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af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] proc sort data =BP_analysis_Data; by sdmvstra sdmvpsu; run ; [Explanation] | Use the SAS procedure, proc sort, to sort the data by the design parameters, strata (sdmvstra) and primary sampling units (sdmvpsu), before running the procedure in SUDAAN. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] proc descript span> data =BP_analysis_Data design =WR noprint DEFT2 ATLEVEL1=1 ATLEVEL2=2 ; [Explanation] | Use proc descript to specify the dataset (BP_analysis_Data), specify the sample design using the design option WR (with replacement). Use the noprint option to suppress printing of results. In this example, you will be sending the results to a SAS data file for further calculations and printing in SAS. Use a DEFT2 statement to request the calculation of the design effect using method 2 (see SUDAAN manual for details on the differing methods for calculating design effect). Method 2 is the method recommended by NCHS for NHANES data. The ATLEVEL1=1 and ATLEVEL2=2 options specify the sampling stages (in NHANES, the number of strata is level 1, and the number of PSUs is level 2) for which you want counts per table cell. ATLEV1 is the number of strata with at least one valid observation and ATLEV2 is the number of PSUs with at least one valid observation. These numbers are used to calculate degrees of freedom. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] nest sdmvstra sdmvpsu; [Explanation] | Use the nest statement to specify the strata and PSU variables to account for the sample design effects. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] Weight wtmec4yr; [Explanation] | Use the weight statement to account for the unequal probability of sampling and nonresponse. In this example, the MEC weight for four years of data is used. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] SUBPOPN riagendr=2 and ridageyr > 19 and ridageyr < 60 ; [Explanation] | Use a subpopn statement to subset on the subgroup of interest. In this example, it is females (riagendr=2) between ages 20-59 years (ridageyr > 19 and ridageyr < 60). | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] Var hbp; [Explanation] | Use a var statement to set the variable of interest as the percent with high blood pressure. (In order to generate results that are expressed in percent with the condition, this variable was coded as 0=no high blood pressure and 100=those with high blood pressure.) | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] class race educ; [Explanation] | Use a class statement to set the categorical variables of interest. In this example, race/ethnic group (race) and education level (educ). | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] Tables race*educ ; [Explanation] | Use a tables statement to request prevalence of high blood pressure stratified on education level (educ) within each race/ethnic group (race). | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] Output atlev1=numstrat atlev2=numpsu mean=mean semean=semean NSUM=N deffmean / filetype=SAS filename=test1 replace; [Explanation] | Use the output statement to request output of results to a SAS data file (filetype=SAS) called test1 (filename=test1). Use a replace statement to replace this file each time this program is run and updated with the latest results. Use an atlev1 option to create the SAS data variable, numstrat, with the value obtained from counting the number of strata in each subdomain requested with at least one valid observation. Use an atlev2 option to create a SAS variable, numpsu, with the value obtained from counting the number of PSU's in each subdomain requested with at least one valid observation. Use the mean option to output the mean to the SAS data set. In this example, the mean is the percent of individuals at each level with high blood pressure. Use the semean option to output the standard error of the mean estimated above to the SAS dataset. Use the nsum option to create the variable N in the SAS dataset which gives the number of observations in each level in each subdomain requested in the table statement. Use the deffmean option to output the design effect for each subdomain requested. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] Rformat race racef. ; Rformat educ educf. ; [Explanation] | Use an rformat option to specify formats for the levels of each categorical variable in the tables statement as needed. Format statements for each variable must be listed individually. In this example, you are setting the formats for the race/ethnic group (race) and education level (educ) variables. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
