pharmaceutical burden from point sources affecting the UNESCO Biosphere Reserve Kristianstads Vattenrike, Sweden. The investigated Biosphere Reserve is a >1000 km(2) wetland system with inflows from lakes, rivers, leachate from landfill, and wastewater-treatment plants (WWTPs). We analysed influent and treated wastewater, leachate water, lake, river, and wetland water alongside sediment for six model pharmaceuticals. The two WWTPs investigated released pharmaceutical residues at levels close to those previously observed in Swedish monitoring exercises. Compound-dependent WWTP removal efficiencies ranging from 12 to 100 % for bendroflumethiazide, oxazepam, atenolol, carbamazepine, and diclofenac were observed. Surface-water concentrations in the most affected lake were ≥100 ng/L for the various pharmaceuticals with atenolol showing the highest levels (>300 ng/L). A small risk assessment showed that adverse single-substance toxicity on aquatic organisms within the UNESCO Biosphere Reserve is unlikely. However, the effects of combinations of a large number of known and unknown pharmaceuticals, metals, and nutrients are still unknown.

6 CFR 7.23 - Emergency release of classified information.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Emergency release of classified information. 7... NATIONAL SECURITY INFORMATION Classified Information § 7.23 Emergency release of classified information. (a... notify the DHS Chief Security Officer and the originating agency of the information disclosed. A copy of...

Effect of water source and trout strain on expression of stress-affected genes in a commercial setting

USDA-ARS?s Scientific Manuscript database

Reduced water quality is a common problem in intensive fish culture that limits production and can affect fish mortality. In this study, two strains of juvenile rainbow trout (approximately 210 g initial weight) were exposed to 1st, 3rd, and 5th use water from raceways (the source spring and after t...

Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy

PubMed Central

Siwy, Justyna; Schanstra, Joost P.; Argiles, Angel; Bakker, Stephan J.L.; Beige, Joachim; Boucek, Petr; Brand, Korbinian; Delles, Christian; Duranton, Flore; Fernandez-Fernandez, Beatriz; Jankowski, Marie-Luise; Al Khatib, Mohammad; Kunt, Thomas; Lajer, Maria; Lichtinghagen, Ralf; Lindhardt, Morten; Maahs, David M; Mischak, Harald; Mullen, William; Navis, Gerjan; Noutsou, Marina; Ortiz, Alberto; Persson, Frederik; Petrie, John R.; Roob, Johannes M.; Rossing, Peter; Ruggenenti, Piero; Rychlik, Ivan; Serra, Andreas L.; Snell-Bergeon, Janet; Spasovski, Goce; Stojceva-Taneva, Olivera; Trillini, Matias; von der Leyen, Heiko; Winklhofer-Roob, Brigitte M.; Zürbig, Petra; Jankowski, Joachim

2014-01-01

Background Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial’ (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273). Methods In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier. Results We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16–89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found. Conclusion We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial. PMID:24589724

An ensemble of SVM classifiers based on gene pairs.

PubMed

Tong, Muchenxuan; Liu, Kun-Hong; Xu, Chungui; Ju, Wenbin

2013-07-01

In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. Copyright © 2013 Elsevier Ltd. All rights reserved.

Inductive Selectivity in Children's Cross-Classified Concepts

ERIC Educational Resources Information Center

Nguyen, Simone P.

2012-01-01

Cross-classified items pose an interesting challenge to children's induction as these items belong to many different categories, each of which may serve as a basis for a different type of inference. Inductive selectivity is the ability to appropriately make different types of inferences about a single cross-classifiable item based on its different…

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

NASA Astrophysics Data System (ADS)

Yu, Dan; Liu, Peng; Ye, Zhipeng; Tang, Xianglong; Zhao, Wei

2016-03-01

Typically, the initial task of classifying indoor scenes is challenging, because the spatial layout and decoration of a scene can vary considerably. Recent efforts at classifying object relationships commonly depend on the results of scene annotation and predefined rules, making classification inflexible. Furthermore, annotation results are easily affected by external factors. Inspired by human cognition, a scene-classification framework was proposed using the empirically based annotation (EBA) and a match-over rule-based (MRB) inference system. The semantic hierarchy of images is exploited by EBA to construct rules empirically for MRB classification. The problem of scene classification is divided into low-level annotation and high-level inference from a macro perspective. Low-level annotation involves detecting the semantic hierarchy and annotating the scene with a deformable-parts model and a bag-of-visual-words model. In high-level inference, hierarchical rules are extracted to train the decision tree for classification. The categories of testing samples are generated from the parts to the whole. Compared with traditional classification strategies, the proposed semantic hierarchy and corresponding rules reduce the effect of a variable background and improve the classification performance. The proposed framework was evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

Survey on the Performance of Source Localization Algorithms.

PubMed

Fresno, José Manuel; Robles, Guillermo; Martínez-Tarifa, Juan Manuel; Stewart, Brian G

2017-11-18

The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton-Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the

Survey on the Performance of Source Localization Algorithms

PubMed Central

2017-01-01

The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the

Prediction of plant lncRNA by ensemble machine learning classifiers.

PubMed

Simopoulos, Caitlin M A; Weretilnyk, Elizabeth A; Golding, G Brian

2018-05-02

In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation. Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are

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 .

Transient emotional events and individual affective traits affect emotion recognition in a perceptual decision-making task.

PubMed

Qiao-Tasserit, Emilie; Garcia Quesada, Maria; Antico, Lia; Bavelier, Daphne; Vuilleumier, Patrik; Pichon, Swann

2017-01-01

Both affective states and personality traits shape how we perceive the social world and interpret emotions. The literature on affective priming has mostly focused on brief influences of emotional stimuli and emotional states on perceptual and cognitive processes. Yet this approach does not fully capture more dynamic processes at the root of emotional states, with such states lingering beyond the duration of the inducing external stimuli. Our goal was to put in perspective three different types of affective states (induced affective states, more sustained mood states and affective traits such as depression and anxiety) and investigate how they may interact and influence emotion perception. Here, we hypothesized that absorption into positive and negative emotional episodes generate sustained affective states that outlast the episode period and bias the interpretation of facial expressions in a perceptual decision-making task. We also investigated how such effects are influenced by more sustained mood states and by individual affect traits (depression and anxiety) and whether they interact. Transient emotional states were induced using movie-clips, after which participants performed a forced-choice emotion classification task with morphed facial expressions ranging from fear to happiness. Using a psychometric approach, we show that negative (vs. neutral) clips increased participants' propensity to classify ambiguous faces as fearful during several minutes. In contrast, positive movies biased classification toward happiness only for those clips perceived as most absorbing. Negative mood, anxiety and depression had a stronger effect than transient states and increased the propensity to classify ambiguous faces as fearful. These results provide the first evidence that absorption and different temporal dimensions of emotions have a significant effect on how we perceive facial expressions.

Transient emotional events and individual affective traits affect emotion recognition in a perceptual decision-making task

PubMed Central

Garcia Quesada, Maria; Antico, Lia; Bavelier, Daphne; Vuilleumier, Patrik; Pichon, Swann

2017-01-01

Both affective states and personality traits shape how we perceive the social world and interpret emotions. The literature on affective priming has mostly focused on brief influences of emotional stimuli and emotional states on perceptual and cognitive processes. Yet this approach does not fully capture more dynamic processes at the root of emotional states, with such states lingering beyond the duration of the inducing external stimuli. Our goal was to put in perspective three different types of affective states (induced affective states, more sustained mood states and affective traits such as depression and anxiety) and investigate how they may interact and influence emotion perception. Here, we hypothesized that absorption into positive and negative emotional episodes generate sustained affective states that outlast the episode period and bias the interpretation of facial expressions in a perceptual decision-making task. We also investigated how such effects are influenced by more sustained mood states and by individual affect traits (depression and anxiety) and whether they interact. Transient emotional states were induced using movie-clips, after which participants performed a forced-choice emotion classification task with morphed facial expressions ranging from fear to happiness. Using a psychometric approach, we show that negative (vs. neutral) clips increased participants’ propensity to classify ambiguous faces as fearful during several minutes. In contrast, positive movies biased classification toward happiness only for those clips perceived as most absorbing. Negative mood, anxiety and depression had a stronger effect than transient states and increased the propensity to classify ambiguous faces as fearful. These results provide the first evidence that absorption and different temporal dimensions of emotions have a significant effect on how we perceive facial expressions. PMID:28151976

Classifying with confidence from incomplete information.

DOE PAGES

Parrish, Nathan; Anderson, Hyrum S.; Gupta, Maya R.; ...

2013-12-01

For this paper, we consider the problem of classifying a test sample given incomplete information. This problem arises naturally when data about a test sample is collected over time, or when costs must be incurred to compute the classification features. For example, in a distributed sensor network only a fraction of the sensors may have reported measurements at a certain time, and additional time, power, and bandwidth is needed to collect the complete data to classify. A practical goal is to assign a class label as soon as enough data is available to make a good decision. We formalize thismore » goal through the notion of reliability—the probability that a label assigned given incomplete data would be the same as the label assigned given the complete data, and we propose a method to classify incomplete data only if some reliability threshold is met. Our approach models the complete data as a random variable whose distribution is dependent on the current incomplete data and the (complete) training data. The method differs from standard imputation strategies in that our focus is on determining the reliability of the classification decision, rather than just the class label. We show that the method provides useful reliability estimates of the correctness of the imputed class labels on a set of experiments on time-series data sets, where the goal is to classify the time-series as early as possible while still guaranteeing that the reliability threshold is met.« less

A Deep XMM-Newton Survey of M33: Point-source Catalog, Source Detection, and Characterization of Overlapping Fields

NASA Astrophysics Data System (ADS)

Williams, Benjamin F.; Wold, Brian; Haberl, Frank; Garofali, Kristen; Blair, William P.; Gaetz, Terrance J.; Kuntz, K. D.; Long, Knox S.; Pannuti, Thomas G.; Pietsch, Wolfgang; Plucinsky, Paul P.; Winkler, P. Frank

2015-05-01

We have obtained a deep 8 field XMM-Newton mosaic of M33 covering the galaxy out to the D25 isophote and beyond to a limiting 0.2-4.5 keV unabsorbed flux of 5 × 10-16 erg cm-2 s-1 (L \\gt 4 × 1034 erg s-1 at the distance of M33). These data allow complete coverage of the galaxy with high sensitivity to soft sources such as diffuse hot gas and supernova remnants (SNRs). Here, we describe the methods we used to identify and characterize 1296 point sources in the 8 fields. We compare our resulting source catalog to the literature, note variable sources, construct hardness ratios, classify soft sources, analyze the source density profile, and measure the X-ray luminosity function (XLF). As a result of the large effective area of XMM-Newton below 1 keV, the survey contains many new soft X-ray sources. The radial source density profile and XLF for the sources suggest that only ˜15% of the 391 bright sources with L \\gt 3.6 × 1035 erg s-1 are likely to be associated with M33, and more than a third of these are known SNRs. The log(N)-log(S) distribution, when corrected for background contamination, is a relatively flat power law with a differential index of 1.5, which suggests that many of the other M33 sources may be high-mass X-ray binaries. Finally, we note the discovery of an interesting new transient X-ray source, which we are unable to classify.

Threat Identification Parameters for a Stolen Category 1 Radioactive Source

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

Ussery, Larry Eugene; Winkler, Ryan; Myers, Steven Charles

2016-02-18

Radioactive sources are used very widely for research and practical applications across medicine, industry, government, universities, and agriculture. The risks associated with these sources vary widely depending on the specific radionuclide used to make the source, source activity, and its chemical and physical form. Sources are categorized by a variety of classification schemes according to the specific risk they pose to the public. This report specifically addresses sources that are classified in the highest category for health risk (category 1). Exposure to an unshielded or lightly shielded category 1 source is extremely dangerous to life and health and can bemore » fatal in relatively short exposure times measured in seconds to minutes. A Category 1 source packaged according to the guidelines dictated by the NRC and U.S. Department of Transportation will typically be surrounded by a large amount of dense shielding material, but will still exhibit a significant dose rate in close proximity. Detection ranges for Category 1 gamma ray sources can extend beyond 5000 ft, but will depend mostly on the source isotope and activity, and the level of shielding around the source. Category 1 sources are easy to detect, but difficult to localize. Dose rates in proximity to an unshielded Category 1 source are extraordinarily high. At distances of a few hundred feet, the functionality of many commonly used handheld instruments will be extremely limited for both the localization and identification of the source. Radiation emitted from a Category 1 source will scatter off of both solid material (ground and buildings) and the atmosphere, a phenomenon known as skyshine. This scattering affects the ability to easily localize and find the source.« less

Frog sound identification using extended k-nearest neighbor classifier

NASA Astrophysics Data System (ADS)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

49 CFR 8.11 - Authority to classify information.

Code of Federal Regulations, 2011 CFR

2011-10-01

....11 Transportation Office of the Secretary of Transportation CLASSIFIED INFORMATION: CLASSIFICATION/DECLASSIFICATION/ACCESS Classification/Declassification of Information § 8.11 Authority to classify information. (a... information as SECRET or CONFIDENTIAL with further authorization to delegate this authority. (b) The following...

