LeadMine: a grammar and dictionary driven approach to entity recognition

PubMed Central

2015-01-01

Background Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Conclusions Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution. PMID:25810776

Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

NASA Astrophysics Data System (ADS)

Liu, Wuying; Wang, Lin

2018-03-01

The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis : Distributed dictionary representation.

PubMed

Garten, Justin; Hoover, Joe; Johnson, Kate M; Boghrati, Reihane; Iskiwitch, Carol; Dehghani, Morteza

2018-02-01

Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience.

Domain Adaptation of Translation Models for Multilingual Applications

DTIC Science & Technology

2009-04-01

expansion effect that corpus (or dictionary ) based trans- lation introduces - however, this effect is maintained even with monolingual query expansion [12...every day; bilingual web pages are harvested as parallel corpora as the quantity of non-English data on the web increases; online dictionaries of...approach is to customize translation models to a domain, by automatically selecting the resources ( dictionaries , parallel corpora) that are best for

Semi-supervised morphosyntactic classification of Old Icelandic.

PubMed

Urban, Kryztof; Tangherlini, Timothy R; Vijūnas, Aurelijus; Broadwell, Peter M

2014-01-01

We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Recent research in data description of the measurement property resource on common data dictionary

NASA Astrophysics Data System (ADS)

Lu, Tielin; Fan, Zitian; Wang, Chunxi; Liu, Xiaojing; Wang, Shuo; Zhao, Hua

2018-03-01

A method for measurement equipment data description has been proposed based on the property resource analysis. The applications of common data dictionary (CDD) to devices and equipment is mainly used in digital factory to advance the management not only in the enterprise, also to the different enterprise in the same data environment. In this paper, we can make a brief of the data flow in the whole manufacture enterprise and the automatic trigger the process of the data exchange. Furthermore,the application of the data dictionary is available for the measurement and control equipment, which can also be used in other different industry in smart manufacture.

CLIR Experiments at Maryland for TREC-2002: Evidence Combination for Arabic-English Retrieval

DTIC Science & Technology

2002-01-01

translation resources of three types (machine translation lexicons, a printed bilingual dictionary that had been manually rekeyed, and translation...on both the term list and the collection). • The Salmone Arabic-to-English dictionary , which was made available for use in the TREC-CLIR track by...Tufts University. No translation preference information is provided in this dictionary , but it does include rich markup describing morphology and part

D1-3: Marshfield Dictionary of Clinical and Translational Science (MD-CTS): An Online Reference for Clinical and Translational Science Terminology

PubMed Central

Finamore, Joe; Ray, William; Kadolph, Chris; Rastegar-Mojarad, Majid; Ye, Zhan; Jacqueline, Bohne; Tachinardi, Umberto; Mendonça, Eneida; Finnegan, Brian; Bartkowiak, Barbara; Weichelt, Bryan; Lin, Simon

2014-01-01

Background/Aims New terms are rapidly appearing in the literature and practice of clinical medicine and translational research. To catalog real-world usage of medical terms, we report the first construction of an online dictionary of clinical and translational medicinal terms, which are computationally generated in near real-time using a big data approach. This project is NIH CTSA-funded and developed by the Marshfield Clinic Research Foundation in conjunction with University of Wisconsin - Madison. Currently titled Marshfield Dictionary of Clinical and Translational Science (MD-CTS), this application is a Google-like word search tool. By entering a term into the search bar, MD-CTS will display that term’s definition, usage examples, contextual terms, related images, and ontological information. A prototype is available for public viewing at http://spellchecker.mfldclin.edu/. Methods We programmatically derived the lexicon for MD-CTS from scholarly communications by parsing through 15,156,745 MEDLINE abstracts and extracting all of the unique words found therein. We then ran this list through several filters in order to remove words that were not relevant for searching, such as common English words and numeric expressions. We then loaded the resulting 1,795,769 terms into SQL tables. Each term is cross-referenced with every occurrence in all abstracts in which it was found. Additional information is aggregated from Wiktionary, Bioportal, and Wikipedia in real-time and displayed on-screen. From this lexicon we created a supplemental dictionary resource (updated quarterly) to be used in Microsoft Office® products. Results We evaluated the utility of MD-CTS by creating a list of 100 words derived from recent clinical and translational medicine publications in the week of July 22, 2013. We then performed comparative searches for each term with Taber’s Cyclopedic Medical Dictionary, Stedman’s Medical Dictionary, Dorland’s Illustrated Medical Dictionary, Medical Subject Headings (MeSH), and MD-CTS. We compared our supplemental dictionary resource to OpenMedSpell for effectiveness in accuracy of term recognition. Conclusions In summary, we developed an online mobile and desktop reference, which comprehensively integrates Wiktionary (term information), Bioportal (ontological information), Wikipedia (related images), and Medline abstract information (term usage) for scientists and clinicians to browse in real-time. We also created a supplemental dictionary resource to be used in Microsoft Office® products.

A Relational Data Dictionary Compatible with the National Bureau of Standards Information Resource Dictionary System.

DTIC Science & Technology

1985-12-01

85 UNCLSSIFIED F/ 3/2 NL mhhhhhhhhhhhhl 4y 1.0 &32 MICROCOPY RESOLUTIOf TEST CKART. N NAVAL POSTGRADUATE SCHOOL Monterey, California DTIC ELECTE...Concern over corporate information resources has resulted from the explosive growth in the size, complexity and number of data bases available to...validity, and relevance, and usability of the data that is available. As a result , there has been a growing interest in two tools which .,%... provide

A dictionary to identify small molecules and drugs in free text.

PubMed

Hettne, Kristina M; Stierum, Rob H; Schuemie, Martijn J; Hendriksen, Peter J M; Schijvenaars, Bob J A; Mulligen, Erik M van; Kleinjans, Jos; Kors, Jan A

2009-11-15

From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers. We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (i) each of the different processing steps increase precision with a minor loss of recall; (ii) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (iii) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (iv) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary. The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web site http://www.biosemantics.org/chemlist.

Classic Classroom Activities: The Oxford Picture Dictionary Program.

ERIC Educational Resources Information Center

Weiss, Renee; Adelson-Goldstein, Jayme; Shapiro, Norma

This teacher resource book offers over 100 reproducible communicative practice activities and 768 picture cards based on the vocabulary of the Oxford Picture Dictionary. Teacher's notes and instructions, including adaptations for multilevel classes, are provided. The activities book has up-to-date art and graphics, explaining over 3700 words. The…

Concordancers and Dictionaries as Problem-Solving Tools for ESL Academic Writing

ERIC Educational Resources Information Center

Yoon, Choongil

2016-01-01

The present study investigated how 6 Korean ESL graduate students in Canada used a suite of freely available reference resources, consisting of Web-based corpus tools, Google search engines, and dictionaries, for solving linguistic problems while completing an authentic academic writing assignment in English. Using a mixed methods design, the…

The Talking Dictionary. The Prospectus Series, Paper No. 2.

ERIC Educational Resources Information Center

Ward, Ted

Three talking dictionaires designed to increase independence and resource-use skills of handicapped children have specific advantages and limitations. System I involves a random access tape recorder, a printed or braille dictionary which contains the inquiry numbers for words, a console (similar to an adding machine) on which the number is…

The Primary Computer Dictionary.

ERIC Educational Resources Information Center

Girard, Suzanne; Willing, Kathlene

Suitable for children from kindergarten to grade three, this dictionary is designed to introduce young children to computer terminology at a level that they will understand and find useful. It is also suitable for parents as a home resource, for library use, and as a handbook for teachers. The first sentence of each definition contains the kernel…

Syntactic and Semantic Specifications in Online English Learners' Dictionaries

ERIC Educational Resources Information Center

Rizo-Rodriguez, Alfonso

2009-01-01

Among the multifarious linguistic resources currently available on the Internet, learners of English as a foreign language, as well as teachers and translators, can effortlessly access a vast variety of electronic dictionaries well suited to a multiplicity of lookup operations. A particular kind of lexicographical work on the Web is the…

The Latent Structure of Dictionaries.

PubMed

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

2016-07-01

How many words-and which ones-are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a "Strongly Connected Subset" of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest-the graph's "minimum feedback vertex set" or MinSet-is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition. Copyright © 2016 Cognitive Science Society, Inc.

Curating and Nudging in Virtual CLIL Environments

ERIC Educational Resources Information Center

Nielsen, Helle Lykke

2014-01-01

Foreign language teachers can benefit substantially from the notions of curation and nudging when scaffolding CLIL activities on the internet. This article shows how these principles can be integrated into CLILstore, a free multimedia-rich learning tool with seamless access to online dictionaries, and presents feedback from first and second year…

Dictionary learning-based CT detection of pulmonary nodules

NASA Astrophysics Data System (ADS)

Wu, Panpan; Xia, Kewen; Zhang, Yanbo; Qian, Xiaohua; Wang, Ge; Yu, Hengyong

2016-10-01

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

Sentiment analysis of political communication: combining a dictionary approach with crowdcoding.

PubMed

Haselmayer, Martin; Jenny, Marcelo

2017-01-01

Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.

Creating a Digital Jamaican Sign Language Dictionary: A R2D2 Approach

ERIC Educational Resources Information Center

MacKinnon, Gregory; Soutar, Iris

2015-01-01

The Jamaican Association for the Deaf, in their responsibilities to oversee education for individuals who are deaf in Jamaica, has demonstrated an urgent need for a dictionary that assists students, educators, and parents with the practical use of "Jamaican Sign Language." While paper versions of a preliminary resource have been explored…

The "Dictionary of Smoky Mountain English" as a Resource for Southern Appalachia.

ERIC Educational Resources Information Center

Montgomery, Michael

This paper argues that one important reflection of a culture's status is the existence of general reference books on it. To this end, it discusses the forthcoming "Dictionary of Smoky Mountain English," a book designed to address the lack of a comprehensive reference work on Appalachian speech and language patterns in this region. The…

Dave Sperling's Guide to the Internet's Best Writing Resources.

ERIC Educational Resources Information Center

Sperling, Dave

2003-01-01

Provides a guide to writing resources on the Internet, including resources for business writing, dictionaries and thesauruses, e-mail, encyclopedias, free Web space, grammar, fun, online help, online writing labs, punctuation, and spelling. Lists useful Internet tips. (Author/VWL)

An Investigation into the Effect of English Learners' Dictionaries on International Students' Acquisition of the English Article System

ERIC Educational Resources Information Center

Miller, Julia

2006-01-01

Learners' dictionaries are a resource which is often overlooked by both students and teachers of English as a Second Language. The wealth of grammatical information contained within them, however, can help students to improve their English language skills and, ipso facto, their academic writing. In this study, four groups of university ESL…

Essential Nursing References.

ERIC Educational Resources Information Center

Nursing and Health Care Perspectives, 2000

2000-01-01

This partially annotated bibliography contains these categories: abstract sources, archives, audiovisuals, bibliographies, databases, dictionaries, directories, drugs/toxicology/environmental health, grant resources, histories, indexes, Internet resources, reviews, statistical sources, and writers' manuals and guides. A supplement lists Canadian…

Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2016-04-01

Increased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches. We obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naïve bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics. Our methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field. Copyright © 2016 Elsevier Inc. All rights reserved.

The Junior Computer Dictionary. 101 Useful Words and Definitions to Introduce Students to Computer Terminology.

ERIC Educational Resources Information Center

Willing, Kathlene R.; Girard, Suzanne

Suitable for children from grades four to seven, this dictionary is designed to introduce children to computer terminology at a level that they will understand and find useful. It is also suitable as a home resource for parents, for library use, and as a handbook for teachers. For each word, the first sentence of the definition contains the kernel…

French as a Second Language. Annotated Bibliography of Learning Resources: Beginning Level. Early Childhood Services - Grade 12.

ERIC Educational Resources Information Center

Alberta Dept. of Education, Edmonton. Language Services Branch.

This annotated bibliography of instructional resources for Alberta (Canada) introductory French second language teaching in early childhood, elementary, and secondary education consists of citations in 10 categories: audio/video recordings; communicative activity resources (primarily texts and workbooks); dictionaries and vocabulary handbooks;…

Exploration of the Factors That Support Learning: Web-Based Activity and Testing Systems in Community College Algebra

ERIC Educational Resources Information Center

Hauk, Shandy; Matlen, Bryan

2016-01-01

A variety of computerized interactive learning platforms exist. Most include instructional supports in the form of problem sets. Feedback to users ranges from a single word like "Correct!" to offers of hints and partially to fully worked examples. Behind-the-scenes design of such systems varies as well --from static dictionaries of…

Exploration of the Factors That Support Learning: Web-Based Activity and Testing Systems in Community College Algebra [Contributed Report

ERIC Educational Resources Information Center

Hauk, Shandy; Matlen, Bryan; Thomas, Larry

2017-01-01

A variety of computerized interactive learning platforms exist. Most include instructional supports in the form of problem sets. Feedback to users ranges from a single word like "Correct!" to offers of hints and partially- to fully-worked examples. Behind-the-scenes design of systems varies as well--from static dictionaries of problems…

Information Storage and Retrieval...Reports on Text Analysis, Dynamic Indexing, Feedback Searches, Dictionary Construction and File Organization.

ERIC Educational Resources Information Center

Salton, Gerald; And Others

The present report is the twenty-first in a series describing research in information storage and retrieval conducted by the Department of Computer Science at Cornell University. The report covering work carried out by the SMART project for approximately two years (summer 1970 to summer 1972) is separated into five parts: automatic content…

Graded Lexicons: New Resources for Educational Purposes and Much More

ERIC Educational Resources Information Center

Gala, Núria; Billami, Mokhtar B.; François, Thomas; Bernhard, Delphine

2015-01-01

Computational tools and resources play an important role for vocabulary acquisition. Although a large variety of dictionaries and learning games are available, few resources provide information about the complexity of a word, either for learning or for comprehension. The idea here is to use frequency counts combined with intralexical variables to…

ESP Students' Views on Online Language Resources for L2 Text Production Purposes

ERIC Educational Resources Information Center

Kozlova, Inna; Presas, Marisa

2013-01-01

The use of online language resources for L2 text production purposes is a recent phenomenon and has not yet been studied in depth. Increasing availability of new online resources seems to be changing the very nature of L2 text production. The traditional dictionary, hitherto a default resource to help with language doubts, is being left behind…

A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources.

PubMed

Moon, Sungrim; Pakhomov, Serguei; Liu, Nathan; Ryan, James O; Melton, Genevieve B

2014-01-01

To create a sense inventory of abbreviations and acronyms from clinical texts. The most frequently occurring abbreviations and acronyms from 352,267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17,359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/research/nlpie/resources/index.htm ('Sense Inventories', website). Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction.

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

"It's Just Reflex Now": German Language Learners' Use of Online Resources

ERIC Educational Resources Information Center

Larson-Guenette, Julie

2013-01-01

This study examined how often and to what extent university learners of German use online resources (e.g., online dictionaries and translators) in relation to German coursework, their motivations for use, and their beliefs about online resources and language learning. Data for this study consisted of open-ended surveys ("n" = 71) and face-to-face…

Information Resource Management in the DCSPLANS (Deputy Chief of Staff for Plans) Branch of the U.S. Army Military Personnel Center

DTIC Science & Technology

1986-11-15

Richard E. Broome [Broome 1985], Major Robert M. DiBona [ DiBona 1985], Major Robert A. Kirsch II [Kirsch 1985], and Major Alan F. Noel, Jr. [Noel 1985...Major Kirsch expanded this prototype to be compatible with the emerging Federal standards for dictionary systems. Major DiBona analyzed data validation...described in [Noel 1985, Kirsch 1985]. The implementation of edit validation rules in a dictionary environment is covered in [ DiBona 1985] and implementation

Resources for Topics in Architecture.

ERIC Educational Resources Information Center

Van Noate, Judith, Comp.

This guide for conducting library research on topics in architecture or on the work of a particular architect presents suggestions for utilizing four categories of resources: books, dictionaries and encyclopedias, indexes, and a periodicals and series list (PASL). Two topics are researched as examples: the contemporary architect Richard Meier, and…

Technical Standards for Command and Control Information Systems (CCISs)

DTIC Science & Technology

1992-01-01

initiation, Conformance Testing 149 management, scheduling, resource allocation , logical and IEEE P1 003 146 physical device access, interrupt handling...70 5.2.3 Remote Data Access (RDA) ........................................... 72 5.2.4 Information Resource Dictionary...146 7.2.1.2 POSIX Conformance Testing .............................. 149 7.2.2 Consortia Recommendations

A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources

PubMed Central

Moon, Sungrim; Pakhomov, Serguei; Liu, Nathan; Ryan, James O; Melton, Genevieve B

2014-01-01

Objective To create a sense inventory of abbreviations and acronyms from clinical texts. Methods The most frequently occurring abbreviations and acronyms from 352 267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). Results The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17 359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/research/nlpie/resources/index.htm (‘Sense Inventories’, website). Conclusions Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction. PMID:23813539

Part 6: The Literature of Inorganic Chemistry, Revised.

ERIC Educational Resources Information Center

Douville, Judith A.

2002-01-01

Presents a list of resources on inorganic chemistry that includes general surveys, nomenclature, dictionaries, handbooks, compilations, and treatises. Selected for use by academic and student chemists. (DDR)

Basic Aerospace Education Library

ERIC Educational Resources Information Center

Journal of Aerospace Education, 1975

1975-01-01

Lists the most significant resource items on aerospace education which are presently available. Includes source books, bibliographies, directories, encyclopedias, dictionaries, audiovisuals, curriculum/planning guides, aerospace statistics, aerospace education statistics and newsletters. (BR)

A study of actions in operative notes.

PubMed

Wang, Yan; Pakhomov, Serguei; Burkart, Nora E; Ryan, James O; Melton, Genevieve B

2012-01-01

Operative notes contain rich information about techniques, instruments, and materials used in procedures. To assist development of effective information extraction (IE) techniques for operative notes, we investigated the sublanguage used to describe actions within the operative report 'procedure description' section. Deep parsing results of 362,310 operative notes with an expanded Stanford parser using the SPECIALIST Lexicon resulted in 200 verbs (92% coverage) including 147 action verbs. Nominal action predicates for each action verb were gathered from WordNet, SPECIALIST Lexicon, New Oxford American Dictionary and Stedman's Medical Dictionary. Coverage gaps were seen in existing lexical, domain, and semantic resources (Unified Medical Language System (UMLS) Metathesaurus, SPECIALIST Lexicon, WordNet and FrameNet). Our findings demonstrate the need to construct surgical domain-specific semantic resources for IE from operative notes.

National Hydrocephalus Foundation

MedlinePlus

... Types of Seizures About the Foundation Mission, History & Philosophy of NHF Treatment of Hydrocephalus What is a Shunt? Treatment Third Ventriculostomy Shunt Malfunction Prognosis and Research Medical Dictionary Resources Success Stories Blessing in Disguise ...

A Graphics Editor for Structured Analysis with a Data Dictionary.

DTIC Science & Technology

1987-12-01

4-3 Human/Computer Interface Considerations 4-3 Screen Layout .... ............. 4-4 Menu System ..... .............. 4-6 Voice Feedback...central computer system . This project is a direct follow on to the 1986 thesis by James W. Urscheler. lie created an initial version of a tool (nicknamed...graphics information. Background r SADT. SADT is the name of SofTech’s methodology for doing requirement analysis and system design. It was first published

Automatic Dictionary Expansion Using Non-parallel Corpora

NASA Astrophysics Data System (ADS)

Rapp, Reinhard; Zock, Michael

Automatically generating bilingual dictionaries from parallel, manually translated texts is a well established technique that works well in practice. However, parallel texts are a scarce resource. Therefore, it is desirable also to be able to generate dictionaries from pairs of comparable monolingual corpora. For most languages, such corpora are much easier to acquire, and often in considerably larger quantities. In this paper we present the implementation of an algorithm which exploits such corpora with good success. Based on the assumption that the co-occurrence patterns between different languages are related, it expands a small base lexicon. For improved performance, it also realizes a novel interlingua approach. That is, if corpora of more than two languages are available, the translations from one language to another can be determined not only directly, but also indirectly via a pivot language.

Information technology principles for management, reporting, and research.

PubMed

Gillam, Michael; Rothenhaus, Todd; Smith, Vernon; Kanhouwa, Meera

2004-11-01

Information technology holds the promise to enhance the ability of individuals and organizations to manage emergency departments, improve data sharing and reporting, and facilitate research. The Society for Academic Emergency Medicine (SAEM) Consensus Committee has identified nine principles to outline a path of optimal features and designs for current and future information technology systems. The principles roughly summarized include the following: utilize open database standards with clear data dictionaries, provide administrative access to necessary data, appoint and recognize individuals with emergency department informatics expertise, allow automated alert and proper identification for enrollment of cases into research, provide visual and statistical tools and training to analyze data, embed automated configurable alarm functionality for clinical and nonclinical systems, allow multiexport standard and format configurable reporting, strategically acquire mission-critical equipment that is networked and capable of automated feedback regarding functional status and location, and dedicate resources toward informatics research and development. The SAEM Consensus Committee concludes that the diligent application of these principles will enhance emergency department management, reporting, and research and ultimately improve the quality of delivered health care.

Foreign Objects in the Rectum

MedlinePlus

... Resources In This Article Medical Dictionary Also of Interest (Quiz) Anal Fissure (News) Could a Common Blood Thinner Lower Cancer Risk? (News) Study Untangles Disparity in Colon Cancer Survival Rates (News) Poor Prognosis for Diabetic Foot Sores (News) ...

Rdesign: A data dictionary with relational database design capabilities in Ada

NASA Technical Reports Server (NTRS)

Lekkos, Anthony A.; Kwok, Teresa Ting-Yin

1986-01-01

Data Dictionary is defined to be the set of all data attributes, which describe data objects in terms of their intrinsic attributes, such as name, type, size, format and definition. It is recognized as the data base for the Information Resource Management, to facilitate understanding and communication about the relationship between systems applications and systems data usage and to help assist in achieving data independence by permitting systems applications to access data knowledge of the location or storage characteristics of the data in the system. A research and development effort to use Ada has produced a data dictionary with data base design capabilities. This project supports data specification and analysis and offers a choice of the relational, network, and hierarchical model for logical data based design. It provides a highly integrated set of analysis and design transformation tools which range from templates for data element definition, spreadsheet for defining functional dependencies, normalization, to logical design generator.

Space platform expendables resupply concept definition study. Volume 3: Work breakdown structure and work breakdown structure dictionary

NASA Technical Reports Server (NTRS)

1984-01-01

The work breakdown structure (WBS) for the Space Platform Expendables Resupply Concept Definition Study is described. The WBS consists of a list of WBS elements, a dictionary of element definitions, and an element logic diagram. The list and logic diagram identify the interrelationships of the elements. The dictionary defines the types of work that may be represented by or be classified under each specific element. The Space Platform Expendable Resupply WBS was selected mainly to support the program planning, scheduling, and costing performed in the programmatics task (task 3). The WBS is neither a statement-of-work nor a work authorization document. Rather, it is a framework around which to define requirements, plan effort, assign responsibilities, allocate and control resources, and report progress, expenditures, technical performance, and schedule performance. The WBS element definitions are independent of make-or-buy decisions, organizational structure, and activity locations unless exceptions are specifically stated.

Fast group matching for MR fingerprinting reconstruction.

PubMed

Cauley, Stephen F; Setsompop, Kawin; Ma, Dan; Jiang, Yun; Ye, Huihui; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L

2015-08-01

MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1 , T2 , proton density, and B0 , the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction. We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach. For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1-2% relative error). The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources. © 2014 Wiley Periodicals, Inc.

Feedback and the rationing of time and effort among competing tasks.

PubMed

Northcraft, Gregory B; Schmidt, Aaron M; Ashford, Susan J

2011-09-01

The study described here tested a model of how characteristics of the feedback environment influence the allocation of resources (time and effort) among competing tasks. Results demonstrated that performers invest more resources on tasks for which higher quality (more timely and more specific) feedback is available; this effect was partially mediated by task salience and task expectancies. Feedback timing and feedback specificity demonstrated both main and interaction effects on resource allocations. Results also demonstrated that performers do better on tasks for which higher quality feedback is available; this effect was mediated by resources allocated to tasks. The practical and theoretical implications of the role of the feedback environment in managing performance are discussed. PsycINFO Database Record (c) 2011 APA, all rights reserved

An Extensible, User- Modifiable Framework for Planning Activities

NASA Technical Reports Server (NTRS)

Joshing, Joseph C.; Abramyan, Lucy; Mickelson, Megan C.; Wallick, Michael N.; Kurien, James A.; Crockett, Thomasa M.; Powell, Mark W.; Pyrzak, Guy; Aghevli, Arash

2013-01-01

This software provides a development framework that allows planning activities for the Mars Science Laboratory rover to be altered at any time, based on changes of the Activity Dictionary. The Activity Dictionary contains the definition of all activities that can be carried out by a particular asset (robotic or human). These definitions (and combinations of these definitions) are used by mission planners to give a daily plan of what a mission should do. During the development and course of the mission, the Activity Dictionary and actions that are going to be carried out will often be changed. Previously, such changes would require a change to the software and redeployment. Now, the Activity Dictionary authors are able to customize activity definitions, parameters, and resource usage without requiring redeployment. This software provides developers and end users the ability to modify the behavior of automatically generated activities using a script. This allows changes to the software behavior without incurring the burden of redeployment. This software is currently being used for the Mars Science Laboratory, and is in the process of being integrated into the LADEE (Lunar Atmosphere and Dust Environment Explorer) mission, as well as the International Space Station.

Data model, dictionaries, and desiderata for biomolecular simulation data indexing and sharing

PubMed Central

2014-01-01

Background Few environments have been developed or deployed to widely share biomolecular simulation data or to enable collaborative networks to facilitate data exploration and reuse. As the amount and complexity of data generated by these simulations is dramatically increasing and the methods are being more widely applied, the need for new tools to manage and share this data has become obvious. In this paper we present the results of a process aimed at assessing the needs of the community for data representation standards to guide the implementation of future repositories for biomolecular simulations. Results We introduce a list of common data elements, inspired by previous work, and updated according to feedback from the community collected through a survey and personal interviews. These data elements integrate the concepts for multiple types of computational methods, including quantum chemistry and molecular dynamics. The identified core data elements were organized into a logical model to guide the design of new databases and application programming interfaces. Finally a set of dictionaries was implemented to be used via SQL queries or locally via a Java API built upon the Apache Lucene text-search engine. Conclusions The model and its associated dictionaries provide a simple yet rich representation of the concepts related to biomolecular simulations, which should guide future developments of repositories and more complex terminologies and ontologies. The model still remains extensible through the decomposition of virtual experiments into tasks and parameter sets, and via the use of extended attributes. The benefits of a common logical model for biomolecular simulations was illustrated through various use cases, including data storage, indexing, and presentation. All the models and dictionaries introduced in this paper are available for download at http://ibiomes.chpc.utah.edu/mediawiki/index.php/Downloads. PMID:24484917

An operational system for subject switching between controlled vocabularies: A computational linguistics approach

NASA Technical Reports Server (NTRS)

Silvester, J. P.; Newton, R.; Klingbiel, P. H.

1984-01-01

The NASA Lexical Dictionary (NLD), a system that automatically translates input subject terms to those of NASA, was developed in four phases. Phase One provided Phrase Matching, a context sensitive word-matching process that matches input phrase words with any NASA Thesaurus posting (i.e., index) term or Use reference. Other Use references have been added to enable the matching of synonyms, variant spellings, and some words with the same root. Phase Two provided the capability of translating any individual DTIC term to one or more NASA terms having the same meaning. Phase Three provided NASA terms having equivalent concepts for two or more DTIC terms, i.e., coordinations of DTIC terms. Phase Four was concerned with indexer feedback and maintenance. Although the original NLD construction involved much manual data entry, ways were found to automate nearly all but the intellectual decision-making processes. In addition to finding improved ways to construct a lexical dictionary, applications for the NLD have been found and are being developed.

Occupations, U. S. A.

ERIC Educational Resources Information Center

Geneva Area City Schools, OH.

