text stringlengths 6 2.91M |
|---|
{
"id": 907290385,
"type": "Feature",
"properties": {
"addr:full":"Unit 10 Broxhead Trading Est Bordon Hampshire GU35 0NY",
"addr:housenumber":"unit 10",
"addr:postcode":"gu35 0ny",
"addr:street":"",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"-0.8528649807,51.1292991638,-0.8528649807,51.1292991638",
"geom:latitude":51.129299,
"geom:longitude":-0.852865,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"Unit 10 Broxhead Trading Est",
"sg:city":"Bordon",
"sg:classifiers":[
{
"category":"Retail",
"subcategory":"Repair Shop",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 1420 474116",
"sg:postcode":"GU35 0NY",
"sg:province":"Hampshire",
"sg:tags":[
"upholsterer"
],
"src:geom":"simplegeo",
"wof:belongsto":[],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_3kzZQuQqyywiATLQVhefDt_51.129299_-0.852865@1300740345"
},
"wof:country":"GB",
"wof:created":1471894590,
"wof:geomhash":"13227b6d92fd5466c23e3611027d1bd4",
"wof:hierarchy":[],
"wof:id":907290385,
"wof:lastmodified":1496971005,
"wof:name":"Treehugger Design Reupholstery",
"wof:parent_id":-1,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"upholsterer"
]
},
"bbox": [
-0.8528649807,
51.1292991638,
-0.8528649807,
51.1292991638
],
"geometry": {"coordinates":[-0.8528649807,51.1292991638],"type":"Point"}
} |
{"entities":{"P7856":{"pageid":82611510,"ns":120,"title":"Property:P7856","lastrevid":1109208264,"modified":"2020-02-03T14:46:14Z","type":"property","datatype":"external-id","id":"P7856","labels":{"en":{"language":"en","value":"museum in Salzburg (AT) ID"},"fr":{"language":"fr","value":"identifiant de mus\u00e9e \u00e0 Salzbourg"},"de":{"language":"de","value":"Museen im Land Salzburg ID"},"nl":{"language":"nl","value":"Salzburg-identificatiecode voor museum"},"mk":{"language":"mk","value":"\u0432\u043e \u041c\u0443\u0437\u0435\u0458\u043e\u0442 \u043d\u0430 \u0421\u0430\u043b\u0446\u0431\u0443\u0440\u0433"},"ca":{"language":"ca","value":"identificador de museu de Salzburg"}},"descriptions":{"en":{"language":"en","value":"identifier to link museums and other GLAM-institutions in the Austrian federal state Salzbourg"},"fr":{"language":"fr","value":"identifiant de GLAM dans l'\u00e9tat de Salzbourg"},"de":{"language":"de","value":"ID in der Museums-Datenbank der Salzburger Landesregierung"}},"aliases":{"de":[{"language":"de","value":"Museen in S (AT) ID"}]},"claims":{"P31":[{"mainsnak":{"snaktype":"value","property":"P31","datavalue":{"value":{"entity-type":"item","numeric-id":21745557,"id":"Q21745557"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","id":"P7856$cb18c79c-41c9-4daa-fd22-ea925fcb14f5","rank":"normal","references":[{"hash":"6ffb117624fb480665a312e8322ee2589a8c6cf6","snaks":{"P4656":[{"snaktype":"value","property":"P4656","datavalue":{"value":"https://www.wikidata.org/wiki/Property:P3066","type":"string"},"datatype":"url"}]},"snaks-order":["P4656"]}]},{"mainsnak":{"snaktype":"value","property":"P31","datavalue":{"value":{"entity-type":"item","numeric-id":18615777,"id":"Q18615777"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","id":"P7856$737fffb4-4a3c-71ca-17c9-d891835edb5d","rank":"normal"}],"P1629":[{"mainsnak":{"snaktype":"value","property":"P1629","datavalue":{"value":{"entity-type":"item","numeric-id":43325,"id":"Q43325"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","id":"P7856$3d55c011-42f4-172e-eb74-4627f44f3f09","rank":"normal"}],"P1855":[{"mainsnak":{"snaktype":"value","property":"P1855","datavalue":{"value":{"entity-type":"item","numeric-id":875740,"id":"Q875740"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P7856":[{"snaktype":"value","property":"P7856","hash":"79efaa5987680d94e0f18c8e25938ebc7d6be904","datavalue":{"value":"2665","type":"string"},"datatype":"external-id"}]},"qualifiers-order":["P7856"],"id":"P7856$16888530-4f9e-d652-8247-44908a12b016","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P1855","datavalue":{"value":{"entity-type":"item","numeric-id":2349556,"id":"Q2349556"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P7856":[{"snaktype":"value","property":"P7856","hash":"b1b5702d9e4e0c647debbb98b69c15247052132f","datavalue":{"value":"2527","type":"string"},"datatype":"external-id"}]},"qualifiers-order":["P7856"],"id":"P7856$deb0c332-46c2-b273-82b1-0ba17c5585ce","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P1855","datavalue":{"value":{"entity-type":"item","numeric-id":3327039,"id":"Q3327039"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P7856":[{"snaktype":"value","property":"P7856","hash":"b5b0ec3f99198011ca359c9883fd7e86a43a08f3","datavalue":{"value":"2553","type":"string"},"datatype":"external-id"}]},"qualifiers-order":["P7856"],"id":"P7856$d3953269-4d3f-58f1-e0b8-8089121782db","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P1855","datavalue":{"value":{"entity-type":"item","numeric-id":1322404,"id":"Q1322404"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P7856":[{"snaktype":"value","property":"P7856","hash":"693548938efe088b8b41d07bb31dd04b0fed844e","datavalue":{"value":"2749","type":"string"},"datatype":"external-id"}]},"qualifiers-order":["P7856"],"id":"P7856$713bcacd-4995-401c-13ba-ee4d2ad93564","rank":"normal"}],"P1630":[{"mainsnak":{"snaktype":"value","property":"P1630","datavalue":{"value":"https://service.salzburg.gv.at/museen/detail.do?museumId=$1","type":"string"},"datatype":"string"},"type":"statement","qualifiers":{"P407":[{"snaktype":"value","property":"P407","hash":"46bfd327b830f66f7061ea92d1be430c135fa91f","datavalue":{"value":{"entity-type":"item","numeric-id":188,"id":"Q188"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P407","hash":"daf1c4fcb58181b02dff9cc89deb084004ddae4b","datavalue":{"value":{"entity-type":"item","numeric-id":1860,"id":"Q1860"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P407"],"id":"P7856$f54c0a37-4eb6-2265-7e26-330d76f6745f","rank":"normal"}],"P1793":[{"mainsnak":{"snaktype":"value","property":"P1793","datavalue":{"value":"^2\\d{3}$","type":"string"},"datatype":"string"},"type":"statement","qualifiers":{"P2916":[{"snaktype":"value","property":"P2916","hash":"d4e88c9d91a1ede1b363bf3c20e5ed80aecc7ab2","datavalue":{"value":{"text":"un chiffre entre 2000 et 2999","language":"fr"},"type":"monolingualtext"},"datatype":"monolingualtext"},{"snaktype":"value","property":"P2916","hash":"909ba82fe715da739215d0d949a418c8e949e53f","datavalue":{"value":{"text":"a number between 2000 and 