af2a44c6_l__Variance_Estimation__Task_3_SUDAAN_proc_descript_procedure_Explanation | [Statements] Rtitle "Prevalence of high blood pressure by race and education level for women age 20-59" ; [Explanation] | Use the rtitle option to set the title for output for procedure. | [] | Continuous NHANES Web Tutorial: Variance Estimation: Task 3 | Explanation | http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm | 39/1438042989126.22_20150728002309-00279-ip-10-236-191-2_363981153_0.json |
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6725f926_nce___BlackBerry_Browser___7_0__Description | [Event type] DOMContentLoaded [Bubbles] no [Cancelable] no [Description] | Occurs when the BlackBerry Browser has completely parsed the document, before any onload events are triggered. | [] | Events API - JavaScript Reference - BlackBerry Browser - 7.0 | Description | http://docs.blackberry.com/pt-br/developers/deliverables/27297/DOM_Events_Objects_1180979_11.jsp | 39/1438043062723.96_20150728002422-00189-ip-10-236-191-2_75426402_23.json |
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9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] General application data [MIME major type] | application | [
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[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] Image data [MIME major type] | image | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
"e"
],
[
"m",
"o",
"d",
"e",
"l"
],
[
"m",
"u",
"l",
"t",
"i",
"p",
"a",
"r",
"t"
],
[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] Mail messages, message status information [MIME major type] | message | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
"e"
],
[
"m",
"o",
"d",
"e",
"l"
],
[
"m",
"u",
"l",
"t",
"i",
"p",
"a",
"r",
"t"
],
[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] VRML and other modeling data [MIME major type] | model | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
"e"
],
[
"m",
"o",
"d",
"e",
"l"
],
[
"m",
"u",
"l",
"t",
"i",
"p",
"a",
"r",
"t"
],
[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] Container for other MIME parts [MIME major type] | multipart | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
"e"
],
[
"m",
"o",
"d",
"e",
"l"
],
[
"m",
"u",
"l",
"t",
"i",
"p",
"a",
"r",
"t"
],
[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] Text data [MIME major type] | text | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
"e"
],
[
"m",
"o",
"d",
"e",
"l"
],
[
"m",
"u",
"l",
"t",
"i",
"p",
"a",
"r",
"t"
],
[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] yes [Description] Video data [MIME major type] | video | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
"e"
],
[
"m",
"o",
"d",
"e",
"l"
],
[
"m",
"u",
"l",
"t",
"i",
"p",
"a",
"r",
"t"
],
[
"t",
"e",
"x",
"t"
],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
9f310afe_The_Mutt_E_Mail_Client__MIME_major_type | [Standard] no [Description] Mostly molecular data [MIME major type] | chemical | [
[
"a",
"p",
"p",
"l",
"i",
"c",
"a",
"t",
"i",
"o",
"n"
],
[
"a",
"u",
"d",
"i",
"o"
],
[
"i",
"m",
"a",
"g",
"e"
],
[
"m",
"e",
"s",
"s",
"a",
"g",
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],
[
"m",
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"e",
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],
[
"m",
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],
[
"t",
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"x",
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],
[
"v",
"i",
"d",
"e",
"o"
],
[
"c",
"h",
"e",
"m",
"i",
"c",
"a",
"l"
]
] | The Mutt E-Mail Client | MIME major type | http://www.mutt.org/doc/devel/manual.html | 39/1438042989126.22_20150728002309-00213-ip-10-236-191-2_610749158_22.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Protects you against malicious mobile apps by dynamically collecting and rating mobile applications for malicious activity, resource usage, and privacy violations [PROTECTION] | Mobile App Reputation | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Protects against false positives using in-the-cloud whitelists from one of the world's largest databases to focus threat research efforts [PROTECTION] | Whitelisting | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Rapidly discovers and protects you against known and zero-day exploits by virtually patching newfound vulnerabilities [PROTECTION] | Vulnerabilities and Exploits | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Quickly identifies botnet or targeted attack behaviors by identifying communications between targets and threat actors' servers [PROTECTION] | Command & Control Communication Protection | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Proactively protects against new threats using active research and investigation to identify new attack methods before they are used by cybercriminals [PROTECTION] | Threat Actor Intelligence | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Blocks users from accessing compromised or malicious websites by tracking the credibility of web pages and domains [PROTECTION] | Enhanced Web Reputation | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Blocks spam and phishing emails that use social engineering before they