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Table 1. Statistics of TDT-2 and TDT-3: our development and evaluation data sets. (The Mandarin audio documents are accompanied by recognized words from the Dragon system).

in Mandarin-English Information (MEI): Investigating Translingual Speech Retrieval
by Helen Meng, Berlin Chen, Sanjeev Khudanpur, Gina-Anne Levow, Wai-Kit Lo, Douglas Oard, Patrick Schone, Karen Tang, Hsin-min Wang, Jianqiang Wang
"... In PAGE 2: ... We used the TDT-2 corpus as our development test set, and TDT-3 as our evaluation test. Table1 describes the... ..."

Table 1 presents an inventory of recent software engineering process evaluation and assessment models. Each of these models was analyzed to identify contributions that could help maintainers. Of the thirty-four proposed models in this inventory, only a handful (shown in bold in table 1) include documented maintenance practices, sometimes accompanied by a rationale and references. However, none of these models covers the entire set of topics and concepts of the body of knowledge specific to software maintenance. This body of knowledge has recently been documented in the chapter 6 of the Guide to the Software Engineering Body of Knowledge [Abr04].

in unknown title
by unknown authors 2004
"... In PAGE 2: ... Table1 : Software Engineering CMM proposals, sorted by year of publication [Apr04] presents the mapping of a much larger number of software maintenance references: a) standards; b) relevant software engineering CMM proposals; and c) recognized software maintenance references. From these mappings, a large number of software maintenance best practices have been identified and listed.... ..."
Cited by 2

Table I - Description of the Variables This table describes the variables in the paper. The Supervisor is the main government agency in charge of supervising stock exchanges. The Issuer is a domestic corporation that raises capital through an initial public offering of common shares. The newly- issued shares will be listed on the country=s largest stock exchange. The Distributor advises the Issuer on the preparation of the prospectus and assists in marketing the securities but does not authorize (or sign) the prospectus unless required by law. The Accountant audits the financial statements and documents that accompany the prospectus. Unless otherwise specified, the source for the variables is the questionnaire of law firms and the laws of each country. The edited answers to the questionnaire are posted at http://post.economics.harvard.edu/faculty/shleifer/papers/securities_documentation.pdf.

in What works in securities laws
by Rafael La Porta, Florencio Lopez-de-silanes, Andrei Shleifer, To Constanza Blanco, John C. Coates Iv, Leyva Martinez, Carlos Orta Tejeda, Tuffic Miguel Ortega, Jorge Gabriel, Taboada Hoyos, L. Nazareth, To Douglas Baird 2006
Cited by 1

Table 1. Subroutine ReLPM

in unknown title
by unknown authors 2006
"... In PAGE 2: ... They are displayed in Tables 1{3, and accompanied by a detailed documentation. Comments to Table1 . This is the main subroutine.... ..."
Cited by 3

Table 3. Recognition results for the SONAR benchmark by various

in Learning and Decision-Making in the Framework of Fuzzy Lattices
by Vassilis G. Kaburlasos, Vassilios Petridis 2000
"... In PAGE 24: ...Table3 summarizes results by various methods. Back propagation and nearest neighbor results are reported in the documentation that accompanies the SONAR data.... ..."
Cited by 5

Table 2: Medical diagnosis tasks of Proben1 benchmark datasets. The datasets have been taken unchanged from an existing collection of real-world benchmark problems, Proben1 [20], that has been established originally for neural networks. The results obtained with one of the fastest learning algorithms for feed-forward neural networks (RPROP) accompany the Proben1 benchmark set to serve as a direct comparison with other methods. Comparability and reproducibility of the results is guaranteed by careful documentation of the experiments. Following the benchmarking idea and in order to increase the con dence in our results with linear GP the results for neural networks have been adopted here from [20] but have been veri ed exemplarily. The main objective is to realize a comparison as fair as possible between genetic programming and neural networks in medical data classi cation and 11

in A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining
by Markus Brameier, Wolfgang Banzhaf 2001
"... In PAGE 11: ... In this contribution genetic programming is applied to six medical problems. Table2 gives a brief description of the diagnosis problems and the diseases that are to be predicted. Medical diagnosis problems always describe classi cation tasks that are much more frequent in medicine than approximation problems.... ..."
Cited by 55