49 CFR 8.11 - Authority to classify information.

Code of Federal Regulations, 2012 CFR

2012-10-01

....11 Transportation Office of the Secretary of Transportation CLASSIFIED INFORMATION: CLASSIFICATION/DECLASSIFICATION/ACCESS Classification/Declassification of Information § 8.11 Authority to classify information. (a... information as SECRET or CONFIDENTIAL with further authorization to delegate this authority. (b) The following...

49 CFR 8.11 - Authority to classify information.

Code of Federal Regulations, 2014 CFR

2014-10-01

....11 Transportation Office of the Secretary of Transportation CLASSIFIED INFORMATION: CLASSIFICATION/DECLASSIFICATION/ACCESS Classification/Declassification of Information § 8.11 Authority to classify information. (a... information as SECRET or CONFIDENTIAL with further authorization to delegate this authority. (b) The following...

49 CFR 8.11 - Authority to classify information.

Code of Federal Regulations, 2013 CFR

2013-10-01

....11 Transportation Office of the Secretary of Transportation CLASSIFIED INFORMATION: CLASSIFICATION/DECLASSIFICATION/ACCESS Classification/Declassification of Information § 8.11 Authority to classify information. (a... information as SECRET or CONFIDENTIAL with further authorization to delegate this authority. (b) The following...

49 CFR 8.11 - Authority to classify information.

Code of Federal Regulations, 2010 CFR

2010-10-01

....11 Transportation Office of the Secretary of Transportation CLASSIFIED INFORMATION: CLASSIFICATION/DECLASSIFICATION/ACCESS Classification/Declassification of Information § 8.11 Authority to classify information. (a... information as SECRET or CONFIDENTIAL with further authorization to delegate this authority. (b) The following...

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

EMISSION CHARACTERIZATION OF STATIONARY NOX SOURCES: VOLUME 1. RESULTS

EPA Science Inventory

The report gives results of an inventory of gaseous, liquid, and solid effluents from stationary NOx sources, projected to the year 2000, and ranks them according to their potential for environmental hazard. It classifies sources according to their pollution formation characteris...

Bioenhanced DNAPL Dissolution: Understanding how Microbial Competition, Biostimulation, and Bioaugmentation Affect Source Zone Longevity

NASA Astrophysics Data System (ADS)

Becker, J. G.; Seagren, E. A.

2006-12-01

The presence of dense non-aqueous phase liquids (DNAPLs) at many chlorinated ethene-contaminated sites can greatly extend the time frames needed to reduce dissolved contaminants to regulatory levels using bioremediation. However, it has been demonstrated that mass removal from chlorinated ethene DNAPLs can potentially be enhanced through dehalorespiration of dissolved contaminants near the NAPL-water interface. Although promising, the amount of "bioenhancement" that can be achieved under optimal conditions is currently not known, and the real significance and engineering potential of this phenomenon currently are not well understood, in part because it can be influenced by a complex set of factors, including DNAPL properties, hydrodynamics, substrate concentrations, and microbial competition for growth substrates. In this study it is hypothesized that: (1) different chlorinated ethene-respiring strains may dominate within different zones of a contaminant plume emanating from a DNAPL source zone due to variations in substrate availability, and microbial competition for chlorinated ethenes and/or electron donors; and (2) the outcome of competitive interactions near the DNAPL source zone will affect the longevity of DNAPL source zones by influencing the degree of dissolution bioenhancement, while the outcome of competitive interactions further downgradient will determine the extent of contaminant dechlorination. To demonstrate the validity of the proposed hypothesis, a series of simple, "proof-of-concept," mathematical simulations evaluating the effects of competitive interactions on the distribution of dehalorespirers at the DNAPL-water interface, the dissolution of tetrachloroethene (PCE), and extent of PCE detoxification were performed in a model competition scenario, in which Dehalococcoides ethenogenes and another dehalorespirer (Desulfuromonas michiganensis) compete for the electron acceptor (PCE) and/or electron donor. The model domain for this evaluation

45 CFR 601.8 - Access to classified materials.

Code of Federal Regulations, 2011 CFR

2011-10-01

....8 Public Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.8 Access to classified materials. No person may be given access to classified information unless that person has been determined to be...

45 CFR 601.8 - Access to classified materials.

Code of Federal Regulations, 2010 CFR

2010-10-01

....8 Public Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.8 Access to classified materials. No person may be given access to classified information unless that person has been determined to be...

45 CFR 601.8 - Access to classified materials.

Code of Federal Regulations, 2013 CFR

2013-10-01

....8 Public Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.8 Access to classified materials. No person may be given access to classified information unless that person has been determined to be...

45 CFR 601.8 - Access to classified materials.

Code of Federal Regulations, 2014 CFR

2014-10-01

....8 Public Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.8 Access to classified materials. No person may be given access to classified information unless that person has been determined to be...

45 CFR 601.8 - Access to classified materials.

Code of Federal Regulations, 2012 CFR

2012-10-01

....8 Public Welfare Regulations Relating to Public Welfare (Continued) NATIONAL SCIENCE FOUNDATION CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.8 Access to classified materials. No person may be given access to classified information unless that person has been determined to be...

Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

NASA Astrophysics Data System (ADS)

Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

2013-03-01

Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

Delineating sources of groundwater recharge in an arsenic-affected Holocene aquifer in Cambodia using stable isotope-based mixing models

NASA Astrophysics Data System (ADS)

Richards, Laura A.; Magnone, Daniel; Boyce, Adrian J.; Casanueva-Marenco, Maria J.; van Dongen, Bart E.; Ballentine, Christopher J.; Polya, David A.

2018-02-01

Chronic exposure to arsenic (As) through the consumption of contaminated groundwaters is a major threat to public health in South and Southeast Asia. The source of As-affected groundwaters is important to the fundamental understanding of the controls on As mobilization and subsequent transport throughout shallow aquifers. Using the stable isotopes of hydrogen and oxygen, the source of groundwater and the interactions between various water bodies were investigated in Cambodia's Kandal Province, an area which is heavily affected by As and typical of many circum-Himalayan shallow aquifers. Two-point mixing models based on δD and δ18O allowed the relative extent of evaporation of groundwater sources to be estimated and allowed various water bodies to be broadly distinguished within the aquifer system. Model limitations are discussed, including the spatial and temporal variation in end member compositions. The conservative tracer Cl/Br is used to further discriminate between groundwater bodies. The stable isotopic signatures of groundwaters containing high As and/or high dissolved organic carbon plot both near the local meteoric water line and near more evaporative lines. The varying degrees of evaporation of high As groundwater sources are indicative of differing recharge contributions (and thus indirectly inferred associated organic matter contributions). The presence of high As groundwaters with recharge derived from both local precipitation and relatively evaporated surface water sources, such as ponds or flooded wetlands, are consistent with (but do not provide direct evidence for) models of a potential dual role of surface-derived and sedimentary organic matter in As mobilization.

CHANDRA ACIS SURVEY OF X-RAY POINT SOURCES: THE SOURCE CATALOG

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

Wang, Song; Liu, Jifeng; Qiu, Yanli

The Chandra archival data is a valuable resource for various studies on different X-ray astronomy topics. In this paper, we utilize this wealth of information and present a uniformly processed data set, which can be used to address a wide range of scientific questions. The data analysis procedures are applied to 10,029 Advanced CCD Imaging Spectrometer observations, which produces 363,530 source detections belonging to 217,828 distinct X-ray sources. This number is twice the size of the Chandra Source Catalog (Version 1.1). The catalogs in this paper provide abundant estimates of the detected X-ray source properties, including source positions, counts, colors,more » fluxes, luminosities, variability statistics, etc. Cross-correlation of these objects with galaxies shows that 17,828 sources are located within the D {sub 25} isophotes of 1110 galaxies, and 7504 sources are located between the D {sub 25} and 2 D {sub 25} isophotes of 910 galaxies. Contamination analysis with the log N –log S relation indicates that 51.3% of objects within 2 D {sub 25} isophotes are truly relevant to galaxies, and the “net” source fraction increases to 58.9%, 67.3%, and 69.1% for sources with luminosities above 10{sup 37}, 10{sup 38}, and 10{sup 39} erg s{sup −1}, respectively. Among the possible scientific uses of this catalog, we discuss the possibility of studying intra-observation variability, inter-observation variability, and supersoft sources (SSSs). About 17,092 detected sources above 10 counts are classified as variable in individual observation with the Kolmogorov–Smirnov (K–S) criterion ( P {sub K–S} < 0.01). There are 99,647 sources observed more than once and 11,843 sources observed 10 times or more, offering us a wealth of data with which to explore the long-term variability. There are 1638 individual objects (∼2350 detections) classified as SSSs. As a quite interesting subclass, detailed studies on X-ray spectra and optical spectroscopic follow-up are

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

NASA Technical Reports Server (NTRS)

Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley

2006-01-01

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

Adaptive Bayes classifiers for remotely sensed data

NASA Technical Reports Server (NTRS)

Raulston, H. S.; Pace, M. O.; Gonzalez, R. C.

1975-01-01

An algorithm is developed for a learning, adaptive, statistical pattern classifier for remotely sensed data. The estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest, and (2) a projection of the parameters in time and space. The results reported are for Gaussian data in which the mean vector of each class may vary with time or position after the classifier is trained.

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…

Bayes Error Rate Estimation Using Classifier Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2003-01-01

The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating th is rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with the Bayes rate, one can quantify how effective that classifier is. Classical approaches for estimating or finding bounds for the Bayes error, in general, yield rather weak results for small sample sizes; unless the problem has some simple characteristics, such as Gaussian class-conditional likelihoods. This article shows how the outputs of a classifier ensemble can be used to provide reliable and easily obtainable estimates of the Bayes error with negligible extra computation. Three methods of varying sophistication are described. First, we present a framework that estimates the Bayes error when multiple classifiers, each providing an estimate of the a posteriori class probabilities, a recombined through averaging. Second, we bolster this approach by adding an information theoretic measure of output correlation to the estimate. Finally, we discuss a more general method that just looks at the class labels indicated by ensem ble members and provides error estimates based on the disagreements among classifiers. The methods are illustrated for artificial data, a difficult four-class problem involving underwater acoustic data, and two problems from the Problem benchmarks. For data sets with known Bayes error, the combiner-based methods introduced in this article outperform existing methods. The estimates obtained by the proposed methods also seem quite reliable for the real-life data sets for which the true Bayes rates are unknown.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

Different carbon sources affect PCB accumulation by marine bivalves.

PubMed

Laitano, M V; Silva Barni, M F; Costa, P G; Cledón, M; Fillmann, G; Miglioranza, K S B; Panarello, H O

2016-02-01

Pampean creeks were evaluated in the present study as potential land-based sources of PCB marine contamination. Different carbon and nitrogen sources from such creeks were analysed as boosters of PCB bioaccumulation by the filter feeder bivalve Brachidontes rodriguezii and grazer limpet Siphonaria lessoni. Carbon of different source than marine and anthropogenic nitrogen assimilated by organisms were estimated through their C and N isotopic composition. PCB concentration in surface sediments and mollusc samples ranged from 2.68 to 6.46 ng g(-1) (wet weight) and from 1074 to 4583 ng g(-1) lipid, respectively, reflecting a punctual source of PCB contamination related to a landfill area. Thus, despite the low flow of creeks, they should not be underestimated as contamination vectors to the marine environment. On the other hand, mussels PCB bioaccumulation was related with the carbon source uptake which highlights the importance to consider this factor when studying PCB distribution in organisms of coastal systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

40 CFR Table 3 to Subpart Dd of... - Tank Control Levels for Tanks at Existing Affected Sources as Required by 40 CFR 63.685(b)(1)

Code of Federal Regulations, 2010 CFR

2010-07-01

... Existing Affected Sources as Required by 40 CFR 63.685(b)(1) 3 Table 3 to Subpart DD of Part 63 Protection... Hazardous Air Pollutants from Off-Site Waste and Recovery Operations Pt. 63, Subpt. DD, Table 3 Table 3 to Subpart DD of Part 63—Tank Control Levels for Tanks at Existing Affected Sources as Required by 40 CFR 63...