The booklet divides job titles, selected from the Dictionary of Occupational Titles, into 15 career clusters: agribusiness and natural resources, business and office education, communication and media, construction, consumer and home economics, fine arts and humanities, health occupations, hospitality and recreation, manufacturing, marine science,…

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

PubMed

Akhondi, Saber A; Hettne, Kristina M; van der Horst, Eelke; van Mulligen, Erik M; Kors, Jan A

2015-01-01

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches

PubMed Central

2015-01-01

Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance. PMID:25810767

Evaluation of Controlled Vocabulary Resources for Development of a Consumer Entry Vocabulary for Diabetes

PubMed Central

Monga, Harpreet K; Sievert, MaryEllen C; Hall, Joan Houston; Longo, Daniel R

2001-01-01

Background Digital information technology can facilitate informed decision making by individuals regarding their personal health care. The digital divide separates those who do and those who do not have access to or otherwise make use of digital information. To close the digital divide, health care communications research must address a fundamental issue, the consumer vocabulary problem: consumers of health care, at least those who are laypersons, are not always familiar with the professional vocabulary and concepts used by providers of health care and by providers of health care information, and, conversely, health care and health care information providers are not always familiar with the vocabulary and concepts used by consumers. One way to address this problem is to develop a consumer entry vocabulary for health care communications. Objectives To evaluate the potential of controlled vocabulary resources for supporting the development of consumer entry vocabulary for diabetes. Methods We used folk medical terms from the Dictionary of American Regional English project to create exended versions of 3 controlled vocabulary resources: the Unified Medical Language System Metathesaurus, the Eurodicautom of the European Commission's Translation Service, and the European Commission Glossary of popular and technical medical terms. We extracted consumer terms from consumer-authored materials, and physician terms from physician-authored materials. We used our extended versions of the vocabulary resources to link diabetes-related terms used by health care consumers to synonymous, nearly-synonymous, or closely-related terms used by family physicians. We also examined whether retrieval of diabetes-related World Wide Web information sites maintained by nonprofit health care professional organizations, academic organizations, or governmental organizations can be improved by substituting a physician term for its related consumer term in the query. Results The Dictionary of American Regional English extension of the Metathesaurus provided coverage, either direct or indirect, of approximately 23% of the natural language consumer-term-physician-term pairs. The Dictionary of American Regional English extension of the Eurodicautom provided coverage for 16% of the term pairs. Both the Metathesaurus and the Eurodicautom indirectly related more terms than they directly related. A high percentage of covered term pairs, with more indirectly covered pairs than directly covered pairs, might be one way to make the most out of expensive controlled vocabulary resources. We compared retrieval of diabetes-related Web information sites using the physician terms to retrieval using related consumer terms We based the comparison on retrieval of sites maintained by non-profit healthcare professional organizations, academic organizations, or governmental organizations. The number of such sites in the first 20 results from a search was increased by substituting a physician term for its related consumer term in the query. This suggests that the Dictionary of American Regional English extensions of the Metathesaurus and Eurodicautom may be used to provide useful links from natural language consumer terms to natural language physician terms. Conclusions The Dictionary of American Regional English extensions of the Metathesaurus and Eurodicautom should be investigated further for support of consumer entry vocabulary for diabetes. PMID:11720966

A Selected Bibliography of Educational Sources.

ERIC Educational Resources Information Center

Campbell, Janet; And Others

Focusing on materials available at the California State University at Long Beach Library, this annotated bibliography lists resources in seven subject categories pertaining to education: (1) guides to the professional educational literature; (2) books about education research methodology; (3) encyclopedias and dictionaries; (4) tests and…

Using TEI for an Endangered Language Lexical Resource: The Nxa?amxcín Database-Dictionary Project

ERIC Educational Resources Information Center

Czaykowska-Higgins, Ewa; Holmes, Martin D.; Kell, Sarah M.

2014-01-01

This paper describes the evolution of a lexical resource project for Nxa?amxcín, an endangered Salish language, from the project's inception in the 1990s, based on legacy materials recorded in the 1960s and 1970s, to its current form as an online database that is transformable into various print and web-based formats for varying uses. We…

Dictionary of Microscopy

NASA Astrophysics Data System (ADS)

Heath, Julian

2005-10-01

The past decade has seen huge advances in the application of microscopy in all areas of science. This welcome development in microscopy has been paralleled by an expansion of the vocabulary of technical terms used in microscopy: terms have been coined for new instruments and techniques and, as microscopes reach even higher resolution, the use of terms that relate to the optical and physical principles underpinning microscopy is now commonplace. The Dictionary of Microscopy was compiled to meet this challenge and provides concise definitions of over 2,500 terms used in the fields of light microscopy, electron microscopy, scanning probe microscopy, x-ray microscopy and related techniques. Written by Dr Julian P. Heath, Editor of Microscopy and Analysis, the dictionary is intended to provide easy navigation through the microscopy terminology and to be a first point of reference for definitions of new and established terms. The Dictionary of Microscopy is an essential, accessible resource for: students who are new to the field and are learning about microscopes equipment purchasers who want an explanation of the terms used in manufacturers' literature scientists who are considering using a new microscopical technique experienced microscopists as an aide mémoire or quick source of reference librarians, the press and marketing personnel who require definitions for technical reports.

Resources into Higher Education. Bibliographic Series No. 31.

ERIC Educational Resources Information Center

Ahrens, Joan

Varied information sources on higher education held by the Arkansas University library are listed. The publications, which are organized by type of material include abstracts and indexes, sources for dissertations and theses, directories of educational personnel and institutions, dictionaries, encyclopedias, handbooks, statistical sources, and…

CIDR

Science.gov Websites

Related Links & Resources Access and Applications Access Applications Example Applications Project Research and Related (R&R) forms and the SF424 (R&R) Application Guide. Access to the CIDR Program Guidelines Example Applications All applications must include a Data Dictionary of phenotypic measures to be

Library Information Resource Book For Staff.

ERIC Educational Resources Information Center

Potts, Ken; And Others

This guide is the Northern Illinois University (NIU) Libraries' quick reference tool for providing information about its collections, facilities, and services. The articles are arranged in an alphabetic, dictionary format with numerous cross-references, and highlight information on the following: administrative offices; company annual reports;…

English for Specific Purposes. Information Guide 2.

ERIC Educational Resources Information Center

British Council, London (England). English-Teaching Information Centre.

This bibliography of materials for teachers of English for specific purposes lists textbooks, technical readers, articles, resource books, reports, dictionaries, reference books, bibliographies, word frequency lists, catalogues of teaching aids, games and activities, current research in Britain, documents available in the archives of the English…

Data Base Directions: Information Resource Management - Strategies and Tools. Proceedings of the Workshop of the National Bureau of Standards and the Association for Computing Machinery (Ft. Lauderdale, Florida, October 20-22, 1980).

ERIC Educational Resources Information Center

Goldfine, Alan H., Ed.

This workshop investigated how managers can evaluate, select, and effectively use information resource management (IRM) tools, especially data dictionary systems (DDS). An executive summary, which provides a definition of IRM as developed by workshop participants, precedes the keynote address, "Data: The Raw Material of a Paper Factory,"…

Beyond Barriers: Encouraging Teacher Use of Feedback Resources. A Report from the Teacher Feedback Resources Project

ERIC Educational Resources Information Center

Morgan, Nicholas; Killion, Joellen

2018-01-01

This report investigates factors that drive teachers to embrace or challenge the use of products and services designed to support improvements in practice. Key elements of the study include: (1) Technology-based resources studied include those designed for video observations, peer feedback and collaboration, online professional learning, and…

Learning Essential Terms and Concepts in Statistics and Accounting

ERIC Educational Resources Information Center

Peters, Pam; Smith, Adam; Middledorp, Jenny; Karpin, Anne; Sin, Samantha; Kilgore, Alan

2014-01-01

This paper describes a terminological approach to the teaching and learning of fundamental concepts in foundation tertiary units in Statistics and Accounting, using an online dictionary-style resource (TermFinder) with customised "termbanks" for each discipline. Designed for independent learning, the termbanks support inquiring students…

The Language Grid: supporting intercultural collaboration

NASA Astrophysics Data System (ADS)

Ishida, T.

2018-03-01

A variety of language resources already exist online. Unfortunately, since many language resources have usage restrictions, it is virtually impossible for each user to negotiate with every language resource provider when combining several resources to achieve the intended purpose. To increase the accessibility and usability of language resources (dictionaries, parallel texts, part-of-speech taggers, machine translators, etc.), we proposed the Language Grid [1]; it wraps existing language resources as atomic services and enables users to create new services by combining the atomic services, and reduces the negotiation costs related to intellectual property rights [4]. Our slogan is “language services from language resources.” We believe that modularization with recombination is the key to creating a full range of customized language environments for various user communities.

Expanding Academic Vocabulary with an Interactive On-Line Database

ERIC Educational Resources Information Center

Horst, Marlise; Cobb, Tom; Nicolae, Ioana

2005-01-01

University students used a set of existing and purpose-built on-line tools for vocabulary learning in an experimental ESL course. The resources included concordance, dictionary, cloze-builder, hypertext, and a database with interactive self-quizzing feature (all freely available at www.lextutor.ca). The vocabulary targeted for learning consisted…

Thumbs Up: High-Quality, Low-Cost Teaching Aids.

ERIC Educational Resources Information Center

Paine, Carolyn

1982-01-01

Exemplary teaching aids--games, workbooks, student and teacher resource books, reading materials, and records--are recommended by subject area and grade level. Materials include an ice cream cone game for mathematics, a "Life Skills Reading" book on telephone usage, a "Dictionary of Recent American History," and many other items. (PP)

ArtMARC Sourcebook: Cataloging Art, Architecture, and Their Visual Images.

ERIC Educational Resources Information Center

McRae, Linda, Ed.; White, Lynda S., Ed.

Profiling the proven cataloging methods of experts from libraries, art galleries, museums, and other institutions, this sourcebook outlines cataloging techniques for a wide variety of resources from ancient artifacts to architectural drawings. A data dictionary of relevant MARC fields is also included, along with data conversion comments. A…

Student Outcomes 2009: Data Dictionary. Support Document

ERIC Educational Resources Information Center

National Centre for Vocational Education Research (NCVER), 2009

2009-01-01

This document was produced as an added resource for the report "Outcomes from the Productivity Places Program, 2009." The study reported the outcomes for students who completed their vocational education and training (VET) under the Productivity Places Program (PPP) during 2008. This document presents an alphabetical arrangement of the…

ARBA Guide to Biographical Resources 1986-1997.

ERIC Educational Resources Information Center

Wick, Robert L., Ed.; Mood, Terry Ann, Ed.

This guide provides a representative selection of biographical dictionaries and related works useful to the reference and collection development processes in all types of libraries. Three criteria were used in selection: (1) each item included was published within the past 12 years; (2) each item has been included in American Reference Books…

Hupa Natural Resources Dictionary.

ERIC Educational Resources Information Center

Bennett, Ruth, Ed.; And Others

Created by children in grades 5-8 who were enrolled in a year-long Hupa language class, this computer-generated, bilingual book contains descriptions and illustrations of local animals, birds, and fish. The introduction explains that students worked on a Macintosh computer able to print the Unifon alphabet used in writing the Hupa language.…

Resources for Teaching Word Identification.

ERIC Educational Resources Information Center

Schell, Leo M., Ed.; And Others

Only materials specifically designed to teach one or more of the following word identification skills were included in this booklet: sight words, context clues, phonic analysis, structural analysis, and dictionary skills. Materials for grades one through six are stressed, although a few materials suitable for secondary school students are listed.…

Linguistic Resource Creation for Research and Technology Development: A Recent Experiment

DTIC Science & Technology

2003-01-01

inflectional morphology. For instance, Tzeltal (a Mayan language of Mexico), Swahili (east Africa), and Shuar (a Jivaroan language of Ecuador) all...or they can come from dictionaries, as was the case for Tzeltal and Shuar . In the case of inflectionally rich languages, when the word forms are

[Systems, boundaries and resources: the lexicographer Gerhard Wahrig (1923-1978) and the genesis of his project "dictionary as database"].

PubMed

Wahrig-Burfeind, Renate; Wahrig, Bettina

2014-09-01

Gerhard Wahrig's private archive has recently been retrieved by the authors and their siblings. We undertake a first survey of the unpublished material and concentrate on those aspects of Wahrig's bio-ergography which stand in relation to his life project "dictionary as database", realised shortly before his death. We argue that this project was conceived in the 1950s, while Wahrig was writing and editing dictionaries and encyclopedias for the Bibliographisches Institut in Leipzig. Wahrig, who had been a wireless operator in WWII, was well informed about the development of computers in West Germany. He was influenced both by Ferdinand de Saussure and by the discussion on language and structure in the Soviet Union. When he crossed the German/German border in 1959, he experienced mechanisms of exclusion before he could establish himself in the West as a lexicographer. We argue that the transfer of symbolic and human capital was problematic due to the cultural differences between the two Germanies. In the 1970s, he became a professor of General and Applied Linguistics. The project of a "dictionary as database" was intended both as a basis for extensive empirical research on the semantic structure of natural languages and as a working tool for the average user of the German language. Due to his untimely death, he could not pursue his idea of exploring semantic networks.

Data dictionary services in XNAT and the Human Connectome Project.

PubMed

Herrick, Rick; McKay, Michael; Olsen, Timothy; Horton, William; Florida, Mark; Moore, Charles J; Marcus, Daniel S

2014-01-01

The XNAT informatics platform is an open source data management tool used by biomedical imaging researchers around the world. An important feature of XNAT is its highly extensible architecture: users of XNAT can add new data types to the system to capture the imaging and phenotypic data generated in their studies. Until recently, XNAT has had limited capacity to broadcast the meaning of these data extensions to users, other XNAT installations, and other software. We have implemented a data dictionary service for XNAT, which is currently being used on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website. The data dictionary service provides a framework to define key relationships between data elements and structures across the XNAT installation. This includes not just core data representing medical imaging data or subject or patient evaluations, but also taxonomical structures, security relationships, subject groups, and research protocols. The data dictionary allows users to define metadata for data structures and their properties, such as value types (e.g., textual, integers, floats) and valid value templates, ranges, or field lists. The service provides compatibility and integration with other research data management services by enabling easy migration of XNAT data to standards-based formats such as the Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML). It also facilitates the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures.

Data dictionary services in XNAT and the Human Connectome Project

PubMed Central

Herrick, Rick; McKay, Michael; Olsen, Timothy; Horton, William; Florida, Mark; Moore, Charles J.; Marcus, Daniel S.

2014-01-01

The XNAT informatics platform is an open source data management tool used by biomedical imaging researchers around the world. An important feature of XNAT is its highly extensible architecture: users of XNAT can add new data types to the system to capture the imaging and phenotypic data generated in their studies. Until recently, XNAT has had limited capacity to broadcast the meaning of these data extensions to users, other XNAT installations, and other software. We have implemented a data dictionary service for XNAT, which is currently being used on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website. The data dictionary service provides a framework to define key relationships between data elements and structures across the XNAT installation. This includes not just core data representing medical imaging data or subject or patient evaluations, but also taxonomical structures, security relationships, subject groups, and research protocols. The data dictionary allows users to define metadata for data structures and their properties, such as value types (e.g., textual, integers, floats) and valid value templates, ranges, or field lists. The service provides compatibility and integration with other research data management services by enabling easy migration of XNAT data to standards-based formats such as the Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML). It also facilitates the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures. PMID:25071542

MD-CTS: An integrated terminology reference of clinical and translational medicine.

PubMed

Ray, Will; Finamore, Joe; Rastegar-Mojarad, Majid; Kadolph, Chris; Ye, Zhan; Bohne, Jacquie; Xu, Yin; Burish, Dan; Sondelski, Joshua; Easker, Melissa; Finnegan, Brian; Bartkowiak, Barbara; Smith, Catherine Arnott; Tachinardi, Umberto; Mendonca, Eneida A; Weichelt, Bryan; Lin, Simon M

2016-01-01

New vocabularies are rapidly evolving in the literature relative to the practice of clinical medicine and translational research. To provide integrated access to new terms, we developed a mobile and desktop online reference-Marshfield Dictionary of Clinical and Translational Science (MD-CTS). It is the first public resource that comprehensively integrates Wiktionary (word definition), BioPortal (ontology), Wiki (image reference), and Medline abstract (word usage) information. MD-CTS is accessible at http://spellchecker.mfldclin.edu/. The website provides a broadened capacity for the wider clinical and translational science community to keep pace with newly emerging scientific vocabulary. An initial evaluation using 63 randomly selected biomedical words suggests that online references generally provided better coverage (73%-95%) than paper-based dictionaries (57-71%).

Booksearch: What Dictionary (General or Specialized) Do You Find Useful or Interesting for Students?

ERIC Educational Resources Information Center

English Journal, 1988

1988-01-01

Presents classroom teachers' recommendations for a variety of dictionaries that may heighten students' interest in language: a reverse dictionary, a visual dictionary, WEIGHTY WORD BOOK, a collegiate desk dictionary, OXFORD ENGLISH DICTIONARY, DICTIONARY OF AMERICAN REGIONAL ENGLISH, and a dictionary of idioms. (ARH)

Improving health care professionals' feedback on communication skills: development of an on-line resource.

PubMed

Harrison, Gill; Hayden, Sheila; Cook, Viv; Cushing, Annie

2012-09-01

This project aimed to develop an open-access on-line resource to assist health care professionals in providing effective feedback on patient-centered clinical and communication skills. The collaborative nature of the development of this learning resource is outlined and evaluation of its use is discussed. An inter-professional team of teaching staff from two London Universities employed a researcher to interview experienced clinical and academic health care professionals and gather examples of difficult feedback situations. Material was used to develop short video clips illustrating some common challenges in giving feedback on clinical and communication skills. Initial evaluation following use of the scenarios in workshops was undertaken by means of a "talking wall" technique. Evaluation indicated that the resource enhanced the learning experience by providing realistic and challenging scenarios to focus discussion. Inter-professional working and piloting the use of the video scenarios in workshops enabled the improvement and refinement of an on-line staff development resource on feedback. The on-line resource is now available as an open access learning tool, with eight scenarios and guidelines for providing effective feedback in the academic or clinical setting. It can be used for self-study or as part of a group training session. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

From Afar to Zulu: A Dictionary of African Cultures.

ERIC Educational Resources Information Center

Haskins, Jim; Biondi, Joann

This resource provides information on over 30 of Africa's most populous and well-known ethnic groups. The text concisely describes the history, traditions, environment, social structure, religion, and daily lifestyles of these diverse cultures. Each entry opens with a map outlining the area populated by the group and a list of key data regarding…

Good Planets Are Hard To Find! Ecology Action Workbook and Dictionary.

ERIC Educational Resources Information Center

Bazar, Ronald M.; Dehr, Roma

Even though the condition of the planet is a serious subject, becoming ecologically aware and active can be fun. This workbook provides ecologically conscious-raising activities that children can do at home or with others. A series of worksheets guides students through eight activities including: (1) assessing community resources and environmental…

Spanish Sign in the Americas.

ERIC Educational Resources Information Center

Schein, Jerome D.

1995-01-01

Spanish Sign Language (SSL) is now the second most used sign language. This article introduces resources for the study of SSL, including three SSL dictionaries--two from Argentina and one from Puerto Rico. Differences in SSL between and within the two countries are noted. Implications for deaf educators in North America are drawn. (Author/DB)

Aerospell Supplemental Spell Check File

NASA Technical Reports Server (NTRS)

2000-01-01

Aerospell is a supplemental spell check file that can be used as a resource for researchers, writers, editors, students, and others who compose scientific and technical texts. The file extends the general spell check dictionaries of word processors by adding more than 13,000 words used in a broad range of aerospace and related disciplines.

Computer-Supported Feedback Message Tailoring for Healthcare Providers in Malawi: Proof-of-Concept.

PubMed

Landis-Lewis, Zach; Douglas, Gerald P; Hochheiser, Harry; Kam, Matthew; Gadabu, Oliver; Bwanali, Mwatha; Jacobson, Rebecca S

2015-01-01

Although performance feedback has the potential to help clinicians improve the quality and safety of care, healthcare organizations generally lack knowledge about how this guidance is best provided. In low-resource settings, tools for theory-informed feedback tailoring may enhance limited clinical supervision resources. Our objectives were to establish proof-of-concept for computer-supported feedback message tailoring in Malawi, Africa. We conducted this research in five stages: clinical performance measurement, modeling the influence of feedback on antiretroviral therapy (ART) performance, creating a rule-based message tailoring process, generating tailored messages for recipients, and finally analysis of performance and message tailoring data. We retrospectively generated tailored messages for 7,448 monthly performance reports from 11 ART clinics. We found that tailored feedback could be routinely generated for four guideline-based performance indicators, with 35% of reports having messages prioritized to optimize the effect of feedback. This research establishes proof-of-concept for a novel approach to improving the use of clinical performance feedback in low-resource settings and suggests possible directions for prospective evaluations comparing alternative designs of feedback messages.

Learners' Dictionaries: State of the Art. Anthology Series 23.

ERIC Educational Resources Information Center

Tickoo, Makhan L., Ed.

A collection of articles on dictionaries for advanced second language learners includes essays on the past, present, and future of learners' dictionaries; alternative dictionaries; dictionary construction; and dictionaries and their users. Titles include: "Idle Thoughts of an Idle Fellow; or Vaticinations on the Learners' Dictionary"…

The SMAP Dictionary Management System

NASA Technical Reports Server (NTRS)

Smith, Kevin A.; Swan, Christoper A.

2014-01-01

The Soil Moisture Active Passive (SMAP) Dictionary Management System is a web-based tool to develop and store a mission dictionary. A mission dictionary defines the interface between a ground system and a spacecraft. In recent years, mission dictionaries have grown in size and scope, making it difficult for engineers across multiple disciplines to coordinate the dictionary development effort. The Dictionary Management Systemaddresses these issues by placing all dictionary information in one place, taking advantage of the efficiencies inherent in co-locating what were once disparate dictionary development efforts.

Dictionaries: British and American. The Language Library.

ERIC Educational Resources Information Center

Hulbert, James Root

An account of the dictionaries, great and small, of the English-speaking world is given in this book. Subjects covered include the origin of English dictionaries, early dictionaries, Noah Webster and his successors to the present, abridged dictionaries, "The Oxford English Dictionary" and later dictionaries patterned after it, the…

The semantics of Chemical Markup Language (CML): dictionaries and conventions.

PubMed

Murray-Rust, Peter; Townsend, Joe A; Adams, Sam E; Phadungsukanan, Weerapong; Thomas, Jens

2011-10-14

The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs.

The semantics of Chemical Markup Language (CML): dictionaries and conventions

PubMed Central

2011-01-01

The semantic architecture of CML consists of conventions, dictionaries and units. The conventions conform to a top-level specification and each convention can constrain compliant documents through machine-processing (validation). Dictionaries conform to a dictionary specification which also imposes machine validation on the dictionaries. Each dictionary can also be used to validate data in a CML document, and provide human-readable descriptions. An additional set of conventions and dictionaries are used to support scientific units. All conventions, dictionaries and dictionary elements are identifiable and addressable through unique URIs. PMID:21999509

Nurses' perceptions of feedback to nursing teams on quality measurements: An embedded case study design.

PubMed

Giesbers, A P M Suzanne; Schouteten, Roel L J; Poutsma, Erik; van der Heijden, Beatrice I J M; van Achterberg, Theo

2016-12-01

Providing nursing teams with feedback on quality measurements is used as a quality improvement instrument in healthcare organizations worldwide. Previous research indicated contradictory results regarding the effect of such feedback on both nurses' well-being and performance. Building on the Job Demands-Resources model this study explores: (1) whether and how nurses' perceptions of feedback on quality measurements (as a burdening job demand or rather as an intrinsically or extrinsically motivating job resource) are respectively related to nurses' well-being and performance; and (2) whether and how team reflection influences nurses' perceptions. An embedded case study. Four surgical wards within three different acute teaching-hospital settings in the Netherlands. During a period of four months, the nurses on each ward were provided with similar feedback on quality measurements. After this period, interviews with eight nurses and the ward manager for each ward were conducted. Additionally, observational data were collected from three oral feedback moments on each of the participating wards. The data revealed that individual nurses perceive the same feedback on quality measurements differently, leading to different effects on nurses' well-being and performance: 1) feedback can be perceived as a job demand that pressures nurses to improve the results on the quality measurements; 2) feedback can be perceived as an extrinsically motivating job resource, that is instrumental to improve the results on quality measurements; 3) feedback can be perceived as an intrinsically motivating job resource that stimulates nurses to improve the results on the quality measurements; and 4) feedback can be perceived neither as a job demand, nor as a job resource, and has no effect on nurses' well-being and performance. Additionally, this study indicates that team reflection after feedback seems to be very low in practice, while our data also provides evidence that nursing teams using the feedback to jointly reflect and analyse their performance and strategies will be able to better translate information about quality measurements into corrective behaviours, which may result in more positive perceptions of feedback on quality measurements among individual nurses. To better understand the impact of feedback to nursing teams on quality measurements, we should take nurses' individual perceptions of this feedback into account. Supporting nursing teams in team reflection after them having received feedback on quality measurements may help in eliciting positive perceptions among nurses, and therewith create positive effects of feedback on both their well-being and performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Role of Dictionaries in Language Learning.

ERIC Educational Resources Information Center

White, Philip A.

1997-01-01

Examines assumptions about dictionaries, especially the bilingual dictionary, and suggests ways of integrating the monolingual dictionary into the second-language instructional process. Findings indicate that the monolingual dictionary can coexist with bilingual dictionaries within a foreign-language course if the latter are appropriately used as…

Training L2 Writers to Reference Corpora as a Self-Correction Tool

ERIC Educational Resources Information Center

Quinn, Cynthia

2015-01-01

Corpora have the potential to support the L2 writing process at the discourse level in contrast to the isolated dictionary entries that many intermediate writers rely on. To take advantage of this resource, learners need to be trained, which involves practising corpus research and referencing skills as well as learning to make data-based…

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

PubMed

Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

2014-02-01

This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

Which Dictionary? A Review of the Leading Learners' Dictionaries.

ERIC Educational Resources Information Center

Nesi, Hilary

Three major dictionaries designed for learners of English as a second language are reviewed, their elements and approaches compared and evaluated, their usefulness for different learners discussed, and recommendations for future dictionary improvement made. The dictionaries in question are the "Oxford Advanced Learner's Dictionary," the…

French Dictionaries. Series: Specialised Bibliographies.

ERIC Educational Resources Information Center

Klaar, R. M.

This is a list of French monolingual, French-English and English-French dictionaries available in December 1975. Dictionaries of etymology, phonetics, place names, proper names, and slang are included, as well as dictionaries for children and dictionaries of Belgian, Canadian, and Swiss French. Most other specialized dictionaries, encyclopedias,…

The Anthropocene Generalized: Evolution of Exo-Civilizations and Their Planetary Feedback.

PubMed

Frank, A; Carroll-Nellenback, Jonathan; Alberti, M; Kleidon, A

2018-05-01

We present a framework for studying generic behaviors possible in the interaction between a resource-harvesting technological civilization (an exo-civilization) and the planetary environment in which it evolves. Using methods from dynamical systems theory, we introduce and analyze a suite of simple equations modeling a population which consumes resources for the purpose of running a technological civilization and the feedback those resources drive on the state of the host planet. The feedbacks can drive the planet away from the initial state the civilization originated in and into domains that are detrimental to its sustainability. Our models conceptualize the problem primarily in terms of feedbacks from the resource use onto the coupled planetary systems. In addition, we also model the population growth advantages gained via the harvesting of these resources. We present three models of increasing complexity: (1) Civilization-planetary interaction with a single resource; (2) Civilization-planetary interaction with two resources each of which has a different level of planetary system feedback; (3) Civilization-planetary interaction with two resources and nonlinear planetary feedback (i.e., runaways). All three models show distinct classes of exo-civilization trajectories. We find smooth entries into long-term, "sustainable" steady states. We also find population booms followed by various levels of "die-off." Finally, we also observe rapid "collapse" trajectories for which the population approaches n = 0. Our results are part of a program for developing an "Astrobiology of the Anthropocene" in which questions of sustainability, centered on the coupled Earth-system, can be seen in their proper astronomical/planetary context. We conclude by discussing the implications of our results for both the coupled Earth system and for the consideration of exo-civilizations across cosmic history. Key Words: Anthropocene-Astrobiology-Civilization-Dynamical system theory-Exoplanets-Population dynamics. Astrobiology 18, 503-518.

Which Desk Dictionary Is Best for Foreign Students of English?

ERIC Educational Resources Information Center

Yorkey, Richard

1969-01-01

"The American College Dictionary, "Funk and Wagnalls Standard College Dictionary," Webster's New World Dictionary of the American Language," The Random House Dictionary of the English Language," and Webster's Seventh New Collegiate Dictionary" are analyzed and ranked as to their usefulness for the foreign learner of English. (FWB)

Supporting infobuttons with terminological knowledge.

PubMed Central

Cimino, J. J.; Elhanan, G.; Zeng, Q.

1997-01-01

We have developed several prototype applications which integrate clinical systems with on-line information resources by using patient data to drive queries in response to user information needs. We refer to these collectively as infobuttons because they are evoked with a minimum of keyboard entry. We make use of knowledge in our terminology, the Medical Entities Dictionary (MED) to assist with the selection of appropriate queries and resources, as well as the translation of patient data to forms recognized by the resources. This paper describes the kinds of knowledge in the MED, including literal attributes, hierarchical links and other semantic links, and how this knowledge is used in system integration. PMID:9357682

Supporting infobuttons with terminological knowledge.

PubMed

Cimino, J J; Elhanan, G; Zeng, Q

1997-01-01

We have developed several prototype applications which integrate clinical systems with on-line information resources by using patient data to drive queries in response to user information needs. We refer to these collectively as infobuttons because they are evoked with a minimum of keyboard entry. We make use of knowledge in our terminology, the Medical Entities Dictionary (MED) to assist with the selection of appropriate queries and resources, as well as the translation of patient data to forms recognized by the resources. This paper describes the kinds of knowledge in the MED, including literal attributes, hierarchical links and other semantic links, and how this knowledge is used in system integration.