2999","language":"en"},"type":"monolingualtext"},"datatype":"monolingualtext"}]},"qualifiers-order":["P2916"],"id":"P7856$209ddf2a-448b-d1a3-75f0-e66397bd487f","rank":"normal"}],"P2302":[{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21502404,"id":"Q21502404"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P1793":[{"snaktype":"value","property":"P1793","hash":"3dc56e388beefad2d0f07a163fcacc9df9200661","datavalue":{"value":"2\\d{3}","type":"string"},"datatype":"string"}],"P2316":[{"snaktype":"value","property":"P2316","hash":"d50c571a43e6102d65b729ccc7049a0a2f867e34","datavalue":{"value":{"entity-type":"item","numeric-id":21502408,"id":"Q21502408"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P1793","P2316"],"id":"P7856$bb1669b5-4a77-5c82-fb06-655556ba5985","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21502410,"id":"Q21502410"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2316":[{"snaktype":"value","property":"P2316","hash":"d50c571a43e6102d65b729ccc7049a0a2f867e34","datavalue":{"value":{"entity-type":"item","numeric-id":21502408,"id":"Q21502408"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P2316"],"id":"P7856$4d2187a7-4f0b-474a-0999-e5f4b341bcde","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21503250,"id":"Q21503250"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2309":[{"snaktype":"value","property":"P2309","hash":"8fb539b5b9fd607316a1edaf9f8fd42a67a51dd2","datavalue":{"value":{"entity-type":"item","numeric-id":30208840,"id":"Q30208840"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}],"P2308":[{"snaktype":"value","property":"P2308","hash":"65484d37e095a7bd892b42eac04b7061e64b8790","datavalue":{"value":{"entity-type":"item","numeric-id":27096235,"id":"Q27096235"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P2308","hash":"6485834e2182db77e8c1810c18b04e341ce92548","datavalue":{"value":{"entity-type":"item","numeric-id":1030034,"id":"Q1030034"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P2308","hash":"dbcd1925f127866d6f4f74d9a41b0778ceaa3d38","datavalue":{"value":{"entity-type":"item","numeric-id":43229,"id":"Q43229"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P2308","hash":"e3dfe8d3416e48d3dd3ee8b3fba25267d9072db6","datavalue":{"value":{"entity-type":"item","numeric-id":210272,"id":"Q210272"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P2308","hash":"47239a163c0cb9f1c9d847c93ec05a95eff46dff","datavalue":{"value":{"entity-type":"item","numeric-id":33506,"id":"Q33506"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P2308","hash":"9c679b60c94dedaa424608d656a1514310a91f26","datavalue":{"value":{"entity-type":"item","numeric-id":166118,"id":"Q166118"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}],"P2316":[{"snaktype":"value","property":"P2316","hash":"d50c571a43e6102d65b729ccc7049a0a2f867e34","datavalue":{"value":{"entity-type":"item","numeric-id":21502408,"id":"Q21502408"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P2309","P2308","P2316"],"id":"P7856$6d08e31f-4bfd-cf93-14d0-8d5f33bb0bb2","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":19474404,"id":"Q19474404"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2316":[{"snaktype":"value","property":"P2316","hash":"d50c571a43e6102d65b729ccc7049a0a2f867e34","datavalue":{"value":{"entity-type":"item","numeric-id":21502408,"id":"Q21502408"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P2316"],"id":"P7856$8f916b01-4b3d-bb3d-3005-6ebe6d5a4562","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21503247,"id":"Q21503247"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2306":[{"snaktype":"value","property":"P2306","hash":"c80b10b5e1e231d8fb29c63903aa48da0afe0b3e","datavalue":{"value":{"entity-type":"property","numeric-id":31,"id":"P31"},"type":"wikibase-entityid"},"datatype":"wikibase-property"}],"P2316":[{"snaktype":"value","property":"P2316","hash":"d50c571a43e6102d65b729ccc7049a0a2f867e34","datavalue":{"value":{"entity-type":"item","numeric-id":21502408,"id":"Q21502408"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P2306","P2316"],"id":"P7856$7D9A50E9-6456-4F74-AD19-4099AAB50381","rank":"normal","references":[{"hash":"c7d3ec48187899f69128918fa1d23f2fee02dbbd","snaks":{"P4656":[{"snaktype":"value","property":"P4656","datavalue":{"value":"https://www.wikidata.org/wiki/Property:P3066#P2302","type":"string"},"datatype":"url"}]},"snaks-order":["P4656"]}]},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21503247,"id":"Q21503247"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2306":[{"snaktype":"value","property":"P2306","hash":"3df82f2a9acae21bb37237b2b8b7bb930a479379","datavalue":{"value":{"entity-type":"property","numeric-id":17,"id":"P17"},"type":"wikibase-entityid"},"datatype":"wikibase-property"}],"P2305":[{"snaktype":"value","property":"P2305","hash":"ce84e8efd706703c64f0e3e7bc69ea637595764a","datavalue":{"value":{"entity-type":"item","numeric-id":40,"id":"Q40"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P2306","P2305"],"id":"P7856$7189fa5f-47e5-e94b-0cea-392048fa04ca","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21503247,"id":"Q21503247"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2306":[{"snaktype":"value","property":"P2306","hash":"632db79c2fd121822c892d35903b82fdb9e82d1d","datavalue":{"value":{"entity-type":"property","numeric-id":625,"id":"P625"},"type":"wikibase-entityid"},"datatype":"wikibase-property"}]},"qualifiers-order":["P2306"],"id":"P7856$3a303b5d-4406-24d2-f446-b5e5c17b953c","rank":"normal"},{"mainsnak":{"snaktype":"value","property":"P2302","datavalue":{"value":{"entity-type":"item","numeric-id":21503247,"id":"Q21503247"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","qualifiers":{"P2306":[{"snaktype":"value","property":"P2306","hash":"98be80b666c50802075a8bdcedaf0ac2335399c1","datavalue":{"value":{"entity-type":"property","numeric-id":131,"id":"P131"},"type":"wikibase-entityid"},"datatype":"wikibase-property"}]},"qualifiers-order":["P2306"],"id":"P7856$3a41982e-48a1-7547-6579-65f501f9d9a4","rank":"normal"}],"P3254":[{"mainsnak":{"snaktype":"value","property":"P3254","datavalue":{"value":"https://www.wikidata.org/wiki/Wikidata:Property_proposal/Museums_in_Salzburg_(AT)_ID","type":"string"},"datatype":"url"},"type":"statement","id":"P7856$a9522d58-4a48-01e6-9009-103d1795a45f","rank":"normal"}],"P4876":[{"mainsnak":{"snaktype":"value","property":"P4876","datavalue":{"value":{"amount":"+143","unit":"1"},"type":"quantity"},"datatype":"quantity"},"type":"statement","qualifiers":{"P585":[{"snaktype":"value","property":"P585","hash":"c5ce8433d9879caead407d3eaa23c51e75eb91cc","datavalue":{"value":{"time":"+2020-01-20T00:00:00Z","timezone":0,"before":0,"after":0,"precision":11,"calendarmodel":"http://www.wikidata.org/entity/Q1985727"},"type":"time"},"datatype":"time"}]},"qualifiers-order":["P585"],"id":"P7856$2994fe08-465a-b224-822e-27589dcb22b4","rank":"normal","references":[{"hash":"f7ee5615135289252354db3c54ab43bee217545b","snaks":{"P854":[{"snaktype":"value","property":"P854","datavalue":{"value":"https://service.salzburg.gv.at/museen/search.do?page=0&size=10&stichwort=&bezirk=0&ort=0&thema=0&schlagwort=0","type":"string"},"datatype":"url"}]},"snaks-order":["P854"]}]}],"P2429":[{"mainsnak":{"snaktype":"value","property":"P2429","datavalue":{"value":{"entity-type":"item","numeric-id":21873974,"id":"Q21873974"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","id":"P7856$cf6a8407-4370-82bc-01cd-d57093a128be","rank":"normal"}],"P1896":[{"mainsnak":{"snaktype":"value","property":"P1896","datavalue":{"value":"https://service.salzburg.gv.at/museen/search.do","type":"string"},"datatype":"url"},"type":"statement","qualifiers