Determining sources of elevated salinity in pre-hydraulic fracturing water quality data using a multivariate discriminant analysis model

NASA Astrophysics Data System (ADS)

Lautz, L. K.; Hoke, G. D.; Lu, Z.; Siegel, D. I.

2013-12-01

Hydraulic fracturing has the potential to introduce saline water into the environment due to migration of deep formation water to shallow aquifers and/or discharge of flowback water to the environment during transport and disposal. It is challenging to definitively identify whether elevated salinity is associated with hydraulic fracturing, in part, due to the real possibility of other anthropogenic sources of salinity in the human-impacted watersheds in which drilling is taking place and some formation water present naturally in shallow groundwater aquifers. We combined new and published chemistry data for private drinking water wells sampled across five southern New York (NY) counties overlying the Marcellus Shale (Broome, Chemung, Chenango, Steuben, and Tioga). Measurements include Cl, Na, Br, I, Ca, Mg, Ba, SO4, and Sr. We compared this baseline groundwater quality data in NY, now under a moratorium on hydraulic fracturing, with published chemistry data for 6 different potential sources of elevated salinity in shallow groundwater, including Appalachian Basin formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. A multivariate random number generator was used to create a synthetic, low salinity (< 20 mg/L Cl) groundwater data set (n=1000) based on the statistical properties of the observed low salinity groundwater. The synthetic, low salinity groundwater was then artificially mixed with variable proportions of different potential sources of salinity to explore chemical differences between groundwater impacted by formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. We then trained a multivariate, discriminant analysis model on the resulting data set to classify observed high salinity groundwater (> 20 mg/L Cl) as being affected by formation water, road salt, septic effluent, landfill leachate, animal waste, or water softeners. Single elements or pairs of

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2013 CFR

2013-07-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2014 CFR

2014-01-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2011 CFR

2011-01-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2012 CFR

2012-01-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2010 CFR

2010-07-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

A cardiorespiratory classifier of voluntary and involuntary electrodermal activity

PubMed Central

2010-01-01

Background Electrodermal reactions (EDRs) can be attributed to many origins, including spontaneous fluctuations of electrodermal activity (EDA) and stimuli such as deep inspirations, voluntary mental activity and startling events. In fields that use EDA as a measure of psychophysiological state, the fact that EDRs may be elicited from many different stimuli is often ignored. This study attempts to classify observed EDRs as voluntary (i.e., generated from intentional respiratory or mental activity) or involuntary (i.e., generated from startling events or spontaneous electrodermal fluctuations). Methods Eight able-bodied participants were subjected to conditions that would cause a change in EDA: music imagery, startling noises, and deep inspirations. A user-centered cardiorespiratory classifier consisting of 1) an EDR detector, 2) a respiratory filter and 3) a cardiorespiratory filter was developed to automatically detect a participant's EDRs and to classify the origin of their stimulation as voluntary or involuntary. Results Detected EDRs were classified with a positive predictive value of 78%, a negative predictive value of 81% and an overall accuracy of 78%. Without the classifier, EDRs could only be correctly attributed as voluntary or involuntary with an accuracy of 50%. Conclusions The proposed classifier may enable investigators to form more accurate interpretations of electrodermal activity as a measure of an individual's psychophysiological state. PMID:20184746

EEG Responses to Auditory Stimuli for Automatic Affect Recognition

PubMed Central

Hettich, Dirk T.; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

2016-01-01

Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

42 CFR 37.50 - Interpreting and classifying chest radiographs-film.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 42 Public Health 1 2014-10-01 2014-10-01 false Interpreting and classifying chest radiographs-film. 37.50 Section 37.50 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES... Interpreting and classifying chest radiographs—film. (a) Chest radiographs must be interpreted and classified...

Blocking Effects in the Learning of Chinese Classifiers

ERIC Educational Resources Information Center

Paul, Jing Z.; Grüter, Theres

2016-01-01

This study investigated order-of-learning effects on the acquisition of classifier-noun associations in Chinese in two experiments modeled after Arnon and Ramscar's (2012) study of artificial language learning. In Experiment 1, learners with no prior exposure to Chinese showed better learning of classifier-noun associations when exposed to larger…

18 CFR 367.18 - Criteria for classifying leases.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Criteria for classifying leases. 367.18 Section 367.18 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT General Instructions § 367.18 Criteria for classifying leases. (a) If, at its inception, a lease...

18 CFR 367.18 - Criteria for classifying leases.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 18 Conservation of Power and Water Resources 1 2013-04-01 2013-04-01 false Criteria for classifying leases. 367.18 Section 367.18 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT General Instructions § 367.18 Criteria for classifying leases. (a) If, at its inception, a lease...

18 CFR 367.18 - Criteria for classifying leases.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 18 Conservation of Power and Water Resources 1 2011-04-01 2011-04-01 false Criteria for classifying leases. 367.18 Section 367.18 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT General Instructions § 367.18 Criteria for classifying leases. (a) If, at its inception, a lease...

18 CFR 367.18 - Criteria for classifying leases.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 18 Conservation of Power and Water Resources 1 2014-04-01 2014-04-01 false Criteria for classifying leases. 367.18 Section 367.18 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT General Instructions § 367.18 Criteria for classifying leases. (a) If, at its inception, a lease...

18 CFR 367.18 - Criteria for classifying leases.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 18 Conservation of Power and Water Resources 1 2012-04-01 2012-04-01 false Criteria for classifying leases. 367.18 Section 367.18 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT General Instructions § 367.18 Criteria for classifying leases. (a) If, at its inception, a lease...

Reciprocity in computer-human interaction: source-based, norm-based, and affect-based explanations.

PubMed

Lee, Seungcheol Austin; Liang, Yuhua Jake

2015-04-01

Individuals often apply social rules when they interact with computers, and this is known as the Computers Are Social Actors (CASA) effect. Following previous work, one approach to understand the mechanism responsible for CASA is to utilize computer agents and have the agents attempt to gain human compliance (e.g., completing a pattern recognition task). The current study focuses on three key factors frequently cited to influence traditional notions of compliance: evaluations toward the source (competence and warmth), normative influence (reciprocity), and affective influence (mood). Structural equation modeling assessed the effects of these factors on human compliance with computer request. The final model shows that norm-based influence (reciprocity) increased the likelihood of compliance, while evaluations toward the computer agent did not significantly influence compliance.

40 CFR Table 3 to Subpart Ppp of... - Group 1 Storage Vessels at Existing and New Affected Sources

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 12 2012-07-01 2011-07-01 true Group 1 Storage Vessels at Existing and... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 3 Table 3 to Subpart PPP of Part 63—Group 1 Storage Vessels at Existing and New Affected Sources Vessel capacity(cubic meters) Vapor Pressure a (kilopascals...

40 CFR Table 3 to Subpart Ppp of... - Group 1 Storage Vessels at Existing and New Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true Group 1 Storage Vessels at Existing and... Polyether Polyols Production Pt. 63, Subpt. PPP, Table 3 Table 3 to Subpart PPP of Part 63—Group 1 Storage Vessels at Existing and New Affected Sources Vessel capacity(cubic meters) Vapor Pressure a (kilopascals...

Constructing better classifier ensemble based on weighted accuracy and diversity measure.

PubMed

Zeng, Xiaodong; Wong, Derek F; Chao, Lidia S

2014-01-01

A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.

Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure

PubMed Central

Chao, Lidia S.

2014-01-01

A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases. PMID:24672402

41 CFR 105-62.102 - Authority to originally classify.

Code of Federal Regulations, 2010 CFR

2010-07-01

... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable...

41 CFR 105-62.102 - Authority to originally classify.

Code of Federal Regulations, 2012 CFR

2012-01-01

... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable...

40 CFR Table 4 to Subpart Dd of... - Tank Control Levels for Tanks at New Affected Sources as Required by 40 CFR 63.685(b)(2)

Code of Federal Regulations, 2010 CFR

2010-07-01

... Affected Sources as Required by 40 CFR 63.685(b)(2) 4 Table 4 to Subpart DD of Part 63 Protection of... Hazardous Air Pollutants from Off-Site Waste and Recovery Operations Pt. 63, Subpt. DD, Table 4 Table 4 to Subpart DD of Part 63—Tank Control Levels for Tanks at New Affected Sources as Required by 40 CFR 63.685(b...

Increasing Children's ASL Classifier Production: A Multicomponent Intervention

ERIC Educational Resources Information Center

Beal-Alvarez, Jennifer S.; Easterbrooks, Susan R.

2013-01-01

The Authors examined classifier production during narrative retells by 10 deaf and hard of hearing students in grades 2-4 at a day school for the deaf following a 6-week intervention of repeated viewings of stories in American Sign Language (ASL) paired with scripted teacher mediation. Classifier production, documented through a…

40 CFR 63.7884 - What are the general standards I must meet for each site remediation with affected sources?

Code of Federal Regulations, 2011 CFR

2011-07-01

... meet for each site remediation with affected sources? 63.7884 Section 63.7884 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS... Pollutants: Site Remediation General Standards § 63.7884 What are the general standards I must meet for each...

40 CFR 63.7884 - What are the general standards I must meet for each site remediation with affected sources?

Code of Federal Regulations, 2012 CFR

2012-07-01

... meet for each site remediation with affected sources? 63.7884 Section 63.7884 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS... Pollutants: Site Remediation General Standards § 63.7884 What are the general standards I must meet for each...

40 CFR 63.7884 - What are the general standards I must meet for each site remediation with affected sources?

Code of Federal Regulations, 2013 CFR

2013-07-01

... meet for each site remediation with affected sources? 63.7884 Section 63.7884 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS... Pollutants: Site Remediation General Standards § 63.7884 What are the general standards I must meet for each...

40 CFR 63.7884 - What are the general standards I must meet for each site remediation with affected sources?

Code of Federal Regulations, 2014 CFR

2014-07-01

... meet for each site remediation with affected sources? 63.7884 Section 63.7884 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS... Pollutants: Site Remediation General Standards § 63.7884 What are the general standards I must meet for each...

40 CFR Table 3 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the...

Code of Federal Regulations, 2013 CFR

2013-07-01

... Foam Production Affected Sources Complying With the Emission Point Specific Limitations 3 Table 3 to...) National Emission Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 3 Table 3 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam...

40 CFR Table 3 to Subpart III of... - Compliance Requirements for Slabstock Foam Production Affected Sources Complying With the...

Code of Federal Regulations, 2014 CFR

2014-07-01

... Foam Production Affected Sources Complying With the Emission Point Specific Limitations 3 Table 3 to...) National Emission Standards for Hazardous Air Pollutants for Flexible Polyurethane Foam Production Pt. 63, Subpt. III, Table 3 Table 3 to Subpart III of Part 63—Compliance Requirements for Slabstock Foam...

New MYC IHC Classifier Integrating Quantitative Architecture Parameters to Predict MYC Gene Translocation in Diffuse Large B-Cell Lymphoma

PubMed Central

Dong, Wei-Feng; Canil, Sarah; Lai, Raymond; Morel, Didier; Swanson, Paul E.; Izevbaye, Iyare

2018-01-01

A new automated MYC IHC classifier based on bivariate logistic regression is presented. The predictor relies on image analysis developed with the open-source ImageJ platform. From a histologic section immunostained for MYC protein, 2 dimensionless quantitative variables are extracted: (a) relative distance between nuclei positive for MYC IHC based on euclidean minimum spanning tree graph and (b) coefficient of variation of the MYC IHC stain intensity among MYC IHC-positive nuclei. Distance between positive nuclei is suggested to inversely correlate MYC gene rearrangement status, whereas coefficient of variation is suggested to inversely correlate physiological regulation of MYC protein expression. The bivariate classifier was compared with 2 other MYC IHC classifiers (based on percentage of MYC IHC positive nuclei), all tested on 113 lymphomas including mostly diffuse large B-cell lymphomas with known MYC fluorescent in situ hybridization (FISH) status. The bivariate classifier strongly outperformed the “percentage of MYC IHC-positive nuclei” methods to predict MYC+ FISH status with 100% sensitivity (95% confidence interval, 94-100) associated with 80% specificity. The test is rapidly performed and might at a minimum provide primary IHC screening for MYC gene rearrangement status in diffuse large B-cell lymphomas. Furthermore, as this bivariate classifier actually predicts “permanent overexpressed MYC protein status,” it might identify nontranslocation-related chromosomal anomalies missed by FISH. PMID:27093450

PPCM: Combing multiple classifiers to improve protein-protein interaction prediction

DOE PAGES

Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

2015-08-01

Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using anmore » assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. Ultimately, this pipeline will be useful for predicting PPI in nonmodel species.« less

Urine cell-based DNA methylation classifier for monitoring bladder cancer.

