Data Preparation. (s,t)∈→−x x=1 i=1 P ((i, j)|f (t), e(s); ←θ−),, (31) Although it is appealing to apply our approach to dealing with real-world non-parallel corpora, it is time-consuming and labor-intensive to manually construct a ground truth parallel corpus. There-
Data Preparation. The Contractor approaches data preparation in a way that is ongoing, automated wherever feasible, scalable, and auditable. The Contractor’s preparation approach must be flexible and extensible to future data sources as well, including State datasets and systems. For the CCRS, data preparation will consist of the following at a minimum:
Data Preparation s,t⟩∈→−x x=1 i=1 P (⟨i, j⟩|f (t), e(s); ←θ−),, (31) Although it is appealing to apply our approach to dealing with real-world non-parallel corpora, it is time-consuming and labor-intensive to manually construct a ground truth parallel corpus. There- ⟨ ⟩| where P ( i, x x(s), f (t); →−θ ) is source-to-target ⟨ ⟩ link posterior probability of the link i, j be- ing present (or absent) in the word align- ment according to the source-to-target model, ⟨ ⟩| P ( i, x x (t), e(s); ←θ−) is target-to-source link pos- terior probability. We follow Xxxxx et al. (2006) to use the product of link posteriors to encourage the agreement at the level of word alignment. xxxx, we follow Xxxx et al. (2015) to build syn- thetic E, F , and G to facilitate the evaluation. We first extract a set of parallel phrases from a sentence-level parallel corpus using the state- of-the-art phrase-based translation system Xxxxx (Xxxxx et al., 2007) and discard low-probability parallel phrases. Then, E and F can be con- structed by corrupting the parallel phrase set by 0.40 0.35 agreement ratio 0.30 0.25 noise inner outer no agreement iteration C → E E → C Outer Inner C E 0 10K 41.0 54.4 83.6 83.8 0 20K 28.3 48.3 80.1 81.2 10K 0 54.7 43.1 84.9 84.3 20K 0 50.4 31.4 83.8 83.6 10K 10K 34.9 34.4 80.0 79.7 20K 20K 22.4 23.1 73.6 74.3 Table 2: Effect of noise in terms of F1 on the de- velopment set. Figure 4: Comparison of agreement ratios on the development set. seed C → E E → C Outer Inner 50 4.1 4.8 60.8 66.2 100 5.1 5.5 65.6 69.8 500 7.5 8.4 70.4 72.5 1,000 22.4 23.1 73.6 74.3 Table 1: Effect of seed lexicon size in terms of F1 on the development set. adding irrelevant source and target phrases ran- domly. Note that the parallel phrase set can serve as the ground truth parallel corpus G. We refer to the non-parallel phrases in E and F as noise. From LDC Chinese-English parallel corpora, we constructed a development set and a test set. The development set contains 20K parallel phras- es, 20K noisy Chinese phrases, and 20K noisy En- glish phrases. The test test contains 20K parallel phrases, 180K noisy Chinese phrases, and 180K noisy English phrases. The seed parallel lexicon contains 1K entries.
Data Preparation. The years that were focused on in this analysis were graduating years of 2017, 2018, and 2019. This is because the senior and one- year out surveys were both available and contained similar formatting. The variables that were focused on were: • Major and major department • Major satisfaction rating • Employment status • Employment position • Employment relation to major • Would you pick Etown again if you started your college search over today? The data preparation and organization were a large part of this project. Although each senior survey contained similar questions, there were some key fields that differed. Each year of the one-year out and the senior survey information was contained within separate excel files. There were years of the surveys that did not contain student id numbers. This is the key identifier of students and acted as the method of joining the senior survey and one-year out survey information to collect those who answered both surveys. Therefore, to correct this information gap, the id numbers needed to be brought in. In addition, there were years of the survey that only contained the major department while other years only contained the individual major itself. Therefore, for years that contained the specific major, the major department was brought in. In addition to the important field gaps, there were smaller adjustments that needed to be made. Although the answer choices for all of the rating field contained Very Dissatisfied, Dissatisfied, Neither Satisfied nor Dissatisfied, Satisfied, and Very Satisfied the capitalization differed and therefore was adjusted for the aggregation of the data to be successful.
Data Preparation. The NREL team will communicate with SEA on the geographic extent of the microsimulation model and the data sources needed for enabling the master function. Besides the existing microsimulation model and the passenger demand profile, the NREL team will work with Port staff to estimate traffic volume entering the simulation area on major access roads, background traffic demand (e.g., recirculating traffic, employee commuting, etc.), bypassing traffic volume on major access roads, and the distribution of passenger origins and destinations inside the airport (e.g., terminals, curb segments, etc.). The NREL team will seek other open sources (such as TomTom API) for any inputs that are not currently available to the Port staff.