":{"P407":[{"snaktype":"value","property":"P407","hash":"c1a01c3aca42c591593d1445ba401fceed7ef251","datavalue":{"value":{"entity-type":"item","numeric-id":306626,"id":"Q306626"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},{"snaktype":"value","property":"P407","hash":"daf1c4fcb58181b02dff9cc89deb084004ddae4b","datavalue":{"value":{"entity-type":"item","numeric-id":1860,"id":"Q1860"},"type":"wikibase-entityid"},"datatype":"wikibase-item"}]},"qualifiers-order":["P407"],"id":"P7856$7afc397c-4190-3b1d-5cac-e83de6112c7a","rank":"normal"}],"P1659":[{"mainsnak":{"snaktype":"value","property":"P1659","datavalue":{"value":{"entity-type":"property","numeric-id":7296,"id":"P7296"},"type":"wikibase-entityid"},"datatype":"wikibase-property"},"type":"statement","id":"P7856$ea7a0522-47a7-9ed8-1054-12552887da65","rank":"normal"}],"P2264":[{"mainsnak":{"snaktype":"value","property":"P2264","datavalue":{"value":"3305","type":"string"},"datatype":"external-id"},"type":"statement","id":"P7856$60587e65-444c-e829-f1a1-9fc6cef8fe92","rank":"normal"}],"P17":[{"mainsnak":{"snaktype":"value","property":"P17","datavalue":{"value":{"entity-type":"item","numeric-id":40,"id":"Q40"},"type":"wikibase-entityid"},"datatype":"wikibase-item"},"type":"statement","id":"P7856$ad8441ac-4e57-6d32-3117-af447b2181b8","rank":"normal"}]}}}} |
{"title":"男「サラリーマンだって妄想ぐらいするんだよ」","url":"http://elephant.2chblog.jp/archives/52210078.html","time":"2017-09-25 18:40:01 UTC"} |
{"uuid":"9dc86cb4-ce4a-4f4c-afa0-6c275dd8067f","name":"com.andersenlab.ponamorev.tinkoffbank.CheckingValuesTest.waitPageLoading","description":"","start":1519116946172,"stop":1519116946280,"children":["d18a9ee9-5ad8-4962-a2a4-ad2e521e68e1"],"befores":[{"name":"waitPageLoading","status":"passed","stage":"finished","description":"","start":1519116946172,"stop":1519116946280}]} |
{
"forum_title": "Fyrirtæki",
"id": "31770",
"title": "Hrikalega öflug höfuðljós til sölu.",
"url": "http://www.jeppaspjall.is/viewtopic.php?f=51&t=31770",
"posts": [
{
"user_name": "Johnboblem",
"text": "Frí heimsending heim að dyrum. Til á lager: \nhttp://gadget.is/is/products/hofudljos\nÖflugt 1800 lumena höfuðljós - Verð 3.990 með heimsendingu.\nÖflugt 3000 lumena höfuðljós. - Verð 5.990 með heimsendingu.",
"date": "2015-12-04 15:52:00",
"post_id": "164411",
"reply_to_id": false
}
],
"date": "2015-12-04 15:52:00"
} |
[
{
"id": "0f76500b-de92-4d89-a5b0-bc8358610031",
"org": "The Quiz (Одесса)",
"latestCheck": "2020-05-09T12:00:00+03:00",
"type": ["events"],
"duration": 2,
"image": "thequizodessa.png",
"price": ["100 UAH/чел", "max 500 UAH/ком"],
"url": ["https://thequiz.com.ua/calendar_odessa", "https://www.facebook.com/thequiz.odessa"],
"games": [
{
"time": "2020-05-01T20:00:00+03:00",
"name": "Юмористическую игру 'Квиздец' Online a.k.a. 'чернуха'"
},
{
"time": "2020-05-04T20:00:00+03:00",
"name": "Тематический квиз по вселенной Star Wars"
},
{
"time": "2020-05-08T20:00:00+03:00",
"name": "The Quiz, четвёртая игра online сезона"
},
{
"time": "2020-05-11T20:00:00+03:00",
"name": "The Quiz Lite, пятая игра online сезона"
},
{
"time": "2020-05-15T20:00:00+03:00",
"name": "КиноКвиз"
},
{
"time": "2020-05-18T20:00:00+03:00",
"name": "Футбольный квиз"
},
{
"time": "2020-05-22T20:00:00+03:00",
"name": "The Quiz, пятая игра online сезона"
},
{
"time": "2020-05-25T20:00:00+03:00",
"name": "The Quiz Lite, шестая игра online сезона"
},
{
"time": "2020-05-29T20:00:00+03:00",
"name": "Мультимедиа-квиз"
}
]
}
] |
[
{
"name": "nginx-backend-ingress-example",
"ingress_host": "nginx.example.com",
"server_host": "nginx-backend",
"port": "80"
}
]
|
{"uuid": "c12d79e7-2e81-45d2-ba3e-7736e7016aa2", "children": ["d5d04042-6dea-4c3a-9cae-9c926f147efe", "77e0cffe-eeb2-45ce-b960-ad4c5594a108", "edb8a89e-6dcb-4c08-bfcd-2edd7b04e199", "bb25133d-ddd2-4c62-8be7-a2248ebdb6c5", "4e73f095-87fe-46e6-9606-6bf217f65b78", "23dd5b1b-e4c3-41e3-b790-06333bc56623", "7fe408fa-3573-449a-8a87-43873e8cefbc", "df9ad70e-7b23-4490-9c82-020d05097196", "11575017-838b-4082-8dc2-1b19ebe6abb1"], "befores": [{"name": "base_url", "status": "passed", "start": 1594179610063, "stop": 1594179610063}], "start": 1594179610063, "stop": 1594179969572} |
{
"filename": "test_action_get_vrfs.py"
}
|
[
{
"id":"01",
"title": "让平淡无奇的日子发光",
"url": "http://p1.music.126.net/G5KpXx4yOLkXaYq4hZGWnA==/109951163041223430.webp?imageView&thumbnail=246x0&quality=75&tostatic=0&type=webp"
},
{
"id":"02",
"title": "相爱，是两个有趣灵魂的势均力敌",
"url": "http://p1.music.126.net/xWjmqjdIAJzSYbsHICkHOQ==/109951163044887239.webp?imageView&thumbnail=246x0&quality=75&tostatic=0&type=webp"
},
{
"id":"03",
"title": "「华语」哭泣使人乞讨，思念使人奔跑",
"url": "http://p1.music.126.net/3LbMYwTiQD5U3MCSgDPgdA==/109951163035317628.webp?imageView&thumbnail=246x0&quality=75&tostatic=0&type=webp"
},
{
"id":"04",
"title": "电音史上最伟大的250首EDM音乐作品",
"url": "http://p1.music.126.net/1BQFT-akODSLM93bj4CQ2Q==/109951163041556034.webp?imageView&thumbnail=246x0&quality=75&tostatic=0&type=webp"
},
{
"id":"05",
"title": "古风殇恋｜待到红颜消 杳杳无归期",
"url": "http://p1.music.126.net/6QbE9r2JeqBz3j3Kva--4A==/109951163038217098.webp?imageView&thumbnail=246x0&quality=75&tostatic=0&type=webp"
},
{
"id":"06",
"title": "国语说唱也很抒情，副歌也美",
"url": "http://p1.music.126.net/iJiLxOuVGLDXEqnJ4GY2Cg==/109951163033285469.webp?imageView&thumbnail=246x0&quality=75&tostatic=0&type=webp"
}
] |
{"pmid":32425955,"pmcid":"PMC7203467","title":"Potential SARS-CoV-2 Preimmune IgM Epitopes.","text":["Potential SARS-CoV-2 Preimmune IgM Epitopes.","While studying the human public IgM igome as represented by a library of 224,087 linear mimotopes, three exact matches to peptides in the proteins of SARS-CoV-2 were found: two in the open reading frame 1ab and one in the spike protein. Joining the efforts to fast track SARS-CoV-2 vaccine development, here we describe briefly these potential epitopes in comparison to mimotopes representing peptides of SARS-CoV, HCoV 229E and OC43.","Front Immunol","Shivarov, Velizar","Petrov, Peter K","Pashov, Anastas D","32425955"],"abstract":["While studying the human public IgM igome as represented by a library of 224,087 linear mimotopes, three exact matches to peptides in the proteins of SARS-CoV-2 were found: two in the open reading frame 1ab and one in the spike protein. Joining the efforts to fast track SARS-CoV-2 vaccine development, here we describe briefly these potential epitopes in comparison to mimotopes representing peptides of SARS-CoV, HCoV 229E and OC43."],"journal":"Front Immunol","authors":["Shivarov, Velizar","Petrov, Peter K","Pashov, Anastas D"],"date":"2020-05-20T11:00:00Z","year":2020,"_id":"32425955","source":"PubMed","week":"202021|May 18 - May 24","doi":"10.3389/fimmu.2020.00932","keywords":["b cell precursors","igm","sars-cov-2","epitope","mimotope"],"topics":["Treatment","Mechanism"],"weight":1,"_version_":1667252837709512704,"score":9.490897,"similar":[{"pmid":32292901,"pmcid":"PMC7142689","title":"Pathogenic Priming Likely Contributes to Serious and Critical Illness and Mortality in COVID-19 via Autoimmunity.","text":["Pathogenic Priming Likely Contributes