PubMed

van der Heijden, Antoine G; Mengual, Lourdes; Ingelmo-Torres, Mercedes; Lozano, Juan J; van Rijt-van de Westerlo, Cindy C M; Baixauli, Montserrat; Geavlete, Bogdan; Moldoveanud, Cristian; Ene, Cosmin; Dinney, Colin P; Czerniak, Bogdan; Schalken, Jack A; Kiemeney, Lambertus A L M; Ribal, Maria J; Witjes, J Alfred; Alcaraz, Antonio

2018-01-01

Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). Voided urine samples ( N  = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients ( N  = 399). In the discovery phase, seven selected genes from the literature ( CDH13 , CFTR , NID2 , SALL3 , TMEFF2 , TWIST1 , and VIM2 ) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). A three-gene methylation classifier containing CFTR , SALL3 , and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented.

Classifying Cereal Data (Earlier Methods)

Cancer.gov

The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

Learning to classify wakes from local sensory information

NASA Astrophysics Data System (ADS)

Alsalman, Mohamad; Colvert, Brendan; Kanso, Eva; Kanso Team

2017-11-01

Aquatic organisms exhibit remarkable abilities to sense local flow signals contained in their fluid environment and to surmise the origins of these flows. For example, fish can discern the information contained in various flow structures and utilize this information for obstacle avoidance and prey tracking. Flow structures created by flapping and swimming bodies are well characterized in the fluid dynamics literature; however, such characterization relies on classical methods that use an external observer to reconstruct global flow fields. The reconstructed flows, or wakes, are then classified according to the unsteady vortex patterns. Here, we propose a new approach for wake identification: we classify the wakes resulting from a flapping airfoil by applying machine learning algorithms to local flow information. In particular, we simulate the wakes of an oscillating airfoil in an incoming flow, extract the downstream vorticity information, and train a classifier to learn the different flow structures and classify new ones. This data-driven approach provides a promising framework for underwater navigation and detection in application to autonomous bio-inspired vehicles.

Is there differential responsiveness to a future cigarette price increase depending on adolescents' source of cigarette access?

PubMed

Hwang, Jun Hyun; Park, Soon-Woo

2017-06-01

We examined whether the responsiveness to an increase in cigarettes price differed by adolescents' cigarette acquisition source. We analyzed data on 6134 youth smokers (grades 7-12) from a cross-sectional survey in Korea with national representativeness. The respondents were classified into one of the following according to their source of cigarette acquisition: commercial-source group, social-source group, and others. Multiple logistic regressions were performed to estimate the effects of an increase in cigarette price on the intention to quit smoking on the basis of the cigarette acquisition source. Of the 6134 youth smokers, 36.0% acquired cigarettes from social sources, compared to the 49.6% who purchased cigarettes directly from commercial sources. In response to a future cigarette price increase, regardless of an individual's smoking level, there was no statistically significant difference in the odds ratio for the intention to stop smoking in association with cigarette acquisition sources. The social-source group had nonsignificant, but consistently positive, odds ratios (1.07-1.30) as compared to that of the commercial-source group. Our findings indicate that the cigarette acquisition source does not affect the responsiveness to an increase in cigarette price. Therefore, a cigarette price policy is a comprehensive strategy to reduce smoking among youth smokers, regardless of their source.

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2014 CFR

2014-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2012 CFR

2012-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2013 CFR

2013-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

Affective design identification on the development of batik convection product

NASA Astrophysics Data System (ADS)

Prastawa, H.; Purwaningsih, R.

2017-11-01

The affective design is increasingly applied to product development in order to meet the desires and preferences of customers. Batik is a traditional Indonesian culture containing historical and cultural values. The development of batik design is one of the efforts to strengthen the identity and superiority of Indonesia’s creative industries as well as to preserve batik as the cultural heritage of the nation. Batik product designs offered by the manufacturers do not necessarily correspond with the wishes of consumers, especially the affective values involved. Therefore it is necessary to identify consumer perceptions of convection- based batik product in the form of clothing and fabrics, especially the affective value as the consideration for the designer or manufacturer to develop design alternatives to batik convection products. This research aims to obtain information on consumer affective value, to identify the affective value perception differences among X and Y Generation and to classify affective value in the corresponding cluster of the batik products convection. This study uses Kansei engineering to determine the perception of affective design in the form of Kansei word. Cluster Analysis was used to form clusters that classify affective value of the same class. The results showed that there were 16 pairs of Kansei word which was worth as an affective consumer desire, the 3 indicators that had significant differences among X and Y Generation and 4 clusters with different characteristics.

40 CFR 63.1345 - Emissions limits for affected sources other than kilns; in-line kiln/raw mills; clinker coolers...

Code of Federal Regulations, 2011 CFR

2011-07-01

... other than kilns; in-line kiln/raw mills; clinker coolers; new and reconstructed raw material dryers; and raw and finish mills, and open clinker piles. 63.1345 Section 63.1345 Protection of Environment... for affected sources other than kilns; in-line kiln/raw mills; clinker coolers; new and reconstructed...

Classifying defects in pallet stringers by ultrasonic scanning

Treesearch

Mohammed F. Kabir; Daniel L. Schmoldt; Philip A. Araman; Mark E. Schafer; Sang-Mook Lee

2003-01-01

Detecting and classifying defects are required to grade and sort pallet parts. Use of quality parts can extend the life cycle of pallets and can reduce long-term cost. An investigation has been carried out to detect and classify defects in yellow-poplar (Liriodendron tulipifera, L.) and red oak (Quercus rubra, L.) stringers using ultrasonic scanning. Data were...

Fisher classifier and its probability of error estimation

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

A consensus prognostic gene expression classifier for ER positive breast cancer

PubMed Central

Teschendorff, Andrew E; Naderi, Ali; Barbosa-Morais, Nuno L; Pinder, Sarah E; Ellis, Ian O; Aparicio, Sam; Brenton, James D; Caldas, Carlos

2006-01-01

Background A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. Results Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. Conclusion The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors. PMID:17076897

nRC: non-coding RNA Classifier based on structural features.

PubMed

Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

2017-01-01

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

Arrogance analysis of several typical pattern recognition classifiers

NASA Astrophysics Data System (ADS)

Jing, Chen; Xia, Shengping; Hu, Weidong

2007-04-01

Various kinds of classification methods have been developed. However, most of these classical methods, such as Back-Propagation (BP), Bayesian method, Support Vector Machine(SVM), Self-Organizing Map (SOM) are arrogant. A so-called arrogance, for a human, means that his decision, which even is a mistake, overstates his actual experience. Accordingly, we say that he is a arrogant if he frequently makes arrogant decisions. Likewise, some classical pattern classifiers represent the similar characteristic of arrogance. Given an input feature vector, we say a classifier is arrogant in its classification if its veracity is high yet its experience is low. Typically, for a new sample which is distinguishable from original training samples, traditional classifiers recognize it as one of the known targets. Clearly, arrogance in classification is an undesirable attribute. Conversely, a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its experience. Inquisitiveness is, in many ways, the opposite of arrogance. In nature, inquisitiveness is an eagerness for knowledge characterized by the drive to question, to seek a deeper understanding. The human capacity to doubt present beliefs allows us to acquire new experiences and to learn from our mistakes. Within the discrete world of computers, inquisitive pattern recognition is the constructive investigation and exploitation of conflict in information. Thus, we quantify this balance and discuss new techniques that will detect arrogance in a classifier.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

NASA Astrophysics Data System (ADS)

Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

2014-03-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

Resistance of Loblolly Pine Sources to Fusiform Rust in Field Progeny Tests

Treesearch

H.R. Powers; E.G. Kuhlman

1987-01-01

Results of concentrated basidiospore spray (CBS) inoculations correlated well with field infection. Generally, the CBS system correctly classified resistant and susceptible sources, but it classed seven sources with field resistance as susceptible.

Inspection of wear particles in oils by using a fuzzy classifier

NASA Astrophysics Data System (ADS)

Hamalainen, Jari J.; Enwald, Petri

1994-11-01

The reliability of stand-alone machines and larger production units can be improved by automated condition monitoring. Analysis of wear particles in lubricating or hydraulic oils helps diagnosing the wear states of machine parts. This paper presents a computer vision system for automated classification of wear particles. Digitized images from experiments with a bearing test bench, a hydraulic system with an industrial company, and oil samples from different industrial sources were used for algorithm development and testing. The wear particles were divided into four classes indicating different wear mechanisms: cutting wear, fatigue wear, adhesive wear, and abrasive wear. The results showed that the fuzzy K-nearest neighbor classifier utilized gave the same distribution of wear particles as the classification by a human expert.

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

PubMed Central

Townsend, K A; Wollstein, G; Danks, D; Sung, K R; Ishikawa, H; Kagemann, L; Gabriele, M L; Schuman, J S

2010-01-01

Aims To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies. Results The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/ D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875. Conclusions Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma. PMID:18523087

Local Subspace Classifier with Transform-Invariance for Image Classification

NASA Astrophysics Data System (ADS)

Hotta, Seiji

A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

Method of generating features optimal to a dataset and classifier

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

Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

[Carbon source metabolic diversity of soil microbial community under different climate types in the area affected by Wenchuan earthquake].

PubMed

Zhang, Guang-Shuai; Lin, Yong-Ming; Ma, Rui-Feng; Deng, Hao-Jun; Du, Kun; Wu, Cheng-Zhen; Hong, Wei

2015-02-01

The MS8.0 Wenchuan earthquake in 2008 led to huge damage to land covers in northwest Sichuan, one of the critical fragile eco-regions in China which can be divided into Semi-arid dry hot climate zone (SDHC) and Subtropical humid monsoon climate zone (SHMC). Using the method of Bilog-ECO-microplate technique, this paper aimed to determine the functional diversity of soil microbial community in the earthquake-affected areas which can be divided into undamaged area (U), recover area (R) and damaged area without recovery (D) under different climate types, in order to provide scientific basis for ecological recovery. The results indicated that the average-well-color-development (AWCD) in undamaged area and recovery area showed SDHC > SHMC, which was contrary to the AWCD in the damaged area without recovery. The AWCD of damaged area without recovery was the lowest in both climate zones. The number of carbon source utilization types of soil microbial in SHMC zone was significantly higher than that in SDHC zone. The carbon source utilization types in both climate zones presented a trend of recover area > undamaged area > damaged area without recovery. The carbon source metabolic diversity characteristic of soil microbial community was significantly different in different climate zones. The diversity index and evenness index both showed a ranking of undamaged area > recover area > damaged area without recovery. In addition, the recovery area had the highest richness index. The soil microbial carbon sources metabolism characteristic was affected by soil nutrient, aboveground vegetation biomass and vegetation coverage to some extent. In conclusion, earthquake and its secondary disasters influenced the carbon source metabolic diversity characteristic of soil microbial community mainly through the change of aboveground vegetation and soil environmental factors.

Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors.

PubMed

Gustafsson, Mats G; Wallman, Mikael; Wickenberg Bolin, Ulrika; Göransson, Hanna; Fryknäs, M; Andersson, Claes R; Isaksson, Anders

2010-06-01

Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (CI) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the CI is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice. It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples. Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets. An empirically derived ME prior seems promising for improving the Bayesian CI for the unknown error rate of a designed classifier. Copyright 2010 Elsevier B.V. All rights reserved.

List of Potentially Affected Sources for the Asphalt Processing and Roofing Manufacturing National Emission Standards for Hazardous Air Pollutants (NESHAP) November 2001

EPA Pesticide Factsheets

This is a November 2001 list of sources identified by EPA as potentially affected by the Asphalt Processing and Roofing Manufacturing National Emission Standards for Hazardous Air Pollutants (NESHAP).