When Cancer Returns

MedlinePlus

... content 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Coping with Advanced Cancer

MedlinePlus

... content 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Thinking about Complementary and Alternative Medicine

MedlinePlus

... content 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Caring for the Caregiver

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

The Use of Monolingual Mobile Dictionaries in the Context of Reading by Intermediate Cantonese EFL Learners in Hong Kong

ERIC Educational Resources Information Center

Zou, Di; Xie, Haoran; Wang, Fu Lee

2015-01-01

Previous studies on dictionary consultation investigated mainly online dictionaries or simple pocket electronic dictionaries as they were commonly used among learners back then, yet the more updated mobile dictionaries were superficially investigated though they have already replaced the pocket electronic dictionaries. These studies are also…

Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

PubMed Central

Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

2014-01-01

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively. PMID:24871988

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

PubMed

Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

2014-05-27

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

Chemical Entity Recognition and Resolution to ChEBI

PubMed Central

Grego, Tiago; Pesquita, Catia; Bastos, Hugo P.; Couto, Francisco M.

2012-01-01

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2–5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks. PMID:25937941

Cancer Information Summaries: Screening/Detection

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Children with Cancer: A Guide for Parents

MedlinePlus

... content 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Usage Notes in the Oxford American Dictionary.

ERIC Educational Resources Information Center

Berner, R. Thomas

1981-01-01

Compares the "Oxford American Dictionary" with the "American Heritage Dictionary." Examines the dictionaries' differences in philosophies of language, introductory essays, and usage notes. Concludes that the "Oxford American Dictionary" is too conservative, paternalistic, and dogmatic for the 1980s. (DMM)

Treatment Choices for Men with Early-Stage Prostate Cancer

MedlinePlus

... content 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Pain Control: Support for People with Cancer

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Chemotherapy and You: Support for People with Cancer

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Facing Forward Series: Life After Cancer Treatment

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Eating Hints: Before, During, and After Cancer Treatment

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Taking Time: Support for People with Cancer

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Data Management Standards in Computer-aided Acquisition and Logistic Support (CALS)

NASA Technical Reports Server (NTRS)

Jefferson, David K.

1990-01-01

Viewgraphs and discussion on data management standards in computer-aided acquisition and logistic support (CALS) are presented. CALS is intended to reduce cost, increase quality, and improve timeliness of weapon system acquisition and support by greatly improving the flow of technical information. The phase 2 standards, industrial environment, are discussed. The information resource dictionary system (IRDS) is described.

Study of Large Data Resources for Multilingual Training and System Porting (Pub Version, Open Access)

DTIC Science & Technology

2016-05-03

extraction trained on a large database corpus – English Fisher. Although the performance of ported monolingual system would be worse in comparison...Language TE LI HA LA ZU LLP hours 8.6 9.6 7.9 8.1 8.4 LM sentences 11935 10743 9861 11577 10644 LM words 68175 83157 93131 93328 60832 dictionary 14505

Radiation Therapy and You: Support for People with Cancer

MedlinePlus

... Español 1-800-4-CANCER Live Chat Publications Dictionary Menu Contact Dictionary Search About Cancer Causes and Prevention Risk Factors ... Levels of Evidence: Integrative Therapies Fact Sheets NCI Dictionaries NCI Dictionary of Cancer Terms NCI Drug Dictionary ...

Develop feedback system for intelligent dynamic resource allocation to improve application performance.

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

Gentile, Ann C.; Brandt, James M.; Tucker, Thomas

2011-09-01

This report provides documentation for the completion of the Sandia Level II milestone 'Develop feedback system for intelligent dynamic resource allocation to improve application performance'. This milestone demonstrates the use of a scalable data collection analysis and feedback system that enables insight into how an application is utilizing the hardware resources of a high performance computing (HPC) platform in a lightweight fashion. Further we demonstrate utilizing the same mechanisms used for transporting data for remote analysis and visualization to provide low latency run-time feedback to applications. The ultimate goal of this body of work is performance optimization in the facemore » of the ever increasing size and complexity of HPC systems.« less

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

5 CFR 9701.407 - Monitoring performance and providing feedback.

Code of Federal Regulations, 2010 CFR

2010-01-01

... feedback. 9701.407 Section 9701.407 Administrative Personnel DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM (DEPARTMENT OF HOMELAND SECURITY-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF HOMELAND SECURITY HUMAN RESOURCES MANAGEMENT SYSTEM Performance Management § 9701.407 Monitoring...

What Dictionary to Use? A Closer Look at the "Oxford Advanced Learner's Dictionary," the "Longman Dictionary of Contemporary English" and the "Longman Lexicon of Contempory English."

ERIC Educational Resources Information Center

Shaw, A. M.

1983-01-01

Three dictionaries are compared for their usefulness to teachers of English as a foreign language, teachers in training, students, and other users of English as a foreign language. The issue of monolingual versus bilingual dictionary format is discussed, and a previous analysis of the two bilingual dictionaries is summarized. Pronunciation…

DICTIONARIES AND LANGUAGE CHANGE.

ERIC Educational Resources Information Center

POOLEY, ROBERT C.

TWO VIEWS OF A DICTIONARY'S PURPOSE CAME INTO SHARP CONFLICT UPON THE PUBLICATION OF WEBSTER'S "THIRD NEW INTERNATIONAL UNABRIDGED DICTIONARY." THE FIRST VIEW IS THAT A DICTIONARY IS A REFERENCE BOOK ON LANGUAGE ETIQUETTE, AN AUTHORITY FOR MAINTAINING THE PURITY OF THE ENGLISH LANGUAGE. THE SECOND IS THAT A DICTIONARY IS A SCIENTIFIC…

Do Dictionaries Help Students Write?

ERIC Educational Resources Information Center

Nesi, Hilary

Examples are given of real lexical errors made by learner writers, and consideration is given to the way in which three learners' dictionaries could deal with the lexical items that were misused. The dictionaries were the "Oxford Advanced Learner's Dictionary," the "Longman Dictionary of Contemporary English," and the "Chambers Universal Learners'…

Information on Quantifiers and Argument Structure in English Learner's Dictionaries.

ERIC Educational Resources Information Center

Lee, Thomas Hun-tak

1993-01-01

Lexicographers have been arguing for the inclusion of abstract and complex grammatical information in dictionaries. This paper examines the extent to which information about quantifiers and the argument structure of verbs is encoded in English learner's dictionaries. The Oxford Advanced Learner's Dictionary (1989), the Longman Dictionary of…

Students' Understanding of Dictionary Entries: A Study with Respect to Four Learners' Dictionaries.

ERIC Educational Resources Information Center

Jana, Abhra; Amritavalli, Vijaya; Amritavalli, R.

2003-01-01

Investigates the effects of definitional information in the form of dictionary entries, on second language learners' vocabulary learning in an instructed setting. Indian students (Native Hindi speakers) of English received monolingual English dictionary entries of five previously unknown words from four different learner's dictionaries. Results…

Seismic classification through sparse filter dictionaries

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

Hickmann, Kyle Scott; Srinivasan, Gowri

We tackle a multi-label classi cation problem involving the relation between acoustic- pro le features and the measured seismogram. To isolate components of the seismo- grams unique to each class of acoustic pro le we build dictionaries of convolutional lters. The convolutional- lter dictionaries for the individual classes are then combined into a large dictionary for the entire seismogram set. A given seismogram is classi ed by computing its representation in the large dictionary and then comparing reconstruction accuracy with this representation using each of the sub-dictionaries. The sub-dictionary with the minimal reconstruction error identi es the seismogram class.

Adaptive structured dictionary learning for image fusion based on group-sparse-representation

NASA Astrophysics Data System (ADS)

Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

2018-04-01

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Chemical entity recognition in patents by combining dictionary-based and statistical approaches

PubMed Central

Akhondi, Saber A.; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F.H.; Hettne, Kristina M.; van Mulligen, Erik M.; Kors, Jan A.

2016-01-01

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small. Database URL: http://biosemantics.org/chemdner-patents PMID:27141091

Coupled dictionary learning for joint MR image restoration and segmentation

NASA Astrophysics Data System (ADS)

Yang, Xuesong; Fan, Yong

2018-03-01

To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

Tensor Dictionary Learning for Positive Definite Matrices.

PubMed

Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikolaos

2015-11-01

Sparse models have proven to be extremely successful in image processing and computer vision. However, a majority of the effort has been focused on sparse representation of vectors and low-rank models for general matrices. The success of sparse modeling, along with popularity of region covariances, has inspired the development of sparse coding approaches for these positive definite descriptors. While in earlier work, the dictionary was formed from all, or a random subset of, the training signals, it is clearly advantageous to learn a concise dictionary from the entire training set. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between sparse coding and dictionary update stages, and different atom update methods are described. A discriminative version of the dictionary learning approach is also proposed, which simultaneously learns dictionaries for different classes in classification or clustering. Experimental results demonstrate the advantage of learning dictionaries from data both from reconstruction and classification viewpoints. Finally, a software library is presented comprising C++ binaries for all the positive definite sparse coding and dictionary learning approaches presented here.

FRS Download Summary and Data Element Dictionary ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

RCRAInfo Download Summary and Data Element Dictionary ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

Consolidated Environmental Resource Database Information Process (CERDIP)

DTIC Science & Technology

2015-11-19

Secretary of the Army for Installations, Energy and Environment [OASA(IE&E)] ESOH 5850 21st Street, Bldg 211, Second Floor Fort Belvoir, VA 22060-5938...Elizabeth J. Keysar 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) National Defense Center for Energy and Environment Operated by Concurrent...Markup Language NDCEE National Defense Center for Energy and Environment NFDD National Geospatial–Intelligence Agency Feature Data Dictionary

A Participatory Research Approach to develop an Arabic Symbol Dictionary.

PubMed

Draffan, E A; Kadous, Amatullah; Idris, Amal; Banes, David; Zeinoun, Nadine; Wald, Mike; Halabi, Nawar

2015-01-01

The purpose of the Arabic Symbol Dictionary research discussed in this paper, is to provide a resource of culturally, environmentally and linguistically suitable symbols to aid communication and literacy skills. A participatory approach with the use of online social media and a bespoke symbol management system has been established to enhance the process of matching a user based Arabic and English core vocabulary with appropriate imagery. Participants including AAC users, their families, carers, teachers and therapists who have been involved in the research from the outset, collating the vocabularies, debating cultural nuances for symbols and critiquing the design of technologies for selection procedures. The positive reaction of those who have voted on the symbols with requests for early use have justified the iterative nature of the methodologies used for this part of the project. However, constant re-evaluation will be necessary and in depth analysis of all the data received has yet to be completed.

Medical and dermatology dictionaries: an examination of unstructured definitions and a proposal for the future.

PubMed

DeVries, David Todd; Papier, Art; Byrnes, Jennifer; Goldsmith, Lowell A

2004-01-01

Medical dictionaries serve to describe and clarify the term set used by medical professionals. In this commentary, we analyze a representative set of skin disease definitions from 2 prominent medical dictionaries, Stedman's Medical Dictionary and Dorland's Illustrated Medical Dictionary. We find that there is an apparent lack of stylistic standards with regard to content and form. We advocate a new standard form for the definition of medical terminology, a standard to complement the easy-to-read yet unstructured style of the traditional dictionary entry. This new form offers a reproducible structure, paving the way for the development of a computer readable "dictionary" of medical terminology. Such a dictionary offers immediate update capability and a fundamental improvement in the ability to search for relationships between terms.

Data-Dictionary-Editing Program

NASA Technical Reports Server (NTRS)

Cumming, A. P.

1989-01-01

Access to data-dictionary relations and attributes made more convenient. Data Dictionary Editor (DDE) application program provides more convenient read/write access to data-dictionary table ("descriptions table") via data screen using SMARTQUERY function keys. Provides three main advantages: (1) User works with table names and field names rather than with table numbers and field numbers, (2) Provides online access to definitions of data-dictionary keys, and (3) Provides displayed summary list that shows, for each datum, which data-dictionary entries currently exist for any specific relation or attribute. Computer program developed to give developers of data bases more convenient access to the OMNIBASE VAX/IDM data-dictionary relations and attributes.

Dictionary Learning Algorithms for Sparse Representation

PubMed Central

Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.

2010-01-01

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial “25 words or less”), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an over-complete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error). PMID:12590811

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.

Fast Low-Rank Shared Dictionary Learning for Image Classification.

PubMed

Tiep Huu Vu; Monga, Vishal

2017-11-01

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

Fast Low-Rank Shared Dictionary Learning for Image Classification

NASA Astrophysics Data System (ADS)

Vu, Tiep Huu; Monga, Vishal

2017-11-01

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image datasets establish the advantages of our method over state-of-the-art dictionary learning methods.

Emo, love and god: making sense of Urban Dictionary, a crowd-sourced online dictionary.

PubMed

Nguyen, Dong; McGillivray, Barbara; Yasseri, Taha

2018-05-01

The Internet facilitates large-scale collaborative projects and the emergence of Web 2.0 platforms, where producers and consumers of content unify, has drastically changed the information market. On the one hand, the promise of the 'wisdom of the crowd' has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages. On the other hand, the decentralized and often unmonitored environment of such projects may make them susceptible to low-quality content. In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary. We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content. We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns. Urban Dictionary also contains offensive content, but highly offensive content tends to receive lower scores through the dictionary's voting system. The low threshold to include new material in Urban Dictionary enables quick recording of new words and new meanings, but the resulting heterogeneous content can pose challenges in using Urban Dictionary as a source to study language innovation.

A Study of Comparatively Low Achievement Students' Bilingualized Dictionary Use and Their English Learning

ERIC Educational Resources Information Center

Chen, Szu-An

2016-01-01

This study investigates bilingualized dictionary use of Taiwanese university students. It aims to examine EFL learners' overall dictionary use behavior and their perspectives on book dictionary as well as the necessity of advance guidance in using dictionaries. Data was collected through questionnaires and analyzed by SPSS 15.0. Findings indicate…

Using Different Types of Dictionaries for Improving EFL Reading Comprehension and Vocabulary Learning

ERIC Educational Resources Information Center

Alharbi, Majed A.

2016-01-01

This study investigated the effects of monolingual book dictionaries, popup dictionaries, and type-in dictionaries on improving reading comprehension and vocabulary learning in an EFL program. An experimental design involving four groups and a post-test was chosen for the experiment: (1) pop-up dictionary (experimental group 1); (2) type-in…

Students Working with an English Learners' Dictionary on CD-ROM.

ERIC Educational Resources Information Center

Winkler, Birgit

This paper examines the growing literature on pedagogical lexicography and the growing focus on how well the learner uses the dictionary in second language learning. Dictionaries are becoming more user-friendly. This study used the writing task to reveal new insights into how students use a CD-ROM dictionary. It found a lack of dictionary-using…

The Effects of Dictionary Use on the Vocabulary Learning Strategies Used by Language Learners of Spanish.

ERIC Educational Resources Information Center

Hsien-jen, Chin

This study investigated the effects of dictionary use on the vocabulary learning strategies used by intermediate college-level Spanish learners to understand new vocabulary items in a reading test. Participants were randomly assigned to one of three groups: control (without a dictionary), bilingual dictionary (using a Spanish-English dictionary),…

Resilience - A Concept

DTIC Science & Technology

2016-04-05

dictionary ]. Retrieved from http://www.investopedia.com/terms/b/blackbox.asp Bodeau, D., Brtis, J., Graubart, R., & Salwen, J. (2013). Resiliency...techniques for systems-of-systems (Report No. 13-3513). Bedford, MA: The MITRE Corporation. Confidence, (n.d.). In Oxford dictionaries [Online dictionary ...Acquisition, Technology and Logistics. Holistic Strategy Approach. (n.d.). In BusinessDictionary.com [Online business dictionary ]. Retrieved from http

Improving the Incoherence of a Learned Dictionary via Rank Shrinkage.

PubMed

Ubaru, Shashanka; Seghouane, Abd-Krim; Saad, Yousef

2017-01-01

This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach. We establish theoretical results that show that the rank shrinkage step included will reduce the coherence of the dictionary, which is further validated by experimental results. Numerical experiments illustrating the performance of the proposed algorithm in comparison to various other well-known dictionary learning algorithms are also presented.

The Making of the "Oxford English Dictionary."

ERIC Educational Resources Information Center

Winchester, Simon

2003-01-01

Summarizes remarks made to open the Gallaudet University conference on Dictionaries and the Standardization of languages. It concerns the making of what is arguably the world's greatest dictionary, "The Oxford English Dictionary." (VWL)

A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising

NASA Astrophysics Data System (ADS)

Karimi, Davood; Ward, Rabab K.

2016-03-01

Sparse representation of signals in learned overcomplete dictionaries has proven to be a powerful tool with applications in denoising, restoration, compression, reconstruction, and more. Recent research has shown that learned overcomplete dictionaries can lead to better results than analytical dictionaries such as wavelets in almost all image processing applications. However, a major disadvantage of these dictionaries is that their learning and usage is very computationally intensive. In particular, finding the sparse representation of a signal in these dictionaries requires solving an optimization problem that leads to very long computational times, especially in 3D image processing. Moreover, the sparse representation found by greedy algorithms is usually sub-optimal. In this paper, we propose a novel two-level dictionary structure that improves the performance and the speed of standard greedy sparse coding methods. The first (i.e., the top) level in our dictionary is a fixed orthonormal basis, whereas the second level includes the atoms that are learned from the training data. We explain how such a dictionary can be learned from the training data and how the sparse representation of a new signal in this dictionary can be computed. As an application, we use the proposed dictionary structure for removing the noise and artifacts in 3D computed tomography (CT) images. Our experiments with real CT images show that the proposed method achieves results that are comparable with standard dictionary-based methods while substantially reducing the computational time.

Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng.

PubMed

Chen, Qian; Ai, Ni; Liao, Jie; Shao, Xin; Liu, Yufeng; Fan, Xiaohui

2017-01-01

Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. The Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies.

MiDas: Automatic Extraction of a Common Domain of Discourse in Sleep Medicine for Multi-center Data Integration

PubMed Central

Sahoo, Satya S.; Ogbuji, Chimezie; Luo, Lingyun; Dong, Xiao; Cui, Licong; Redline, Susan S.; Zhang, Guo-Qiang

2011-01-01

Clinical studies often use data dictionaries with controlled sets of terms to facilitate data collection, limited interoperability and sharing at a local site. Multi-center retrospective clinical studies require that these data dictionaries, originating from individual participating centers, be harmonized in preparation for the integration of the corresponding clinical research data. Domain ontologies are often used to facilitate multi-center data integration by modeling terms from data dictionaries in a logic-based language, but interoperability among domain ontologies (using automated techniques) is an unresolved issue. Although many upper-level reference ontologies have been proposed to address this challenge, our experience in integrating multi-center sleep medicine data highlights the need for an upper level ontology that models a common set of terms at multiple-levels of abstraction, which is not covered by the existing upper-level ontologies. We introduce a methodology underpinned by a Minimal Domain of Discourse (MiDas) algorithm to automatically extract a minimal common domain of discourse (upper-domain ontology) from an existing domain ontology. Using the Multi-Modality, Multi-Resource Environment for Physiological and Clinical Research (Physio-MIMI) multi-center project in sleep medicine as a use case, we demonstrate the use of MiDas in extracting a minimal domain of discourse for sleep medicine, from Physio-MIMI’s Sleep Domain Ontology (SDO). We then extend the resulting domain of discourse with terms from the data dictionary of the Sleep Heart and Health Study (SHHS) to validate MiDas. To illustrate the wider applicability of MiDas, we automatically extract the respective domains of discourse from 6 sample domain ontologies from the National Center for Biomedical Ontologies (NCBO) and the OBO Foundry. PMID:22195180

MiDas: automatic extraction of a common domain of discourse in sleep medicine for multi-center data integration.

PubMed

Sahoo, Satya S; Ogbuji, Chimezie; Luo, Lingyun; Dong, Xiao; Cui, Licong; Redline, Susan S; Zhang, Guo-Qiang

2011-01-01

Clinical studies often use data dictionaries with controlled sets of terms to facilitate data collection, limited interoperability and sharing at a local site. Multi-center retrospective clinical studies require that these data dictionaries, originating from individual participating centers, be harmonized in preparation for the integration of the corresponding clinical research data. Domain ontologies are often used to facilitate multi-center data integration by modeling terms from data dictionaries in a logic-based language, but interoperability among domain ontologies (using automated techniques) is an unresolved issue. Although many upper-level reference ontologies have been proposed to address this challenge, our experience in integrating multi-center sleep medicine data highlights the need for an upper level ontology that models a common set of terms at multiple-levels of abstraction, which is not covered by the existing upper-level ontologies. We introduce a methodology underpinned by a Minimal Domain of Discourse (MiDas) algorithm to automatically extract a minimal common domain of discourse (upper-domain ontology) from an existing domain ontology. Using the Multi-Modality, Multi-Resource Environment for Physiological and Clinical Research (Physio-MIMI) multi-center project in sleep medicine as a use case, we demonstrate the use of MiDas in extracting a minimal domain of discourse for sleep medicine, from Physio-MIMI's Sleep Domain Ontology (SDO). We then extend the resulting domain of discourse with terms from the data dictionary of the Sleep Heart and Health Study (SHHS) to validate MiDas. To illustrate the wider applicability of MiDas, we automatically extract the respective domains of discourse from 6 sample domain ontologies from the National Center for Biomedical Ontologies (NCBO) and the OBO Foundry.

Dictionary Approaches to Image Compression and Reconstruction

NASA Technical Reports Server (NTRS)

Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

1998-01-01

This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as phi(sub gamma), are discrete time signals, where gamma represents the dictionary index. A dictionary with a collection of these waveforms is typically complete or overcomplete. Given such a dictionary, the goal is to obtain a representation image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

Polarimetric SAR image classification based on discriminative dictionary learning model

NASA Astrophysics Data System (ADS)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Dictionary Approaches to Image Compression and Reconstruction

NASA Technical Reports Server (NTRS)

Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

1998-01-01

This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as lambda, are discrete time signals, where y represents the dictionary index. A dictionary with a collection of these waveforms Is typically complete or over complete. Given such a dictionary, the goal is to obtain a representation Image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

The ABCs of Data Dictionaries

ERIC Educational Resources Information Center

Gould, Tate; Nicholas, Amy; Blandford, William; Ruggiero, Tony; Peters, Mary; Thayer, Sara

2014-01-01

This overview of the basic components of a data dictionary is designed to educate and inform IDEA Part C and Part B 619 state staff about the purpose and benefits of having up-to-date data dictionaries for their data systems. This report discusses the following topics: (1) What Is a Data Dictionary?; (2) Why Is a Data Dictionary Needed and How Can…

Evaluating Online Dictionaries From Faculty Prospective: A Case Study Performed On English Faculty Members At King Saud University--Wadi Aldawaser Branch

ERIC Educational Resources Information Center

Abouserie, Hossam Eldin Mohamed Refaat

2010-01-01

The purpose of this study was to evaluate online dictionaries from faculty prospective. The study tried to obtain in depth information about various forms of dictionaries the faculty used; degree of awareness and accessing online dictionaries; types of online dictionaries accessed; basic features of information provided; major benefits gained…

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

Xu, Q; Han, H; Xing, L

Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, amore » conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning based method.« less

Individual differences in the benefits of feedback for learning.

PubMed

Kelley, Christopher M; McLaughlin, Anne Collins

2012-02-01

Research on learning from feedback has produced ambiguous guidelines for feedback design--some have advocated minimal feedback, whereas others have recommended more extensive feedback that highly supported performance. The objective of the current study was to investigate how individual differences in cognitive resources may predict feedback requirements and resolve previous conflicted findings. Cognitive resources were controlled for by comparing samples from populations with known differences, older and younger adults.To control for task demands, a simple rule-based learning task was created in which participants learned to identify fake Windows pop-ups. Pop-ups were divided into two categories--those that required fluid ability to identify and those that could be identified using crystallized intelligence. In general, results showed participants given higher feedback learned more. However, when analyzed by type of task demand, younger adults performed comparably with both levels of feedback for both cues whereas older adults benefited from increased feedbackfor fluid ability cues but from decreased feedback for crystallized ability cues. One explanation for the current findings is feedback requirements are connected to the cognitive abilities of the learner-those with higher abilities for the type of demands imposed by the task are likely to benefit from reduced feedback. We suggest the following considerations for feedback design: Incorporate learner characteristics and task demands when designing learning support via feedback.

Standardized Representation of Clinical Study Data Dictionaries with CIMI Archetypes.

PubMed

Sharma, Deepak K; Solbrig, Harold R; Prud'hommeaux, Eric; Pathak, Jyotishman; Jiang, Guoqian

2016-01-01

Researchers commonly use a tabular format to describe and represent clinical study data. The lack of standardization of data dictionary's metadata elements presents challenges for their harmonization for similar studies and impedes interoperability outside the local context. We propose that representing data dictionaries in the form of standardized archetypes can help to overcome this problem. The Archetype Modeling Language (AML) as developed by the Clinical Information Modeling Initiative (CIMI) can serve as a common format for the representation of data dictionary models. We mapped three different data dictionaries (identified from dbGAP, PheKB and TCGA) onto AML archetypes by aligning dictionary variable definitions with the AML archetype elements. The near complete alignment of data dictionaries helped map them into valid AML models that captured all data dictionary model metadata. The outcome of the work would help subject matter experts harmonize data models for quality, semantic interoperability and better downstream data integration.

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

PubMed Central

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-01-01

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

PubMed

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-09-22

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

Giving Feedback: Development of Scales for the Mum Effect, Discomfort Giving Feedback, and Feedback Medium Preference

ERIC Educational Resources Information Center

Cox, Susie S.; Marler, Laura E.; Simmering, Marcia J.; Totten, Jeff W.

2011-01-01

Research in organizational behavior and human resources promotes the view that it is critical for managers to provide accurate feedback to employees, yet little research addresses rater tendencies (i.e., the "mum effect") and attitudes that influence how performance feedback is given. Because technology has changed the nature of…

Developing a National-Level Concept Dictionary for EHR Implementations in Kenya.

PubMed

Keny, Aggrey; Wanyee, Steven; Kwaro, Daniel; Mulwa, Edwin; Were, Martin C

2015-01-01

The increasing adoption of Electronic Health Records (EHR) by developing countries comes with the need to develop common terminology standards to assure semantic interoperability. In Kenya, where the Ministry of Health has rolled out an EHR at 646 sites, several challenges have emerged including variable dictionaries across implementations, inability to easily share data across systems, lack of expertise in dictionary management, lack of central coordination and custody of a terminology service, inadequately defined policies and processes, insufficient infrastructure, among others. A Concept Working Group was constituted to address these challenges. The country settled on a common Kenya data dictionary, initially derived as a subset of the Columbia International eHealth Laboratory (CIEL)/Millennium Villages Project (MVP) dictionary. The initial dictionary scope largely focuses on clinical needs. Processes and policies around dictionary management are being guided by the framework developed by Bakhshi-Raiez et al. Technical and infrastructure-based approaches are also underway to streamline workflow for dictionary management and distribution across implementations. Kenya's approach on comprehensive common dictionary can serve as a model for other countries in similar settings.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

PubMed

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

ICIS-Air Download Summary and Data Element Dictionary ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

ICIS-FE&C Download Summary and Data Element Dictionary ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

ICIS-NPDES Limit Summary and Data Element Dictionary ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

Civil Enforcement Case Report Data Dictionary | ECHO | US ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

ICIS-NPDES DMR Summary and Data Element Dictionary ...

EPA Pesticide Factsheets

ECHO, Enforcement and Compliance History Online, provides compliance and enforcement information for approximately 800,000 EPA-regulated facilities nationwide. ECHO includes permit, inspection, violation, enforcement action, and penalty information about facilities regulated under the Clean Air Act (CAA) Stationary Source Program, Clean Water Act (CWA) National Pollutant Elimination Discharge System (NPDES), and/or Resource Conservation and Recovery Act (RCRA). Information also is provided on surrounding demographics when available.

The Text Retrieval Conferences (TRECs)

DTIC Science & Technology

1998-10-01

per- form a monolingual run in the target language to act as a baseline. Thirteen groups participated in the TREC-6 CLIR track. Three major...language; the use of machine-readable bilingual dictionaries or other existing linguistic re- sources; and the use of corpus resources to train or...formance for each method. In general, the best cross- language performance was between 50%-75% as ef- fective as a quality monolingual run. The TREC-7

Specifications for a Federal Information Processing Standard Data Dictionary System

NASA Technical Reports Server (NTRS)

Goldfine, A.

1984-01-01

The development of a software specification that Federal agencies may use in evaluating and selecting data dictionary systems (DDS) is discussed. To supply the flexibility needed by widely different applications and environments in the Federal Government, the Federal Information Processing Standard (FIPS) specifies a core DDS together with an optimal set of modules. The focus and status of the development project are described. Functional specifications for the FIPS DDS are examined for the dictionary, the dictionary schema, and the dictionary processing system. The DDS user interfaces and DDS software interfaces are discussed as well as dictionary administration.

Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

PubMed

Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

2015-07-21

Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer.