reach you [PROTECTION] | Enhanced Email Reputation | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Checks the reputation of each file (including metadata like prevalence, age, geo-location) against an extensive database in the cloud before permitting user access, freeing you from dealing with oppressive pattern files [PROTECTION] | Enhanced File Reputation | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Provides immediate and automatic protection from the multitude of threats by continuously updating and correlating massive amounts of global threat intelligence [PROTECTION] | Big Data Analytics and Data Mining Correlation | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Safeguards network bandwidth, endpoint efficiency, and privacy by performing web and file reputation queries directly to local servers, instead to the public cloud [PROTECTION] | Smart Protection Server | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
d0407483____Cloud_Data_Mining_Framework__PROTECTION | [WHY IT MATTERS TO YOU] Speeds protection by automatically updating Trend Micro's global threat intelligence each time a new threat is identified on a single customer's routine reputation check [PROTECTION] | Smart Feedback | [] | Global Threat Intelligence – Cloud Data Mining Framework | PROTECTION | http://www.trendmicro.ca/en-ca/technology-innovation/our-technology/smart-protection-network/index.html | 39/1438042989126.22_20150728002309-00286-ip-10-236-191-2_780999264_1.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] B [Replace Engine Oil] | Replace Engine Oil and Filter. Inspect various systems and components | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] 1 [Replace Engine Oil] | Rotate Tires and check tire pressure and condition | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] 2 [Replace Engine Oil] | Replace air cleaner element, check drive belt and replace dust and pollen filter | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] 3 [Replace Engine Oil] | Replace transmission fluid and transfer fluid (if equipped) | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] 4 [Replace Engine Oil] | Replace spark plugs, replace timing belt (if equipped), inspect water pump, and inspect value clearance | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] 5 [Replace Engine Oil] | Replace Engine Coolant | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
a038632e_ese_Imports_at_Los_Gatos_Acura__Replace_Engine_Oil | [A] 6 [Replace Engine Oil] | Replace rear differential fluid (if equipped) | [] | Acura Car Service Los Gatos & Sunnydale | Get Acura & Honda Service or Repairs for Japanese Imports at Los Gatos Acura | Replace Engine Oil | http://www.losgatosacura.com/service/index.htm | 39/1438042989126.22_20150728002309-00058-ip-10-236-191-2_565549962_0.json |
00545926_Index_Program__Col_2 | [Col 1] diversions [Col 3] diversity [Col 4] divert [Col 2] | diversities | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] diverted [Col 3] Divett [Col 4] dividable [Col 2] | dives | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] Divide [Col 3] DIVIDED [Col 4] Divided [Col 2] | divide | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] divided [Col 3] Dividends [Col 4] divider [Col 2] | dividend | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] DIVIDERS [Col 3] dividers [Col 4] divides [Col 2] | Dividers | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] DIVIDING [Col 3] dividing [Col 4] dividion [Col 2] | Dividing | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] Divination [Col 3] Divine [Col 4] divine [Col 2] | divination | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] diving [Col 3] divisable [Col 4] divisible [Col 2] | Divinity | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] DIVISION [Col 3] division [Col 4] Divisions [Col 2] | Division | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] divisions [Col 3] divison [Col 4] divisor [Col 2] | Divison | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] divisors [Col 3] divits [Col 4] divorce [Col 2] | divistions | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] divorced [Col 3] divsion [Col 4] divulge [Col 2] | divs | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] divvy [Col 3] Diwa [Col 4] diwa901 [Col 2] | DIWA | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] DIWAs [Col 3] diwstance [Col 4] Dix [Col 2] | Diwas | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] Dixie [Col 3] Dixon [Col 4] DIY [Col 2] | dixneuf | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] diyaudio [Col 3] DJ [Col 4] djehrma [Col 2] | dizzy | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] djehrman [Col 3] djreprints [Col 4] dk [Col 2] | DJIA | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] dkirchge [Col 3] Dl [Col 4] dL [Col 2] | dkirchgesler | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] dl [Col 3] dlb [Col 4] DLet [Col 2] | DL8SEL | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