COMPARATIVE STUDY OF AIR CLASSIFIERS

EPA Science Inventory

This report describes the results of field tests of seven different air classifier systems. The systems are compared in regard to (a) their capacity to handle refuse and separate it into a heavy fraction and a light fuel fraction, (b) their ability to concentrate lights in the li...

Global biodiversity monitoring: from data sources to essential biodiversity variables

USGS Publications Warehouse

Proenca, Vania; Martin, Laura J.; Pereira, Henrique M.; Fernandez, Miguel; McRae, Louise; Belnap, Jayne; Böhm, Monika; Brummitt, Neil; Garcia-Moreno, Jaime; Gregory, Richard D.; Honrado, Joao P; Jürgens, Norbert; Opige, Michael; Schmeller, Dirk S.; Tiago, Patricia; van Sway, Chris A

2016-01-01

Essential Biodiversity Variables (EBVs) consolidate information from varied biodiversity observation sources. Here we demonstrate the links between data sources, EBVs and indicators and discuss how different sources of biodiversity observations can be harnessed to inform EBVs. We classify sources of primary observations into four types: extensive and intensive monitoring schemes, ecological field studies and satellite remote sensing. We characterize their geographic, taxonomic and temporal coverage. Ecological field studies and intensive monitoring schemes inform a wide range of EBVs, but the former tend to deliver short-term data, while the geographic coverage of the latter is limited. In contrast, extensive monitoring schemes mostly inform the population abundance EBV, but deliver long-term data across an extensive network of sites. Satellite remote sensing is particularly suited to providing information on ecosystem function and structure EBVs. Biases behind data sources may affect the representativeness of global biodiversity datasets. To improve them, researchers must assess data sources and then develop strategies to compensate for identified gaps. We draw on the population abundance dataset informing the Living Planet Index (LPI) to illustrate the effects of data sources on EBV representativeness. We find that long-term monitoring schemes informing the LPI are still scarce outside of Europe and North America and that ecological field studies play a key role in covering that gap. Achieving representative EBV datasets will depend both on the ability to integrate available data, through data harmonization and modeling efforts, and on the establishment of new monitoring programs to address critical data gaps.

On an Allan variance approach to classify VLBI radio-sources on the basis of their astrometric stability

NASA Astrophysics Data System (ADS)

Gattano, C.; Lambert, S.; Bizouard, C.

2017-12-01

In the context of selecting sources defining the celestial reference frame, we compute astrometric time series of all VLBI radio-sources from observations in the International VLBI Service database. The time series are then analyzed with Allan variance in order to estimate the astrometric stability. From results, we establish a new classification that takes into account the whole multi-time scales information. The algorithm is flexible on the definition of ``stable source" through an adjustable threshold.

41 CFR 105-62.102 - Authority to originally classify.

Code of Federal Regulations, 2013 CFR

2013-07-01

... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable... classification authority. Delegations of original classification authority are limited to the minimum number...

41 CFR 105-62.102 - Authority to originally classify.

Code of Federal Regulations, 2011 CFR

2011-01-01

... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable... classification authority. Delegations of original classification authority are limited to the minimum number...

41 CFR 105-62.102 - Authority to originally classify.

Code of Federal Regulations, 2014 CFR

2014-01-01

... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable... classification authority. Delegations of original classification authority are limited to the minimum number...

Bayesian network classifiers for categorizing cortical GABAergic interneurons.

PubMed

Mihaljević, Bojan; Benavides-Piccione, Ruth; Bielza, Concha; DeFelipe, Javier; Larrañaga, Pedro

2015-04-01

An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

46 CFR 108.177 - Electrical equipment in classified locations.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Electrical equipment in classified locations. 108.177 Section 108.177 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) A-MOBILE OFFSHORE... equipment in classified locations. Electrical equipment and devices installed in spaces made non-hazardous...

Verb-raising and Numeral Classifiers in Japanese: Incompatible Bedfellows.

ERIC Educational Resources Information Center

Fukushima, Kazuhiko

2003-01-01

Examines verb raising in Japanese and looks at Koizumi's (2000) evidence for verb-raising based on data involving, among other things, numeral classifiers. Demonstrates that Koizumi's evidence based on numeral classifiers does not support his claim that verb-raising occurs in Japanese. (Author/VWL)

Human resources management and firm performance: The differential role of managerial affective and continuance commitment.

PubMed

Gong, Yaping; Law, Kenneth S; Chang, Song; Xin, Katherine R

2009-01-01

In this study, the authors developed a dual-concern (i.e., maintenance and performance) model of human resources (HR) management. The authors identified commonly examined HR practices that apply to the middle manager level and classified them into the maintenance- and performance-oriented HR subsystems. The authors found support for the 2-factor model on the basis of responses from 2,148 managers from 463 firms operating in China. Regression results indicate that the performance-oriented HR subsystems had a positive relationship with firm performance and that the relationship was mediated by middle managers' affective commitment to the firm. The maintenance-oriented HR subsystems had a positive relationship with middle managers' continuance commitment but not with their affective commitment and firm performance. This study contributes to the understanding of how HR practices relate to firm performance and offers an improved test of the argument that valuable and firm-specific HR provide a source of competitive advantage. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

SVM classifier on chip for melanoma detection.

PubMed

Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak

2017-07-01

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

Pairwise diversity ranking of polychotomous features for ensemble physiological signal classifiers.

PubMed

Gupta, Lalit; Kota, Srinivas; Molfese, Dennis L; Vaidyanathan, Ravi

2013-06-01

It is well known that fusion classifiers for physiological signal classification with diverse components (classifiers or data sets) outperform those with less diverse components. Determining component diversity, therefore, is of the utmost importance in the design of fusion classifiers that are often employed in clinical diagnostic and numerous other pattern recognition problems. In this article, a new pairwise diversity-based ranking strategy is introduced to select a subset of ensemble components, which when combined will be more diverse than any other component subset of the same size. The strategy is unified in the sense that the components can be classifiers or data sets. Moreover, the classifiers and data sets can be polychotomous. Classifier-fusion and data-fusion systems are formulated based on the diversity-based selection strategy, and the application of the two fusion strategies are demonstrated through the classification of multichannel event-related potentials. It is observed that for both classifier and data fusion, the classification accuracy tends to increase/decrease when the diversity of the component ensemble increases/decreases. For the four sets of 14-channel event-related potentials considered, it is shown that data fusion outperforms classifier fusion. Furthermore, it is demonstrated that the combination of data components that yield the best performance, in a relative sense, can be determined through the diversity-based selection strategy.

Exploring the Power of Heterogeneous Information Sources

DTIC Science & Technology

2011-01-01

Individual movies are classified as being of one or more of 18 genres , such as Comedy and Thriller , which can be treated as binary vectors. 2) User... genres , from different sources, in different formats, and with different types of representation. Many interesting patterns cannot be extracted from a...provide better web services or help film distributors in decision making, we need to conduct integrative analysis of all the information sources. For

On the statistical assessment of classifiers using DNA microarray data

PubMed Central

Ancona, N; Maglietta, R; Piepoli, A; D'Addabbo, A; Cotugno, R; Savino, M; Liuni, S; Carella, M; Pesole, G; Perri, F

2006-01-01

Background In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. Results We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set

10 CFR 1016.24 - Special handling of classified material.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Special handling of classified material. 1016.24 Section 1016.24 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) SAFEGUARDING OF RESTRICTED DATA Physical Security § 1016.24 Special handling of classified material. When the Restricted Data contained in material...

10 CFR 1016.24 - Special handling of classified material.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Special handling of classified material. 1016.24 Section 1016.24 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) SAFEGUARDING OF RESTRICTED DATA Physical Security § 1016.24 Special handling of classified material. When the Restricted Data contained in material...

18 CFR 3a.71 - Accountability for classified material.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 18 Conservation of Power and Water Resources 1 2014-04-01 2014-04-01 false Accountability for classified material. 3a.71 Section 3a.71 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY COMMISSION, DEPARTMENT OF ENERGY GENERAL RULES NATIONAL SECURITY INFORMATION Accountability for Classified...

18 CFR 3a.71 - Accountability for classified material.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 18 Conservation of Power and Water Resources 1 2013-04-01 2013-04-01 false Accountability for classified material. 3a.71 Section 3a.71 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY COMMISSION, DEPARTMENT OF ENERGY GENERAL RULES NATIONAL SECURITY INFORMATION Accountability for Classified...

18 CFR 3a.71 - Accountability for classified material.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 18 Conservation of Power and Water Resources 1 2012-04-01 2012-04-01 false Accountability for classified material. 3a.71 Section 3a.71 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY COMMISSION, DEPARTMENT OF ENERGY GENERAL RULES NATIONAL SECURITY INFORMATION Accountability for Classified...

18 CFR 3a.71 - Accountability for classified material.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Accountability for classified material. 3a.71 Section 3a.71 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY COMMISSION, DEPARTMENT OF ENERGY GENERAL RULES NATIONAL SECURITY INFORMATION Accountability for Classified...

An ANN-based HRV classifier for cardiac health prognosis.

PubMed

Sunkaria, Ramesh Kumar; Kumar, Vinod; Saxena, Suresh Chandra; Singhal, Achala M

2014-01-01

A multi-layer artificial neural network (ANN)-based heart rate variability (HRV) classifier has been proposed, which gives the cardiac health status as the output based on HRV of the patients independently of the cardiologists' view. The electrocardiogram (ECG) data of 46 patients were recorded in the out-patient department (OPD) of a hospital and HRV was evaluated using self-designed autoregressive-model-based technique. These patients suspected to be suffering from cardiac abnormalities were thoroughly examined by experienced cardiologists. On the basis of symptoms and other investigations, the attending cardiologists advised them to be classified into four categories as per the severity of cardiac health. Out of 46, the HRV data of 28 patients were used for training and data of 18 patients were used for testing of the proposed classifier. The cardiac health classification of each tested patient with the proposed classifier matches with the medical opinion of the cardiologists.

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.

29 CFR 14.21 - Release of classified information to foreign governments.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 1 2011-07-01 2011-07-01 false Release of classified information to foreign governments. 14.21 Section 14.21 Labor Office of the Secretary of Labor SECURITY REGULATIONS Transmission of Classified Information § 14.21 Release of classified information to foreign governments. National security...

29 CFR 14.21 - Release of classified information to foreign governments.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 29 Labor 1 2010-07-01 2010-07-01 true Release of classified information to foreign governments. 14.21 Section 14.21 Labor Office of the Secretary of Labor SECURITY REGULATIONS Transmission of Classified Information § 14.21 Release of classified information to foreign governments. National security...

PRIORITIZATION OF GROUND WATER CONTAMINANTS AND SOURCES

EPA Science Inventory

The objective of this research was to identify chemical, physical, bacteriological, and viral contaminants, and their sources, which present the greatest health threat in public ground water supplies in the USA; and to classify (prioritize) such contaminants and relative to their...

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

PubMed

Haker, Steven; Wells, William M; Warfield, Simon K; Talos, Ion-Florin; Bhagwat, Jui G; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H

2005-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation*

PubMed Central

Haker, Steven; Wells, William M.; Warfield, Simon K.; Talos, Ion-Florin; Bhagwat, Jui G.; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H.

2010-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging. PMID:16685884

THE DETROIT RIVER AS A CHEMICAL LOADING SOURCE TO LAKE ERIE

EPA Science Inventory

The Detroit River, one of 42 designated areas of concern., has been classified as one of the most polluted rivers in North America. This system receives chemical loadings from a variety of sources including upstream discharges, industrial/municipal point sources, combined sewage ...

Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

PubMed

Akhtar, Naveed; Mian, Ajmal

2017-10-03

We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

Enhancing atlas based segmentation with multiclass linear classifiers

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

Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr

Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible localmore » registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.« less

Hunt for Federal Funds Gives Classified Research a Lift

ERIC Educational Resources Information Center

Basken, Paul

2012-01-01

For some colleges and professors, classified research promises prestige and money. Powerhouses like the Massachusetts Institute of Technology and the Johns Hopkins University have for decades run large classified laboratories. But most other universities either do not allow such research or conduct it quietly, and in small doses. The…

Toward an efficient Photometric Supernova Classifier

NASA Astrophysics Data System (ADS)

McClain, Bradley

2018-01-01

The Sloan Digital Sky Survey Supernova Survey (SDSS) discovered more than 1,000 Type Ia Supernovae, yet less than half of these have spectroscopic measurements. As wide-field imaging telescopes such as The Dark Energy Survey (DES) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) discover more supernovae, the need for accurate and computationally cheap photometric classifiers increases. My goal is to use a photometric classification algorithm based on Sncosmo, a python library for supernova cosmology analysis, to reclassify previously identified Hubble SN and other non-spectroscopically confirmed surveys. My results will be compared to other photometric classifiers such as PSNID and STARDUST. In the near future, I expect to have the algorithm validated with simulated data, optimized for efficiency, and applied with high performance computing to real data.

An empirical assessment of validation practices for molecular classifiers

PubMed Central

Castaldi, Peter J.; Dahabreh, Issa J.

2011-01-01

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21–61%) and 29% (IQR, 15–65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04–5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice. PMID:21300697

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.

Dimensionality Reduction Through Classifier Ensembles

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

1999-01-01

In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

Does source population size affect performance in new environments?

PubMed Central

Yates, Matthew C; Fraser, Dylan J

2014-01-01

Small populations are predicted to perform poorly relative to large populations when experiencing environmental change. To explore this prediction in nature, data from reciprocal transplant, common garden, and translocation studies were compared meta-analytically. We contrasted changes in performance resulting from transplantation to new environments among individuals originating from different sized source populations from plants and salmonids. We then evaluated the effect of source population size on performance in natural common garden environments and the relationship between population size and habitat quality. In ‘home-away’ contrasts, large populations exhibited reduced performance in new environments. In common gardens, the effect of source population size on performance was inconsistent across life-history stages (LHS) and environments. When transplanted to the same set of new environments, small populations either performed equally well or better than large populations, depending on life stage. Conversely, large populations outperformed small populations within native environments, but only at later life stages. Population size was not associated with habitat quality. Several factors might explain the negative association between source population size and performance in new environments: (i) stronger local adaptation in large populations and antagonistic pleiotropy, (ii) the maintenance of genetic variation in small populations, and (iii) potential environmental differences between large and small populations. PMID:25469166

Is there differential responsiveness to a future cigarette price increase depending on adolescents’ source of cigarette access?

PubMed Central

Hwang, Jun Hyun; Park, Soon-Woo

2017-01-01

Abstract We examined whether the responsiveness to an increase in cigarettes price differed by adolescents’ cigarette acquisition source. We analyzed data on 6134 youth smokers (grades 7–12) from a cross-sectional survey in Korea with national representativeness. The respondents were classified into one of the following according to their source of cigarette acquisition: commercial-source group, social-source group, and others. Multiple logistic regressions were performed to estimate the effects of an increase in cigarette price on the intention to quit smoking on the basis of the cigarette acquisition source. Of the 6134 youth smokers, 36.0% acquired cigarettes from social sources, compared to the 49.6% who purchased cigarettes directly from commercial sources. In response to a future cigarette price increase, regardless of an individual's smoking level, there was no statistically significant difference in the odds ratio for the intention to stop smoking in association with cigarette acquisition sources. The social-source group had nonsignificant, but consistently positive, odds ratios (1.07–1.30) as compared to that of the commercial-source group. Our findings indicate that the cigarette acquisition source does not affect the responsiveness to an increase in cigarette price. Therefore, a cigarette price policy is a comprehensive strategy to reduce smoking among youth smokers, regardless of their source. PMID:28658140

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

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.

Diagnostic index: an open-source tool to classify TMJ OA condyles

NASA Astrophysics Data System (ADS)

Paniagua, Beatriz; Pascal, Laura; Prieto, Juan; Vimort, Jean Baptiste; Gomes, Liliane; Yatabe, Marilia; Ruellas, Antonio Carlos; Budin, Francois; Pieper, Steve; Styner, Martin; Benavides, Erika; Cevidanes, Lucia

2017-03-01

Osteoarthritis (OA) of temporomandibular joints (TMJ) occurs in about 40% of the patients who present TMJ disorders. Despite its prevalence, OA diagnosis and treatment remain controversial since there are no clear symptoms of the disease, especially in early stages. Quantitative tools based on 3D imaging of the TMJ condyle have the potential to help characterize TMJ OA changes. The goals of the tools proposed in this study are to ultimately develop robust imaging markers for diagnosis and assessment of treatment efficacy. This work proposes to identify differences among asymptomatic controls and different clinical phenotypes of TMJ OA by means of Statistical Shape Modeling (SSM), obtained via clinical expert consensus. From three different grouping schemes (with 3, 5 and 7 groups), our best results reveal that that the majority (74.5%) of the classifications occur in agreement with the groups assigned by consensus between our clinical experts. Our findings suggest the existence of different disease-based phenotypic morphologies in TMJ OA. Our preliminary findings with statistical shape modeling based biomarkers may provide a quantitative staging of the disease. The methodology used in this study is included in an open source image analysis toolbox, to ensure reproducibility and appropriate distribution and dissemination of the solution proposed.

Centre-based restricted nearest feature plane with angle classifier for face recognition

NASA Astrophysics Data System (ADS)

Tang, Linlin; Lu, Huifen; Zhao, Liang; Li, Zuohua

2017-10-01

An improved classifier based on the nearest feature plane (NFP), called the centre-based restricted nearest feature plane with the angle (RNFPA) classifier, is proposed for the face recognition problems here. The famous NFP uses the geometrical information of samples to increase the number of training samples, but it increases the computation complexity and it also has an inaccuracy problem coursed by the extended feature plane. To solve the above problems, RNFPA exploits a centre-based feature plane and utilizes a threshold of angle to restrict extended feature space. By choosing the appropriate angle threshold, RNFPA can improve the performance and decrease computation complexity. Experiments in the AT&T face database, AR face database and FERET face database are used to evaluate the proposed classifier. Compared with the original NFP classifier, the nearest feature line (NFL) classifier, the nearest neighbour (NN) classifier and some other improved NFP classifiers, the proposed one achieves competitive performance.

Procedures to cover Spillage of Classified Information Onto Unclassified Systems

EPA Pesticide Factsheets

The purpose of this is to implement the security control requirements and outline actions required when responding to electronic spillage of classified national security information (classified information) onto unclassified information systems or devices.

Classifying a Smoker Scale in Adult Daily and Nondaily Smokers

PubMed Central

2014-01-01

Introduction: Smoker identity, or the strength of beliefs about oneself as a smoker, is a robust marker of smoking behavior. However, many nondaily smokers do not identify as smokers, underestimating their risk for tobacco-related disease and resulting in missed intervention opportunities. Assessing underlying beliefs about characteristics used to classify smokers may help explain the discrepancy between smoking behavior and smoker identity. This study examines the factor structure, reliability, and validity of the Classifying a Smoker scale among a racially diverse sample of adult smokers. Methods: A cross-sectional survey was administered through an online panel survey service to 2,376 current smokers who were at least 25 years of age. The sample was stratified to obtain equal numbers of 3 racial/ethnic groups (African American, Latino, and White) across smoking level (nondaily and daily smoking). Results: The Classifying a Smoker scale displayed a single factor structure and excellent internal consistency (α = .91). Classifying a Smoker scores significantly increased at each level of smoking, F(3,2375) = 23.68, p < .0001. Those with higher scores had a stronger smoker identity, stronger dependence on cigarettes, greater health risk perceptions, more smoking friends, and were more likely to carry cigarettes. Classifying a Smoker scores explained unique variance in smoking variables above and beyond that explained by smoker identity. Conclusions: The present study supports the use of the Classifying a Smoker scale among diverse, experienced smokers. Stronger endorsement of characteristics used to classify a smoker (i.e., stricter criteria) was positively associated with heavier smoking and related characteristics. Prospective studies are needed to inform prevention and treatment efforts. PMID:24297807

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

PubMed Central

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance. PMID:24695550

Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

"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…

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Comparison of Classifier Architectures for Online Neural Spike Sorting.

PubMed

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

A native Bayesian classifier based routing protocol for VANETS

NASA Astrophysics Data System (ADS)

Bao, Zhenshan; Zhou, Keqin; Zhang, Wenbo; Gong, Xiaolei

2016-12-01

Geographic routing protocols are one of the most hot research areas in VANET (Vehicular Ad-hoc Network). However, there are few routing protocols can take both the transmission efficient and the usage of ratio into account. As we have noticed, different messages in VANET may ask different quality of service. So we raised a Native Bayesian Classifier based routing protocol (Naive Bayesian Classifier-Greedy, NBC-Greedy), which can classify and transmit different messages by its emergency degree. As a result, we can balance the transmission efficient and the usage of ratio with this protocol. Based on Matlab simulation, we can draw a conclusion that NBC-Greedy is more efficient and stable than LR-Greedy and GPSR.

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

2017-09-01

Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard

Personality and affect characteristics of outpatients with depression.

PubMed

Petrocelli, J V; Glaser, B A; Calhoun, G B; Campbell, L F

2001-08-01

This investigation was designed to examine the relationship between depression severity and personality disorders measured by the Millon Clinical Multiaxial Inventory-II (Millon, 1987) and affectivity measured by the Positive Affectivity/Negative Affectivity Schedule (Watson, Clark, & Tellegen, 1988). Discriminant analyses were employed to identify the personality and affective dimensions that maximally discriminate between 4 different levels of depressive severity. Differences between the 4 levels of depressive severity are suggestive of unique patterns of personality characteristics. Discriminant analysis showed that 74.8% of the cases were correctly classified by a single linear discriminant function, and that 61% of the variance in depression severity was accounted for by selected personality and affect variables. Results extend current conceptualizations of comorbidity and are discussed with respect to depression severity.

Preliminary analysis of the JAPE ground vehicle test data with an artificial neural network classifier

NASA Technical Reports Server (NTRS)

Larsen, Nathan F.; Carnes, Ben L.

1993-01-01

Remotely sensing and classifying military vehicles in a battlefield environment have been the source of much research over the past 20 years. The ability to know where threat vehicles are located is an obvious advantage to military personnel. In the past active methods of ground vehicle detection such as radar have been used, but with the advancement of technology to locate these active sensors, passive sensors are preferred. Passive sensors detect acoustic emissions, seismic movement, electromagnetic radiation, etc., produced by the target and use this information to describe it. Deriving the mathematical models to classify vehicles in this manner has been, and is, quite complex and not always reliable. However, with the resurgence of artificial neural network (ANN) research in the past few years, developing models for this work may be a thing of the past. Preliminary results from an ANN analysis to the tank signatures recorded at the Joint Acoustic Propagation Experiment (JAPE) at the US Army White Sands Missile Range, NM, in July 1991, are presented.

69. VIEW FROM ABOVE OF PRIMARY MILL AND CLASSIFIER No. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

69. VIEW FROM ABOVE OF PRIMARY MILL AND CLASSIFIER No. 2. PRIMARY CLASSIFIER No. 1 AT RIGHT EDGE OF VIEW. - Bald Mountain Gold Mill, Nevada Gulch at head of False Bottom Creek, Lead, Lawrence County, SD

40 CFR Table 12 to Subpart Xxxx of... - Continuous Compliance With the Emission Limits for Tire Cord Production Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 12 2010-07-01 2010-07-01 true Continuous Compliance With the Emission Limits for Tire Cord Production Affected Sources 12 Table 12 to Subpart XXXX of Part 63 Protection of... Pollutants: Rubber Tire Manufacturing Pt. 63, Subpt. XXXX, Table 12 Table 12 to Subpart XXXX of Part 63...

40 CFR Table 3 to Subpart Jjj of... - Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 11 2011-07-01 2011-07-01 false Group 1 Storage Vessels at Existing... Pollutant Emissions: Group IV Polymers and Resins Pt. 63, Subpt. JJJ, Table 3 Table 3 to Subpart JJJ of Part 63—Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics...

40 CFR Table 3 to Subpart Jjj of... - Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 12 2012-07-01 2011-07-01 true Group 1 Storage Vessels at Existing... Pollutant Emissions: Group IV Polymers and Resins Pt. 63, Subpt. JJJ, Table 3 Table 3 to Subpart JJJ of Part 63—Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics...