PubMed

Li, Zhenni; Ding, Shuxue; Li, Yujie; Yang, Zuyuan; Xie, Shengli; Chen, Wuhui

2018-02-01

Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Emo, love and god: making sense of Urban Dictionary, a crowd-sourced online dictionary

PubMed Central

McGillivray, Barbara

2018-01-01

The Internet facilitates large-scale collaborative projects and the emergence of Web 2.0 platforms, where producers and consumers of content unify, has drastically changed the information market. On the one hand, the promise of the ‘wisdom of the crowd’ has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages. On the other hand, the decentralized and often unmonitored environment of such projects may make them susceptible to low-quality content. In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary. We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content. We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns. Urban Dictionary also contains offensive content, but highly offensive content tends to receive lower scores through the dictionary’s voting system. The low threshold to include new material in Urban Dictionary enables quick recording of new words and new meanings, but the resulting heterogeneous content can pose challenges in using Urban Dictionary as a source to study language innovation. PMID:29892417

Cost Analysis, Evaluation and Feedback. Symposium.

ERIC Educational Resources Information Center

2002

This document contains four papers from a symposium on cost analysis, evaluation, and feedback in human resource development. "Training Evaluation with 360-Degree Feedback" (Froukje A. Jellema) reports on a quasi-experimental study that examined the effectiveness of 360-degree feedback in evaluating the training received by nurses in a…

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Effects of Feedback on Memory Strategies of Younger and Older Adults

PubMed Central

Zhang, Fan; Zhang, Xin; Luo, Meng; Geng, Haiyan

2016-01-01

Existing literature suggests that feedback could effectively reduce false memories in younger adults. However, it is unclear whether memory performance in older adults also might be affected by feedback. The current study tested the hypothesis that older adults can use immediate feedback to adjust their memory strategy, similar to younger adults, but after feedback is removed, older adults may not be able to maintain using the memory strategy. Older adults will display more false memories than younger adults due to a reduction in attentional resources. In Study 1, both younger and older adults adjusted gist processing and item-specific processing biases based on the feedback given (i.e., biased and objective feedback). In Study 2 after the feedback was removed, only younger adults with full attention were able to maintain the feedback-shaped memory strategy; whereas, both younger adults with divided attention and older adults had increased false memories after feedback was removed. The findings suggest that environmental support helps older adults as well as younger adults to adopt a memory strategy that demands high attentional resources, but when the support is removed, older adults can no longer maintain such a strategy. PMID:28033327

The Effects of Feedback on Memory Strategies of Younger and Older Adults.

PubMed

Zhang, Fan; Zhang, Xin; Luo, Meng; Geng, Haiyan

2016-01-01

Existing literature suggests that feedback could effectively reduce false memories in younger adults. However, it is unclear whether memory performance in older adults also might be affected by feedback. The current study tested the hypothesis that older adults can use immediate feedback to adjust their memory strategy, similar to younger adults, but after feedback is removed, older adults may not be able to maintain using the memory strategy. Older adults will display more false memories than younger adults due to a reduction in attentional resources. In Study 1, both younger and older adults adjusted gist processing and item-specific processing biases based on the feedback given (i.e., biased and objective feedback). In Study 2 after the feedback was removed, only younger adults with full attention were able to maintain the feedback-shaped memory strategy; whereas, both younger adults with divided attention and older adults had increased false memories after feedback was removed. The findings suggest that environmental support helps older adults as well as younger adults to adopt a memory strategy that demands high attentional resources, but when the support is removed, older adults can no longer maintain such a strategy.

The efficacy of dictionary use while reading for learning new words.

PubMed

Hamilton, Harley

2012-01-01

The researcher investigated the use of three types of dictionaries while reading by high school students with severe to profound hearing loss. The objective of the study was to determine the effectiveness of each type of dictionary for acquiring the meanings of unknown vocabulary in text. The three types of dictionaries were (a) an online bilingual multimedia English-American Sign Language (ASL) dictionary (OBMEAD), (b) a paper English-ASL dictionary (PBEAD), and (c) an online monolingual English dictionary (OMED). It was found that for immediate recall of target words, the OBMEAD was superior to both the PBEAD and the OMED. For later recall, no significant difference appeared between the OBMEAD and the PBEAD. For both of these, recall was statistically superior to recall for words learned via the OMED.

What Online Traditional Medicine Dictionaries Bring To English Speakers Now? Concepts or Equivalents?

PubMed Central

Fang, Lu

2018-01-01

Nowadays, more and more Chinese medicine practices are applied in the world and popularizing that becomes an urgent task. To meet the requiremets, an increasing number of Chinese - English traditional medicine dictionaries have been produced at home or abroad in recent decades. Nevertheless, the users are still struggling to spot the information in dictionaries. What traditional medicine dictionaries are needed for the English speakers now? To identify an entry model for online TCM dictionaries, I compared the entries in five printed traditional medicine dictionaries and two online ones. Based upon this, I tentatively put forward two samples, “阳经 (yángjīng)” and “阴经 (yīn jīng)”, focusing on concepts transmitting, for online Chinese - English TCM dictionaries. PMID:29875861

Gapped Spectral Dictionaries and Their Applications for Database Searches of Tandem Mass Spectra*

PubMed Central

Jeong, Kyowon; Kim, Sangtae; Bandeira, Nuno; Pevzner, Pavel A.

2011-01-01

Generating all plausible de novo interpretations of a peptide tandem mass (MS/MS) spectrum (Spectral Dictionary) and quickly matching them against the database represent a recently emerged alternative approach to peptide identification. However, the sizes of the Spectral Dictionaries quickly grow with the peptide length making their generation impractical for long peptides. We introduce Gapped Spectral Dictionaries (all plausible de novo interpretations with gaps) that can be easily generated for any peptide length thus addressing the limitation of the Spectral Dictionary approach. We show that Gapped Spectral Dictionaries are small thus opening a possibility of using them to speed-up MS/MS searches. Our MS-GappedDictionary algorithm (based on Gapped Spectral Dictionaries) enables proteogenomics applications (such as searches in the six-frame translation of the human genome) that are prohibitively time consuming with existing approaches. MS-GappedDictionary generates gapped peptides that occupy a niche between accurate but short peptide sequence tags and long but inaccurate full length peptide reconstructions. We show that, contrary to conventional wisdom, some high-quality spectra do not have good peptide sequence tags and introduce gapped tags that have advantages over the conventional peptide sequence tags in MS/MS database searches. PMID:21444829

Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

PubMed

Akhondi, Saber A; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F H; Hettne, Kristina M; van Mulligen, Erik M; Kors, Jan A

2016-01-01

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents. © The Author(s) 2016. Published by Oxford University Press.

Debriefing Can Reduce Misperceptions of Feedback: The Case of Renewable Resource Management

ERIC Educational Resources Information Center

Qudrat-Ullah, Hassan

2007-01-01

According to the hypothesis of misperception of feedback, people's poor performance in renewable resource management tasks can be attributed to their general tendency to systematically misperceive the dynamics of bioeconomic systems. The thesis of this article is that dynamic decision performance can be improved by helping individuals develop more…

Chinese-English Nuclear and Physics Dictionary.

ERIC Educational Resources Information Center

Air Force Systems Command, Wright-Patterson AFB, OH. Foreign Technology Div.

The Nuclear and Physics Dictionary is one of a series of Chinese-English technical dictionaries prepared by the Foreign Technology Division, United States Air Force Systems Command. The purpose of this dictionary is to provide rapid reference tools for translators, abstractors, and research analysts concerned with scientific and technical…

Mandarin Chinese Dictionary: English-Chinese.

ERIC Educational Resources Information Center

Wang, Fred Fangyu

This dictionary is a companion volume to the "Mandarin Chinese Dictionary (Chinese-English)" published in 1967 by Seton Hall University. The purpose of the dictionary is to help English-speaking students produce Chinese sentences in certain cultural situations by looking up the English expressions. Natural, spoken Chinese expressions within the…

Intertwining thesauri and dictionaries

NASA Technical Reports Server (NTRS)

Buchan, R. L.

1989-01-01

The use of dictionaries and thesauri in information retrieval is discussed. The structure and functions of thesauri and dictionaries are described. Particular attention is given to the format of the NASA Thesaurus. The relationship between thesauri and dictionaries, the need to regularize terminology, and the capitalization of words are examined.

MEANING DISCRIMINATION IN BILINGUAL DICTIONARIES.

ERIC Educational Resources Information Center

IANNUCCI, JAMES E.

SEMANTIC DISCRIMINATION OF POLYSEMOUS ENTRY WORDS IN BILINGUAL DICTIONARIES WAS DISCUSSED IN THE PAPER. HANDICAPS OF PRESENT BILINGUAL DICTIONARIES AND BARRIERS TO THEIR FULL UTILIZATION WERE ENUMERATED. THE AUTHOR CONCLUDED THAT (1) A BILINGUAL DICTIONARY SHOULD HAVE A DISCRIMINATION FOR EVERY TRANSLATION OF AN ENTRY WORD WHICH HAS SEVERAL…

The Use of Hyper-Reference and Conventional Dictionaries.

ERIC Educational Resources Information Center

Aust, Ronald; And Others

1993-01-01

Describes a study of 80 undergraduate foreign language learners that compared the use of a hyper-reference source incorporating an electronic dictionary and a conventional paper dictionary. Measures of consultation frequency, study time, efficiency, and comprehension are examined; bilingual and monolingual dictionary use is compared; and further…

TSAR (Theater Simulation of Airbase Resources) Database Dictionary F-4G.

DTIC Science & Technology

1987-06-05

REC/TRANS RT-1159 388 71ZDO CNTL UNIT C- 10062 /A 391 71320 CNTL ARN-127 393 723A0 REC/TRANS RT-689 394 723B0 INDIC, HEIGHT TOTAL NUMBER OF PART REPAIR...PROCEDURES - 2 LOU " 4"p" IW qg t~l pb i.go stanc E lm FIGURE 98 111-260 S.. RESOURCE REQUIREMENTS 111.1.5.25 LIU’S #45 - #49 - *LRU PART TIME...RT-1159 AG 386 71ZBO ADAPTER MX9577 AG 387 71ZCO MOUNT (REC/TRANS) AG 388 71ZDO CNTL UNIT C- 10062 /A AG 389 71ZEO MOUNT (DIG TO ANALOG CONVERTER) AG

The Impact of Feedback Frequency on Learning and Task Performance: Challenging the "More Is Better" Assumption

ERIC Educational Resources Information Center

Lam, Chak Fu; DeRue, D. Scott; Karam, Elizabeth P.; Hollenbeck, John R.

2011-01-01

Previous research on feedback frequency suggests that more frequent feedback improves learning and task performance (Salmoni, Schmidt, & Walter, 1984). Drawing from resource allocation theory (Kanfer & Ackerman, 1989), we challenge the "more is better" assumption and propose that frequent feedback can overwhelm an individual's cognitive resource…

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

The Oxford English Dictionary: A Brief History.

ERIC Educational Resources Information Center

Fritze, Ronald H.

1989-01-01

Reviews the development of English dictionaries in general and the Oxford English Dictionary (OED) in particular. The discussion covers the decision by the Philological Society to create the dictionary, the principles that guided its development, the involvement of James Augustus Henry Murray, the magnitude and progress of the project, and the…

Dictionary Making: A Case of Kiswahili Dictionaries.

ERIC Educational Resources Information Center

Mohamed, Mohamed A.

Two Swahili dictionaries and two bilingual dictionaries by the same author (one English-Swahili and one Swahili-English) are evaluated for their form and content, with illustrations offered from each. Aspects examined include: the compilation of headwords, including their meanings with relation to basic and extended meanings; treatment of…

Buying and Selling Words: What Every Good Librarian Should Know about the Dictionary Business.

ERIC Educational Resources Information Center

Kister, Ken

1993-01-01

Discusses features to consider when selecting dictionaries. Topics addressed include the publishing industry; the dictionary market; profits from dictionaries; pricing; competitive marketing tactics, including similar titles, claims to numbers of entries and numbers of definitions, and similar physical appearance; a trademark infringement case;…

The New Unabridged English-Persian Dictionary.

ERIC Educational Resources Information Center

Aryanpur, Abbas; Saleh, Jahan Shah

This five-volume English-Persian dictionary is based on Webster's International Dictionary (1960 and 1961) and The Shorter Oxford English Dictionary (1959); it attempts to provide Persian equivalents of all the words of Oxford and all the key-words of Webster. Pronunciation keys for the English phonetic transcription and for the difficult Persian…

Evaluating L2 Readers' Vocabulary Strategies and Dictionary Use

ERIC Educational Resources Information Center

Prichard, Caleb

2008-01-01

A review of the relevant literature concerning second language dictionary use while reading suggests that selective dictionary use may lead to improved comprehension and efficient vocabulary development. This study aims to examine the dictionary use of Japanese university students to determine just how selective they are when reading nonfiction…

Online English-English Learner Dictionaries Boost Word Learning

ERIC Educational Resources Information Center

Nurmukhamedov, Ulugbek

2012-01-01

Learners of English might be familiar with several online monolingual dictionaries that are not necessarily the best choices for the English as Second/Foreign Language (ESL/EFL) context. Although these monolingual online dictionaries contain definitions, pronunciation guides, and other elements normally found in general-use dictionaries, they are…

Research Timeline: Dictionary Use by English Language Learners

ERIC Educational Resources Information Center

Nesi, Hilary

2014-01-01

The history of research into dictionary use tends to be characterised by small-scale studies undertaken in a variety of different contexts, rather than larger-scale, longer-term funded projects. The research conducted by dictionary publishers is not generally made public, because of its commercial sensitivity, yet because dictionary production is…

The Dictionary and Vocabulary Behavior: A Single Word or a Handful?

ERIC Educational Resources Information Center

Baxter, James

1980-01-01

To provide a context for dictionary selection, the vocabulary behavior of students is examined. Distinguishing between written and spoken English, the relation between dictionary use, classroom vocabulary behavior, and students' success in meeting their communicative needs is discussed. The choice of a monolingual English learners' dictionary is…

UMass at TREC 2002: Cross Language and Novelty Tracks

DTIC Science & Technology

2002-01-01

resources – stemmers, dictionaries , machine translation, and an acronym database. We found that proper names were extremely important in this year’s queries...data by manually annotating 48 additional topics. 1. Cross Language Track We submitted one monolingual run and four cross-language runs. For the... monolingual run, the technology was essentially the same as the system we used for TREC 2001. For the cross-language run, we integrated some new

Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. Copyright © 2013 Elsevier B.V. All rights reserved.

Standardized Representation of Clinical Study Data Dictionaries with CIMI Archetypes

PubMed Central

Sharma, Deepak K.; Solbrig, Harold R.; Prud’hommeaux, Eric; Pathak, Jyotishman; Jiang, Guoqian

2016-01-01

Researchers commonly use a tabular format to describe and represent clinical study data. The lack of standardization of data dictionary’s metadata elements presents challenges for their harmonization for similar studies and impedes interoperability outside the local context. We propose that representing data dictionaries in the form of standardized archetypes can help to overcome this problem. The Archetype Modeling Language (AML) as developed by the Clinical Information Modeling Initiative (CIMI) can serve as a common format for the representation of data dictionary models. We mapped three different data dictionaries (identified from dbGAP, PheKB and TCGA) onto AML archetypes by aligning dictionary variable definitions with the AML archetype elements. The near complete alignment of data dictionaries helped map them into valid AML models that captured all data dictionary model metadata. The outcome of the work would help subject matter experts harmonize data models for quality, semantic interoperability and better downstream data integration. PMID:28269909

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

NHEXAS PHASE I ARIZONA STUDY--STANDARD OPERATING PROCEDURE FOR THE GENERATION AND OPERATION OF DATA DICTIONARIES (UA-D-4.0)

EPA Science Inventory

The purpose of this SOP is to provide a standard method for the writing of data dictionaries. This procedure applies to the dictionaries used during the Arizona NHEXAS project and the "Border" study. Keywords: guidelines; data dictionaries.

The National Human Exposure Assessme...

Should Dictionaries Be Used in Translation Tests and Examinations?

ERIC Educational Resources Information Center

Mahmoud, Abdulmoneim

2017-01-01

Motivated by the conflicting views regarding the use of the dictionary in translation tests and examinations this study was intended to verify the dictionary-free vs dictionary-based translation hypotheses. The subjects were 135 Arabic-speaking male and female EFL third-year university students. A group consisting of 62 students translated a text…

Corpora and Collocations in Chinese-English Dictionaries for Chinese Users

ERIC Educational Resources Information Center

Xia, Lixin

2015-01-01

The paper identifies the major problems of the Chinese-English dictionary in representing collocational information after an extensive survey of nine dictionaries popular among Chinese users. It is found that the Chinese-English dictionary only provides the collocation types of "v+n" and "v+n," but completely ignores those of…

The Creation of Learner-Centred Dictionaries for Endangered Languages: A Rotuman Example

ERIC Educational Resources Information Center

Vamarasi, M.

2014-01-01

This article examines the creation of dictionaries for endangered languages (ELs). Though each dictionary is uniquely prepared for its users, all dictionaries should be based on sound principles of vocabulary learning, including the importance of lexical chunks, as emphasised by Michael Lewis in his "Lexical Approach." Many of the…

Marks, Spaces and Boundaries: Punctuation (and Other Effects) in the Typography of Dictionaries

ERIC Educational Resources Information Center

Luna, Paul

2011-01-01

Dictionary compilers and designers use punctuation to structure and clarify entries and to encode information. Dictionaries with a relatively simple structure can have simple typography and simple punctuation; as dictionaries grew more complex, and encountered the space constraints of the printed page, complex encoding systems were developed,…

Evaluating Bilingual and Monolingual Dictionaries for L2 Learners.

ERIC Educational Resources Information Center

Hunt, Alan

1997-01-01

A discussion of dictionaries and their use for second language (L2) learning suggests that lack of computerized modern language corpora can adversely affect bilingual dictionaries, commonly used by L2 learners, and shows how use of such corpora has benefitted two contemporary monolingual L2 learner dictionaries (1995 editions of the Longman…

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

PubMed

Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang

2017-07-01

It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

PubMed

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems.

PubMed

Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-12-01

The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction.

Cross-View Action Recognition via Transferable Dictionary Learning.

PubMed

Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

2016-05-01

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.

PubMed

Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD's robustness and wide applicability.

Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

PubMed Central

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality. PMID:25961715

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems

PubMed Central

Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A.

2017-01-01

The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction. PMID:29376111

Online multi-modal robust non-negative dictionary learning for visual tracking.

PubMed

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning.

PubMed

Song, Ying; Zhu, Zhen; Lu, Yang; Liu, Qiegen; Zhao, Jun

2014-03-01

To improve the magnetic resonance imaging (MRI) data acquisition speed while maintaining the reconstruction quality, a novel method is proposed for multislice MRI reconstruction from undersampled k-space data based on compressed-sensing theory using dictionary learning. There are two aspects to improve the reconstruction quality. One is that spatial correlation among slices is used by extending the atoms in dictionary learning from patches to blocks. The other is that the dictionary-learning scheme is used at two resolution levels; i.e., a low-resolution dictionary is used for sparse coding and a high-resolution dictionary is used for image updating. Numerical experiments are carried out on in vivo 3D MR images of brains and abdomens with a variety of undersampling schemes and ratios. The proposed method (dual-DLMRI) achieves better reconstruction quality than conventional reconstruction methods, with the peak signal-to-noise ratio being 7 dB higher. The advantages of the dual dictionaries are obvious compared with the single dictionary. Parameter variations ranging from 50% to 200% only bias the image quality within 15% in terms of the peak signal-to-noise ratio. Dual-DLMRI effectively uses the a priori information in the dual-dictionary scheme and provides dramatically improved reconstruction quality. Copyright © 2013 Wiley Periodicals, Inc.

Developing a Cardiac Rehabilitation Education Resource for Rural Health Workers in Queensland: Reviewing the Process and Outcomes.

ERIC Educational Resources Information Center

Parker, Elizabeth; O'Connor-Fleming, M.; Tooth, L.; Humphries, R. J.

2002-01-01

A resource manual on cardiac rehabilitation education was developed for health workers providing patient education in rural and remote Queensland (Australia). A process evaluation of the manual received feedback from 5 key informants and 31 rural health care workers following a 3-month trial. Overall, feedback was positive. Recommended content…

The Effects of Expectancy-Incongruent Feedback and Self-Affirmation on Task Performance of Secondary School Students

ERIC Educational Resources Information Center

Baadte, Christiane; Kurenbach, Friederike

2017-01-01

In the present study, the assumption was tested that expectancy-incongruent feedback in conjunction with explicit self-affirmation directs attention away from the task and to the self. As a result, performance should decrease in resource-sensitive text/picture comprehension tasks as compared to resource-insensitive tasks. Three hundred and…

Alternatively Constrained Dictionary Learning For Image Superresolution.

PubMed

Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun

2014-03-01

Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to simultaneously consider the geometrical structure of the dictionary and the corresponding coefficients, which may result in noticeable superresolution reconstruction artifacts. In other words, when a low-resolution image and its corresponding high-resolution image are represented in their feature spaces, the two sets of dictionaries and the obtained coefficients have intrinsic links, which has not yet been well studied. Motivated by the development on nonlocal self-similarity and manifold learning, a novel sparse coding method is reported to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries and provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Furthermore, to utilize the model of the proposed method more effectively for single-image superresolution, this paper also proposes a novel dictionary-pair learning method, which is named as two-stage dictionary training. Extensive experiments are carried out on a large set of images comparing with other popular algorithms for the same purpose, and the results clearly demonstrate the effectiveness of the proposed sparse representation model and the corresponding dictionary learning algorithm.

Risk assessment and adaptive runoff utilization in water resource system considering the complex relationship among water supply, electricity generation and environment

NASA Astrophysics Data System (ADS)

Zhou, J.; Zeng, X.; Mo, L.; Chen, L.; Jiang, Z.; Feng, Z.; Yuan, L.; He, Z.

2017-12-01

Generally, the adaptive utilization and regulation of runoff in the source region of China's southwest rivers is classified as a typical multi-objective collaborative optimization problem. There are grim competitions and incidence relation in the subsystems of water supply, electricity generation and environment, which leads to a series of complex problems represented by hydrological process variation, blocked electricity output and water environment risk. Mathematically, the difficulties of multi-objective collaborative optimization focus on the description of reciprocal relationships and the establishment of evolving model of adaptive systems. Thus, based on the theory of complex systems science, this project tries to carry out the research from the following aspects: the changing trend of coupled water resource, the covariant factor and driving mechanism, the dynamic evolution law of mutual feedback dynamic process in the supply-generation-environment coupled system, the environmental response and influence mechanism of coupled mutual feedback water resource system, the relationship between leading risk factor and multiple risk based on evolutionary stability and dynamic balance, the transfer mechanism of multiple risk response with the variation of the leading risk factor, the multidimensional coupled feedback system of multiple risk assessment index system and optimized decision theory. Based on the above-mentioned research results, the dynamic method balancing the efficiency of multiple objectives in the coupled feedback system and optimized regulation model of water resources is proposed, and the adaptive scheduling mode considering the internal characteristics and external response of coupled mutual feedback system of water resource is established. In this way, the project can make a contribution to the optimal scheduling theory and methodology of water resource management under uncertainty in the source region of Southwest River.

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

What Do Students Want Most from Written Feedback Information? Distinguishing Necessities from Luxuries Using a Budgeting Methodology

ERIC Educational Resources Information Center

Winstone, Naomi E.; Nash, Robert A.; Rowntree, James; Menezes, Richard

2016-01-01

Feedback is a key concern for higher education practitioners, yet there is little evidence concerning the aspects of assessment feedback information that higher education students prioritise when their lecturers' time and resources are stretched. One recent study found that, in such circumstances, students actually perceive feedback information…

Using Bilingual Dictionaries.

ERIC Educational Resources Information Center

Thompson, Geoff

1987-01-01

Monolingual dictionaries have serious disadvantages in many language teaching situations; bilingual dictionaries are potentially more efficient and more motivating sources of information for language learners. (Author/CB)

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

Blind Linguistic Steganalysis against Translation Based Steganography

NASA Astrophysics Data System (ADS)

Chen, Zhili; Huang, Liusheng; Meng, Peng; Yang, Wei; Miao, Haibo

Translation based steganography (TBS) is a kind of relatively new and secure linguistic steganography. It takes advantage of the "noise" created by automatic translation of natural language text to encode the secret information. Up to date, there is little research on the steganalysis against this kind of linguistic steganography. In this paper, a blind steganalytic method, which is named natural frequency zoned word distribution analysis (NFZ-WDA), is presented. This method has improved on a previously proposed linguistic steganalysis method based on word distribution which is targeted for the detection of linguistic steganography like nicetext and texto. The new method aims to detect the application of TBS and uses none of the related information about TBS, its only used resource is a word frequency dictionary obtained from a large corpus, or a so called natural frequency dictionary, so it is totally blind. To verify the effectiveness of NFZ-WDA, two experiments with two-class and multi-class SVM classifiers respectively are carried out. The experimental results show that the steganalytic method is pretty promising.

Grammar Coding in the "Oxford Advanced Learner's Dictionary of Current English."

ERIC Educational Resources Information Center

Wekker, Herman

1992-01-01

Focuses on the revised system of grammar coding for verbs in the fourth edition of the "Oxford Advanced Learner's Dictionary of Current English" (OALD4), comparing it with two other similar dictionaries. It is shown that the OALD4 is found to be more favorable on many criteria than the other comparable dictionaries. (16 references) (VWL)

A Study on the Use of Mobile Dictionaries in Vocabulary Teaching

ERIC Educational Resources Information Center

Aslan, Erdinç

2016-01-01

In recent years, rapid developments in technology have placed books and notebooks into the mobile phones and tablets and also the dictionaries into these small boxes. Giant dictionaries, which we once barely managed to carry, have been replaced by mobile dictionaries through which we can reach any words we want with only few touches. Mobile…

Letters to a Dictionary: Competing Views of Language in the Reception of "Webster's Third New International Dictionary"

ERIC Educational Resources Information Center

Bello, Anne Pence

2013-01-01

The publication of "Webster's Third New International Dictionary" in September 1961 set off a national controversy about dictionaries and language that ultimately included issues related to linguistics and English education. The negative reviews published in the press about the "Third" have shaped beliefs about the nature of…

Effects of Printed, Pocket Electronic, and Online Dictionaries on High School Students' English Vocabulary Retention

ERIC Educational Resources Information Center

Chiu, Li-Ling; Liu, Gi-Zen

2013-01-01

This study obtained empirical evidence regarding the effects of using printed dictionaries (PD), pocket electronic dictionaries (PED), and online type-in dictionaries (OTID) on English vocabulary retention at a junior high school. A mixed-methods research methodology was adopted in this study. Thirty-three seventh graders were asked to use all…

The Efficacy of Dictionary Use while Reading for Learning New Words

ERIC Educational Resources Information Center

Hamilton, Harley

2012-01-01

The researcher investigated the use of three types of dictionaries while reading by high school students with severe to profound hearing loss. The objective of the study was to determine the effectiveness of each type of dictionary for acquiring the meanings of unknown vocabulary in text. The three types of dictionaries were (a) an online…

A Selected Bibliography of Dictionaries. General Information Series, No. 9. Indochinese Refugee Education Guides. Revised.

ERIC Educational Resources Information Center

Center for Applied Linguistics, Arlington, VA.

The purpose of this bulletin is to provide the American teacher or sponsor with information on the use, limitations and availability of dictionaries that can be used by Indochinese refugees. The introductory material contains descriptions of both monolingual and bilingual dictionaries, a discussion of the inadequacies of bilingual dictionaries in…

Dictionaries Can Help Writing--If Students Know How To Use Them.

ERIC Educational Resources Information Center

Jacobs, George M.

A study investigated whether instruction in how to use a dictionary led to improved second language performance and greater dictionary use among English majors (N=54) in a reading and writing course at a Thai university. One of three participating classes was instructed in the use of a monolingual learner's dictionary. A passage correction test…

Dictionary-Based Tensor Canonical Polyadic Decomposition

NASA Astrophysics Data System (ADS)

Cohen, Jeremy Emile; Gillis, Nicolas

2018-04-01

To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.

Data dictionaries in information systems - Standards, usage , and application

NASA Technical Reports Server (NTRS)

Johnson, Margaret

1990-01-01

An overview of data dictionary systems and the role of standardization in the interchange of data dictionaries is presented. The development of the data dictionary for the Planetary Data System is cited as an example. The data element dictionary (DED), which is the repository of the definitions of the vocabulary utilized in an information system, is an important part of this service. A DED provides the definitions of the fields of the data set as well as the data elements of the catalog system. Finally, international efforts such as the Consultative Committee on Space Data Systems and other committees set up to provide standard recommendations on the usage and structure of data dictionaries in the international space science community are discussed.