00545926_Index_Program__Col_2 | [Col 1] Dlevinas79 [Col 3] dlide [Col 4] DLL [Col 2] | dlevinas79 | [] | Index Program | Col 2 | http://www.bundesverband-pokervereine.de/Index-Program.htm | 39/1438043062723.96_20150728002422-00287-ip-10-236-191-2_342521320_205.json |
89b5b414_S_III___Phone_specs_comparison__Hardware | [1] Qualcomm Snapdragon 800 MSM8974 [2] Samsung Exynos 4 (4412) [Hardware] | System chip System chip - Most modern handsets use an advanced chip that includes many of the device’s hardware modules like the processor, Wi-Fi, Bluetooth and sometimes even the wireless radio. This field shows what particular system chip (or System-on-a-Chip) is used in the phone. | [] | Sony Xperia Z1 vs Samsung Galaxy S III - Phone specs comparison | Hardware | http://www.phonearena.com/phones/compare/Sony-Xperia-Z1,Samsung-Galaxy-S-III/phones/8000,6330 | 39/1438043062723.96_20150728002422-00255-ip-10-236-191-2_654462313_3.json |
89b5b414_S_III___Phone_specs_comparison__Hardware | [1] Quad core, 2200 MHz, Krait 400 [2] Quad core, 1400 MHz, ARM Cortex-A9 [Hardware] | Processor Processor - The processor is the main computing component of a phone and is a major factor when it comes to the overall speed of the device. Some more powerful smartphones use dual-core and quad-core processors designed to deliver greater performance. | [] | Sony Xperia Z1 vs Samsung Galaxy S III - Phone specs comparison | Hardware | http://www.phonearena.com/phones/compare/Sony-Xperia-Z1,Samsung-Galaxy-S-III/phones/8000,6330 | 39/1438043062723.96_20150728002422-00255-ip-10-236-191-2_654462313_3.json |
89b5b414_S_III___Phone_specs_comparison__Hardware | [1] Adreno 330 [2] ARM Mali-400/MP4 [Hardware] | Graphics processor | [] | Sony Xperia Z1 vs Samsung Galaxy S III - Phone specs comparison | Hardware | http://www.phonearena.com/phones/compare/Sony-Xperia-Z1,Samsung-Galaxy-S-III/phones/8000,6330 | 39/1438043062723.96_20150728002422-00255-ip-10-236-191-2_654462313_3.json |
89b5b414_S_III___Phone_specs_comparison__Hardware | [1] 2048 MB RAM [2] 1024 MB RAM [Hardware] | System memory System memory - System memory, or RAM memory is the type of memory that the device uses to temporarily store data from the OS or currently-running apps. The more RAM available to the device, the better the performance will be when multiple or heavier programs are running. | [] | Sony Xperia Z1 vs Samsung Galaxy S III - Phone specs comparison | Hardware | http://www.phonearena.com/phones/compare/Sony-Xperia-Z1,Samsung-Galaxy-S-III/phones/8000,6330 | 39/1438043062723.96_20150728002422-00255-ip-10-236-191-2_654462313_3.json |
89b5b414_S_III___Phone_specs_comparison__Hardware | [1] 16 GB [2] 32 GB [Hardware] | Built-in storage | [] | Sony Xperia Z1 vs Samsung Galaxy S III - Phone specs comparison | Hardware | http://www.phonearena.com/phones/compare/Sony-Xperia-Z1,Samsung-Galaxy-S-III/phones/8000,6330 | 39/1438043062723.96_20150728002422-00255-ip-10-236-191-2_654462313_3.json |
89b5b414_S_III___Phone_specs_comparison__Hardware | [1] microSD, microSDHC, microSDXC up to 64 GB [2] microSD, microSDHC, microSDXC up to 64 GB [Hardware] | Storage expansion | [] | Sony Xperia Z1 vs Samsung Galaxy S III - Phone specs comparison | Hardware | http://www.phonearena.com/phones/compare/Sony-Xperia-Z1,Samsung-Galaxy-S-III/phones/8000,6330 | 39/1438043062723.96_20150728002422-00255-ip-10-236-191-2_654462313_3.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20090091489 [Published] 04-09-2009 [Description] | REARVIEW MIRROR ASSEMBLY ENCOMPASSING A RADAR DETECTOR AND/OR LASER DETECTOR - One embodiment of a rearview mirror encompassing a plurality of radar detectors and laser detectors comprises of an opaque housing, a rearview mirror assembly, a universal mount, and a PCB assembly providing all of the illumination, audible indication, logic, user inputs, and radar and laser detection. Visual indications of radar or laser signals via illuminated indicators visible through the mirror, provide a fully functioning rearview mirror, when in standby mode. The device accommodates a plurality of mounts in order to accommodate all vehicle types. The geometry and mounting locations provide for more sensitive and accurate detection of radar and laser signals, while neither distracting the driver, detracting from the aesthetic nature of the vehicle, or causing a safety hazard. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20110025176 [Published] 02-03-2011 [Description] | Multiple Position Stand - A support mechanism for supporting an object on a surface is disclosed. The support mechanism includes a joint connected to the object; and a stand connected to the joint. The joint may selectively rotate to allow the stand to support the object on the surface in: a first position comprising a landscape orientation at a first angle between the object and the surface, a second position comprising a landscape orientation at a second angle between the object and the surface, a third position comprising a portrait orientation at a third angle between the object and the surface, and a fourth position comprising a portrait orientation at a fourth angle between the object and the surface. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20110164365 [Published] 07-07-2011 [Description] | COMPONENT ASSEMBLY - A portable computing device is disclosed. The portable computing device can take many forms such as a laptop computer, a tablet computer, and so on. The portable computing device can include at least a single piece housing. The single piece housing including a plurality of steps. The plurality of mounting steps are formed by at least removing a preselected amount of housing material at predetermined locations on the interior surface. At least some of the mounting steps are used to mount at least some of the plurality of internal operating components to the housing. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20110164370 [Published] 07-07-2011 [Description] | ASSEMBLY OF DISPLAY MODULE - A portable computing device is disclosed. The portable computing device can take many forms such as a laptop computer, a tablet computer, and so on. The portable computing device can include a single piece housing and a display module. The display module can include a protective top glass cover that is bonded to a plastic display frame and surrounded by a seal frame. The display module can also include a display panel and its associated circuitry suspended from the display frame below the protective glass cover. The display module is coupled to the single piece housing mounting clips in a manner that allows the display module to move relative to the single piece housing during an impact event. The plastic display frame can include inserts that provide structural support near openings in the single piece housing. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20110164371 [Published] 07-07-2011 [Description] | PRINTED CIRCUIT BOARD - A methodology for connecting device components with circuitry located at different levels and orientations relative to one another is described. First circuitry can be located on a multi-plane rigid circuit board where the multi-plane rigid circuit board can include at least one flexible member sharing a common substrate with the multi-plane rigid circuit board that extends from a body portion of the multi-plane rigid circuit board. The flexible member can include traces used to convey power and/or data and an interface coupled to the power and/or data traces. The flexible member can be deflected or twisted to connect first circuitry on the body portion of the multi-plane rigid circuit board to second circuitry associated with another device component. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20110164372 [Published] 07-07-2011 [Description] | DISPLAY MODULE - A portable computing device is disclosed. The portable computing device can take many forms such as a laptop computer, a tablet computer, and so on. The portable computing device can include a single piece housing and a display module. The display module can include a protective top glass cover that is bonded to a plastic display frame and surrounded by a seal frame. The display module can also include a display panel and its associated circuitry suspended from the display frame below the protective glass cover. The display module is coupled to the single piece housing mounting clips in a manner that allows the display module to move relative to the single piece housing during an impact event. The plastic display frame can include inserts that provide structural support near openings in the single piece housing. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
d46d3ef8_Banko___Patent_applications__Description | [Patent application number] 20110166690 [Published] 07-07-2011 [Description] | HANDHELD COMPUTING DEVICE - A portable computing device is disclosed. The portable computing device can take many forms such as a laptop computer, a tablet computer, and so on. The portable computing device can include at least a single piece housing. The single piece housing including a plurality of steps. The plurality of mounting steps is formed by at least removing a preselected amount of housing material at predetermined locations on the interior surface. At least some of the mounting steps are used to mount at least some of the plurality of internal operating components to the housing. | [] | Banko - Patent applications | Description | http://www.faqs.org/patents/inventor/banko-6/ | 39/1438042989126.22_20150728002309-00060-ip-10-236-191-2_439703848_6.json |
Dataset Card for "UnpredicTable-cluster-noise" - Dataset of Few-shot Tasks from Tables
Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
UnpredicTable-full: Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, UnpredicTable-full, which comprises 413,299 tasks from 23,744 unique websites.