40 CFR Table 3 to Subpart Jjj of... - Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 12 2014-07-01 2014-07-01 false Group 1 Storage Vessels at Existing... Hazardous Air Pollutant Emissions: Group IV Polymers and Resins Pt. 63, Subpt. JJJ, Table 3 Table 3 to Subpart JJJ of Part 63—Group 1 Storage Vessels at Existing Affected Sources Producing the Listed...

40 CFR Table 3 to Subpart Jjj of... - Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true Group 1 Storage Vessels at Existing... Pollutant Emissions: Group IV Polymers and Resins Pt. 63, Subpt. JJJ, Table 3 Table 3 to Subpart JJJ of Part 63—Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics...

40 CFR Table 3 to Subpart Jjj of... - Group 1 Storage Vessels at Existing Affected Sources Producing the Listed Thermoplastics

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 12 2013-07-01 2013-07-01 false Group 1 Storage Vessels at Existing... Hazardous Air Pollutant Emissions: Group IV Polymers and Resins Pt. 63, Subpt. JJJ, Table 3 Table 3 to Subpart JJJ of Part 63—Group 1 Storage Vessels at Existing Affected Sources Producing the Listed...

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Evolving fuzzy rules in a learning classifier system

NASA Technical Reports Server (NTRS)

Valenzuela-Rendon, Manuel

1993-01-01

The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

The Variables Affecting the Success of Students

ERIC Educational Resources Information Center

Savas, Behsat; Gurel, Ramazan

2014-01-01

The aim of this study is to determine the variables affecting the success of students. This research, which was conducted through the relational screening model, has a sampling of students who were selected from a middle city in Turkey. The schools are classified into three as low, medium and high. A total of 3491 students are selected by using…

Hygroscopic growth of size-resolved, emission-source classified, aerosol particles sampled across the United States

NASA Astrophysics Data System (ADS)

Shingler, T.; Crosbie, E. C.; Ziemba, L. D.; Anderson, B. E.; Campuzano Jost, P.; Jimenez, J. L.; Mikoviny, T.; Wisthaler, A.; Sorooshian, A.

2014-12-01

The hygroscopic growth of atmospheric aerosol particles is a key air quality parameter, impacting the radiation budget, visibility, and cloud formation. During the DC3 and SEAC4RS field campaigns (>300 total flight hours), measurements were made over 32 US states, Canada, the Pacific Ocean, and the Gulf of Mexico, between the surface and 41,000 feet ASL. The aircraft research payloads included a suite of in-situ aerosol and gas phase instruments. The Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP) and the Langley Aerosol Research Group Experiment (LARGE) humidified nephelometer instrument applied different techniques to measure water uptake by aerosol particles at prescribed relative humidity values. Size-resolved growth factor (GF ≡ Dp,wet/Dp,dry) measurements by the DASH-SP are compared to bulk scattering measurements (f(RH) ≡ σscat,wet/σscat,dry) by the LARGE instrument. Spatial location and volatile organic compound tracers such as isoprene and acetonitrile are used to classify the origin of distinct air masses, including: forest fires, biogenic-emitting forests, agricultural use lands, marine boundary layer, urban, and rural background. Analyses of GF results by air mass origin are reported and results are compared with f(RH) measurements. A parameterization between the f(RH) and GF measurements and its potential uses are discussed.

Sources and Processes Affecting Particulate Matter Pollution over North China

NASA Astrophysics Data System (ADS)

Zhang, L.; Shao, J.; Lu, X.; Zhao, Y.; Gong, S.; Henze, D. K.

2015-12-01

Severe fine particulate matter (PM2.5) pollution over North China has received broad attention worldwide in recent years. Better understanding the sources and processes controlling pollution over this region is of great importance with urgent implications for air quality policy. We will present a four-dimensional variational (4D-Var) data assimilation system using the GEOS-Chem chemical transport model and its adjoint model at 0.25° × 0.3125° horizontal resolution, and apply it to analyze the factors affecting PM2.5 concentrations over North China. Hourly surface observations of PM2.5 and sulfur dioxide (SO2) from the China National Environmental Monitoring Center (CNEMC) can be assimilated into the model to evaluate and constrain aerosol (primary and precursors) emissions. Application of the data assimilation system to the APEC period (the Asia-Pacific Economic Cooperation summit; 5-11 November 2014) shows that 46% of the PM2.5 pollution reduction during APEC ("The APEC Blue") can be attributed to meteorology conditions and the rest 54% to emission reductions due to strict emission controls. Ammonia emissions are shown to significantly contribute to PM2.5 over North China in the fall. By converting sulfuric acid and nitric acid to longer-lived ammonium sulfate and ammonium nitrate aerosols, ammonia plays an important role in promoting their regional transport influences. We will also discuss the pathways and mechanisms of external long-range transport influences to the PM2.5 pollution over North China.

Exploiting Language Models to Classify Events from Twitter

PubMed Central

Vo, Duc-Thuan; Hai, Vo Thuan; Ock, Cheol-Young

2015-01-01

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events. PMID:26451139

40 CFR 63.821 - Designation of affected sources.

Code of Federal Regulations, 2011 CFR

2011-07-01

....821 Section 63.821 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) NATIONAL EMISSION STANDARDS FOR HAZARDOUS AIR POLLUTANTS FOR SOURCE CATEGORIES (CONTINUED... presses and all related equipment, including proof presses, cylinder and parts cleaners, ink and solvent...

40 CFR Table 7 to Subpart Xxxx of... - Initial Compliance With the Emission Limits for Tire Cord Production Affected Sources

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 12 2010-07-01 2010-07-01 true Initial Compliance With the Emission Limits for Tire Cord Production Affected Sources 7 Table 7 to Subpart XXXX of Part 63 Protection of... Pollutants: Rubber Tire Manufacturing Pt. 63, Subpt. XXXX, Table 7 Table 7 to Subpart XXXX of Part 63—Initial...

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.

Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.

PubMed

Kahn, Charles E; Kalpathy-Cramer, Jayashree; Lam, Cesar A; Eldredge, Christina E

2012-02-01

Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities--computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiograph-to each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A "Simple Vote" ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers' votes. A "Weighted Vote" classifier weighted each individual classifier's vote based on performance over a training set. For each image, this classifier's output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier. Individual classifiers' F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905-0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927-0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval.

Gently does it: Humans outperform a software classifier in recognizing subtle, nonstereotypical facial expressions.

PubMed

Yitzhak, Neta; Giladi, Nir; Gurevich, Tanya; Messinger, Daniel S; Prince, Emily B; Martin, Katherine; Aviezer, Hillel

2017-12-01

According to dominant theories of affect, humans innately and universally express a set of emotions using specific configurations of prototypical facial activity. Accordingly, thousands of studies have tested emotion recognition using sets of highly intense and stereotypical facial expressions, yet their incidence in real life is virtually unknown. In fact, a commonplace experience is that emotions are expressed in subtle and nonprototypical forms. Such facial expressions are at the focus of the current study. In Experiment 1, we present the development and validation of a novel stimulus set consisting of dynamic and subtle emotional facial displays conveyed without constraining expressers to using prototypical configurations. Although these subtle expressions were more challenging to recognize than prototypical dynamic expressions, they were still well recognized by human raters, and perhaps most importantly, they were rated as more ecological and naturalistic than the prototypical expressions. In Experiment 2, we examined the characteristics of subtle versus prototypical expressions by subjecting them to a software classifier, which used prototypical basic emotion criteria. Although the software was highly successful at classifying prototypical expressions, it performed very poorly at classifying the subtle expressions. Further validation was obtained from human expert face coders: Subtle stimuli did not contain many of the key facial movements present in prototypical expressions. Together, these findings suggest that emotions may be successfully conveyed to human viewers using subtle nonprototypical expressions. Although classic prototypical facial expressions are well recognized, they appear less naturalistic and may not capture the richness of everyday emotional communication. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

36 CFR 1256.70 - What controls access to national security-classified information?

Code of Federal Regulations, 2010 CFR

2010-07-01

... national security-classified information? 1256.70 Section 1256.70 Parks, Forests, and Public Property... HISTORICAL MATERIALS Access to Materials Containing National Security-Classified Information § 1256.70 What controls access to national security-classified information? (a) The declassification of and public access...

Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

Glycemic index of American-grown jasmine rice classified as high.

PubMed

Truong, Teresa H; Yuet, Wei Cheng; Hall, Micki D

2014-06-01

The primary objective was to determine the glycemic index (GI) of jasmine rice grown in the United States (US). Secondary objective was to compare the GI of US grown jasmine rice to those grown in Thailand. Twelve healthy subjects were served all four brands of jasmine rice and a reference food (glucose), each containing 50 g of available carbohydrate. Fingerstick blood glucose was measured at 0, 15, 30, 45, 60, 90, and 120 min after consumption following a fasting state. The GI was calculated using the standard equation. The GI values for test foods ranged from 96 to 116 and were all classified as high GI foods. No difference in GI was found between American-grown and Thailand-grown jasmine rice. Although not statistically significant, observations show glycemic response among Asian American participants may be different. GI should be considered when planning meals with jasmine rice as the main source carbohydrate.

Macro- and Micro-Validation: Beyond the "Five Sources" Framework for Classifying Validation Evidence and Analysis

ERIC Educational Resources Information Center

Newton, Paul E.

2016-01-01

This paper argues that the dominant framework for conceptualizing validation evidence and analysis--the "five sources" framework from the 1999 "Standards"--is seriously limited. Its limitation raises a significant barrier to understanding the nature of comprehensive validation, and this presents a significant threat to…

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

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

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug

2013-05-15

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessedmore » using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same

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

Criminal Prohibitions on the Publication of Classified Defense Information

DTIC Science & Technology

2013-09-09

States. While prosecutions appear to be on the rise, leaks of classified information to the press have relatively infrequently been punished as crimes ... Crime Victims Fund. 76 §795. Photographing and sketching defense installations (a) Whenever, in the interests of national defense, the President...Publication of Classified Defense Information Congressional Research Service 13 and crimes and offenses not capital”85 that are not enumerated elsewhere in

Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble

PubMed Central

Liu, Hang; Chu, Renzhi; Tang, Zhenan

2015-01-01

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. PMID:25942640

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.

Spectral classifier design with ensemble classifiers and misclassification-rejection: application to elastic-scattering spectroscopy for detection of colonic neoplasia.

PubMed

Rodriguez-Diaz, Eladio; Castanon, David A; Singh, Satish K; Bigio, Irving J

2011-06-01

Optical spectroscopy has shown potential as a real-time, in vivo, diagnostic tool for identifying neoplasia during endoscopy. We present the development of a diagnostic algorithm to classify elastic-scattering spectroscopy (ESS) spectra as either neoplastic or non-neoplastic. The algorithm is based on pattern recognition methods, including ensemble classifiers, in which members of the ensemble are trained on different regions of the ESS spectrum, and misclassification-rejection, where the algorithm identifies and refrains from classifying samples that are at higher risk of being misclassified. These "rejected" samples can be reexamined by simply repositioning the probe to obtain additional optical readings or ultimately by sending the polyp for histopathological assessment, as per standard practice. Prospective validation using separate training and testing sets result in a baseline performance of sensitivity = .83, specificity = .79, using the standard framework of feature extraction (principal component analysis) followed by classification (with linear support vector machines). With the developed algorithm, performance improves to Se ∼ 0.90, Sp ∼ 0.90, at a cost of rejecting 20-33% of the samples. These results are on par with a panel of expert pathologists. For colonoscopic prevention of colorectal cancer, our system could reduce biopsy risk and cost, obviate retrieval of non-neoplastic polyps, decrease procedure time, and improve assessment of cancer risk.

5 CFR 1312.4 - Classified designations.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., DOWNGRADING, DECLASSIFICATION AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION Classification and Declassification of National Security Information § 1312.4 Classified designations. (a) Except as provided by the Atomic Energy Act of 1954, as amended, (42 U.S.C. 2011) or the National Security Act of 1947, as amended...

VLDL metabolism in rats is affected by the concentration and source of dietary protein.