Incorporating E-learning in teaching English language to medical students: exploring its potential contributions

PubMed Central

Navidinia, Hossein; Zare Bidaki, Majid; Hekmati, Nargess

2016-01-01

Background: The spread of technology has influenced different aspects of human life, and teaching and learning are not exceptions. This study aimed to examine the potential contribution of the use of technology in teaching English language to medical students. Methods: This qualitative-action research study was conducted in Birjand University of Medical Sciences (BUMS), with 60 medical students taking a general English course in the Fall Semester of 2015. The class favored different tools and multimedia facilities such as a tube channel, e-dictionaries, educational films, and etextbooks to enhance students’ learning. In addition, the class had a weblog in which students could upload assignments and receive feedback from peers and the instructors. Results: The results revealed that e-learning could enhance students’ language proficiency and facilitate the teaching process. Learners preferred to use more e-dictionaries to learn the meaning of the new words, watch English medical films to boost their speaking and listening skills, and use the electronic version of their textbook as they could carry it wherever they wanted. Conclusion: The students preferred this method of learning English as they became more independent by using the electronic facilities. They found that learning English did not have a fixed institutionalized method, and e-learning activities could provide them with authentic input for language learning even outside of the classroom. PMID:28491837

Incorporating E-learning in teaching English language to medical students: exploring its potential contributions.

PubMed

Navidinia, Hossein; Zare Bidaki, Majid; Hekmati, Nargess

2016-01-01

Background: The spread of technology has influenced different aspects of human life, and teaching and learning are not exceptions. This study aimed to examine the potential contribution of the use of technology in teaching English language to medical students. Methods: This qualitative-action research study was conducted in Birjand University of Medical Sciences (BUMS), with 60 medical students taking a general English course in the Fall Semester of 2015. The class favored different tools and multimedia facilities such as a tube channel, e-dictionaries, educational films, and etextbooks to enhance students' learning. In addition, the class had a weblog in which students could upload assignments and receive feedback from peers and the instructors. Results: The results revealed that e-learning could enhance students' language proficiency and facilitate the teaching process. Learners preferred to use more e-dictionaries to learn the meaning of the new words, watch English medical films to boost their speaking and listening skills, and use the electronic version of their textbook as they could carry it wherever they wanted. Conclusion: The students preferred this method of learning English as they became more independent by using the electronic facilities. They found that learning English did not have a fixed institutionalized method, and e-learning activities could provide them with authentic input for language learning even outside of the classroom.

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

PubMed Central

Grossi, Giuliano; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability. PMID:28103283

Personalized Age Progression with Bi-Level Aging Dictionary Learning.

PubMed

Shu, Xiangbo; Tang, Jinhui; Li, Zechao; Lai, Hanjiang; Zhang, Liyan; Yan, Shuicheng

2018-04-01

Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g., mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.

The Impact of Disciplinary Background and Teaching Experience on the Use of Evaluative Language in Teacher Feedback

ERIC Educational Resources Information Center

Hu, Guangwei; Choo, Lilin

2016-01-01

This study was designed to examine secondary teachers' use of evaluative language resources in their qualitative written feedback on student work and factors shaping the deployment of such resources. Drawing on appraisal theory as an analytic framework for the language of evaluation, the study analyzed 84 teachers' evaluative reports on their…

U.S.-MEXICO BORDER PROGRAM ARIZONA BORDER STUDY--STANDARD OPERATING PROCEDURE FOR THE GENERATION AND OPERATION OF DATA DICTIONARIES (UA-D-4.0)

EPA Science Inventory

The purpose of this SOP is to provide a standard method for the writing of data dictionaries. This procedure applies to the dictionaries used during the Arizona NHEXAS project and the Border study. Keywords: guidelines; data dictionaries.

The U.S.-Mexico Border Program is spon...

Multimodal Task-Driven Dictionary Learning for Image Classification

DTIC Science & Technology

2015-12-18

1 Multimodal Task-Driven Dictionary Learning for Image Classification Soheil Bahrampour, Student Member, IEEE, Nasser M. Nasrabadi, Fellow, IEEE...Asok Ray, Fellow, IEEE, and W. Kenneth Jenkins, Life Fellow, IEEE Abstract— Dictionary learning algorithms have been suc- cessfully used for both...reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are

A Study of the Relationship between Type of Dictionary Used and Lexical Proficiency in Writings of Iranian EFL Students

ERIC Educational Resources Information Center

Vahdany, Fereidoon; Abdollahzadeh, Milad; Gholami, Shokoufeh; Ghanipoor, Mahmood

2014-01-01

This study aimed at investigating the relationship between types of dictionaries used and lexical proficiency in writing. Eighty TOEFL students took part in responding to two Questionnaires collecting information about their dictionary type preferences and habits of dictionary use, along with an interview for further in-depth responses. They were…

English-Chinese Cross-Language IR Using Bilingual Dictionaries

DTIC Science & Technology

2006-01-01

specialized dictionaries together contain about two million entries [6]. 4 Monolingual Experiment The Chinese documents and the Chinese translations of... monolingual performance. The main performance-limiting factor is the limited coverage of the dictionary used in query translation. Some of the key con...English-Chinese Cross-Language IR using Bilingual Dictionaries Aitao Chen , Hailing Jiang , and Fredric Gey School of Information Management

Predicate Argument Structure Analysis for Use Case Description Modeling

NASA Astrophysics Data System (ADS)

Takeuchi, Hironori; Nakamura, Taiga; Yamaguchi, Takahira

In a large software system development project, many documents are prepared and updated frequently. In such a situation, support is needed for looking through these documents easily to identify inconsistencies and to maintain traceability. In this research, we focus on the requirements documents such as use cases and consider how to create models from the use case descriptions in unformatted text. In the model construction, we propose a few semantic constraints based on the features of the use cases and use them for a predicate argument structure analysis to assign semantic labels to actors and actions. With this approach, we show that we can assign semantic labels without enhancing any existing general lexical resources such as case frame dictionaries and design a less language-dependent model construction architecture. By using the constructed model, we consider a system for quality analysis of the use cases and automated test case generation to keep the traceability between document sets. We evaluated the reuse of the existing use cases and generated test case steps automatically with the proposed prototype system from real-world use cases in the development of a system using a packaged application. Based on the evaluation, we show how to construct models with high precision from English and Japanese use case data. Also, we could generate good test cases for about 90% of the real use cases through the manual improvement of the descriptions based on the feedback from the quality analysis system.

Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

PubMed

Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

2014-01-01

An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

Discriminative object tracking via sparse representation and online dictionary learning.

PubMed

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-09-01

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

A Knowledge Dictionary System for Scheduling Support

DTIC Science & Technology

1988-10-01

quantity of the resource is allocated; (g) two (or more) activities that conflict temporall , can only proceed if one or more of the activities are re...does not allow any parameter substitution but merely processes the contents of the file as a series of I:eystrokes. To create a macro, simply type...bytes in the system. When the hardware actually uses such a struc- ture (e.g., the Motorola 68000 series CPU) the OS will almost always present it that

Ambiguity and variability of database and software names in bioinformatics.

PubMed

Duck, Geraint; Kovacevic, Aleksandar; Robertson, David L; Stevens, Robert; Nenadic, Goran

2015-01-01

There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification. Through the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions. On a test set of 25 full-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and ambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an F-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively. We characterise the issues with various mention types and propose potential ways of capturing additional database and software mentions in the literature. Our analyses show that identification of mentions of databases and tools is a challenging task that cannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows improvement and promise (primarily in precision), more contextual information needs to be taken into account to achieve a good degree of accuracy.

High-recall protein entity recognition using a dictionary

PubMed Central

Kou, Zhenzhen; Cohen, William W.; Murphy, Robert F.

2010-01-01

Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches to that of Maximum Entropy (Max-Ent) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary—the measure of most interest in our intended application. PMID:15961466

Organizing the present, looking to the future: an online knowledge repository to facilitate collaboration.

PubMed

Burchill, C; Roos, L L; Fergusson, P; Jebamani, L; Turner, K; Dueck, S

2000-01-01

Comprehensive data available in the Canadian province of Manitoba since 1970 have aided study of the interaction between population health, health care utilization, and structural features of the health care system. Given a complex linked database and many ongoing projects, better organization of available epidemiological, institutional, and technical information was needed. The Manitoba Centre for Health Policy and Evaluation wished to develop a knowledge repository to handle data, document research Methods, and facilitate both internal communication and collaboration with other sites. This evolving knowledge repository consists of both public and internal (restricted access) pages on the World Wide Web (WWW). Information can be accessed using an indexed logical format or queried to allow entry at user-defined points. The main topics are: Concept Dictionary, Research Definitions, Meta-Index, and Glossary. The Concept Dictionary operationalizes concepts used in health research using administrative data, outlining the creation of complex variables. Research Definitions specify the codes for common surgical procedures, tests, and diagnoses. The Meta-Index organizes concepts and definitions according to the Medical Sub-Heading (MeSH) system developed by the National Library of Medicine. The Glossary facilitates navigation through the research terms and abbreviations in the knowledge repository. An Education Resources heading presents a web-based graduate course using substantial amounts of material in the Concept Dictionary, a lecture in the Epidemiology Supercourse, and material for Manitoba's Regional Health Authorities. Confidential information (including Data Dictionaries) is available on the Centre's internal website. Use of the public pages has increased dramatically since January 1998, with almost 6,000 page hits from 250 different hosts in May 1999. More recently, the number of page hits has averaged around 4,000 per month, while the number of unique hosts has climbed to around 400. This knowledge repository promotes standardization and increases efficiency by placing concepts and associated programming in the Centre's collective memory. Collaboration and project management are facilitated.

The Database Query Support Processor (QSP)

NASA Technical Reports Server (NTRS)

1993-01-01

The number and diversity of databases available to users continues to increase dramatically. Currently, the trend is towards decentralized, client server architectures that (on the surface) are less expensive to acquire, operate, and maintain than information architectures based on centralized, monolithic mainframes. The database query support processor (QSP) effort evaluates the performance of a network level, heterogeneous database access capability. Air Force Material Command's Rome Laboratory has developed an approach, based on ANSI standard X3.138 - 1988, 'The Information Resource Dictionary System (IRDS)' to seamless access to heterogeneous databases based on extensions to data dictionary technology. To successfully query a decentralized information system, users must know what data are available from which source, or have the knowledge and system privileges necessary to find out this information. Privacy and security considerations prohibit free and open access to every information system in every network. Even in completely open systems, time required to locate relevant data (in systems of any appreciable size) would be better spent analyzing the data, assuming the original question was not forgotten. Extensions to data dictionary technology have the potential to more fully automate the search and retrieval for relevant data in a decentralized environment. Substantial amounts of time and money could be saved by not having to teach users what data resides in which systems and how to access each of those systems. Information describing data and how to get it could be removed from the application and placed in a dedicated repository where it belongs. The result simplified applications that are less brittle and less expensive to build and maintain. Software technology providing the required functionality is off the shelf. The key difficulty is in defining the metadata required to support the process. The database query support processor effort will provide quantitative data on the amount of effort required to implement an extended data dictionary at the network level, add new systems, adapt to changing user needs, and provide sound estimates on operations and maintenance costs and savings.

Organizing the Present, Looking to the Future: An Online Knowledge Repository to Facilitate Collaboration

PubMed Central

Burchill, Charles; Fergusson, Patricia; Jebamani, Laurel; Turner, Ken; Dueck, Stephen

2000-01-01

Background Comprehensive data available in the Canadian province of Manitoba since 1970 have aided study of the interaction between population health, health care utilization, and structural features of the health care system. Given a complex linked database and many ongoing projects, better organization of available epidemiological, institutional, and technical information was needed. Objective The Manitoba Centre for Health Policy and Evaluation wished to develop a knowledge repository to handle data, document research methods, and facilitate both internal communication and collaboration with other sites. Methods This evolving knowledge repository consists of both public and internal (restricted access) pages on the World Wide Web (WWW). Information can be accessed using an indexed logical format or queried to allow entry at user-defined points. The main topics are: Concept Dictionary, Research Definitions, Meta-Index, and Glossary. The Concept Dictionary operationalizes concepts used in health research using administrative data, outlining the creation of complex variables. Research Definitions specify the codes for common surgical procedures, tests, and diagnoses. The Meta-Index organizes concepts and definitions according to the Medical Sub-Heading (MeSH) system developed by the National Library of Medicine. The Glossary facilitates navigation through the research terms and abbreviations in the knowledge repository. An Education Resources heading presents a web-based graduate course using substantial amounts of material in the Concept Dictionary, a lecture in the Epidemiology Supercourse, and material for Manitoba's Regional Health Authorities. Confidential information (including Data Dictionaries) is available on the Centre's internal website. Results Use of the public pages has increased dramatically since January 1998, with almost 6,000 page hits from 250 different hosts in May 1999. More recently, the number of page hits has averaged around 4,000 per month, while the number of unique hosts has climbed to around 400. Conclusions This knowledge repository promotes standardization and increases efficiency by placing concepts and associated programming in the Centre's collective memory. Collaboration and project management are facilitated. PMID:11720929

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining.

PubMed

Hettne, Kristina M; Williams, Antony J; van Mulligen, Erik M; Kleinjans, Jos; Tkachenko, Valery; Kors, Jan A

2010-03-23

Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining

PubMed Central

2010-01-01

Background Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. Results We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. Conclusions We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist. PMID:20331846

Environmental Health and Toxicology Resources of the United States National Library of Medicine

PubMed Central

Hochstein, Colette; Arnesen, Stacey; Goshorn, Jeanne

2009-01-01

For over 40 years, the National Library of Medicine’s (NLM) Toxicology and Environmental Health Information Program (TEHIP) has worked to organize and to provide access to an extensive array of environmental health and toxicology resources. During these years, the TEHIP program has evolved from a handful of databases developed primarily for researchers to a broad range of products and services that also serve industry, students, and the general public. TEHIP’s resources include TOXNET® , a collection of databases, including online handbooks, bibliographic references, information on the release of chemicals in the environment, and a chemical dictionary. TEHIP also produces several resources aimed towards the general public, such as the Household Products Database , which helps users explore chemicals often found in common household products, and Tox Town® , an interactive guide to commonly encountered toxic substances, health, and the environment. This paper introduces some of NLM’s environmental health and toxicology resources. PMID:17915629

Relaxations to Sparse Optimization Problems and Applications

NASA Astrophysics Data System (ADS)

Skau, Erik West

Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we apply a maximum entropy model, guided by the social media data, to estimate the flooded regions during a 2013 flood in Boulder, CO and show that the results are comparable to those obtained using expert information.

miRPathDB: a new dictionary on microRNAs and target pathways.

PubMed

Backes, Christina; Kehl, Tim; Stöckel, Daniel; Fehlmann, Tobias; Schneider, Lara; Meese, Eckart; Lenhof, Hans-Peter; Keller, Andreas

2017-01-04

In the last decade, miRNAs and their regulatory mechanisms have been intensively studied and many tools for the analysis of miRNAs and their targets have been developed. We previously presented a dictionary on single miRNAs and their putative target pathways. Since then, the number of miRNAs has tripled and the knowledge on miRNAs and targets has grown substantially. This, along with changes in pathway resources such as KEGG, leads to an improved understanding of miRNAs, their target genes and related pathways. Here, we introduce the miRNA Pathway Dictionary Database (miRPathDB), freely accessible at https://mpd.bioinf.uni-sb.de/ With the database we aim to complement available target pathway web-servers by providing researchers easy access to the information which pathways are regulated by a miRNA, which miRNAs target a pathway and how specific these regulations are. The database contains a large number of miRNAs (2595 human miRNAs), different miRNA target sets (14 773 experimentally validated target genes as well as 19 281 predicted targets genes) and a broad selection of functional biochemical categories (KEGG-, WikiPathways-, BioCarta-, SMPDB-, PID-, Reactome pathways, functional categories from gene ontology (GO), protein families from Pfam and chromosomal locations totaling 12 875 categories). In addition to Homo sapiens, also Mus musculus data are stored and can be compared to human target pathways. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Developing a distributed data dictionary service

NASA Technical Reports Server (NTRS)

U'Ren, J.

2000-01-01

This paper will explore the use of the Lightweight Directory Access Protocol (LDAP) using the ISO 11179 Data Dictionary Schema as a mechanism for standardizing the structure and communication links between data dictionaries.

Multiple Sparse Representations Classification

PubMed Central

Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik

2015-01-01

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level. PMID:26177106

Multimodal Strategies Allowing Corrective Feedback to Be Softened during Webconferencing-Supported Interactions

ERIC Educational Resources Information Center

Wigham, Ciara R.; Vidal, Julie

2016-01-01

This paper focuses on corrective feedback and examines how trainee-teachers use different semiotic resources to soften feedback sequences during synchronous online interactions. The ISMAEL corpus of webconferencing-supported L2 interactions in French provided data for this qualitative study. Using multimodal transcriptions, the analysis describes…

A dictionary server for supplying context sensitive medical knowledge.

PubMed

Ruan, W; Bürkle, T; Dudeck, J

2000-01-01

The Giessen Data Dictionary Server (GDDS), developed at Giessen University Hospital, integrates clinical systems with on-line, context sensitive medical knowledge to help with making medical decisions. By "context" we mean the clinical information that is being presented at the moment the information need is occurring. The dictionary server makes use of a semantic network supported by a medical data dictionary to link terms from clinical applications to their proper information sources. It has been designed to analyze the network structure itself instead of knowing the layout of the semantic net in advance. This enables us to map appropriate information sources to various clinical applications, such as nursing documentation, drug prescription and cancer follow up systems. This paper describes the function of the dictionary server and shows how the knowledge stored in the semantic network is used in the dictionary service.

Sparsity and Nullity: Paradigm for Analysis Dictionary Learning

DTIC Science & Technology

2016-08-09

16. SECURITY CLASSIFICATION OF: Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and...we investigate the relation between the SNS problem and the analysis dictionary learning problem, and show that the SNS problem plays a central role...and may be utilized to solve dictionary learning problems. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12

Readers' opinions of romantic poetry are consistent with emotional measures based on the Dictionary of Affect in Language.

PubMed

Whissell, Cynthia

2003-06-01

A principal components analysis of 68 volunteers' subjective ratings of 20 excerpts of Romantic poetry and of Dictionary of Affect scores for the same excerpts produced four components representing Pleasantness, Activation, Romanticism, and Nature. Dictionary measures and subjective ratings of the same constructs loaded on the same factor. Results are interpreted as providing construct validity for the Dictionary of Affect.

Fast dictionary-based reconstruction for diffusion spectrum imaging.

PubMed

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Weakly supervised visual dictionary learning by harnessing image attributes.

PubMed

Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

2014-12-01

Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

Password-only authenticated three-party key exchange proven secure against insider dictionary attacks.

PubMed

Nam, Junghyun; Choo, Kim-Kwang Raymond; Paik, Juryon; Won, Dongho

2014-01-01

While a number of protocols for password-only authenticated key exchange (PAKE) in the 3-party setting have been proposed, it still remains a challenging task to prove the security of a 3-party PAKE protocol against insider dictionary attacks. To the best of our knowledge, there is no 3-party PAKE protocol that carries a formal proof, or even definition, of security against insider dictionary attacks. In this paper, we present the first 3-party PAKE protocol proven secure against both online and offline dictionary attacks as well as insider and outsider dictionary attacks. Our construct can be viewed as a protocol compiler that transforms any 2-party PAKE protocol into a 3-party PAKE protocol with 2 additional rounds of communication. We also present a simple and intuitive approach of formally modelling dictionary attacks in the password-only 3-party setting, which significantly reduces the complexity of proving the security of 3-party PAKE protocols against dictionary attacks. In addition, we investigate the security of the well-known 3-party PAKE protocol, called GPAKE, due to Abdalla et al. (2005, 2006), and demonstrate that the security of GPAKE against online dictionary attacks depends heavily on the composition of its two building blocks, namely a 2-party PAKE protocol and a 3-party key distribution protocol.

Adaptive Greedy Dictionary Selection for Web Media Summarization.

PubMed

Cong, Yang; Liu, Ji; Sun, Gan; You, Quanzeng; Li, Yuncheng; Luo, Jiebo

2017-01-01

Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l 2,0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.

Multi-level discriminative dictionary learning with application to large scale image classification.

PubMed

Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

2015-10-01

The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

PubMed Central

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

2015-01-01

Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format

PubMed Central

Kinjo, Akira R.; Suzuki, Hirofumi; Yamashita, Reiko; Ikegawa, Yasuyo; Kudou, Takahiro; Igarashi, Reiko; Kengaku, Yumiko; Cho, Hasumi; Standley, Daron M.; Nakagawa, Atsushi; Nakamura, Haruki

2012-01-01

The Protein Data Bank Japan (PDBj, http://pdbj.org) is a member of the worldwide Protein Data Bank (wwPDB) and accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins, which are summarized in this article. To enhance the interoperability of the PDB data, we have recently developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, along with its ontology in the Web Ontology Language (OWL) based on the PDB mmCIF Exchange Dictionary. Being in the standard format for the Semantic Web, the PDB/RDF data provide a means to integrate the PDB with other biological information resources. PMID:21976737

The Pocket Dictionary: A Textbook for Spelling.

ERIC Educational Resources Information Center

Doggett, Maran

1982-01-01

Reports on a productive approach to secondary-school spelling instruction--one that emphasizes how and when to use the dictionary. Describes two of the many class activities that cultivate student use of the dictionary. (RL)

Cheap Words: A Paperback Dictionary Roundup.

ERIC Educational Resources Information Center

Kister, Ken

1979-01-01

Surveys currently available paperback editions in three classes of dictionaries: collegiate, abridged, and pocket. A general discussion distinguishes among the classes and offers seven consumer tips, followed by an annotated listing of dictionaries now available. (SW)

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

PubMed

Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

2013-01-01

Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning

PubMed Central

Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

2013-01-01

Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝd, and the dictionary is learned from the training data using the vector space structure of ℝd and its Euclidean L2-metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis. PMID:24129583

An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

PubMed

Zhang, Ye; Yu, Tenglong; Wang, Wenwu

2014-01-01

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining.

PubMed

Salhi, Adil; Essack, Magbubah; Alam, Tanvir; Bajic, Vladan P; Ma, Lina; Radovanovic, Aleksandar; Marchand, Benoit; Schmeier, Sebastian; Zhang, Zhang; Bajic, Vladimir B

2017-07-03

Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA ( www.cbrc.kaust.edu.sa/des_ncrna ). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining

PubMed Central

Salhi, Adil; Essack, Magbubah; Alam, Tanvir; Bajic, Vladan P.; Ma, Lina; Radovanovic, Aleksandar; Marchand, Benoit; Zhang, Zhang; Bajic, Vladimir B.

2017-01-01

ABSTRACT Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA (www.cbrc.kaust.edu.sa/des_ncrna). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource. PMID:28387604

A selective annotated bibliography for clinical audiology (1988-2008): reference works.

PubMed

Ferrer-Vinent, Susan T; Ferrer-Vinent, Ignacio J

2009-06-01

This is the 1st in a series of 3 planned companion articles that present a selected, annotated, and indexed bibliography of clinical audiology publications from 1988 to 2008. Research and preparation of the bibliography were based on published guidelines, professional audiology experience, and professional librarian experience. This article presents reference works (dictionaries, encyclopedias, handbooks, and manuals). The future planned articles will cover other monographs, periodicals, and online resources. Audiologists and librarians can use these lists as a guide when seeking clinical audiology literature.

An Analysis of Data Dictionaries and Their Role in Information Resource Management.

DTIC Science & Technology

1984-09-01

management system DBMS). It manages data by utilizing software routines built Lato the idta di:-tionary package and thus is not dependent 3n D.IrS soft- wace ...described as having active or passive interfaces or a combination of the two. An inter- faze is a series of commands which connact the data...carefully conceived examples in the ii:tionary’s refer- enzc manuals. A hierarchy of menus can reduce complex oper- ations to a series of smaller

Sexting--it's in the dictionary.

PubMed

Mattey, Beth; Diliberto, Gail Mattey

2013-03-01

Sexting has become commonplace in our vocabulary, as commonplace as technology use is to our youth. The role of the school nurse necessitates awareness of issues surrounding sexting along with the capability to proactively educate students, staff and parents on the dangers of sexting. Students are empowered when provided the knowledge that only they control their own image. This article explores current terminology, incidence of sexting among today's youth, legal implications, as well as strategies and resources for schools to assist in dealing with sexting.

SaRAD: a Simple and Robust Abbreviation Dictionary.

PubMed

Adar, Eytan

2004-03-01

Due to recent interest in the use of textual material to augment traditional experiments it has become necessary to automatically cluster, classify and filter natural language information. The Simple and Robust Abbreviation Dictionary (SaRAD) provides an easy to implement, high performance tool for the construction of a biomedical symbol dictionary. The algorithms, applied to the MEDLINE document set, result in a high quality dictionary and toolset to disambiguate abbreviation symbols automatically.

The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval

DTIC Science & Technology

2006-01-01

The Effect of Bilingual Term List Size on Dictionary -Based Cross-Language Information Retrieval Dina Demner-Fushman Department of Computer Science... dictionary -based Cross-Language Information Retrieval (CLIR), in which the goal is to find documents written in one natural language based on queries that...in which the documents are written. In dictionary -based CLIR techniques, the princi- pal source of translation knowledge is a translation lexicon

Robust Multimodal Dictionary Learning

PubMed Central

Cao, Tian; Jojic, Vladimir; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

2014-01-01

We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities. Dictionaries learned with such non-corresponding data will induce uncertainty about image representation. In this paper, we propose a probabilistic model that accounts for image areas that are poorly corresponding between the image modalities. We cast the problem of learning a dictionary in presence of problematic image patches as a likelihood maximization problem and solve it with a variant of the EM algorithm. Our algorithm iterates identification of poorly corresponding patches and re-finements of the dictionary. We tested our method on synthetic and real data. We show improvements in image prediction quality and alignment accuracy when using the method for multimodal image registration. PMID:24505674

Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification.

PubMed

Schuemie, Martijn J; Mons, Barend; Weeber, Marc; Kors, Jan A

2007-06-01

Gene and protein name identification in text requires a dictionary approach to relate synonyms to the same gene or protein, and to link names to external databases. However, existing dictionaries are incomplete. We investigate two complementary methods for automatic generation of a comprehensive dictionary: combination of information from existing gene and protein databases and rule-based generation of spelling variations. Both methods have been reported in literature before, but have hitherto not been combined and evaluated systematically. We combined gene and protein names from several existing databases of four different organisms. The combined dictionaries showed a substantial increase in recall on three different test sets, as compared to any single database. Application of 23 spelling variation rules to the combined dictionaries further increased recall. However, many rules appeared to have no effect and some appear to have a detrimental effect on precision.

Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields*

PubMed Central

Sun, Jiaqi; Xie, Yuchen; Ye, Wenxing; Ho, Jeffrey; Entezari, Alireza; Blackband, Stephen J.

2013-01-01

In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate the intrinsic geometric structure of manifolds and performs better than traditional dictionary learning approaches when applied to data lying on the manifold of square root densities. Non-negativity as well as smoothness across the whole field of the reconstructed DPs is guaranteed in our approach. We demonstrate the advantage of our approach by comparing it with an existing dictionary based reconstruction method on synthetic and real multi-shell MRI data. PMID:24684004

A dictionary server for supplying context sensitive medical knowledge.

PubMed Central

Ruan, W.; Bürkle, T.; Dudeck, J.

2000-01-01

The Giessen Data Dictionary Server (GDDS), developed at Giessen University Hospital, integrates clinical systems with on-line, context sensitive medical knowledge to help with making medical decisions. By "context" we mean the clinical information that is being presented at the moment the information need is occurring. The dictionary server makes use of a semantic network supported by a medical data dictionary to link terms from clinical applications to their proper information sources. It has been designed to analyze the network structure itself instead of knowing the layout of the semantic net in advance. This enables us to map appropriate information sources to various clinical applications, such as nursing documentation, drug prescription and cancer follow up systems. This paper describes the function of the dictionary server and shows how the knowledge stored in the semantic network is used in the dictionary service. PMID:11079978

Plasma Dictionary Website

NASA Astrophysics Data System (ADS)

Correll, Don; Heeter, Robert; Alvarez, Mitch

2000-10-01

In response to many inquiries for a list of plasma terms, a database driven Plasma Dictionary website (plasmadictionary.llnl.gov) was created that allows users to submit new terms, search for specific terms or browse alphabetic listings. The Plasma Dictionary website contents began with the Fusion & Plasma Glossary terms available at the Fusion Energy Educational website (fusedweb.llnl.gov). Plasma researchers are encouraged to add terms and definitions. By clarifying the meanings of specific plasma terms, it is envisioned that the primary use of the Plasma Dictionary website will be by students, teachers, researchers, and writers for (1) Enhancing literacy in plasma science, (2) Serving as an educational aid, (3) Providing practical information, and (4) Helping clarify plasma writings. The Plasma Dictionary website has already proved useful in responding to a request from the CRC Press (www.crcpress.com) to add plasma terms to its CRC physics dictionary project (members.aol.com/physdict/).

The Research on Denoising of SAR Image Based on Improved K-SVD Algorithm

NASA Astrophysics Data System (ADS)

Tan, Linglong; Li, Changkai; Wang, Yueqin

2018-04-01

SAR images often receive noise interference in the process of acquisition and transmission, which can greatly reduce the quality of images and cause great difficulties for image processing. The existing complete DCT dictionary algorithm is fast in processing speed, but its denoising effect is poor. In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure is introduced in detail. The dictionary has a compact representation and can effectively train the image signal. Then, the sparse dictionary is trained by K-SVD algorithm according to the sparse representation of the dictionary. The algorithm has more advantages in high dimensional data processing. Experimental results show that the proposed algorithm can remove the speckle noise more effectively than the complete DCT dictionary and retain the edge details better.