UnpredicTable-unique: This is the same as UnpredicTable-full but filtered to have a maximum of one task per website. UnpredicTable-unique contains exactly 23,744 tasks from 23,744 websites.
UnpredicTable-5k: This dataset contains 5k random tables from the full dataset.
UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
UnpredicTable data subsets based on the website of origin:
- UnpredicTable-baseball-fantasysports-yahoo-com
- UnpredicTable-bulbapedia-bulbagarden-net
- UnpredicTable-cappex-com
- UnpredicTable-cram-com
- UnpredicTable-dividend-com
- UnpredicTable-dummies-com
- UnpredicTable-en-wikipedia-org
- UnpredicTable-ensembl-org
- UnpredicTable-gamefaqs-com
- UnpredicTable-mgoblog-com
- UnpredicTable-mmo-champion-com
- UnpredicTable-msdn-microsoft-com
- UnpredicTable-phonearena-com
- UnpredicTable-sittercity-com
- UnpredicTable-sporcle-com
- UnpredicTable-studystack-com
- UnpredicTable-support-google-com
- UnpredicTable-w3-org
- UnpredicTable-wiki-openmoko-org
- UnpredicTable-wkdu-org
UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
- UnpredicTable-cluster00
- UnpredicTable-cluster01
- UnpredicTable-cluster02
- UnpredicTable-cluster03
- UnpredicTable-cluster04
- UnpredicTable-cluster05
- UnpredicTable-cluster06
- UnpredicTable-cluster07
- UnpredicTable-cluster08
- UnpredicTable-cluster09
- UnpredicTable-cluster10
- UnpredicTable-cluster11
- UnpredicTable-cluster12
- UnpredicTable-cluster13
- UnpredicTable-cluster14
- UnpredicTable-cluster15
- UnpredicTable-cluster16
- UnpredicTable-cluster17
- UnpredicTable-cluster18
- UnpredicTable-cluster19
- UnpredicTable-cluster20
- UnpredicTable-cluster21
- UnpredicTable-cluster22
- UnpredicTable-cluster23
- UnpredicTable-cluster24
- UnpredicTable-cluster25
- UnpredicTable-cluster26
- UnpredicTable-cluster27
- UnpredicTable-cluster28
- UnpredicTable-cluster29
- UnpredicTable-cluster-noise
Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
Languages
English
Dataset Structure
Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
Data Splits
The UnpredicTable datasets do not come with additional data splits.
Dataset Creation
Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
Source Data
Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
Who are the source language producers?
The dataset is extracted from WDC Web Table Corpora.
Annotations
Annotation process
Manual annotation was only carried out for the UnpredicTable-rated-low, UnpredicTable-rated-medium, and UnpredicTable-rated-high data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
Who are the annotators?
Annotations were carried out by a lab assistant.
Personal and Sensitive Information
The data was extracted from WDC Web Table Corpora, which in turn extracted tables from the Common Crawl. We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
Considerations for Using the Data
Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
Other Known Limitations
No additional known limitations.
Additional Information
Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
Licensing Information
Apache 2.0
Citation Information
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
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