to Serious and Critical Illness and Mortality in COVID-19 via Autoimmunity.","Homology between human and viral proteins is an established factor in viral- or vaccine-induced autoimmunity. Failure of SARS and MERS vaccines in animal trials involved pathogenesis consistent with an immunological priming that could involve autoimmunity in lung tissues due to previous exposure to the SARS and MERS spike protein. Exposure pathogenesis to SARS-CoV-2 in COVID-19 likely will lead to similar outcomes. Immunogenic peptides in viruses or bacteria that match human proteins are good candidates for pathogenic priming peptides (similar to the more diffuse idea of \"immune enhancement\"). Here I provide an assessment of potential for human pathogenesis via autoimmunity via exposure, via infection or injection. SAR-CoV-2 spike proteins, and all other SARS-CoV-2 proteins, immunogenic epitopes in each SARS-CoV-2 protein were compared to human proteins in search of high local homologous matching. Only one immunogenic epitope in a SARS-CoV-2 had no homology to human proteins. If all of the parts of the epitopes that are homologous to human proteins are excluded from consideration due to risk of pathogenic priming, the remaining immunogenic parts of the epitopes may be still immunogenic and remain as potentially viable candidates for vaccine development. Mapping of the genes encoding human protein matches to pathways point to targets that could explain the observed presentation of symptoms in COVID-19 disease. It also strongly points to a large number of opportunities for expected disturbances in the immune system itself, targeting elements of MCH Class I and Class II antigen presentation, PD-1 signaling, cross-presentation of soluble exogenous antigens and the ER-Phagosome pathway. Translational consequences of these findings are explored.","J Transl Autoimmun","Lyons-Weiler, James","32292901"],"abstract":["Homology between human and viral proteins is an established factor in viral- or vaccine-induced autoimmunity. Failure of SARS and MERS vaccines in animal trials involved pathogenesis consistent with an immunological priming that could involve autoimmunity in lung tissues due to previous exposure to the SARS and MERS spike protein. Exposure pathogenesis to SARS-CoV-2 in COVID-19 likely will lead to similar outcomes. Immunogenic peptides in viruses or bacteria that match human proteins are good candidates for pathogenic priming peptides (similar to the more diffuse idea of \"immune enhancement\"). Here I provide an assessment of potential for human pathogenesis via autoimmunity via exposure, via infection or injection. SAR-CoV-2 spike proteins, and all other SARS-CoV-2 proteins, immunogenic epitopes in each SARS-CoV-2 protein were compared to human proteins in search of high local homologous matching. Only one immunogenic epitope in a SARS-CoV-2 had no homology to human proteins. If all of the parts of the epitopes that are homologous to human proteins are excluded from consideration due to risk of pathogenic priming, the remaining immunogenic parts of the epitopes may be still immunogenic and remain as potentially viable candidates for vaccine development. Mapping of the genes encoding human protein matches to pathways point to targets that could explain the observed presentation of symptoms in COVID-19 disease. It also strongly points to a large number of opportunities for expected disturbances in the immune system itself, targeting elements of MCH Class I and Class II antigen presentation, PD-1 signaling, cross-presentation of soluble exogenous antigens and the ER-Phagosome pathway. Translational consequences of these findings are explored."],"journal":"J Transl Autoimmun","authors":["Lyons-Weiler, James"],"date":"2020-04-16T11:00:00Z","year":2020,"_id":"32292901","source":"PubMed","week":"202016|Apr 13 - Apr 19","doi":"10.1016/j.jtauto.2020.100051","keywords":["autoimmunity","covid-19","immuneenhancement","pathogenic priming","sars-cov-2"],"topics":["Mechanism"],"weight":1,"_version_":1666138494482776064,"score":153.82253},{"pmid":32372051,"title":"Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2.","text":["Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2.","To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information is currently available about the immune target epitopes of novel coronavirus (SARS-CoV-2) to induce host immune responses. Through a comprehensive bioinformatic screening of potential epitopes derived from the SARS-CoV-2 sequences for HLAs commonly present in the Japanese population, we identified 2013 and 1399 possible peptide epitopes that are likely to have the high affinity (<0.5%- and 2%-rank, respectively) to HLA class I and II molecules, respectively, that may induce CD8(+) and CD4(+) T-cell responses. These epitopes distributed across the structural (spike, envelope, membrane, and nucleocapsid proteins) and the nonstructural proteins (proteins corresponding to six open reading frames); however, we found several regions where high-affinity epitopes were significantly enriched. By comparing the sequences of these predicted T cell epitopes to the other coronaviruses, we identified 781 HLA-class I and 418 HLA-class II epitopes that have high homologies to SARS-CoV. To further select commonly-available epitopes that would be applicable to larger populations, we calculated population coverages based on the allele frequencies of HLA molecules, and found 2 HLA-class I epitopes covering 83.8% of the Japanese population. The findings in the current study provide us valuable information to design widely-available vaccine epitopes against SARS-CoV-2 and also provide the useful information for monitoring T-cell responses.","J Hum Genet","Kiyotani, Kazuma","Toyoshima, Yujiro","Nemoto, Kensaku","Nakamura, Yusuke","32372051"],"abstract":["To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information is currently available about the immune target epitopes of novel coronavirus (SARS-CoV-2) to induce host immune responses. Through a comprehensive bioinformatic screening of potential epitopes derived from the SARS-CoV-2 sequences for HLAs commonly present in the Japanese population, we identified 2013 and 1399 possible peptide epitopes that are likely to have the high affinity (<0.5%- and 2%-rank, respectively) to HLA class I and II molecules, respectively, that may induce CD8(+) and CD4(+) T-cell responses. These epitopes distributed across the structural (spike, envelope, membrane, and nucleocapsid proteins) and the nonstructural proteins (proteins corresponding to six open reading frames); however, we found several regions where high-affinity epitopes were significantly enriched. By comparing the sequences of these predicted T cell epitopes to the other coronaviruses, we identified 781 HLA-class I and 418 HLA-class II epitopes that have high homologies to SARS-CoV. To further select commonly-available epitopes that would be applicable to larger populations, we calculated population coverages based on the allele frequencies