Speaking truth to power: the effect of candid feedback on how individuals with power allocate resources.

PubMed

Oc, Burak; Bashshur, Michael R; Moore, Celia

2015-03-01

Subordinates are often seen as impotent, able to react to but not affect how powerholders treat them. Instead, we conceptualize subordinate feedback as an important trigger of powerholders' behavioral self-regulation and explore subordinates' reciprocal influence on how powerholders allocate resources to them over time. In 2 experiments using a multiparty, multiround dictator game paradigm, we found that when subordinates provided candid feedback about whether they found prior allocations to be fair or unfair, powerholders regulated how self-interested their allocations were over time. However, when subordinates provided compliant feedback about powerholders' prior allocation decisions (offered consistently positive feedback, regardless of the powerholders' prior allocation), those powerholders made increasingly self-interested allocations over time. In addition, we showed that guilt partially mediates this relationship: powerholders feel more guilty after receiving negative feedback about an allocation, subsequently leading to a less self-interested allocation, whereas they feel less guilty after receiving positive feedback about an allocation, subsequently taking more for themselves. Our findings integrate the literature on upward feedback with theory about moral self-regulation to support the idea that subordinates are an important source of influence over those who hold power over them. PsycINFO Database Record (c) 2015 APA, all rights reserved.

A Standard-Driven Data Dictionary for Data Harmonization of Heterogeneous Datasets in Urban Geological Information Systems

NASA Astrophysics Data System (ADS)

Liu, G.; Wu, C.; Li, X.; Song, P.

2013-12-01

The 3D urban geological information system has been a major part of the national urban geological survey project of China Geological Survey in recent years. Large amount of multi-source and multi-subject data are to be stored in the urban geological databases. There are various models and vocabularies drafted and applied by industrial companies in urban geological data. The issues such as duplicate and ambiguous definition of terms and different coding structure increase the difficulty of information sharing and data integration. To solve this problem, we proposed a national standard-driven information classification and coding method to effectively store and integrate urban geological data, and we applied the data dictionary technology to achieve structural and standard data storage. The overall purpose of this work is to set up a common data platform to provide information sharing service. Research progresses are as follows: (1) A unified classification and coding method for multi-source data based on national standards. Underlying national standards include GB 9649-88 for geology and GB/T 13923-2006 for geography. Current industrial models are compared with national standards to build a mapping table. The attributes of various urban geological data entity models are reduced to several categories according to their application phases and domains. Then a logical data model is set up as a standard format to design data file structures for a relational database. (2) A multi-level data dictionary for data standardization constraint. Three levels of data dictionary are designed: model data dictionary is used to manage system database files and enhance maintenance of the whole database system; attribute dictionary organizes fields used in database tables; term and code dictionary is applied to provide a standard for urban information system by adopting appropriate classification and coding methods; comprehensive data dictionary manages system operation and security. (3) An extension to system data management function based on data dictionary. Data item constraint input function is making use of the standard term and code dictionary to get standard input result. Attribute dictionary organizes all the fields of an urban geological information database to ensure the consistency of term use for fields. Model dictionary is used to generate a database operation interface automatically with standard semantic content via term and code dictionary. The above method and technology have been applied to the construction of Fuzhou Urban Geological Information System, South-East China with satisfactory results.

Dormitory Residents Reduce Electricity Consumption when Exposed to Real-Time Visual Feedback and Incentives

ERIC Educational Resources Information Center

Petersen, John E.; Shunturov, Vladislav; Janda, Kathryn; Platt, Gavin; Weinberger, Kate

2007-01-01

Purpose: In residential buildings, personal choices influence electricity and water consumption. Prior studies indicate that information feedback can stimulate resource conservation. College dormitories provide an excellent venue for controlled study of the effects of feedback. The goal of this study is to assess how different resolutions of…

Performance Feedback Utility in a Small Organization: Effects on Organizational Outcomes and Managerial Decision Processes.

ERIC Educational Resources Information Center

Florin-Thuma, Beth C.; Boudreau, John W.

1987-01-01

Investigated the frequent but previously untested assertion that utility analysis can improve communication and decision making about human resource management programs by examining a performance feedback intervention in a small fast-food store. Results suggest substantial payoffs from performance feedback, though the store's owner-managers had…

Teacher Trainers' Beliefs about Feedback on Teaching Practice: Negotiating the Tensions between Authoritativeness and Dialogic Space

ERIC Educational Resources Information Center

Louw, Stephen; Todd, Richard Watson; Jimarkon, Pattamawan

2016-01-01

Contradictory tensions are apparent during the feedback given to pre-service English language teachers by trainers following their practice teaching. Trainers' beliefs may serve as a resource in dealing with these conflicting tensions. Trainers' beliefs about the feedback they provide during teaching practice conferences were elicited, and…

Improving Performance in Very Small Firms through Effective Assessment and Feedback

ERIC Educational Resources Information Center

Lorenzet, Steven J.; Cook, Ronald G.; Ozeki, Cynthia

2006-01-01

Purpose: The purpose of this paper is to improve assessment and feedback processes in the training practices of very small firms, thereby improving the firms' human capital. Design/methodology/approach: The paper reviews research and practice on effective assessment and feedback. Findings: Based on this paper, human resources are increasingly seen…

Learning overcomplete representations from distributed data: a brief review

NASA Astrophysics Data System (ADS)

Raja, Haroon; Bajwa, Waheed U.

2016-05-01

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates the design of dictionary learning algorithms that consider distributed nature of data as one of the problem variables. Just like centralized settings, distributed dictionary learning problem can be posed in more than one way depending on the problem setup. Most notable distinguishing features are the online versus batch nature of data and the representative versus discriminative nature of the dictionaries. In this paper, several distributed dictionary learning algorithms that are designed to tackle different problem setups are reviewed. One of these algorithms is cloud K-SVD, which solves the dictionary learning problem for batch data in distributed settings. One distinguishing feature of cloud K-SVD is that it has been shown to converge to its centralized counterpart, namely, the K-SVD solution. On the other hand, no such guarantees are provided for other distributed dictionary learning algorithms. Convergence of cloud K-SVD to the centralized K-SVD solution means problems that are solvable by K-SVD in centralized settings can now be solved in distributed settings with similar performance. Finally, cloud K-SVD is used as an example to show the advantages that are attainable by deploying distributed dictionary algorithms for real world distributed datasets.

Dictionnaires et encyclopedies: cuvee 89 (Dictionaries and Encyclopedias: Vintage 89).

ERIC Educational Resources Information Center

Ibrahim, Amr Helmy

1989-01-01

For the first time since its initial publication in 1905, the much-imitated "Petit Larousse" dictionary/reference book has a true competitor in Hachette's "Le Dictionnaire de notre temps", a new dictionary reflecting modern French usage. (MSE)

Terminological reference of a knowledge-based system: the data dictionary.

PubMed

Stausberg, J; Wormek, A; Kraut, U

1995-01-01

The development of open and integrated knowledge bases makes new demands on the definition of the used terminology. The definition should be realized in a data dictionary separated from the knowledge base. Within the works done at a reference model of medical knowledge, a data dictionary has been developed and used in different applications: a term definition shell, a documentation tool and a knowledge base. The data dictionary includes that part of terminology, which is largely independent of a certain knowledge model. For that reason, the data dictionary can be used as a basis for integrating knowledge bases into information systems, for knowledge sharing and reuse and for modular development of knowledge-based systems.

Using dictionaries to study the mental lexicon.

PubMed

Anshen, F; Aronoff, M

The notion of a mental lexicon has its historical roots in practical reference dictionaries. The distributional analysis of dictionaries provides one means of investigating the structure of the mental lexicon. We review our earlier work with dictionaries, based on a three-way horserace model of lexical access and production, and then present the most recent results of our ongoing analysis of the Oxford English Dictionary, Second Edition on CD-ROM, which traces changes in productivity over time of the English suffixes -ment and -ity, both of which originate in French borrowings. Our results lead us to question the validity of automatic analogy from a set of existing words as the driving force behind morphological productivity. Copyright 1999 Academic Press.

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

PubMed

Weiss, Christian; Zoubir, Abdelhak M

2017-05-01

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

The T.M.R. Data Dictionary: A Management Tool for Data Base Design

PubMed Central

Ostrowski, Maureen; Bernes, Marshall R.

1984-01-01

In January 1981, a dictionary-driven ambulatory care information system known as TMR (The Medical Record) was installed at a large private medical group practice in Los Angeles. TMR's data dictionary has enabled the medical group to adapt the software to meet changing user needs largely without programming support. For top management, the dictionary is also a tool for navigating through the system's complexity and assuring the integrity of management goals.

Sensitivity computation of the ell1 minimization problem and its application to dictionary design of ill-posed problems

NASA Astrophysics Data System (ADS)

Horesh, L.; Haber, E.

2009-09-01

The ell1 minimization problem has been studied extensively in the past few years. Recently, there has been a growing interest in its application for inverse problems. Most studies have concentrated in devising ways for sparse representation of a solution using a given prototype dictionary. Very few studies have addressed the more challenging problem of optimal dictionary construction, and even these were primarily devoted to the simplistic sparse coding application. In this paper, sensitivity analysis of the inverse solution with respect to the dictionary is presented. This analysis reveals some of the salient features and intrinsic difficulties which are associated with the dictionary design problem. Equipped with these insights, we propose an optimization strategy that alleviates these hurdles while utilizing the derived sensitivity relations for the design of a locally optimal dictionary. Our optimality criterion is based on local minimization of the Bayesian risk, given a set of training models. We present a mathematical formulation and an algorithmic framework to achieve this goal. The proposed framework offers the design of dictionaries for inverse problems that incorporate non-trivial, non-injective observation operators, where the data and the recovered parameters may reside in different spaces. We test our algorithm and show that it yields improved dictionaries for a diverse set of inverse problems in geophysics and medical imaging.

Password-Only Authenticated Three-Party Key Exchange Proven Secure against Insider Dictionary Attacks

PubMed Central

Nam, Junghyun; Choo, Kim-Kwang Raymond

2014-01-01

While a number of protocols for password-only authenticated key exchange (PAKE) in the 3-party setting have been proposed, it still remains a challenging task to prove the security of a 3-party PAKE protocol against insider dictionary attacks. To the best of our knowledge, there is no 3-party PAKE protocol that carries a formal proof, or even definition, of security against insider dictionary attacks. In this paper, we present the first 3-party PAKE protocol proven secure against both online and offline dictionary attacks as well as insider and outsider dictionary attacks. Our construct can be viewed as a protocol compiler that transforms any 2-party PAKE protocol into a 3-party PAKE protocol with 2 additional rounds of communication. We also present a simple and intuitive approach of formally modelling dictionary attacks in the password-only 3-party setting, which significantly reduces the complexity of proving the security of 3-party PAKE protocols against dictionary attacks. In addition, we investigate the security of the well-known 3-party PAKE protocol, called GPAKE, due to Abdalla et al. (2005, 2006), and demonstrate that the security of GPAKE against online dictionary attacks depends heavily on the composition of its two building blocks, namely a 2-party PAKE protocol and a 3-party key distribution protocol. PMID:25309956

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

PubMed

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Image fusion via nonlocal sparse K-SVD dictionary learning.

PubMed

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Dictionary as Database.

ERIC Educational Resources Information Center

Painter, Derrick

1996-01-01

Discussion of dictionaries as databases focuses on the digitizing of The Oxford English dictionary (OED) and the use of Standard Generalized Mark-Up Language (SGML). Topics include the creation of a consortium to digitize the OED, document structure, relational databases, text forms, sequence, and discourse. (LRW)

Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

PubMed

Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

2013-01-01

Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces sparser coefficients than the original SPF basis and results in significantly lower reconstruction error than Bilgic et al.'s method.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

Measurement of negativity bias in personal narratives using corpus-based emotion dictionaries.

PubMed

Cohen, Shuki J

2011-04-01

This study presents a novel methodology for the measurement of negativity bias using positive and negative dictionaries of emotion words applied to autobiographical narratives. At odds with the cognitive theory of mood dysregulation, previous text-analytical studies have failed to find significant correlation between emotion dictionaries and negative affectivity or dysphoria. In the present study, an a priori list dictionary of emotion words was refined based on the actual use of these words in personal narratives collected from close to 500 college students. Half of the corpus was used to construct, via concordance analysis, the grammatical structures associated with the words in their emotional sense. The second half of the corpus served as a validation corpus. The resulting dictionary ignores words that are not used in their intended emotional sense, including negated emotions, homophones, frozen idioms etc. Correlations of the resulting corpus-based negative and positive emotion dictionaries with self-report measures of negative affectivity were in the expected direction, and were statistically significant, with medium effect size. The potential use of these dictionaries as implicit measures of negativity bias and in the analysis of psychotherapy transcripts is discussed.

Joint seismic data denoising and interpolation with double-sparsity dictionary learning

NASA Astrophysics Data System (ADS)

Zhu, Lingchen; Liu, Entao; McClellan, James H.

2017-08-01

Seismic data quality is vital to geophysical applications, so that methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double-sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data, while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the data set without introducing pseudo-Gibbs artifacts when compared to other directional multi-scale transform methods such as curvelets.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

PubMed

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-03-27

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K -nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Bilevel Model-Based Discriminative Dictionary Learning for Recognition.

PubMed

Zhou, Pan; Zhang, Chao; Lin, Zhouchen

2017-03-01

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l 0 or l 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2014-04-01

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Improving Teach Astronomy: A Survey of Instructors

NASA Astrophysics Data System (ADS)

Wenger, Matthew; Riabokin, Malanka; Impey, Chris David

2018-01-01

Teach Astronomy is a website that provides educational resources for introductory astronomy. The motivation behind constructing this site was to provide quality online educational tools for use as a primary or supplementary instructional resource for teachers and students. The website provides an online textbook, glossary, podcasts and video summaries of concepts. As the popularity of online courses steadily increases, so does the demand for robust online educational resources. In order to cater to our users, our team conducted a survey of the instructors that use Teach Astronomy site for feedback for use in updating and streamlining the website content. The survey collected feedback regarding functionality of each of the website tools, in which courses the site was being used, and the motivation of the instructors use of our site. The overwhelming majority of responses indicate that instructors use the website as a class textbook in introductory astronomy courses for non-science majors, and instructors also generally tended to agree that the site content was comprehensive and lucid. One interesting result of the survey is to cluster topics in a way that is consistent with different levels of instruction (i.e. grouping middle-school level content and university level content distinctly). Our team will use this feedback to improve the Teach Astronomy website and maintain it as a high-quality, free online resource. We will also continue to gather feedback from instructors to ensure that the Teach Astronomy website stays current and remains a valuable online resource for instructors around the country.

Evaluating Feedback Systems by Civil Service Employees. HRDV 6000 Integrated Studies in Human Resources Development

DTIC Science & Technology

2001-07-01

Specifically, it gives leaders an azimuth check in their self-awareness, Evaluating feedback systems by civil service employees 16 gives feedback to assist ...constructive feedback and that managers needed to be taught how to communicate with employees . Currently, DOE has run this program since 1996 and...providing employees with developmental information. Information that would be used to assist leaders in improving in both their managerial and

Feedback Cheerleader, Mean-Spirited Feedback Estudio Critico Discursivo De Ideologias Sobre El Feedback Escrito En Un Departamento Universitario De Espanol En Estados Unidos

ERIC Educational Resources Information Center

Dominguez-Mujica, Maria Eugenia

2012-01-01

The aim of this research is to investigate ideologies that monitor and control styles of written feedback as well as discursive resources used in the expression of those ideologies. Using socio-cognitive analysis, a development of critical discourse studies, I have analyzed 44 sessions of chat on the Internet, 78 journal notes, 31 emails and 12…

Automatic de-identification of textual documents in the electronic health record: a review of recent research

PubMed Central

2010-01-01

Background In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here. Methods This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers. Results The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries. Conclusions In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication. PMID:20678228

Automatic de-identification of textual documents in the electronic health record: a review of recent research.

PubMed

Meystre, Stephane M; Friedlin, F Jeffrey; South, Brett R; Shen, Shuying; Samore, Matthew H

2010-08-02

In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here. This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers. The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries. In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.

Trying Out a New Dictionary.

ERIC Educational Resources Information Center

Benson, Morton; Benson, Evelyn

1988-01-01

Describes the BBI Combinatory Dictionary of English and demonstrates its usefulness for advanced learners of English by administering a monolingual completion test, first without a dictionary and then with the BBI, to Hungarian and Russian English teachers. Both groups' scores improved dramatically on the posttest. (LMO)

Travel time tomography with local image regularization by sparsity constrained dictionary learning

NASA Astrophysics Data System (ADS)

Bianco, M.; Gerstoft, P.

2017-12-01

We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

The chemical component dictionary: complete descriptions of constituent molecules in experimentally determined 3D macromolecules in the Protein Data Bank

PubMed Central

Westbrook, John D.; Shao, Chenghua; Feng, Zukang; Zhuravleva, Marina; Velankar, Sameer; Young, Jasmine

2015-01-01

Summary: The Chemical Component Dictionary (CCD) is a chemical reference data resource that describes all residue and small molecule components found in Protein Data Bank (PDB) entries. The CCD contains detailed chemical descriptions for standard and modified amino acids/nucleotides, small molecule ligands and solvent molecules. Each chemical definition includes descriptions of chemical properties such as stereochemical assignments, chemical descriptors, systematic chemical names and idealized coordinates. The content, preparation, validation and distribution of this CCD chemical reference dataset are described. Availability and implementation: The CCD is updated regularly in conjunction with the scheduled weekly release of new PDB structure data. The CCD and amino acid variant reference datasets are hosted in the public PDB ftp repository at ftp://ftp.wwpdb.org/pub/pdb/data/monomers/components.cif.gz, ftp://ftp.wwpdb.org/pub/pdb/data/monomers/aa-variants-v1.cif.gz, and its mirror sites, and can be accessed from http://wwpdb.org. Contact: jwest@rcsb.rutgers.edu. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25540181

Model-based semantic dictionaries for medical language understanding.

PubMed Central

Rassinoux, A. M.; Baud, R. H.; Ruch, P.; Trombert-Paviot, B.; Rodrigues, J. M.

1999-01-01

Semantic dictionaries are emerging as a major cornerstone towards achieving sound natural language understanding. Indeed, they constitute the main bridge between words and conceptual entities that reflect their meanings. Nowadays, more and more wide-coverage lexical dictionaries are electronically available in the public domain. However, associating a semantic content with lexical entries is not a straightforward task as it is subordinate to the existence of a fine-grained concept model of the treated domain. This paper presents the benefits and pitfalls in building and maintaining multilingual dictionaries, the semantics of which is directly established on an existing concept model. Concrete cases, handled through the GALEN-IN-USE project, illustrate the use of such semantic dictionaries for the analysis and generation of multilingual surgical procedures. PMID:10566333

Talking Shop with Moira Runcie.

ERIC Educational Resources Information Center

Bowers, Rogers

1998-01-01

Presents an interview with Moira Runcie, Editorial Director for ELT (English Language Teaching) dictionaries at Oxford University Press. The interview focuses on the work of A.S. Hornby in creating the first learners dictionary of English and shows how modern dictionaries draw on his work. (Author/JL)

Cross-label Suppression: a Discriminative and Fast Dictionary Learning with Group Regularization.

PubMed

Wang, Xiudong; Gu, Yuantao

2017-05-10

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used `0-norm or `1- norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

Metacognitive factors that impact student nurse use of point of care technology in clinical settings.

PubMed

Kuiper, RuthAnne

2010-01-01

The utility of personal digital assistants (PDA) as a point of care resource in health care practice and education presents new challenges for nursing faculty. While there is a plethora of PDA resources available, little is known about the variables that effect student learning and technology adoption. In this study nursing students used PDA software programs which included a drug guide, medical dictionary, laboratory manual and nursing diagnosis manual during acute care clinical experiences. Analysis of student journals comparative reflective statements about the PDA as an adjunct to other available resources in clinical practice are presented. The benefits of having a PDA included readily available data, validation of thinking processes, and facilitation of care plan re-evaluation. Students reported increased frequency of use and independence. Significant correlations between user perceptions and computer self-efficacy suggested greater confidence in abilities with technology resulting in increased self-awareness and achievement of learning outcomes.

USEEIO Satellite Tables

EPA Pesticide Factsheets

These files contain the environmental data as particular emissions or resources associated with a BEA sectors that are used in the USEEIO model. They are organized by the emission or resources type, as described in the manuscript. The main files (without SI) show the final satellite tables in the 'Exchanges' sheet which have emissions or resource use per USD for 2013. The other sheets in these files provide meta data for the create of the tables, including general information, sources, etc. The 'export' sheet is used for saving the satellite table for csv export. The data dictionary describes the fields in this sheet. The supporting files provide all the details data transformation and organization for the development of the satellite tables.This dataset is associated with the following publication:Yang, Y., W. Ingwersen, T. Hawkins, and D. Meyer. USEEIO: a New and Transparent United States Environmentally Extended Input-Output Model. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA,

AdjScales: Visualizing Differences between Adjectives for Language Learners

NASA Astrophysics Data System (ADS)

Sheinman, Vera; Tokunaga, Takenobu

In this study we introduce AdjScales, a method for scaling similar adjectives by their strength. It combines existing Web-based computational linguistic techniques in order to automatically differentiate between similar adjectives that describe the same property by strength. Though this kind of information is rarely present in most of the lexical resources and dictionaries, it may be useful for language learners that try to distinguish between similar words. Additionally, learners might gain from a simple visualization of these differences using unidimensional scales. The method is evaluated by comparison with annotation on a subset of adjectives from WordNet by four native English speakers. It is also compared against two non-native speakers of English. The collected annotation is an interesting resource in its own right. This work is a first step toward automatic differentiation of meaning between similar words for language learners. AdjScales can be useful for lexical resource enhancement.

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

PubMed

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging

PubMed Central

Ravishankar, Saiprasad; Moore, Brian E.; Nadakuditi, Raj Rao; Fessler, Jeffrey A.

2017-01-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamic magnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method. PMID:28092528

Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging.

PubMed

Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-05-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamicmagnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method.

49 CFR Appendix B to Part 604 - Reasons for Removal

Code of Federal Regulations, 2012 CFR

2012-10-01

... honest mistake. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn... performing it. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn., 1968... force. In addition, no other policy of insurance has taken its place. Black's Law Dictionary, Revised...

49 CFR Appendix B to Part 604 - Reasons for Removal

Code of Federal Regulations, 2013 CFR

2013-10-01

... honest mistake. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn... performing it. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn., 1968... force. In addition, no other policy of insurance has taken its place. Black's Law Dictionary, Revised...

49 CFR Appendix B to Part 604 - Reasons for Removal

Code of Federal Regulations, 2014 CFR

2014-10-01

... honest mistake. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn... performing it. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn., 1968... force. In addition, no other policy of insurance has taken its place. Black's Law Dictionary, Revised...

Ahtna Athabaskan Dictionary.

ERIC Educational Resources Information Center

Kari, James, Ed.

This dictionary of Ahtna, a dialect of the Athabaskan language family, is the first to integrate all morphemes into a single alphabetically arranged section of main entries, with verbs arranged according to a theory of Ahtna (and Athabascan) verb theme categories. An introductory section details dictionary format conventions used, presents a brief…

A Novel Approach to Creating Disambiguated Multilingual Dictionaries

ERIC Educational Resources Information Center

Boguslavsky, Igor; Cardenosa, Jesus; Gallardo, Carolina

2009-01-01

Multilingual lexicons are needed in various applications, such as cross-lingual information retrieval, machine translation, and some others. Often, these applications suffer from the ambiguity of dictionary items, especially when an intermediate natural language is involved in the process of the dictionary construction, since this language adds…

49 CFR Appendix B to Part 604 - Reasons for Removal

Code of Federal Regulations, 2010 CFR

2010-10-01

... honest mistake. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn... performing it. Black's Law Dictionary, Revised Fourth Edition, West Publishing Company, St. Paul, Minn., 1968... force. In addition, no other policy of insurance has taken its place. Black's Law Dictionary, Revised...

The Lexicographic Treatment of Color Terms

ERIC Educational Resources Information Center

Williams, Krista

2014-01-01

This dissertation explores the main question, "What are the issues involved in the definition and translation of color terms in dictionaries?" To answer this question, I examined color term definitions in monolingual dictionaries of French and English, and color term translations in bilingual dictionaries of French paired with nine…

21 CFR 701.3 - Designation of ingredients.

Code of Federal Regulations, 2010 CFR

2010-04-01

....) Cosmetic Ingredient Dictionary, Second Ed., 1977 (available from the Cosmetic, Toiletry and Fragrance... revised monographs are published in supplements to this dictionary edition by July 18, 1980. Acid Black 2.../federal_register/code_of_federal_regulations/ibr_locations.html. (v) USAN and the USP dictionary of drug...

Web-Based Learning Information System for Web 3.0

NASA Astrophysics Data System (ADS)

Rego, Hugo; Moreira, Tiago; García-Peñalvo, Francisco Jose

With the emergence of Web/eLearning 3.0 we have been developing/adjusting AHKME in order to face this great challenge. One of our goals is to allow the instructional designer and teacher to access standardized resources and evaluate the possibility of integration and reuse in eLearning systems, not only content but also the learning strategy. We have also integrated some collaborative tools for the adaptation of resources, as well as the collection of feedback from users to provide feedback to the system. We also provide tools for the instructional designer to create/customize specifications/ontologies to give structure and meaning to resources, manual and automatic search with recommendation of resources and instructional design based on the context, as well as recommendation of adaptations in learning resources. We also consider the concept of mobility and mobile technology applied to eLearning, allowing access by teachers and students to learning resources, regardless of time and space.

The Use of Electronic Dictionaries for Pronunciation Practice by University EFL Students

ERIC Educational Resources Information Center

Metruk, Rastislav

2017-01-01

This paper attempts to explore how Slovak learners of English use electronic dictionaries with regard to pronunciation practice and improvement. A total of 24 Slovak university students (subjects) completed a questionnaire which contained pronunciation-related questions in connection with the use of electronic dictionaries. The questions primarily…

Dictionary of Multicultural Education.

ERIC Educational Resources Information Center

Grant, Carl A., Ed.; Ladson-Billings, Gloria, Ed.

The focus of this dictionary is the meanings and perspectives of various terms that are used in multicultural education. Contributors have often addressed the literal meanings of words and terms as well as contextual meanings and examples that helped create those meanings. Like other dictionaries, this one is arranged alphabetically, but it goes…

Sparse Representation Based Classification with Structure Preserving Dimension Reduction

DTIC Science & Technology

2014-03-13

dictionary learning [39] used stochastic approximations to update dictionary with a large data set. Laplacian score dictionary ( LSD ) [58], which is based on...vol. 4. 2003. p. 864–7. 47. Shaw B, Jebara T. Structure preserving embedding. In: The 26th annual international conference on machine learning, ICML

Chinese-English Aviation and Space Dictionary.

ERIC Educational Resources Information Center

Air Force Systems Command, Wright-Patterson AFB, OH. Foreign Technology Div.

The Aviation and Space Dictionary is the second of a series of Chinese-English technical dictionaries under preparation by the Foreign Technology Division, United States Air Force Systems Command. The purpose of the series is to provide rapid reference tools for translators, abstracters, and research analysts concerned with scientific and…

Dictionary of Marketing Terms.

ERIC Educational Resources Information Center

Everhardt, Richard M.

A listing of words and definitions compiled from more than 10 college and high school textbooks are presented in this dictionary of marketing terms. Over 1,200 entries of terms used in retailing, wholesaling, economics, and investments are included. This dictionary was designed to aid both instructors and students to better understand the…

EFL Students' "Yahoo!" Online Bilingual Dictionary Use Behavior

ERIC Educational Resources Information Center

Tseng, Fan-ping

2009-01-01

This study examined 38 EFL senior high school students' "Yahoo!" online dictionary look-up behavior. In a language laboratory, the participants read an article on a reading sheet, underlined any words they did not know, looked up their unknown words in "Yahoo!" online bilingual dictionary, and wrote down the definitions of…

Chinese-Cantonese Dictionary of Common Chinese-Cantonese Characters.

ERIC Educational Resources Information Center

Defense Language Inst., Washington, DC.

This dictionary contains 1,500 Chinese-Cantonese characters (selected from three frequency lists), and more than 6,000 Chinese-Cantonese terms (selected from three Cantonese-English dictionaries). The characters are arranged alphabetically according to the U.S. Army Language School System of Romanization, which is described in the…

Chinese-English Electronics and Telecommunications Dictionary, Vol. 2.

ERIC Educational Resources Information Center

Air Force Systems Command, Wright-Patterson AFB, OH. Foreign Technology Div.

This is the second volume of the Electronics and Telecommunications Dictionary, the third of the series of Chinese-English technical dictionaries under preparation by the Foreign Technology Division, United States Air Force Systems Command. The purpose of the series is to provide rapid reference tools for translators, abstracters, and research…

Chinese-English Electronics and Telecommunications Dictionary. Vol. 1.