PubMed

Madani, Sihem; Prost, Josiane; Narce, Michel; Belleville, Jacques

2003-12-01

The present study was designed to determine if changes in dietary protein level and source are related to changes in VLDL lipid concentrations and VLDL binding by hepatic membranes and isolated hepatocytes. Male Wistar rats were fed cholesterol-free diets containing 10, 20 or 30 g/100 g casein or highly purified soybean protein for 4 wk. Hepatic, plasma and VLDL lipids, VLDL apo B-100 and VLDL uptake by isolated hepatocytes and VLDL binding to hepatic membrane were determined. Increasing casein or soybean protein level (from 10 to 30 g/100 g) in the diet increased VLDL apo B-100, indicating an increase in the number of VLDL particles. VLDL uptake by isolated hepatocytes and VLDL binding to hepatic membrane increased when the protein level increased from 10 to 20 g/100 g in the diet and decreased with 30 g/100 g protein, regardless of protein type. The dietary protein source did not affect plasma total cholesterol concentrations at any protein level. Feeding 20 g/100 g soybean protein compared with casein lowered plasma triglyceride concentrations and VLDL number as measured by decreased VLDL-protein, -phospholipid, -triglyceride, -cholesterol and -apo B-100. VLDL uptake by isolated hepatocytes and VLDL binding to hepatic membrane were higher in rats fed soybean protein than those fed casein. The higher VLDL uptake could be responsible for the hypotriglyceridemia in rats fed soybean protein.

Classifying multispectral data by neural networks

NASA Technical Reports Server (NTRS)

Telfer, Brian A.; Szu, Harold H.; Kiang, Richard K.

1993-01-01

Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data.

Human Activity Recognition by Combining a Small Number of Classifiers.

PubMed

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Modeling and Classifying Six-Dimensional Trajectories for Teleoperation Under a Time Delay

NASA Technical Reports Server (NTRS)

SunSpiral, Vytas; Wheeler, Kevin R.; Allan, Mark B.; Martin, Rodney

2006-01-01

Within the context of teleoperating the JSC Robonaut humanoid robot under 2-10 second time delays, this paper explores the technical problem of modeling and classifying human motions represented as six-dimensional (position and orientation) trajectories. A dual path research agenda is reviewed which explored both deterministic approaches and stochastic approaches using Hidden Markov Models. Finally, recent results are shown from a new model which represents the fusion of these two research paths. Questions are also raised about the possibility of automatically generating autonomous actions by reusing the same predictive models of human behavior to be the source of autonomous control. This approach changes the role of teleoperation from being a stand-in for autonomy into the first data collection step for developing generative models capable of autonomous control of the robot.

Dealing with contaminated datasets: An approach to classifier training

NASA Astrophysics Data System (ADS)

Homenda, Wladyslaw; Jastrzebska, Agnieszka; Rybnik, Mariusz

2016-06-01

The paper presents a novel approach to classification reinforced with rejection mechanism. The method is based on a two-tier set of classifiers. First layer classifies elements, second layer separates native elements from foreign ones in each distinguished class. The key novelty presented here is rejection mechanism training scheme according to the philosophy "one-against-all-other-classes". Proposed method was tested in an empirical study of handwritten digits recognition.

A time-frequency classifier for human gait recognition

NASA Astrophysics Data System (ADS)

Mobasseri, Bijan G.; Amin, Moeness G.

2009-05-01

Radar has established itself as an effective all-weather, day or night sensor. Radar signals can penetrate walls and provide information on moving targets. Recently, radar has been used as an effective biometric sensor for classification of gait. The return from a coherent radar system contains a frequency offset in the carrier frequency, known as the Doppler Effect. The movements of arms and legs give rise to micro Doppler which can be clearly detailed in the time-frequency domain using traditional or modern time-frequency signal representation. In this paper we propose a gait classifier based on subspace learning using principal components analysis(PCA). The training set consists of feature vectors defined as either time or frequency snapshots taken from the spectrogram of radar backscatter. We show that gait signature is captured effectively in feature vectors. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Results show that gait classification with high accuracy and short observation window is achievable using the proposed classifier.

32 CFR 148.2 - Classified programs.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 1 2011-07-01 2011-07-01 false Classified programs. 148.2 Section 148.2 National Defense Department of Defense OFFICE OF THE SECRETARY OF DEFENSE PERSONNEL, MILITARY AND CIVILIAN NATIONAL POLICY AND IMPLEMENTATION OF RECIPROCITY OF FACILITIES National Policy on Reciprocity of Use and...

32 CFR 148.2 - Classified programs.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 1 2010-07-01 2010-07-01 false Classified programs. 148.2 Section 148.2 National Defense Department of Defense OFFICE OF THE SECRETARY OF DEFENSE PERSONNEL, MILITARY AND CIVILIAN NATIONAL POLICY AND IMPLEMENTATION OF RECIPROCITY OF FACILITIES National Policy on Reciprocity of Use and...

32 CFR 148.2 - Classified programs.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 1 2014-07-01 2014-07-01 false Classified programs. 148.2 Section 148.2 National Defense Department of Defense OFFICE OF THE SECRETARY OF DEFENSE PERSONNEL, MILITARY AND CIVILIAN NATIONAL POLICY AND IMPLEMENTATION OF RECIPROCITY OF FACILITIES National Policy on Reciprocity of Use and...

32 CFR 148.2 - Classified programs.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 1 2013-07-01 2013-07-01 false Classified programs. 148.2 Section 148.2 National Defense Department of Defense OFFICE OF THE SECRETARY OF DEFENSE PERSONNEL, MILITARY AND CIVILIAN NATIONAL POLICY AND IMPLEMENTATION OF RECIPROCITY OF FACILITIES National Policy on Reciprocity of Use and...

32 CFR 148.2 - Classified programs.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 1 2012-07-01 2012-07-01 false Classified programs. 148.2 Section 148.2 National Defense Department of Defense OFFICE OF THE SECRETARY OF DEFENSE PERSONNEL, MILITARY AND CIVILIAN NATIONAL POLICY AND IMPLEMENTATION OF RECIPROCITY OF FACILITIES National Policy on Reciprocity of Use and...

28 CFR 700.14 - Classified information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... INFORMATION OF THE OFFICE OF INDEPENDENT COUNSEL Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 700.14 Classified information. In processing a request for access to a... Executive order concerning the classification of records, the Office shall review the information to...

42 CFR 37.50 - Interpreting and classifying chest radiographs-film.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 42 Public Health 1 2013-10-01 2013-10-01 false Interpreting and classifying chest radiographs-film. 37.50 Section 37.50 Public Health PUBLIC HEALTH SERVICE, DEPARTMENT OF HEALTH AND HUMAN SERVICES... Radiographs § 37.50 Interpreting and classifying chest radiographs—film. (a) Chest radiographs must be...

Effects of classified paper waste on warm season grass establishment

USDA-ARS?s Scientific Manuscript database

The goal of this project is to investigate utilization of pulverized classified paper waste as an organic soil amendment for rehabilitation of severely disturbed training lands. Federal regulations require that classified documents be pulverized to 0.9 x 4.2 mm. These minute fiber sizes cannot be re...

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data

PubMed Central

Yang, Yang; Niehaus, Katherine E; Walker, Timothy M; Iqbal, Zamin; Walker, A Sarah; Wilson, Daniel J; Peto, Tim E A; Crook, Derrick W; Smith, E Grace; Zhu, Tingting; Clifton, David A

2018-01-01

Abstract Motivation Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Summary Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance. Results Compared to previous rules-based approach, the sensitivities from the best-performing models increased by 2-4% for isoniazid, rifampicin and ethambutol to 97% (P < 0.01), respectively; for ciprofloxacin and multi-drug resistant TB, they increased to 96%. For moxifloxacin and ofloxacin, sensitivities increased by 12 and 15% from 83 and 81% based on existing known resistance alleles to 95% and 96% (P < 0.01), respectively. Particularly, our models improved sensitivities compared to the previous rules-based approach by 15 and 24% to 84 and 87% for pyrazinamide and streptomycin (P < 0.01), respectively. The best-performing models increase the area-under-the-ROC curve by 10% for pyrazinamide and streptomycin (P < 0.01), and 4–8% for other drugs (P < 0.01). Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/~davidc/code.php. Contact david.clifton@eng.ox.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29240876

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data.

PubMed

Yang, Yang; Niehaus, Katherine E; Walker, Timothy M; Iqbal, Zamin; Walker, A Sarah; Wilson, Daniel J; Peto, Tim E A; Crook, Derrick W; Smith, E Grace; Zhu, Tingting; Clifton, David A

2018-05-15

Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance. Compared to previous rules-based approach, the sensitivities from the best-performing models increased by 2-4% for isoniazid, rifampicin and ethambutol to 97% (P < 0.01), respectively; for ciprofloxacin and multi-drug resistant TB, they increased to 96%. For moxifloxacin and ofloxacin, sensitivities increased by 12 and 15% from 83 and 81% based on existing known resistance alleles to 95% and 96% (P < 0.01), respectively. Particularly, our models improved sensitivities compared to the previous rules-based approach by 15 and 24% to 84 and 87% for pyrazinamide and streptomycin (P < 0.01), respectively. The best-performing models increase the area-under-the-ROC curve by 10% for pyrazinamide and streptomycin (P < 0.01), and 4-8% for other drugs (P < 0.01). The details of source code are provided at http://www.robots.ox.ac.uk/~davidc/code.php. david.clifton@eng.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Classifying short genomic fragments from novel lineages using composition and homology

PubMed Central

2011-01-01

Background The assignment of taxonomic attributions to DNA fragments recovered directly from the environment is a vital step in metagenomic data analysis. Assignments can be made using rank-specific classifiers, which assign reads to taxonomic labels from a predetermined level such as named species or strain, or rank-flexible classifiers, which choose an appropriate taxonomic rank for each sequence in a data set. The choice of rank typically depends on the optimal model for a given sequence and on the breadth of taxonomic groups seen in a set of close-to-optimal models. Homology-based (e.g., LCA) and composition-based (e.g., PhyloPythia, TACOA) rank-flexible classifiers have been proposed, but there is at present no hybrid approach that utilizes both homology and composition. Results We first develop a hybrid, rank-specific classifier based on BLAST and Naïve Bayes (NB) that has comparable accuracy and a faster running time than the current best approach, PhymmBL. By substituting LCA for BLAST or allowing the inclusion of suboptimal NB models, we obtain a rank-flexible classifier. This hybrid classifier outperforms established rank-flexible approaches on simulated metagenomic fragments of length 200 bp to 1000 bp and is able to assign taxonomic attributions to a subset of sequences with few misclassifications. We then demonstrate the performance of different classifiers on an enhanced biological phosphorous removal metagenome, illustrating the advantages of rank-flexible classifiers when representative genomes are absent from the set of reference genomes. Application to a glacier ice metagenome demonstrates that similar taxonomic profiles are obtained across a set of classifiers which are increasingly conservative in their classification. Conclusions Our NB-based classification scheme is faster than the current best composition-based algorithm, Phymm, while providing equally accurate predictions. The rank-flexible variant of NB, which we term ε-NB, is

Preliminary investigation of processes that affect source term identification

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

Wickliff, D.S.; Solomon, D.K.; Farrow, N.D.

Solid Waste Storage Area (SWSA) 5 is known to be a significant source of contaminants, especially tritium ({sup 3}H), to the White Oak Creek (WOC) watershed. For example, Solomon et al. (1991) estimated the total {sup 3}H discharge in Melton Branch (most of which originates in SWSA 5) for the 1988 water year to be 1210 Ci. A critical issue for making decisions concerning remedial actions at SWSA 5 is knowing whether the annual contaminant discharge is increasing or decreasing. Because (1) the magnitude of the annual contaminant discharge is highly correlated to the amount of annual precipitation (Solomon etmore » al., 1991) and (2) a significant lag may exist between the time of peak contaminant release from primary sources (i.e., waste trenches) and the time of peak discharge into streams, short-term stream monitoring by itself is not sufficient for predicting future contaminant discharges. In this study we use {sup 3}H to examine the link between contaminant release from primary waste sources and contaminant discharge into streams. By understanding and quantifying subsurface transport processes, realistic predictions of future contaminant discharge, along with an evaluation of the effectiveness of remedial action alternatives, will be possible. The objectives of this study are (1) to characterize the subsurface movement of contaminants (primarily {sup 3}H) with an emphasis on the effects of matrix diffusion; (2) to determine the relative strength of primary vs secondary sources; and (3) to establish a methodology capable of determining whether the {sup 3}H discharge from SWSA 5 to streams is increasing or decreasing.« less

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-01-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices. PMID:25177107

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

PubMed

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-11-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

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.

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 expert computer program for classifying stars on the MK spectral classification system

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

Gray, R. O.; Corbally, C. J.

2014-04-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectralmore » type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.« less