of HLA molecules, and found 2 HLA-class I epitopes covering 83.8% of the Japanese population. The findings in the current study provide us valuable information to design widely-available vaccine epitopes against SARS-CoV-2 and also provide the useful information for monitoring T-cell responses."],"journal":"J Hum Genet","authors":["Kiyotani, Kazuma","Toyoshima, Yujiro","Nemoto, Kensaku","Nakamura, Yusuke"],"date":"2020-05-07T11:00:00Z","year":2020,"_id":"32372051","source":"PubMed","week":"202019|May 04 - May 10","doi":"10.1038/s10038-020-0771-5","locations":["Japanese","Japanese"],"countries":["Japan"],"countries_codes":["JPN|Japan"],"topics":["Mechanism","Treatment"],"weight":1,"_version_":1666138496429981697,"score":150.71773},{"pmid":32352026,"pmcid":"PMC7189872","title":"Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach.","text":["Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach.","A new virus termed SARS-COV-2 (causing COVID-19 disease) can exhibit a progressive, fatal impact on individuals. The World Health Organization (WHO) has declared the spread of the virus to be a global pandemic. Currently, there are over 1 million cases and over 100,000 confirmed deaths due to the virus. Hence, prophylactic and therapeutic strategies are promptly needed. In this study we report an epitope, ITLCFTLKR, which is biochemically fit to HLA allelic proteins. We propose that this could be used as a potential vaccine candidate against SARS-COV-2. A selected putative epitope and HLA-allelic complexes show not only better binding scores, but also RMSD values in the range of 0-1A. This epitope was found to have a 99.8% structural favorability as per Ramachandran-plot analysis. Similarly, a suitable range of IC50 values and population coverage was obtained to represent greater validation of T-cell epitope analysis. Stability analysis using MDWeb and half-life analysis using the ProtParam tool has confirmed that this epitope is well-selected. This new methodology of epitope-based vaccine prediction is fundamental and fast in application, ad can be economically beneficial and viable.","Inform Med Unlocked","Joshi, Amit","Joshi, Bhuwan Chandra","Mannan, M Amin-Ul","Kaushik, Vikas","32352026"],"abstract":["A new virus termed SARS-COV-2 (causing COVID-19 disease) can exhibit a progressive, fatal impact on individuals. The World Health Organization (WHO) has declared the spread of the virus to be a global pandemic. Currently, there are over 1 million cases and over 100,000 confirmed deaths due to the virus. Hence, prophylactic and therapeutic strategies are promptly needed. In this study we report an epitope, ITLCFTLKR, which is biochemically fit to HLA allelic proteins. We propose that this could be used as a potential vaccine candidate against SARS-COV-2. A selected putative epitope and HLA-allelic complexes show not only better binding scores, but also RMSD values in the range of 0-1A. This epitope was found to have a 99.8% structural favorability as per Ramachandran-plot analysis. Similarly, a suitable range of IC50 values and population coverage was obtained to represent greater validation of T-cell epitope analysis. Stability analysis using MDWeb and half-life analysis using the ProtParam tool has confirmed that this epitope is well-selected. This new methodology of epitope-based vaccine prediction is fundamental and fast in application, ad can be economically beneficial and viable."],"journal":"Inform Med Unlocked","authors":["Joshi, Amit","Joshi, Bhuwan Chandra","Mannan, M Amin-Ul","Kaushik, Vikas"],"date":"2020-05-01T11:00:00Z","year":2020,"_id":"32352026","source":"PubMed","week":"202018|Apr 27 - May 03","doi":"10.1016/j.imu.2020.100338","keywords":["epitope","hla-alleles","immuno-informatics","sars-cov-2","simulation","vaccine"],"topics":["Treatment"],"weight":1,"_version_":1666138495730581505,"score":146.24928},{"pmid":32483236,"title":"Two linear epitopes on the SARS-CoV-2 spike protein that elicit neutralising antibodies in COVID-19 patients.","text":["Two linear epitopes on the SARS-CoV-2 spike protein that elicit neutralising antibodies in COVID-19 patients.","Given the ongoing SARS-CoV-2 pandemic, identification of immunogenic targets against the coronavirus spike glycoprotein will provide crucial advances towards the development of sensitive diagnostic tools and potential vaccine candidate targets. In this study, using pools of overlapping linear B-cell peptides, we report two IgG immunodominant regions on SARS-CoV-2 spike glycoprotein that are recognised by sera from COVID-19 convalescent patients. Notably, one is specific to SARS-CoV-2, which is located in close proximity to the receptor binding domain. The other region, which is localised at the fusion peptide, could potentially function as a pan-SARS target. Functionally, antibody depletion assays demonstrate that antibodies targeting these immunodominant regions significantly alter virus neutralisation capacities. Taken together, identification and validation of these neutralising B-cell epitopes will provide insights towards the design of diagnostics and vaccine candidates against this high priority coronavirus.","Nat Commun","Poh, Chek Meng","Carissimo, Guillaume","Wang, Bei","Amrun, Siti Naqiah","Lee, Cheryl Yi-Pin","Chee, Rhonda Sin-Ling","Fong, Siew-Wai","Yeo, Nicholas Kim-Wah","Lee, Wen-Hsin","Torres-Ruesta, Anthony","Leo, Yee-Sin","Chen, Mark I-Cheng","Tan, Seow-Yen","Chai, Louis Yi Ann","Kalimuddin, Shirin","Kheng, Shirley Seah Gek","Thien, Siew-Yee","Young, Barnaby Edward","Lye, David C","Hanson, Brendon John","Wang, Cheng-I","Renia, Laurent","Ng, Lisa F P","32483236"],"abstract":["Given the ongoing SARS-CoV-2 pandemic, identification of immunogenic targets against the coronavirus spike glycoprotein will provide crucial advances towards the development of sensitive diagnostic tools and potential vaccine candidate targets. In this study, using pools of overlapping linear B-cell peptides, we report two IgG immunodominant regions on SARS-CoV-2 spike glycoprotein that are recognised by sera from COVID-19 convalescent patients. Notably, one is specific to SARS-CoV-2, which is located in close proximity to the receptor binding domain. The other region, which is localised at the fusion peptide, could potentially function as a pan-SARS target. Functionally, antibody depletion assays demonstrate that antibodies targeting these immunodominant regions significantly alter virus neutralisation capacities. Taken together, identification and validation of these neutralising B-cell epitopes will provide insights towards the design of diagnostics and vaccine candidates against this high priority coronavirus."],"journal":"Nat Commun","authors":["Poh, Chek Meng","Carissimo, Guillaume","Wang, Bei","Amrun, Siti Naqiah","Lee, Cheryl Yi-Pin","Chee, Rhonda Sin-Ling","Fong, Siew-Wai","Yeo, Nicholas Kim-Wah","Lee, Wen-Hsin","Torres-Ruesta, Anthony","Leo, Yee-Sin","Chen, Mark I-Cheng","Tan, Seow-Yen","Chai, Louis Yi Ann","Kalimuddin, Shirin","Kheng, Shirley Seah Gek","Thien, Siew-Yee","Young, Barnaby