Table 1. Properties of input devices with accompanying explanations.

in A Survey of Manual Input Devices
by Martin J. Schedlbauer 2007
Cited by 1

Table 6: Technical Documentation for Supported Routing Platforms

in unknown title
by unknown authors 2008
"... In PAGE 5: ...he following features have been added to JUNOS Release 8.4. Following the description is the title of the manual or manuals to consult for further information. For a complete list of manuals, see Table6 on page 96 and Table 7 on page 99. NOTE: Starting with JUNOS Release 8.... In PAGE 95: ...onger available. PDF output for each manual as a whole continues to be available. a73 For some of the software manuals, HTML filenames now incorporate numerical IDs instead of chapter names. List of Technical Publications Table6 on page 96 lists the software and hardware guides and release notes for Juniper Networks J-series, M-series, MX-series, and T-series routing platforms and Changes to Documentation a73 95... In PAGE 97: ...Table6 : Technical Documentation for Supported Routing Platforms (continued) Description Book Describes the JUNOS software components and packaging and explains how to initially configure, reinstall, and upgrade the JUNOS system software. This material was formerly covered in the JUNOS System Basics Configuration Guide.... In PAGE 98: ...Table6 : Technical Documentation for Supported Routing Platforms (continued) Description Book Provides reference pages for the operational tag elements in the JUNOS XML API. JUNOS XML API Operational Reference Describes how to use the NETCONF API to monitor and configure Juniper Networks routing platforms.... In PAGE 99: ...Table6 : Technical Documentation for Supported Routing Platforms (continued) Description Book Summarize new features and known problems for a particular software release, provide corrections and updates to published JUNOS, JUNOScript, and NETCONF manuals, provide information that might have been omitted from the manuals, and describe upgrade and downgrade procedures. JUNOS Release Notes Describe the available documentation for the routing platform and summarize known problems with the hardware and accompanying software.... ..."

Table 11: Experimental factor values example. The Characteristics categories used in column headings (i.e., the terms in square brackets) are taken from the MGED Ontology quot;BioMaterialCharacteristics quot; class [26]. The values contained in the body of these columns may be either free text, or terms from an ontology as indicated by an quot;OI quot; tag in the column heading (relating to the MAGEv2 concept quot;OntologyIndividual quot;). For example, the quot;OI:nci_meta quot; tag indicates that terms are taken from the NCI Metathesaurus [27]. The sources for these database tags ( quot;nci_meta quot;, quot;CTO quot;) are defined in the IDF, as shown in Table 3. Biological replicates are indicated by shared experimental factor values ( quot;Time quot; in this example; the columns containing experimental factors would be specified in the accompanying IDF). Most of the protocols have been omitted for brevity. Please see the detailed MAGE-TAB specification document [22] for more information.

in unknown title
by unknown authors 2006
"... In PAGE 13: ... The experimental factors are the principal variables in the investigation, for instance quot;time quot; in time series investigations, quot;dose quot; in dose response investigations, quot;compound quot; in compound have several experimental factors; for example, com- pound, dose and time may all be experimental factors in a dose response investigation in which several com- pounds are added to the samples over a time course. Experimental factors and their values can appear in the SDRF file in any column ( Table11 ), and are annotated as such by being listed in the IDF file. For example, the IDF linked to the SDRF in Table 11 would include the MGED Ontology term quot;Time quot; in its list of experimental factors (see Table 3), indicating that the quot;ParameterValue [Time] quot; column represents an experimental variable.... In PAGE 13: ... Experimental factors and their values can appear in the SDRF file in any column (Table 11), and are annotated as such by being listed in the IDF file. For example, the IDF linked to the SDRF in Table11 would include the MGED Ontology term quot;Time quot; in its list of experimental factors (see Table 3), indicating that the quot;ParameterValue [Time] quot; column represents an experimental variable. Biological replicates are represented by distinct biological sources, Table 6: Representation of the investigation design in Figure 5 as an SDRF.... ..."

Table 2: Summaries of productivity and accompanying size for 150 IT-projects.

in Quantifying the Effects of IT-Governance Rules
by unknown authors
"... In PAGE 12: ...Table 2: Summaries of productivity and accompanying size for 150 IT-projects. From Table2 , we learn that the productivity ranges between 4.5 and 15.... ..."
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