ERIC Educational Resources Information Center

Air Force Systems Command, Wright-Patterson AFB, OH. Foreign Technology Div.

This is the first volume of the Electronics and Telecommunications Dictionary, the third of the series of Chinese-English technical dictionaries under preparation by the Foreign Technology Division, United States Air Force Systems Command. The purpose of the series is to provide rapid reference tools for translators, abstracters, and research…

Binukid Dictionary.

ERIC Educational Resources Information Center

Otanes, Fe T., Ed.; Wrigglesworth, Hazel

1992-01-01

The dictionary of Binukid, a language spoken in the Bukidnon province of the Philippines, is intended as a tool for students of Binukid and for native Binukid-speakers interested in learning English. A single dialect was chosen for this work. The dictionary is introduced by notes on Binukid grammar, including basic information about phonology and…

Learning the Language of Difference: The Dictionary in the High School.

ERIC Educational Resources Information Center

Willinsky, John

1987-01-01

Reports on dictionaries' power to misrepresent gender. Examines the definitions of three terms (clitoris, penis, and vagina) in eight leading high school dictionaries. Concludes that the absence of certain female gender-related terms represents another instance of institutionalized silence about the experience of women. (MM)

75 FR 22805 - Federal Travel Regulation; Relocation Allowances; Standard Data Dictionary for Collection of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-30

... GENERAL SERVICES ADMINISTRATION [Proposed GSA Bulletin FTR 10-XXX; Docket 2010-0009; Sequence 1] Federal Travel Regulation; Relocation Allowances; Standard Data Dictionary for Collection of Transaction... GSA is posting online a proposed FTR bulletin that contains the data dictionary that large Federal...

Defining Moments \\ di-'fi-ning 'mo-mnts \\

ERIC Educational Resources Information Center

Kilman, Carrie

2012-01-01

Children encounter new words every day. Although dictionaries designed for young readers can help students explore and experiment with language, it turns out many mainstream children's dictionaries fail to accurately describe the world in which many students live. The challenges to children's dictionary publishers can be steep. First, there is the…

Getting the Most out of the Dictionary

ERIC Educational Resources Information Center

Marckwardt, Albert H.

2012-01-01

The usefulness of the dictionary as a reliable source of information for word meanings, spelling, and pronunciation is widely recognized. But even in these obvious matters, the information that the dictionary has to offer is not always accurately interpreted. With respect to pronunciation there seem to be two general pitfalls: (1) the…

A dictionary of commonly used terms and terminologies in nonwovens

USDA-ARS?s Scientific Manuscript database

A need for a comprehensive dictionary of cotton was assessed by the International Cotton Advisory Committee (ICAC), Washington, DC. The ICAC has selected the topics (from the fiber to fabric) to be covered in the dictionary. The ICAC has invited researchers/scientists from across the globe, to compi...

78 FR 68343 - Homeownership Counseling Organizations Lists Interpretive Rule

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-14

... into their definitional meanings, according to the Data Dictionary,\\7\\ to ensure clarity. This will be... dictionary for the Field ``Services'' can be found at http://data.hud.gov/Housing_Counselor/getServices , and a data dictionary for ``Languages'' can be found at http://data.hud.gov/Housing_Counselor/get...

Measurement of Negativity Bias in Personal Narratives Using Corpus-Based Emotion Dictionaries

ERIC Educational Resources Information Center

Cohen, Shuki J.

2011-01-01

This study presents a novel methodology for the measurement of negativity bias using positive and negative dictionaries of emotion words applied to autobiographical narratives. At odds with the cognitive theory of mood dysregulation, previous text-analytical studies have failed to find significant correlation between emotion dictionaries and…

A Proposal To Develop the Axiological Aspect in Onomasiological Dictionaries.

ERIC Educational Resources Information Center

Felices Lago, Angel Miguel

It is argued that English dictionaries currently provide evaluative information in addition to descriptive information about the words they contain, and that this aspect of dictionaries should be developed and expanded on. First, the historical background and distribution of the axiological parameter in English-language onomasiological…

Earliest English Definitions of Anaisthesia and Anaesthesia.

PubMed

Haridas, Rajesh P

2017-11-01

The earliest identified English definition of the word anaisthesia was discovered in the first edition (1684) of A Physical Dictionary, an English translation of Steven Blankaart's medical dictionary, Lexicon Medicum Graeco-Latinum. This definition was almost certainly the source of the definition of anaesthesia which appeared in Dictionarium Anglo-Britannicum (1708), a general-purpose English dictionary compiled by the lexicographer John Kersey. The words anaisthesia and anaesthesia have not been identified in English medical or surgical publications that antedate the earliest English dictionaries in which they are known to have been defined.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

MR fingerprinting reconstruction with Kalman filter.

PubMed

Zhang, Xiaodi; Zhou, Zechen; Chen, Shiyang; Chen, Shuo; Li, Rui; Hu, Xiaoping

2017-09-01

Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

Weakly Supervised Dictionary Learning

NASA Astrophysics Data System (ADS)

You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

2018-05-01

We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

PubMed Central

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-01-01

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. PMID:28346385

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Blind compressive sensing dynamic MRI

PubMed Central

Lingala, Sajan Goud; Jacob, Mathews

2013-01-01

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the non-orthogonal nature of the dictionary basis functions. Since the number of degrees of freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting ℓ1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler sub problems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the ℓ1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the ℓ0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic MRI applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes. PMID:23542951

Toward Harnessing User Feedback For Machine Learning

DTIC Science & Technology

2006-10-02

machine learning systems. If this resource-the users themselves-could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users? understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users

IRDS prototyping with applications to the representation of EA/RA models

NASA Technical Reports Server (NTRS)

Lekkos, Anthony A.; Greenwood, Bruce

1988-01-01

The requirements and system overview for the Information Resources Dictionary System (IRDS) are described. A formal design specification for a scaled down IRDS implementation compatible with the proposed FIPS IRDS standard is contained. The major design objectives for this IRDS will include a menu driven user interface, implementation of basic IRDS operations, and PC compatibility. The IRDS was implemented using Smalltalk/5 object oriented programming system and an ATT 6300 personal computer running under MS-DOS 3.1. The difficulties encountered in using Smalltalk are discussed.

Review of "A Dictionary of Global Huayu"

ERIC Educational Resources Information Center

Li, Rui

2016-01-01

As the first Huayu dictionary published by the Commercial Press, "A Dictionary of Global Huayu" (Chinese Language) did a pioneer work in many aspects. It did expand the influence of Chinese and provided Chinese speaker abroad a valuable reference book for study and communication. Nevertheless, there are still some demerits. First of all,…

Variant Spellings in Modern American Dictionaries.

ERIC Educational Resources Information Center

Emery, Donald W.

A record of how present-day desk dictionaries are recognizing the existence of variant or secondary spellings for many common English words, this reference list can be used by teachers of English and authors of spelling lists. Originally published in 1958, this revised edition uses two dictionaries not in existence then and the revised editions of…

A Survey of Meaning Discrimination in Selected English/Spanish Dictionaries.

ERIC Educational Resources Information Center

Powers, Michael D.

1985-01-01

Examines the treatment of sense discrimination in eight Spanish/English English/Spanish bilingual dictionaries and one specialized dictionary. Does this by analyzing 30 words that Torrents des Prats determined have at least nine different sense discriminations from English into Spanish. Larousse was found to be far superior to the others. (SED)

Aleut Dictionary (Unangam Tunudgusii). An Unabridged Lexicon of the Aleutian, Pribilof, and Commander Islands Aleut Language.

ERIC Educational Resources Information Center

Bergsland, Knut, Comp.

This comprehensive dictionary draws on ethnographic and linguistic work of the Aleut language and culture dating to 1745. An introductory section explains the dictionary's format, offers a brief historical survey, and contains notes on Aleut phonology and orthography, dialectal differences and developments, Eskimo-Aleut phonological…

Usage and Efficacy of Electronic Dictionaries for a Language without Word Boundaries

ERIC Educational Resources Information Center

Toyoda, Etsuko

2016-01-01

There is cumulative evidence suggesting that hyper-glossing facilitates lower-level processing and enhances reading comprehension. There are plentiful studies on electronic dictionaries for English. However, research on e-dictionaries for languages with no boundaries between words is still scarce. The main aim for the current study is to…

Chinese-English Technical Dictionaries. Volume 1, Aviation and Space.

ERIC Educational Resources Information Center

Library of Congress, Washington, DC. Aerospace Technology Div.

The present dictionary is the first of a series of Chinese-English technical dictionaries under preparation by the Aerospace Technology Division of the Library of Congress. The purpose of the series is to provide rapid reference tools for translators, abstractors, and research analysts concerned with scientific and technical materials published in…

Chinese-English and English-Chinese Dictionaries in the Library of Congress. An Annotated Bibliography.

ERIC Educational Resources Information Center

Dunn, Robert, Comp.

An annotated bibliography of the Library of Congress' Chinese-English holdings on all subjects, as well as certain polyglot and multilingual dictionaries with English and Chinese entries. Included are general, encyclopaedic and comprehensive dictionaries; vocabularies; word lists; syllabaries; lists of place names, personal names, nomenclature,…

The New Oxford Picture Dictionary, English/Navajo Edition.

ERIC Educational Resources Information Center

Parnwell, E. C.

This picture dictionary illustrates over 2,400 words. The dictionary is organized thematically, beginning with topics most useful for the survival needs of students in an English speaking country. However, teachers may adapt the order to reflect the needs of their students. Verbs are included on separate pages, but within topic areas in which they…

The Oxford Picture Dictionary. Beginning Workbook.

ERIC Educational Resources Information Center

Fuchs, Marjorie

The beginning workbook of the Oxford Picture Dictionary is in full color and offers vocabulary reinforcement activities that correspond page for page with the dictionary. Clear and simple instructions with examples make it suitable for independent use in the classroom or at home. The workbook has up-to-date art and graphics, explaining over 3700…

English-Dari Dictionary.

ERIC Educational Resources Information Center

Peace Corps, Washington, DC.

This 7,000-word dictionary is designed for English speakers learning Dari. The dictionary consists of two parts, the first a reference to find words easily translatable from one language to the other, the second a list of idioms and short phrases commonly used in everyday conversation, yet not readily translatable. Many of these entries have no…

An Electronic Dictionary and Translation System for Murrinh-Patha

ERIC Educational Resources Information Center

Seiss, Melanie; Nordlinger, Rachel

2012-01-01

This paper presents an electronic dictionary and translation system for the Australian language Murrinh-Patha. Its complex verbal structure makes learning Murrinh-Patha very difficult. Design learning materials or a dictionary which is easy to understand and to use also presents a challenge. This paper discusses some of the difficulties posed by…

Linguistic and Cultural Strategies in ELT Dictionaries

ERIC Educational Resources Information Center

Corrius, Montse; Pujol, Didac

2010-01-01

There are three main types of ELT dictionaries: monolingual, bilingual, and bilingualized. Each type of dictionary, while having its own advantages, also hinders the learning of English as a foreign language and culture in so far as it is written from a homogenizing (linguistic- and culture-centric) perspective. This paper presents a new type of…

Dictionaries of African Sign Languages: An Overview

ERIC Educational Resources Information Center

Schmaling, Constanze H.

2012-01-01

This article gives an overview of dictionaries of African sign languages that have been published to date most of which have not been widely distributed. After an introduction into the field of sign language lexicography and a discussion of some of the obstacles that authors of sign language dictionaries face in general, I will show problems…

Supporting Social Studies Reading Comprehension with an Electronic Pop-Up Dictionary

ERIC Educational Resources Information Center

Fry, Sara Winstead; Gosky, Ross

2008-01-01

This study investigated how middle school students' comprehension was impacted by reading social studies texts online with a pop-up dictionary function for every word in the text. A quantitative counterbalance design was used to determine how 129 middle school students' reading comprehension test scores for the pop-up dictionary reading differed…

76 FR 10055 - Changes to the Public Housing Assessment System (PHAS): Physical Condition Scoring Notice

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-23

... Weights and Criticality Levels, and Dictionary of Deficiency Definitions The Item Weights and Criticality Levels tables and the Dictionary of Deficiency Definitions, currently in use, were published as... Dictionary of Deficiency Definitions is found at http://www.hud.gov/offices/reac/pdf/pass_dict2.3.pdf . V...

A Dictionary of Hindi Verbal Expressions (Hindi-English). Final Report.

ERIC Educational Resources Information Center

Bahl, Kali Charan, Comp.

This dictionary covers approximately 28,277 verbal expressions in modern standard Hindi and their rendered English equivalents. The study lists longer verbal expressions which are generally matched by single verbs in English. The lexicographer notes that the majority of entries in this dictionary do not appear in their present form in most other…

Aspects of Sentence Retrieval

DTIC Science & Technology

2006-09-01

English-to-Arabic-to-English Lexicon . . . . . . . . . . . . . . . . . . . . . 89 6.2.4 A WordNet Probabilistic Dictionary ...19 4.1 Examples of “translations” of the terms “zebra” and “galileo” from a translation dictionary trained...106 6.13 Comparing the use of WordNet as a translation table, and as a dictionary during the training of a translation table

Bilingualised Dictionaries: How Learners Really Use Them.

ERIC Educational Resources Information Center

Laufer, Batia; Kimmel, Michal

1997-01-01

Seventy native Hebrew-speaking English-as-a-Second-Language students participated in a study that investigated what part of an entry second-language learners read when they look up an unfamiliar word in a bilingualised dictionary: the monolingual, the bilingual, or both. Results suggest the bilingualised dictionary is very effective because it is…

Dictionnaires du francais langue etrangere (Dictionaries for French as a Second Language).

ERIC Educational Resources Information Center

Gross, Gaston; Ibrahim, Amr

1981-01-01

Examines the purposes served by native language dictionaries as an introduction to the review of three monolingual French dictionaries for foreigners. Devotes particular attention to the most recent, the "Dictionnaire du francais langue etrangere", published by Larousse. Stresses the characteristics that are considered desirable for this type of…

Aveiro method in reproducing kernel Hilbert spaces under complete dictionary

NASA Astrophysics Data System (ADS)

Mai, Weixiong; Qian, Tao

2017-12-01

Aveiro Method is a sparse representation method in reproducing kernel Hilbert spaces (RKHS) that gives orthogonal projections in linear combinations of reproducing kernels over uniqueness sets. It, however, suffers from determination of uniqueness sets in the underlying RKHS. In fact, in general spaces, uniqueness sets are not easy to be identified, let alone the convergence speed aspect with Aveiro Method. To avoid those difficulties we propose an anew Aveiro Method based on a dictionary and the matching pursuit idea. What we do, in fact, are more: The new Aveiro method will be in relation to the recently proposed, the so called Pre-Orthogonal Greedy Algorithm (P-OGA) involving completion of a given dictionary. The new method is called Aveiro Method Under Complete Dictionary (AMUCD). The complete dictionary consists of all directional derivatives of the underlying reproducing kernels. We show that, under the boundary vanishing condition, bring available for the classical Hardy and Paley-Wiener spaces, the complete dictionary enables an efficient expansion of any given element in the Hilbert space. The proposed method reveals new and advanced aspects in both the Aveiro Method and the greedy algorithm.

Assigning categorical information to Japanese medical terms using MeSH and MEDLINE.

PubMed

Onogi, Yuzo

2007-01-01

This paper reports on the assigning of MeSH (Medical Subject Headings) categories to Japanese terms in an English-Japanese dictionary using the titles and abstracts of articles indexed in MEDLINE. In a previous study, 30,000 of 80,000 terms in the dictionary were mapped to MeSH terms by normalized comparison. It was reasoned that if the remaining dictionary terms appeared in MEDLINE-indexed articles that are indexed using MeSH terms, then relevancies between the dictionary terms and MeSH terms could be calculated, and thus MeSH categories assigned. This study compares two approaches for calculating the weight matrix. One is the TF*IDF method and the other uses the inner product of two weight matrices. About 20,000 additional dictionary terms were identified in MEDLINE-indexed articles published between 2000 and 2004. The precision and recall of these algorithms were evaluated separately for MeSH terms and non-MeSH terms. Unfortunately, the precision and recall of the algorithms was not good, but this method will help with manual assignment of MeSH categories to dictionary terms.

Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity

NASA Astrophysics Data System (ADS)

Cui, Lingli; Gong, Xiangyang; Zhang, Jianyu; Wang, Huaqing

2016-12-01

The quantitative diagnosis of rolling bearing fault severity is particularly crucial to realize a proper maintenance decision. Aiming at the fault feature of rolling bearing, a novel double-dictionary matching pursuit (DDMP) for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity (LZC) index is proposed in this paper. In order to match the features of rolling bearing fault, the impulse time-frequency dictionary and modulation dictionary are constructed to form the double-dictionary by using the method of parameterized function model. Then a novel matching pursuit method is proposed based on the new double-dictionary. For rolling bearing vibration signals with different fault sizes, the signals are decomposed and reconstructed by the DDMP. After the noise reduced and signals reconstructed, the LZC index is introduced to realize the fault extent evaluation. The applications of this method to the fault experimental signals of bearing outer race and inner race with different degree of injury have shown that the proposed method can effectively realize the fault extent evaluation.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

PubMed Central

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13. Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract PMID:27504009

A dictionary without definitions: romanticist science in the production and presentation of the Grimm brothers' German dictionary, 1838-1863.

PubMed

Kistner, Kelly

2014-12-01

Between 1838 and 1863 the Grimm brothers led a collaborative research project to create a new kind of dictionary documenting the history of the German language. They imagined the work would present a scientific account of linguistic cohesiveness and strengthen German unity. However, their dictionary volumes (most of which were arranged and written by Jacob Grimm) would be variously criticized for their idiosyncratic character and ultimately seen as a poor, and even prejudicial, piece of scholarship. This paper argues that such criticisms may reflect a misunderstanding of the dictionary. I claim it can be best understood as an artifact of romanticist science and its epistemological privileging of subjective perception coupled with a deeply-held faith in inter-subjective congruence. Thus situated, it is a rare and detailed case of Romantic ideas and ideals applied to the scientific study of social artifacts. Moreover, the dictionary's organization, reception, and legacy provide insights into the changing landscape of scientific practice in Germany, showcasing the difficulties of implementing a romanticist vision of science amidst widening gaps between the public and professionals, generalists and specialists.

Using interpersonal affect regulation in simulated healthcare consultations: an experimental investigation of self-control resource depletion.

PubMed

Martínez-Íñigo, David; Mercado, Francisco; Totterdell, Peter

2015-01-01

Controlled Interpersonal Affect Regulation -the process of deliberately influencing the internal feeling states of others- occurs in a variety of interpersonal relationships and contexts. An incipient corpus of research shows that interpersonal affect regulation can be characterized as a goal-directed behavior that uses self-control processes which, according to the strength model of self-regulation, consumes a limited resource that is also used by other self-control processes. Using interpersonal affect-improving and affect-worsening regulation strategies can increase agent's resource depletion but there is reason to think that effects will partially rely on target's feedback in response to the regulation. Using a healthcare paradigm, an experiment was conducted to test the combined effects of interpersonal affect regulation use and patient feedback on healthcare workers' resource depletion, measured as self-reported experienced and expected emotional exhaustion, and persistence on a self-regulation task. Medical students (N = 78) were randomly assigned to a 2(interpersonal affect regulation: affect-worsening vs. affect-improving) × 2(patients' feedback: positive vs. negative) factorial between-subjects design and given instructions to play the role of doctors in interactions with two professional actors trained to act as patients. Analysis of covariance showed that affect-worsening was more depleting than affect-improving for all measures, whereas the recovery effects of positive feedback varied depending on strategy type and measure. The findings confirm the characterization of interpersonal affect regulation as potentially depleting, but suggest that the correspondence between the agent's strategy and the target's response needs to be taken into consideration. Use of affect-improving and positive feedback showed positive effects on self-rated performance, indicating that interpersonal affect regulation is relevant for organizational as well as personal outcomes.

Using interpersonal affect regulation in simulated healthcare consultations: an experimental investigation of self-control resource depletion

PubMed Central

Martínez-Íñigo, David; Mercado, Francisco; Totterdell, Peter

2015-01-01

Controlled Interpersonal Affect Regulation –the process of deliberately influencing the internal feeling states of others– occurs in a variety of interpersonal relationships and contexts. An incipient corpus of research shows that interpersonal affect regulation can be characterized as a goal-directed behavior that uses self-control processes which, according to the strength model of self-regulation, consumes a limited resource that is also used by other self-control processes. Using interpersonal affect-improving and affect-worsening regulation strategies can increase agent’s resource depletion but there is reason to think that effects will partially rely on target’s feedback in response to the regulation. Using a healthcare paradigm, an experiment was conducted to test the combined effects of interpersonal affect regulation use and patient feedback on healthcare workers’ resource depletion, measured as self-reported experienced and expected emotional exhaustion, and persistence on a self-regulation task. Medical students (N = 78) were randomly assigned to a 2(interpersonal affect regulation: affect-worsening vs. affect-improving) × 2(patients’ feedback: positive vs. negative) factorial between-subjects design and given instructions to play the role of doctors in interactions with two professional actors trained to act as patients. Analysis of covariance showed that affect-worsening was more depleting than affect-improving for all measures, whereas the recovery effects of positive feedback varied depending on strategy type and measure. The findings confirm the characterization of interpersonal affect regulation as potentially depleting, but suggest that the correspondence between the agent’s strategy and the target’s response needs to be taken into consideration. Use of affect-improving and positive feedback showed positive effects on self-rated performance, indicating that interpersonal affect regulation is relevant for organizational as well as personal outcomes. PMID:26483737

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

DOEpatents

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Image fusion using sparse overcomplete feature dictionaries

DOEpatents

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

PubMed

Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

2017-04-01

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

PubMed

Wu, Lin; Wang, Yang; Pan, Shirui

2017-12-01

It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

Do volunteer community-based preceptors value students' feedback?

PubMed

Dent, M Marie; Boltri, John; Okosun, Ike S

2004-11-01

A key component of educational practice is to provide feedback and evaluation to teachers and learners to improve the teaching and learning process. The purpose of this study was to determine whether volunteer community preceptors value evaluation and feedback by students as much as they value other resources or rewards. In Fall 1999, a questionnaire concerning the resources and rewards of preceptorship was mailed to 236 community preceptors affiliated with the Mercer University School of Medicine, Macon, Georgia. Preceptors were asked to rate 20 factors on a five-point Likert scale (5 = very important to 1 = not very important). The mean values were compared using t-tests. One hundred sixty-eight preceptors (71%) completed questionnaires. Preceptors rated evaluation and feedback from students significantly higher (p < .001) than all other factors (mean = 4.02, standard deviation [SD] = .87). Continuing medical education for teaching was the next most highly valued factor (mean = 3.67, SD = 1.14). Preceptors rated financial compensation the lowest (mean = 2.01, SD = 1.19) of all factors. The high rank of feedback and evaluation from students persisted across gender, specialty, length of time as a preceptor, practice location, and years practicing medicine. This study demonstrates that feedback and evaluation from students is highly valued. The knowledge that community-based preceptors highly value feedback and evaluation from students should stimulate medical school programs to provide feedback and evaluation to preceptors that will enhance the educational outcomes for both faculty and learners.

The Yale Kamusi Project: A Swahili-English, English-Swahili Dictionary.

ERIC Educational Resources Information Center

Hinnebusch, Thomas

2001-01-01

Evaluates the strengths and weaknesses of the Yale Online Kamusi project, an electronic Web-based Swahili-English and English-Swahili dictionary. The dictionary is described and checked for comprehensiveness, the adequacy and quality of the glosses and definitions are tested, and a number of recommendations are made to help make it a better and…

Dictionary Form in Decoding, Encoding and Retention: Further Insights

ERIC Educational Resources Information Center

Dziemianko, Anna

2017-01-01

The aim of the paper is to investigate the role of dictionary form (paper versus electronic) in language reception, production and retention. The body of existing research does not give a clear answer as to which dictionary medium benefits users more. Divergent findings from many studies into the topic might stem from differences in research…

Review of EFL Learners' Habits in the Use of Pedagogical Dictionaries

ERIC Educational Resources Information Center

El-Sayed, Al-Nauman Al-Amin Ali; Siddiek, Ahmed Gumaa

2013-01-01

A dictionary is an important device for both: EFL teachers and EFL learners. It is highly needed to conduct effective teaching and learning. Many investigations were carried out to study the foreign language learners' habits in the use of their dictionaries in reading, writing, testing and translating. This paper is shedding light on this issue;…

75 FR 17003 - Reconsideration of Interpretation of Regulations That Determine Pollutants Covered by Clean Air...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-02

... Law Dictionary (8th Ed.) is ``the act or process of controlling by rule or restriction.'' However, an alternative meaning in this same dictionary defines the term as ``a rule or order, having legal force, usu. issued by an administrative agency or local government.'' The primary meaning in Webster's dictionary for...

Dictionary Culture of University Students Learning English as a Foreign Language in Turkey

ERIC Educational Resources Information Center

Baskin, Sami; Mumcu, Muhsin

2018-01-01

Dictionaries, one of the oldest tools of language education, have continued to be a part of education although information technologies and concept of education has changed over time. Until today, with the help of the developments in technology both types of dictionaries have increased, and usage areas have expanded. Therefore, it is possible to…

The Efficacy of Dictionary Use while Reading for Learning New Words

ERIC Educational Resources Information Center

Hamilton, Harley

2012-01-01

This paper describes a study investigating the use of three types of dictionaries by deaf (i.e., with severe to profound hearing loss) high school students while reading to determine the effectiveness of each type for acquiring the meanings of unknown vocabulary in text. The dictionary types used include an online bilingual multimedia English-ASL…

Bilingualised or Monolingual Dictionaries? Preferences and Practices of Advanced ESL Learners in Hong Kong

ERIC Educational Resources Information Center

Chan, Alice Y. W.

2011-01-01

This article reports on the results of a questionnaire and interview survey on Cantonese ESL learners' preference for bilingualised dictionaries or monolingual dictionaries. The questionnaire survey was implemented with about 160 university English majors in Hong Kong and three focus group interviews were conducted with 14 of these participants.…

Dictionary Use of Undergraduate Students in Foreign Language Departments in Turkey at Present

ERIC Educational Resources Information Center

Tulgar, Aysegül Takkaç

2017-01-01

Foreign language learning has always been a process carried out with the help of dictionaries which are both in target language and from native language to target language/from target language to native language. Dictionary use is an especially delicate issue for students in foreign language departments because students in those departments are…

The Effect of a Simplified English Language Dictionary on a Reading Test. LEP Projects Report 1.

ERIC Educational Resources Information Center

Albus, Deb; Bielinski, John; Thurlow, Martha; Liu, Kristin

This study was conducted to examine whether using a monolingual, simplified English dictionary as an accommodation on a reading test with limited-English-proficient (LEP) Hmong students improved test performance. Hmong students were chosen because they are often not literate in their first language. For these students, bilingual dictionaries are…

Enhancing a Web Crawler with Arabic Search Capability

DTIC Science & Technology

2010-09-01

7 Figure 2. Monolingual 11-point precision results. From [14]...........................................8 Figure 3. Lucene...libraries (prefixes dictionary , stems dictionary and suffixes dictionary ). If all the word elements (prefix, stem, suffix) are found in their...stemmer improved over 90% in average precision from raw retrieval. The authors concluded that stemming is very effective on Arabic IR. For monolingual

Dictionaries without Borders: Expanding the Limits of the Academy

ERIC Educational Resources Information Center

Miller, Julia

2012-01-01

Many people imagine dictionaries to be bulky tomes that are hard to lift and are only useful for quick translations or to check the meaning or spelling of difficult words. This paper aims to dispel that myth and show how online versions of monolingual English learners' dictionaries (MELDs) can be used pedagogically to engage students in academic…

A Selected Bibliography of Dictionaries. General Information Series, No. 9. Indochinese Refugee Education Guides.

ERIC Educational Resources Information Center

Center for Applied Linguistics, Arlington, VA.

This is a selected, annotated bibliography of dictionaries useful to Indochinese refugees. The purpose of this guide is to provide the American teacher or sponsor with information on the use, limitations and availability of monolingual and bilingual dictionaries which can be used by refugees. The bibliography is preceded by notes on problems with…

A Generative Theory of Relevance

DTIC Science & Technology

2004-09-01

73 5.3.1.4 Parameter estimation with a dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.3.1.5 Document ranking...engine [3]. The stemmer combines morphological rules with a large dictionary of special cases and exceptions. After stemming, 418 stop-words from the...goes over all Arabic training strings. Bulgarian definitions are identical. 5.3.1.4 Parameter estimation with a dictionary Parallel and comparable

The Role of Electronic Pocket Dictionaries as an English Learning Tool among Chinese Students

ERIC Educational Resources Information Center

Jian, Hua-Li; Sandnes, Frode Eika; Law, Kris M. Y.; Huang, Yo-Ping; Huang, Yueh-Min

2009-01-01

This study addressed the role of electronic pocket dictionaries as a language learning tool among university students in Hong Kong and Taiwan. The target groups included engineering and humanities students at both undergraduate and graduate level. Speed of reference was found to be the main motivator for using an electronic pocket dictionary.…

Progressive multi-atlas label fusion by dictionary evolution.