Edward","Lye, David C","Hanson, Brendon John","Wang, Cheng-I","Renia, Laurent","Ng, Lisa F P"],"date":"2020-06-03T11:00:00Z","year":2020,"_id":"32483236","source":"PubMed","week":"202023|Jun 01 - Jun 07","doi":"10.1038/s41467-020-16638-2","topics":["Treatment","Mechanism","Diagnosis"],"weight":1,"_version_":1668704334372667393,"score":146.01749},{"pmid":32473352,"title":"Understanding the B and T cell epitopes of spike protein of severe acute respiratory syndrome coronavirus-2: A computational way to predict the immunogens.","text":["Understanding the B and T cell epitopes of spike protein of severe acute respiratory syndrome coronavirus-2: A computational way to predict the immunogens.","The 2019 novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak has caused a large number of deaths, with thousands of confirmed cases worldwide. The present study followed computational approaches to identify B- and T-cell epitopes for the spike (S) glycoprotein of SARS-CoV-2 by its interactions with the human leukocyte antigen alleles. We identified 24 peptide stretches on the SARS-CoV-2S protein that are well conserved among the reported strains. The S protein structure further validated the presence of predicted peptides on the surface, of which 20 are surface exposed and predicted to have reasonable epitope binding efficiency. The work could be useful for understanding the immunodominant regions in the surface protein of SARS-CoV-2 and could potentially help in designing some peptide-based diagnostics. Also, identified T-cell epitopes might be considered for incorporation in vaccine designs.","Infect Genet Evol","Vashi, Yoya","Jagrit, Vipin","Kumar, Sachin","32473352"],"abstract":["The 2019 novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak has caused a large number of deaths, with thousands of confirmed cases worldwide. The present study followed computational approaches to identify B- and T-cell epitopes for the spike (S) glycoprotein of SARS-CoV-2 by its interactions with the human leukocyte antigen alleles. We identified 24 peptide stretches on the SARS-CoV-2S protein that are well conserved among the reported strains. The S protein structure further validated the presence of predicted peptides on the surface, of which 20 are surface exposed and predicted to have reasonable epitope binding efficiency. The work could be useful for understanding the immunodominant regions in the surface protein of SARS-CoV-2 and could potentially help in designing some peptide-based diagnostics. Also, identified T-cell epitopes might be considered for incorporation in vaccine designs."],"journal":"Infect Genet Evol","authors":["Vashi, Yoya","Jagrit, Vipin","Kumar, Sachin"],"date":"2020-05-31T11:00:00Z","year":2020,"_id":"32473352","source":"PubMed","week":"202022|May 25 - May 31","doi":"10.1016/j.meegid.2020.104382","keywords":["diagnostics","epitopes","sars-cov-2","spike protein"],"topics":["Treatment","Mechanism"],"weight":1,"_version_":1668255193388548096,"score":145.54123}]} |
{"poster":"ShadowIceWolf","date":"2017-03-02T19:43:28.089+0000","title":"Edge of Night Anzeiegfehler","subforum":"Melde einen Bug","up_votes":1,"down_votes":0,"body":"Bei Saum der Nacht {{item:3814}} wird trotz der Veränderung weiterhin angezeigt, dass das Item noch 60 AD gibt und der Cooldown der aktivierbaren Fähigkeit 30 Sekunden beträgt. Wenn man es kauft bringt es allerdings nur die 55 AD und die aktivierbare Fähigkeit hat auch nach der Nutzung 45 Sekunden Cooldown, wie es bei den Patchnotizen steht.","replies":[{"poster":"Crounty","date":"2017-03-02T19:49:10.940+0000","up_votes":1,"down_votes":0,"body":"Da es ein Hotfix war konnte man nur die tatsächlichen Stats ändern und nicht das, was angezeigt wird.\n\nWird mit dem nächsten Patch behoben.","replies":[]}]} |
{
"id": 219524935,
"type": "Feature",
"properties": {
"addr:full":"15 Cormorant Close Washington Durham NE38 0DE",
"addr:housenumber":"15",
"addr:postcode":"ne38 0de",
"addr:street":"Cormorant Close",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"-1.554314,54.891933,-1.554314,54.891933",
"geom:latitude":54.891933,
"geom:longitude":-1.554314,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"15 Cormorant Close",
"sg:city":"Washington",
"sg:classifiers":[
{
"category":"Organizations",
"subcategory":"Non-profit Organization",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 191 417 3858",
"sg:postcode":"NE38 0DE",
"sg:province":"Durham",
"sg:tags":[
"organisation",
"voluntary",
"charity"
],
"src:geom":"simplegeo",
"wof:belongsto":[
85787265,
85633159,
85684483
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_3wCwUUc6aIO4239xlrA5ke_54.891933_-1.554314@1300740215"
},
"wof:country":"GB",
"wof:geomhash":"ce32c105f3b95998d73fbd605fcaf1a9",
"wof:hierarchy":[
{
"continent_id":-1,
"country_id":85633159,
"locality_id":-1,
"neighbourhood_id":85787265,
"region_id":85684483,
"venue_id":219524935
}
],
"wof:id":219524935,
"wof:lastmodified":1496966480,
"wof:name":"VSO Sunderland",
"wof:parent_id":85787265,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"organisation",
"voluntary",
"charity"
]
},
"bbox": [
-1.554314,
54.891933,
-1.554314,
54.891933
],
"geometry": {"coordinates":[-1.554314,54.891933],"type":"Point"}
} |
{
"jwtExpired": "Votre code a expiré. Si vous souhaitez changer votre mot de passe cliquez sur le lien suivant",
"invalidToken": "Code invalide",
"emailMandatory": "Email obligatoire",
"passwordMandatory": "Mot de passe obligatoire",
"422": "Erreur durant l'action",
"mailSent": "Mail sent to {{email}}. Please check your email. Don't forget to check your spam as well. Please follow the e-mail's instructions",
"emailUsed": "Cet email est déjà utilisé.",
"emailUsedProvider": "Ce compte E2 existe, mais vous l'avez crée via {{provider}}. Veuillez selectionner le lien {{provider}} pour vous identifier",
"userNotFound": "Utilisateur introuvable",
"loginFailed": "Erreur de connexion: Email ou mot passe invalide",
"invalidValue": "Format invalide",
"notAllowed": "Vous n'êtes pas autorisé",
"messagePwd": {
"error": "{{message}}",
"success": "{{message}}",
"passwordRequested": "E² demande de changement de mot de passe",
"invalidPassword": "Mot de passe incorrect"
}
}
|
{"id":2017-06-01_3.,
"date":"2017-06-01",
"report":"A8-0152/2017",
"name":"CHANGE ME",
"rapporteur":"RAPPORTEUR",
"desc":"Joëlle Bergeron - Vote unique 01/06/2017 11:37:43.000",
"for":539,"against":23,"abstention":72}
|
{
"sn1.51:0.1": "Verbundene Lehrreden 1.51 ",
"sn1.51:0.2": "6. Das Kapitel über das Alter ",
"sn1.51:0.3": "Alter ",
"sn1.51:1.1": "„Was ist noch im Alter gut? ",
"sn1.51:1.2": "Was ist gut, wenn es verankert ist? ",
"sn1.51:1.3": "Was ist der Schatz der Menschen? ",
"sn1.51:1.4": "Was ist für Diebe schwer zu stehlen?“ ",
"sn1.51:2.1": "„Sittlichkeit ist noch im Alter gut. ",
"sn1.51:2.2": "Vertrauen ist gut, wenn es verankert ist. ",
"sn1.51:2.3": "Weisheit ist der Schatz der Menschen. ",
"sn1.51:2.4": "Und Verdienst ist für Diebe schwer zu stehlen.“ "
} |