Data Preparation. All documents, instruments and data supplied by Client to TCS will be supplied in accordance with the previously agreed upon time requirements and specifications set forth in Schedule 1. Client shall be responsible for all consequences of its failure to supply TCS with accurate documents and data within prescribed time periods. Client agrees to retain duplicate copies of all documents, instruments and data supplied by Client to TCS hereunder; or, if the production and retention of such copies is not practical, Client holds TCS blameless for loss or damage to said documents. Client is responsible for the accuracy and completeness of its own information and documents and Client is responsible for all of its acts, omissions and representations pertaining to or contained in all such information or documents. Unless Client previously informs TCS in writing of exceptions or qualifications, TCS has the right to rely upon the accuracy and completeness of the information and documents provided by Client and TCS assumes no liability for services performed in reliance thereon. TCS shall inform Client of any erroneous, inaccurate or incomplete information or documents from the Client to the extent such becomes apparent or known to TCS. However, unless expressly accepted in writing as a part of the service to be performed, TCS shall have no obligation to audit or review Client's information or documents for accuracy or completeness.
Data Preparation. Well water test data was provided to Emory University by ARK starting in November 2019. The ARK dataset contained censored data consisting of values below their respective limits of detection (LODs) and missing data for variables that were not tested. Censored data consists of unknown values beyond a certain threshold. In this study, censored data refers to data points below the parameter’s LOD. These parameters resulted in little variation across observations due to their data points being below the limit of detection (LOD) and/or were not tested for throughout the duration of the sampling time period and therefore were not used in correlation or regression analysis. Censored data points were imputed using their respective LOD divided by the square root of 2, per EPA guidance [24].
Data Preparation. In preparing the data for subsequent analyses, several iterations were required to detect potential outliers, errors and other data anomalies. Reviews included multiple scatter plot comparisons, source plot card reviews, as well as between-measurement data checks. Corrections were made where noted, and plot measurement deletions only occurred in a few instances. SAS programs were written so that compilations could be easily adjusted or modified (e.g., changes in utilization standards). All SAS programs and input data files will be made available to ASRD.
Data Preparation. The data available from various sources was collected. The ground maps, contour information, etc. were scanned, digitized and registered as per the requirement. Data was prepared depending on the level of accuracy required and any corrections required were made. All the layers were geo-referenced and brought to a common scale (real coordinates), so that overlay could be performed. A computer programme was used to estimate the soil loss. The formats of outputs from each layer were firmed up to match the formats of inputs in the program. The grid size to be used was also decided to match the level of accuracy required, the data availability and the software and time limitations. The format of output was finalized. Ground truthing and data collection was also included in the procedure.
Data Preparation. HDM-4’s required input is organized into data sets that describe road networks, vehicle fleets, pavement preservation standards, traffic and speed flow patterns, and climate conditions. Most of the required pavement performance information was obtained from 2002 data within the Washington State Pavement Management System (WSPMS) (Xxxxxxxxxxxx et al., 2002). Other data were obtained through available literature and interviews with WSDOT personnel. The Road Networks data set contains a detailed account of each road section’s physical attributes. HDM-4 uses this information to model pavement deterioration and to provide input to other models. The Vehicle Fleet data set contains vehicle characteristics that are used for calculating speeds, operating costs, and travel times to determine traffic impacts on roads and the resulting costs for the economic analysis. The WSPMS vehicle classification was used for HDM-4 input and included passenger cars, single-unit trucks, double-unit trucks, and truck trains (Xxxxxxxxxxxx et al., 2003). Preservation standards define pavement preservation practices, including their costs and effects on pavement conditions when they are applied. Although WSDOT uses a number of different preservation practices, the most common one for flexible pavement is a 45-mm HMA overlay (Xxx et al., 1993). The typical target distress for application of a 45-mm HMA overlay is when the total area of pavement cracking is ≥ 10 percent (total roadway area), rut depth is ≥ 10 mm, or the IRI is ≥ 3.5 m/km (although the “trigger” IRI used by WSDOT may be reduced to about 2.8 m/km). Table 1 lists the major inputs. Specific inputs shown in Table 1 are not described in this report. Table 1: Maintenance standard of 45-mm HMA overlay in HDM-4 version 1.3 General Name: 45-mm HMA Overlay Short Code: 45 OVER Intervention Type: Responsive Design Surface Material: Asphalt Concrete Thickness: 45 mm Dry Season a: 0.44 CDS: 1 Intervention Responsive Criteria: Total cracked area ≥ 10% or Rutting ≥ 10 mm or IRI ≥ 3.5 m/km Min. Interval: 1 Max. Interval: 9999 Last Year: 2099 Max Roughness: 16 m/km Min ADT: 0 Max ADT: 500,000 Costs Overlay Economic: 19 dollars/m2 * Financial: 19 dollars/m2 * Patching Economic: 47 dollars/m2 * Financial: 47 dollars/m2 * Edge Repair Economic: 47 dollars/m2 Financial: 47 dollars/m2 Effects Roughness: Use generalized bilinear model a0 = 0.5244 a1 = 0.5353 a2 = 0.5244 a3 = 0.5353 Rutting: Use rutting reset coefficient = 0 Texture Depth: Use def...