PubMed

Song, Yantao; Wu, Guorong; Bahrami, Khosro; Sun, Quansen; Shen, Dinggang

2017-02-01

Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary. Copyright © 2016 Elsevier B.V. All rights reserved.

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

NASA Astrophysics Data System (ADS)

Li, Jun-Bao; Liu, Jing; Pan, Jeng-Shyang; Yao, Hongxun

2017-06-01

Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

A feature dictionary supporting a multi-domain medical knowledge base.

PubMed

Naeymi-Rad, F

1989-01-01

Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Embedded sparse representation of fMRI data via group-wise dictionary optimization

NASA Astrophysics Data System (ADS)

Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

2016-03-01

Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints.

PubMed

Xiao Yang; Jianjiang Feng; Jie Zhou

2014-05-01

Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.

Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

NASA Astrophysics Data System (ADS)

Wu, Juan; Bai, Min

2018-05-01

We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

Fast dictionary generation and searching for magnetic resonance fingerprinting.

PubMed

Jun Xie; Mengye Lyu; Jian Zhang; Hui, Edward S; Wu, Ed X; Ze Wang

2017-07-01

A super-fast dictionary generation and searching (DGS) algorithm was developed for MR parameter quantification using magnetic resonance fingerprinting (MRF). MRF is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But it has a multiplicative computation complexity, resulting in a big burden of dictionary generating, saving, and retrieving, which can easily be intractable for any state-of-art computers. Based on retrospective analysis of the dictionary matching object function, a multi-scale ZOOM like DGS algorithm, dubbed as MRF-ZOOM, was proposed. MRF ZOOM is quasi-parameter-separable so the multiplicative computation complexity is broken into additive one. Evaluations showed that MRF ZOOM was hundreds or thousands of times faster than the original MRF parameter quantification method even without counting the dictionary generation time in. Using real data, it yielded nearly the same results as produced by the original method. MRF ZOOM provides a super-fast solution for MR parameter quantification.

75 FR 78682 - Renewal of a Currently Approved Information Collection

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-16

..., reporting burden (time and financial resources) is minimized, collection instruments are clearly understood... Corporation's AmeriCorps' Member Feedback Survey (formerly known as AmeriCorps' Performance Measurement... title change to the AmeriCorps' Member Feedback Survey, from AmeriCorps' Performance Measurement...

Evaluating Programs That Promote Climate and Energy Education-Meeting Teacher Needs for Online Resources

NASA Astrophysics Data System (ADS)

Lynds, S. E.; Buhr, S. M.

2011-12-01

The Climate Literacy and Energy Awareness Network (CLEAN) Pathway, is a National Science Digital Library (NSDL) Pathways project that was begun in 2010. The main goal of CLEAN is to generate a reviewed collection of educational resources that are aligned with the Essential Principles of Climate Science (EPCS). Another goal of the project is to support a community that will assist students, teachers, and citizens in climate literacy. A complementary program begun in 2010 is the ICEE (Inspiring Climate Education Excellence) program, which is developing online modules and courses designed around the climate literacy principles for use by teachers and other interested citizens. In these projects, we learn about teacher needs through a variety of evaluation mechanisms. The programs use evaluation to assist in the process of providing easy access to high quality climate and energy learning resources that meet classroom requirements. The internal evaluation of the CLEAN program is multidimensional. At the CLEAN resource review camps, teachers and scientists work together in small groups to assess the value of online resources for use in the classroom. The review camps are evaluated using observation and feedback surveys; the resulting evaluation reports provide information to managers to fine-tune future camps. In this way, a model for effective climate resource development meetings has been refined. Evaluation methods used in ICEE and CLEAN include teacher needs assessment surveys, teacher feedback at professional development opportunities, scientist feedback at resource review workshops, and regular analysis of online usage of resources, forums, and education modules. This paper will review the most successful strategies for evaluating the effectiveness of online climate and energy education resources and their use by educators and the general public.

Effective Feedback to Improve Primary Care Prescribing Safety (EFIPPS) a pragmatic three-arm cluster randomised trial: designing the intervention (ClinicalTrials.gov registration NCT01602705).

PubMed

Barnett, Karen N; Bennie, Marion; Treweek, Shaun; Robertson, Christopher; Petrie, Dennis J; Ritchie, Lewis D; Guthrie, Bruce

2014-10-11

High-risk prescribing in primary care is common and causes considerable harm. Feedback interventions have small/moderate effects on clinical practice, but few trials explicitly compare different forms of feedback. There is growing recognition that intervention development should be theory-informed, and that comprehensive reporting of intervention design is required by potential users of trial findings. The paper describes intervention development for the Effective Feedback to Improve Primary Care Prescribing Safety (EFIPPS) study, a pragmatic three-arm cluster randomised trial in 262 Scottish general practices. The NHS chose to implement a feedback intervention to utilise a new resource, new Prescribing Information System (newPIS). The development phase required selection of high-risk prescribing outcome measures and design of intervention components: (1) educational material (the usual care comparison), (2) feedback of practice rates of high-risk prescribing received by both intervention arms and (3) a theory-informed behaviour change component to be received by one intervention arm. Outcome measures, educational material and feedback design, were developed with a National Health Service Advisory Group. The behaviour change component was informed by the Theory of Planned Behaviour and the Health Action Process Approach. A focus group elicitation study and an email Delphi study with general practitioners (GPs) identified key attitudes and barriers of responding to the prescribing feedback. Behaviour change techniques were mapped to the psychological constructs, and the content was informed by the results of the elicitation and Delphi study. Six high-risk prescribing measures were selected in a consensus process based on importance and feasibility. Educational material and feedback design were based on current NHS Scotland practice and Advisory Group recommendations. The behaviour change component was resource constrained in development, mirroring what is feasible in an NHS context. Four behaviour change interventions were developed and embedded in five quarterly rounds of feedback targeting attitudes, subjective norms, perceived behavioural control and action planning (2×). The paper describes a process which is feasible to use in the resource-constrained environment of NHS-led intervention development and documents the intervention to make its design and implementation explicit to potential users of the trial findings. ClinicalTrials.gov: NCT01602705.

Manual for Bilingual Dictionaries. Textbook, Word List A-L, and Word List LL-Z.

ERIC Educational Resources Information Center

Robinson, Dow F.

Volume One of this handbook for the preparation of bilingual dictionaries deals with (1) the purpose and structure of the bilingual dictionary for which this manual is designed; (2) the grammatical form of a main entry; (3) the grammatical designation of vernacular entries; (4) gloss in Spanish and vernacular; (5) sense discriminations; (6)…

Digitizing Consumption Across the Operational Spectrum

DTIC Science & Technology

2014-09-01

Figure 14.  Java -implemented Dictionary and Query: Result ............................................22  Figure 15.  Global Database Architecture...format. Figure 14 is an illustration of the query submitted in Java and the result which would be shown using the data shown in Figure 13. Figure...13. NoSQL (key, value) Dictionary Example 22 Figure 14. Java -implemented Dictionary and Query: Result While a

Dictionary of Films. Translated, Edited, and Updated by Peter Morris.

ERIC Educational Resources Information Center

Sadoul, Georges

In an attempt ot give a panorama of world cinema since its origins, this dictionary contains entries for about 1200 films from all over the world. A brief description of the plot of the film, the personnel involved in the production, and often some short, critical comments are included for each film. This dictionary is a companion volume to a…

Yeni Redhouse Lugati; Ingilizce-Turkce (Revised Redhouse Dictionairy; English-Turkish).

ERIC Educational Resources Information Center

United Church Board for World Ministries, Istanbul (Turkey). Near East Mission.

The general plan of this dictionary, first prepared by Sir James Redhouse in 1861 and revised in 1950 and 1953, has been to include all words which appear in the Oxford Concise Dictionary and Webster's Collegiate Dictionary. In addition, a great number of idioms have been added; the volume now contains between 60,000 and 70,000 definitions.…

Word Function and Dictionary Use; A Work-Book for Advanced Learners of English.

ERIC Educational Resources Information Center

Osman, Neile

The present volume is designed as a workbook for advanced learners of English as a second or foreign language which will train them through instruction and exercises to use an all-English dictionary. The contents are based on the second edition of Hornby, Gatenby, and Wakefield's "The Advanced Learner's Dictionary of Current English," 1963, Oxford…

Strategies for Reading Chinese Texts with and without Pop-Up Dictionary for Beginning Learners of Chinese

ERIC Educational Resources Information Center

Wang, Jing

2014-01-01

This study is aimed at identifying reading strategies of beginning learners of Chinese as a foreign language (CFL) with and without a pop-up dictionary and at determining if learners retain the reading comprehension gained from using the dictionary. Beginning CFL learners at a Midwestern university answered questions about their reading strategies…

The Use of a Monolingual Dictionary for Meaning Determination by Advanced Cantonese ESL Learners in Hong Kong

ERIC Educational Resources Information Center

Chan, Alice Y. W.

2012-01-01

This article reports on the results of a study which investigated advanced Cantonese English as a Second Language (ESL) learners' use of a monolingual dictionary for determining the meanings of familiar English words used in less familiar contexts. Thirty-two university English majors in Hong Kong participated in a dictionary consultation task,…

RoLo: A Dictionary Interface that Minimizes Extraneous Cognitive Load of Lookup and Supports Incidental and Incremental Learning of Vocabulary

ERIC Educational Resources Information Center

Dang, Thanh-Dung; Chen, Gwo-Dong; Dang, Giao; Li, Liang-Yi; Nurkhamid

2013-01-01

Dictionary use can improve reading comprehension and incidental vocabulary learning. Nevertheless, great extraneous cognitive load imposed by the search process may reduce or even prevent the improvement. With the help of technology, dictionary users can now instantly access the meaning list of a searched word using a mouse click. However, they…

Terminological Multifaceted Educational Dictionary of Active Type as a Possible Way of Special Discourse Presentation

ERIC Educational Resources Information Center

Fatkullina, Flyuza; Morozkina, Eugenia; Suleimanova, Almira; Khayrullina, Rayca

2016-01-01

The purpose of this article is to disclose the scientific basis of the author's academic terminological dictionary for future oil industry experts. Multifaceted terminological dictionary with several different entries is considered to be one of the possible ways to present a special discourse in the classroom. As a result of the study the authors…

76 FR 39090 - Contract Reporting Requirements of Intrastate Natural Gas Companies; Notice of Extension of Time...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-05

... delay until 90 days after the revised Form No. 549D, XML schema format, and Data Dictionary and... Form 549D, the Data Dictionary and Instructions, notice is hereby given that all section 311 and... Data Dictionary and Instructions for filing Form 549D. Staff also corrected and completed testing of a...

Testing Aspects of the Usability of an Online Learner Dictionary Prototype: A Product- and Process-Oriented Study

ERIC Educational Resources Information Center

Hamel, Marie-Josee

2012-01-01

This article reports on a study which took place in the context of the design and development of an online dictionary prototype for learners of French. Aspects of the "usability", i.e. the quality of the "learner-task-dictionary interaction" of the prototype were tested. Micro-tasks were designed to focus on learners'…

In Search of the Optimal Path: How Learners at Task Use an Online Dictionary

ERIC Educational Resources Information Center

Hamel, Marie-Josee

2012-01-01

We have analyzed circa 180 navigation paths followed by six learners while they performed three language encoding tasks at the computer using an online dictionary prototype. Our hypothesis was that learners who follow an "optimal path" while navigating within the dictionary, using its search and look-up functions, would have a high chance of…

Tactics Employed and Problems Encountered by University English Majors in Hong Kong in Using a Dictionary

ERIC Educational Resources Information Center

Chan, Alice Yin Wa

2005-01-01

Building on the results of a small-scale survey which investigated the general use of dictionaries by university English majors in Hong Kong using a questionnaire survey and their specific use of dictionaries using an error correction task, this article discusses the tactics these students employed and the problems they encountered when using a…

Standardized terminology for clinical trial protocols based on top-level ontological categories.

PubMed

Heller, B; Herre, H; Lippoldt, K; Loeffler, M

2004-01-01

This paper describes a new method for the ontologically based standardization of concepts with regard to the quality assurance of clinical trial protocols. We developed a data dictionary for medical and trial-specific terms in which concepts and relations are defined context-dependently. The data dictionary is provided to different medical research networks by means of the software tool Onto-Builder via the internet. The data dictionary is based on domain-specific ontologies and the top-level ontology of GOL. The concepts and relations described in the data dictionary are represented in natural language, semi-formally or formally according to their use.

Application of composite dictionary multi-atom matching in gear fault diagnosis.

PubMed

Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

2011-01-01

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.

Reducible dictionaries for single image super-resolution based on patch matching and mean shifting

NASA Astrophysics Data System (ADS)

Rasti, Pejman; Nasrollahi, Kamal; Orlova, Olga; Tamberg, Gert; Moeslund, Thomas B.; Anbarjafari, Gholamreza

2017-03-01

A single-image super-resolution (SR) method is proposed. The proposed method uses a generated dictionary from pairs of high resolution (HR) images and their corresponding low resolution (LR) representations. First, HR images and the corresponding LR ones are divided into patches of HR and LR, respectively, and then they are collected into separate dictionaries. Afterward, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary is calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary is passed through an illumination enhancement process. By this technique, the noticeable change of illumination between neighbor patches in the super-resolved image is significantly reduced. The enhanced HR patch represents the HR patch of the super-resolved image. Finally, to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image obtained using bicubic interpolation is calculated. The quantitative and qualitative analyses show the superiority of the proposed technique over the conventional and state-of-art methods.

Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-03-22

Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithm account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms are illustrated through simulations and applications on real fMRI data.

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

PubMed

Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

2015-06-01

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.

PubMed

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13.Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract. © The Author(s) 2016. Published by Oxford University Press.

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

PubMed

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

Progressive Dictionary Learning with Hierarchical Predictive Structure for Scalable Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Xiong, Hongkai; Jiang, Xiaoqian; Zou, Junni; Taubman, David

2017-04-12

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a close-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the stateof- the-art scalable extension of H.264/AVC and latest HEVC, standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest SHVC and HEVC simulcast over extensive test sequences with various resolutions.

Comprehensive human transcription factor binding site map for combinatory binding motifs discovery.

PubMed

Müller-Molina, Arnoldo J; Schöler, Hans R; Araúzo-Bravo, Marcos J

2012-01-01

To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.

Comprehensive Human Transcription Factor Binding Site Map for Combinatory Binding Motifs Discovery

PubMed Central

Müller-Molina, Arnoldo J.; Schöler, Hans R.; Araúzo-Bravo, Marcos J.

2012-01-01

To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%–20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory “DNA words.” From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%—far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of “DNA words,” newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters. PMID:23209563

Small molecule annotation for the Protein Data Bank

PubMed Central

Sen, Sanchayita; Young, Jasmine; Berrisford, John M.; Chen, Minyu; Conroy, Matthew J.; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P.; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A.

2014-01-01

The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100 000 structures contain more than 20 000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. PMID:25425036

Small molecule annotation for the Protein Data Bank.

PubMed

Sen, Sanchayita; Young, Jasmine; Berrisford, John M; Chen, Minyu; Conroy, Matthew J; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A

2014-01-01

The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100,000 structures contain more than 20,000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. © The Author(s) 2014. Published by Oxford University Press.

VASP- VARIABLE DIMENSION AUTOMATIC SYNTHESIS PROGRAM

NASA Technical Reports Server (NTRS)

White, J. S.

1994-01-01

VASP is a variable dimension Fortran version of the Automatic Synthesis Program, ASP. The program is used to implement Kalman filtering and control theory. Basically, it consists of 31 subprograms for solving most modern control problems in linear, time-variant (or time-invariant) control systems. These subprograms include operations of matrix algebra, computation of the exponential of a matrix and its convolution integral, and the solution of the matrix Riccati equation. The user calls these subprograms by means of a FORTRAN main program, and so can easily obtain solutions to most general problems of extremization of a quadratic functional of the state of the linear dynamical system. Particularly, these problems include the synthesis of the Kalman filter gains and the optimal feedback gains for minimization of a quadratic performance index. VASP, as an outgrowth of the Automatic Synthesis Program, has the following improvements: more versatile programming language; more convenient input/output format; some new subprograms which consolidate certain groups of statements that are often repeated; and variable dimensioning. The pertinent difference between the two programs is that VASP has variable dimensioning and more efficient storage. The documentation for the VASP program contains a VASP dictionary and example problems. The dictionary contains a description of each subroutine and instructions on its use. The example problems include dynamic response, optimal control gain, solution of the sampled data matrix Riccati equation, matrix decomposition, and a pseudo-inverse of a matrix. This program is written in FORTRAN IV and has been implemented on the IBM 360. The VASP program was developed in 1971.

Normalizing biomedical terms by minimizing ambiguity and variability

PubMed Central

Tsuruoka, Yoshimasa; McNaught, John; Ananiadou, Sophia

2008-01-01

Background One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach. Results We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS. Conclusions The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known. PMID:18426547

Implementation and management of a biomedical observation dictionary in a large healthcare information system.

PubMed

Vandenbussche, Pierre-Yves; Cormont, Sylvie; André, Christophe; Daniel, Christel; Delahousse, Jean; Charlet, Jean; Lepage, Eric

2013-01-01

This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system. AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary. This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC. This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text.

PubMed

Eriksson, Robert; Jensen, Peter Bjødstrup; Frankild, Sune; Jensen, Lars Juhl; Brunak, Søren

2013-01-01

Drugs have tremendous potential to cure and relieve disease, but the risk of unintended effects is always present. Healthcare providers increasingly record data in electronic patient records (EPRs), in which we aim to identify possible adverse events (AEs) and, specifically, possible adverse drug events (ADEs). Based on the undesirable effects section from the summary of product characteristics (SPC) of 7446 drugs, we have built a Danish ADE dictionary. Starting from this dictionary we have developed a pipeline for identifying possible ADEs in unstructured clinical narrative text. We use a named entity recognition (NER) tagger to identify dictionary matches in the text and post-coordination rules to construct ADE compound terms. Finally, we apply post-processing rules and filters to handle, for example, negations and sentences about subjects other than the patient. Moreover, this method allows synonyms to be identified and anatomical location descriptions can be merged to allow appropriate grouping of effects in the same location. The method identified 1 970 731 (35 477 unique) possible ADEs in a large corpus of 6011 psychiatric hospital patient records. Validation was performed through manual inspection of possible ADEs, resulting in precision of 89% and recall of 75%. The presented dictionary-building method could be used to construct other ADE dictionaries. The complication of compound words in Germanic languages was addressed. Additionally, the synonym and anatomical location collapse improve the method. The developed dictionary and method can be used to identify possible ADEs in Danish clinical narratives.

Ecosystem properties self-organize in response to a directional fog-vegetation interaction.

PubMed

Stanton, Daniel E; Armesto, Juan J; Hedin, Lars O

2014-05-01

Feedbacks between vegetation and resource inputs can lead to the local, self-organization of ecosystem properties. In particular, feedbacks in response to directional resources (e.g., coastal fog, slope runoff) can create complex spatial patterns, such as vegetation banding. Although similar feedbacks are thought to be involved in the development of ecosystems, clear empirical examples are rare. We created a simple model of a fog-influenced, temperate rainforest in central Chile, which allows the comparison of natural banding patterns to simulations of various putative mechanisms. We show that only feedbacks between plants and fog were able to replicate the characteristic distributions of vegetation, soil water, and soil nutrients observed in field transects. Other processes, such as rainfall, were unable to match these diagnostic distributions. Furthermore, fog interception by windward trees leads to increased downwind mortality, leading to progressive extinction of the leeward edge. This pattern of ecosystem development and decay through self-organized processes illustrates, on a relatively small spatial and temporal scale, the patterns predicted for ecosystem evolution.

The chemical component dictionary: complete descriptions of constituent molecules in experimentally determined 3D macromolecules in the Protein Data Bank.

PubMed

Westbrook, John D; Shao, Chenghua; Feng, Zukang; Zhuravleva, Marina; Velankar, Sameer; Young, Jasmine

2015-04-15

The Chemical Component Dictionary (CCD) is a chemical reference data resource that describes all residue and small molecule components found in Protein Data Bank (PDB) entries. The CCD contains detailed chemical descriptions for standard and modified amino acids/nucleotides, small molecule ligands and solvent molecules. Each chemical definition includes descriptions of chemical properties such as stereochemical assignments, chemical descriptors, systematic chemical names and idealized coordinates. The content, preparation, validation and distribution of this CCD chemical reference dataset are described. The CCD is updated regularly in conjunction with the scheduled weekly release of new PDB structure data. The CCD and amino acid variant reference datasets are hosted in the public PDB ftp repository at ftp://ftp.wwpdb.org/pub/pdb/data/monomers/components.cif.gz, ftp://ftp.wwpdb.org/pub/pdb/data/monomers/aa-variants-v1.cif.gz, and its mirror sites, and can be accessed from http://wwpdb.org. jwest@rcsb.rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

Madduri, Kamesh; Wu, Kesheng

The Resource Description Framework (RDF) is a popular data model for representing linked data sets arising from the web, as well as large scienti c data repositories such as UniProt. RDF data intrinsically represents a labeled and directed multi-graph. SPARQL is a query language for RDF that expresses subgraph pattern- nding queries on this implicit multigraph in a SQL- like syntax. SPARQL queries generate complex intermediate join queries; to compute these joins e ciently, we propose a new strategy based on bitmap indexes. We store the RDF data in column-oriented structures as compressed bitmaps along with two dictionaries. This papermore » makes three new contributions. (i) We present an e cient parallel strategy for parsing the raw RDF data, building dictionaries of unique entities, and creating compressed bitmap indexes of the data. (ii) We utilize the constructed bitmap indexes to e ciently answer SPARQL queries, simplifying the join evaluations. (iii) To quantify the performance impact of using bitmap indexes, we compare our approach to the state-of-the-art triple-store RDF-3X. We nd that our bitmap index-based approach to answering queries is up to an order of magnitude faster for a variety of SPARQL queries, on gigascale RDF data sets.« less

Working More Productively: Tools for Administrative Data

PubMed Central

Roos, Leslie L; Soodeen, Ruth-Ann; Bond, Ruth; Burchill, Charles

2003-01-01

Objective This paper describes a web-based resource () that contains a series of tools for working with administrative data. This work in knowledge management represents an effort to document, find, and transfer concepts and techniques, both within the local research group and to a more broadly defined user community. Concepts and associated computer programs are made as “modular” as possible to facilitate easy transfer from one project to another. Study Setting/Data Sources Tools to work with a registry, longitudinal administrative data, and special files (survey and clinical) from the Province of Manitoba, Canada in the 1990–2003 period. Data Collection Literature review and analyses of web site utilization were used to generate the findings. Principal Findings The Internet-based Concept Dictionary and SAS macros developed in Manitoba are being used in a growing number of research centers. Nearly 32,000 hits from more than 10,200 hosts in a recent month demonstrate broad interest in the Concept Dictionary. Conclusions The tools, taken together, make up a knowledge repository and research production system that aid local work and have great potential internationally. Modular software provides considerable efficiency. The merging of documentation and researcher-to-researcher dissemination keeps costs manageable. PMID:14596394

Optimal feedback scheme and universal time scaling for Hamiltonian parameter estimation.

PubMed

Yuan, Haidong; Fung, Chi-Hang Fred

2015-09-11

Time is a valuable resource and it is expected that a longer time period should lead to better precision in Hamiltonian parameter estimation. However, recent studies in quantum metrology have shown that in certain cases more time may even lead to worse estimations, which puts this intuition into question. In this Letter we show that by including feedback controls this intuition can be restored. By deriving asymptotically optimal feedback controls we quantify the maximal improvement feedback controls can provide in Hamiltonian parameter estimation and show a universal time scaling for the precision limit under the optimal feedback scheme. Our study reveals an intriguing connection between noncommutativity in the dynamics and the gain of feedback controls in Hamiltonian parameter estimation.

Combining the Benefits of Electronic and Online Dictionaries with CALL Web Sites to Produce Effective and Enjoyable Vocabulary and Language Learning Lessons

ERIC Educational Resources Information Center

Loucky, John Paul

2005-01-01

To more thoroughly analyze and compare the types of dictionaries being used by Japanese college students in three college engineering classes, two kinds of surveys were designed. The first was a general survey about purchase, use and preferences regarding electronic dictionaries. The second survey asked questions about how various computerised…

Disruptive Innovation: Value-Based Health Plans

PubMed Central

Vogenberg, F. Randy

2008-01-01

Value and a Complex Healthcare Market What Is Value to an Employer? “Worth in usefulness or importance to the possessor; utility or merit.” American Heritage Dictionary “A principle, standard, or quality considered worthwhile or desirable.” American Heritage Stedman's Medical Dictionary “A fair return or equivalent in goods, services, or money for something exchanged.” Merriam-Webster's Dictionary of Law PMID:25128808

The Use of E-Dictionary to Read E-Text by Intermediate and Advanced Learners of Chinese

ERIC Educational Resources Information Center

Wang, Jing

2012-01-01

This study focuses on the pedagogical outcomes connected with the use of an e-dictionary by intermediate and advanced learners of Chinese to aid in reading an expository Chinese e-text. Twenty intermediate and advanced participants read an e-text twice aided by an e-dictionary and wrote recalls of the text in English. In addition to low frequency…

The Use of Electronic Dictionary in the Language Classroom: The Views of Language Learners

ERIC Educational Resources Information Center

Barham, Kefah A.

2017-01-01

E- Dictionaries have the potential to be a useful instrument in English Language classes, at the same time; it can be seen as a waste of time and a hindrance tool in the English Language classroom. This paper reports on students' use of e-dictionary in two of "Educational Readings in the English Language" course sections through in-depth…

JPRS Report, Soviet Union, Political Affairs, Republic Language Legislation.

DTIC Science & Technology

1989-12-05

reference materials ( dictionaries , termi- nology glossaries, phrase books, self-taught books, and so on), and qualified specialists in the field of...textbooks; d) to publish self-taught manuals, phrase books, and explanatory and bilingual dictionaries for the aid of persons desiring to study...Armenian. To create the nec- essary printing facility base to publish high-quality illus- trated dictionaries ; to provide uninterrupted delivery of

Creating a Chinese suicide dictionary for identifying suicide risk on social media.

PubMed

Lv, Meizhen; Li, Ang; Liu, Tianli; Zhu, Tingshao

2015-01-01

Introduction. Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary. Methods. To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models. Results and Discussion. Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r = 0.507) and evaluating individual suicide risk (r = 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1: F 1 = 0.48; t2: F 1 = 0.56) produced a more accurate identification than SCLIWC (t1: F 1 = 0.41; t2: F 1 = 0.48) on different observation windows. Conclusions. This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries.

Creating a Chinese suicide dictionary for identifying suicide risk on social media

PubMed Central

Liu, Tianli

2015-01-01

Introduction. Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary. Methods. To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models. Results and Discussion. Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r = 0.507) and evaluating individual suicide risk (r = 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1: F1 = 0.48; t2: F1 = 0.56) produced a more accurate identification than SCLIWC (t1: F1 = 0.41; t2: F1 = 0.48) on different observation windows. Conclusions. This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries. PMID:26713232

Resource-driven changes to host population stability alter the evolution of virulence and transmission.

PubMed

Hite, Jessica L; Cressler, Clayton E

2018-05-05

What drives the evolution of parasite life-history traits? Recent studies suggest that linking within- and between-host processes can provide key insight into both disease dynamics and parasite evolution. Still, it remains difficult to understand how to pinpoint the critical factors connecting these cross-scale feedbacks, particularly under non-equilibrium conditions; many natural host populations inherently fluctuate and parasites themselves can strongly alter the stability of host populations. Here, we develop a general model framework that mechanistically links resources to parasite evolution across a gradient of stable and unstable conditions. First, we dynamically link resources and between-host processes (host density, stability, transmission) to virulence evolution, using a 'non-nested' model. Then, we consider a 'nested' model where population-level processes (transmission and virulence) depend on resource-driven changes to individual-level (within-host) processes (energetics, immune function, parasite production). Contrary to 'non-nested' model predictions, the 'nested' model reveals complex effects of host population dynamics on parasite evolution, including regions of evolutionary bistability; evolution can push parasites towards strongly or weakly stabilizing strategies. This bistability results from dynamic feedbacks between resource-driven changes to host density, host immune function and parasite production. Together, these results highlight how cross-scale feedbacks can provide key insights into the structuring role of parasites and parasite evolution.This article is part of the theme issue 'Anthropogenic resource subsidies and host-parasite dynamics in wildlife'. © 2018 The Author(s).

Leveraging Student Feedback To Improve Teaching in Web-Based Courses.

ERIC Educational Resources Information Center

Schnorr, Donna; Hazari, Sunil

1999-01-01

Explores cognitive aspects of learning and demonstrates how student feedback can be applied to improve Web-based teaching drawing on experiences with undergraduate courses at California State University, San Bernardino. Highlights include Web interactivity; HTML forms; and Web resources with forms for informal assessment. (LRW)