{"data":[{"id":"303471226332209_307942435885088","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"A person doing self swot analysis: \nStrength is my wife. \nWeakness is my neighbour's wife. \nOpportunity is when neighbour is on tour. \nThreat is when i am on tour.","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307942435885088"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307942435885088"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T19:52:27+0000","updated_time":"2011-11-15T19:52:27+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Harshad Lohande","id":"100002321695136"}],"count":2},"comments":{"count":0}},{"id":"303471226332209_307938472552151","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"A Boy pulls down his pant & ask a girl \" do u have this ?\" \nGirl lifts her skirt, slips the panty & says,\"My mom says if u \nhave this u can get plenty of those................! \"","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307938472552151"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307938472552151"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T19:44:32+0000","updated_time":"2011-11-15T19:44:32+0000","likes":{"data":[{"name":"Sanjay Khan","id":"624923774"},{"name":"Dharmendra Solanki","id":"1256097477"},{"name":"Sanjeet Phougat","id":"100002066013864"},{"name":"Harshad Lohande","id":"100002321695136"}],"count":5},"comments":{"count":0}},{"id":"303471226332209_307903405888991","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"A girl selling SANDWICH on the beach in goa, asked a \nsardar:\"sardar ji ,sandwich loge? \" \nsardar ji replied,\"o,kamliye sand wich kyon?, room wich kyon \nnahi?","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307903405888991"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307903405888991"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T18:31:41+0000","updated_time":"2011-11-15T18:41:42+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Sanjay Khan","id":"624923774"},{"name":"Vipin Pal Bisht","id":"100000173345940"},{"name":"Naushad Ahmad","id":"100001434085647"}],"count":8},"comments":{"data":[{"id":"303471226332209_307903405888991_307904335888898","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"SAHI H SARKAO YAAR..MOOD M H KAFI..PURA MATCH DEKHA KYA?","created_time":"2011-11-15T18:33:41+0000"},{"id":"303471226332209_307903405888991_307904885888843","from":{"name":"Dharmendra Solanki","id":"1256097477"},"message":"vo wali jung khatam karke aya hun..ab fursat se sarkaunga yajan!!","created_time":"2011-11-15T18:34:46+0000"},{"id":"303471226332209_307903405888991_307905152555483","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"ABE AB M TO RESGN KR CHUKA...","created_time":"2011-11-15T18:35:19+0000"},{"id":"303471226332209_307903405888991_307905285888803","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"SAMAN TO PACK KR LIYA BS JA RHA HU..","created_time":"2011-11-15T18:35:36+0000"},{"id":"303471226332209_307903405888991_307905505888781","from":{"name":"Dharmendra Solanki","id":"1256097477"},"message":"mein sambhal lunga morcha..tu rest le le!!","created_time":"2011-11-15T18:36:04+0000"},{"id":"303471226332209_307903405888991_307905889222076","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"ABE JOKES RPT BHI KR SKTA H..PURANE SAARE DELETE KR DIYE...GRP HI CHHOD RHA HU..","created_time":"2011-11-15T18:37:10+0000"},{"id":"303471226332209_307903405888991_307906139222051","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"LAST ROUND MAAR RHA HU KOI BACHA HO TO..","created_time":"2011-11-15T18:37:23+0000","likes":1},{"id":"303471226332209_307903405888991_307906209222044","from":{"name":"Dharmendra Solanki","id":"1256097477"},"message":"aisa julm mat kar hum gareebon pe!!","created_time":"2011-11-15T18:37:32+0000"},{"id":"303471226332209_307903405888991_307906512555347","from":{"name":"Dharmendra Solanki","id":"1256097477"},"message":"tera promotion karwa denge..thehar ja..","created_time":"2011-11-15T18:38:07+0000"},{"id":"303471226332209_307903405888991_307906922555306","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"ABE AESA KUCH NI H...AESI BKCHODI SE HI PARESAN AAKE CHHOD RHA HU..","created_time":"2011-11-15T18:38:59+0000"},{"id":"303471226332209_307903405888991_307907539221911","from":{"name":"Dharmendra Solanki","id":"1256097477"},"message":"lol!! tujh jaise bakchod pareshan ho jaayenge to logon ka bakchodi se bharosa hi uth jaayega!!","created_time":"2011-11-15T18:40:18+0000"},{"id":"303471226332209_307903405888991_307907855888546","from":{"name":"Omvir Singh","id":"100000890803498"},"message":":-)","created_time":"2011-11-15T18:40:52+0000"},{"id":"303471226332209_307903405888991_307908252555173","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"ABE YAAR M TUJHE UDHR PING DE RHA HU ....IDHAR AB LIKHNA HI NI H","created_time":"2011-11-15T18:41:42+0000"}],"count":13}},{"id":"303471226332209_307901275889204","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Rail ki patri par mat hagaa karo,train aayegi gaand kat \njaayegi.abhi haath se \ngaand dhotay ho,baad mein gaand se haath dho \nbaithogay!!!!!!!!!!","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307901275889204"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307901275889204"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T18:27:24+0000","updated_time":"2011-11-15T18:27:24+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Abhishek Jha","id":"1209012298"},{"name":"Pawan Sihag","id":"1111364038"}],"count":7},"comments":{"count":0}},{"id":"303471226332209_307896435889688","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Iss jahan main aae ho to ,kuch aaisa kar jaao kadardaan, jiss \ngali se guzro, aawaaz aae --\"ABBAJAAN - ABBAJAAN\"","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307896435889688"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307896435889688"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T18:19:03+0000","updated_time":"2011-11-15T18:19:03+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"}],"count":1},"comments":{"count":0}},{"id":"303471226332209_307893092556689","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Love is not measured by hugging, kissing & sex . luv is respect \n& trust, accepting a person with open legs.. closed eyes.. wet lips.. \nsaying \"push it more\"- 2","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307893092556689"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307893092556689"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T18:11:49+0000","updated_time":"2011-11-15T18:11:49+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Sanjay Khan","id":"624923774"},{"name":"Ankit Choudhary","id":"834854815"}],"count":3},"comments":{"count":0}},{"id":"303471226332209_307889415890390","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Man 2 wife : \"business is going down,if u learn to cook,we can remove bavarchi.\" \nwife : \"asshole, if u learn to fuck, we can remove driver, Gardener & watchman!!!!!!!!\"","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307889415890390"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307889415890390"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T18:04:33+0000","updated_time":"2011-11-15T18:04:33+0000","likes":{"data":[{"name":"Naushad Ahmad","id":"100001434085647"},{"name":"Dharmendra Solanki","id":"1256097477"},{"name":"Alok Shukla","id":"100000311878204"}],"count":7},"comments":{"count":0}},{"id":"303471226332209_307886465890685","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Teacher: Larki or Larkay me kia faraq ha?\nChintu: Larki 1 saal me 1 he bachay ki Maa bn skti ha jb k Larka 1 saal me 365 bachon ka Baap bn skta hy. :D","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307886465890685"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307886465890685"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T17:58:11+0000","updated_time":"2011-11-15T17:58:11+0000","likes":{"data":[{"name":"Pawan Sihag","id":"1111364038"},{"name":"Upendra Pratap Singh Bhadauria","id":"100000742601193"}],"count":2},"comments":{"count":0}},{"id":"303471226332209_307878875891444","from":{"name":"Omvir Singh","id":"100000890803498"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"**GNG TO RESGN ND DELETE STUFF FROM THIS GRP SHORTLY**","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307878875891444"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307878875891444"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T17:43:28+0000","updated_time":"2011-11-15T17:55:58+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Sanjay Khan","id":"624923774"}],"count":2},"comments":{"data":[{"id":"303471226332209_307878875891444_307880042557994","from":{"name":"Satvin Sato","id":"1241400157"},"message":"BC, Publicity Stunt... !!! :D:D:D\\","created_time":"2011-11-15T17:45:54+0000"},{"id":"303471226332209_307878875891444_307880705891261","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"NO NEED OF PUBLICITY....","created_time":"2011-11-15T17:47:14+0000","likes":1},{"id":"303471226332209_307878875891444_307885345890797","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"APKI VAJAH SE TO THODI DER RUK GYA THA...","created_time":"2011-11-15T17:55:58+0000"}],"count":4}},{"id":"303471226332209_307882822557716","from":{"name":"Dharmendra Solanki","id":"1256097477"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Sex life of a couple according to ages:-- \n18+ DINRAAT \n28+ ROZRAAT \n38+ JUMERAAT \n48+ CHANDRAAT \n58+ JAJBAAT AUR GAL BAAT.","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307882822557716"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307882822557716"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T17:51:08+0000","updated_time":"2011-11-15T17:51:08+0000","comments":{"count":0}},{"id":"303471226332209_307882462557752","from":{"name":"Sandeep Kumar","id":"662383539"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"What do u call a group of ppl where two ppl are thinking of sex & all other are thinking of food ???.....\n\n\"A wedding\". =))\n`","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307882462557752"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307882462557752"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T17:50:24+0000","updated_time":"2011-11-15T17:50:24+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Anand Prajapati","id":"100001302354536"},{"name":"Pawan Sihag","id":"1111364038"},{"name":"Raghvendra Singh","id":"100000980191257"}],"count":6},"comments":{"count":0}},{"id":"303471226332209_307877099224955","from":{"name":"Sanjay Khan","id":"624923774"},"to":{"data":[{"name":"Kaustubh Dharmadhikari","id":"737370555"},{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Kaustubh Dharmadhikari Sir Ke exp. Ko Dekh ke... Inko Marketing Dept. ka Head banaya ja raha hai...","message_tags":{"0":[{"id":"737370555","name":"Kaustubh Dharmadhikari","type":"user","offset":0,"length":22}]},"actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307877099224955"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307877099224955"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T17:39:50+0000","updated_time":"2011-11-15T17:44:17+0000","comments":{"data":[{"id":"303471226332209_307877099224955_307877945891537","from":{"name":"Satvin Sato","id":"1241400157"},"message":"Bc, doobegi apni company abb.... :D:D:D","created_time":"2011-11-15T17:41:32+0000","likes":1},{"id":"303471226332209_307877099224955_307878169224848","from":{"name":"Satvin Sato","id":"1241400157"},"message":"KD ko security me daal de, Jaggu ke under...","created_time":"2011-11-15T17:42:05+0000","likes":1},{"id":"303471226332209_307877099224955_307879239224741","from":{"name":"Sanjay Khan","id":"624923774"},"message":"Chal thik hai.. Kaustubh Dharmadhikari Sir bol rahe hai.. Unka Exp. Security mein hai..!!","message_tags":[{"id":"737370555","name":"Kaustubh Dharmadhikari","type":"user","offset":16,"length":22}],"created_time":"2011-11-15T17:44:17+0000","likes":1}],"count":3}},{"id":"303471226332209_307854092560589","from":{"name":"Pradeep Paliwal","id":"100000109252637"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"picture":"https:\/\/fbcdn-photos-a.akamaihd.net\/hphotos-ak-ash4\/383833_255948327787946_1229982723_s.jpg","link":"http:\/\/www.facebook.com\/photo.php?fbid=255948327787946&set=a.255948324454613.56540.100001183418998&type=1","name":"Timeline Photos","properties":[{"name":"By","text":"Manish Jha","href":"http:\/\/www.facebook.com\/manish92.jha"}],"icon":"https:\/\/fbstatic-a.akamaihd.net\/rsrc.php\/v2\/yD\/r\/aS8ecmYRys0.gif","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307854092560589"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307854092560589"}],"privacy":{"value":""},"type":"photo","object_id":"255948327787946","created_time":"2011-11-15T16:50:03+0000","updated_time":"2011-11-15T16:50:03+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"}],"count":1},"comments":{"count":0}},{"id":"303471226332209_307822012563797","from":{"name":"Satvin Sato","id":"1241400157"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"http:\/\/www.facebook.com\/photo.php?fbid=10150367015293676&set=a.231863553675.135481.205051538675&type=1&ref=nf","picture":"https:\/\/fbcdn-photos-a.akamaihd.net\/hphotos-ak-ash4\/299714_10150367015293676_53432880_s.jpg","link":"http:\/\/www.facebook.com\/photo.php?fbid=10150367015293676&set=a.231863553675.135481.205051538675&type=1","name":"Timeline Photos","description":"Join Tintu-Mon\nhttp:\/\/www.facebook.com\/mrtintumon","properties":[{"name":"By","text":"Tintu-Mon","href":"http:\/\/www.facebook.com\/mrtintumon?ref=stream"}],"icon":"https:\/\/fbstatic-a.akamaihd.net\/rsrc.php\/v2\/yD\/r\/aS8ecmYRys0.gif","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307822012563797"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307822012563797"}],"privacy":{"value":""},"type":"photo","object_id":"10150367015293676","created_time":"2011-11-15T15:41:46+0000","updated_time":"2011-11-15T16:34:11+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Sanjay Khan","id":"624923774"},{"name":"Gagan Sharma","id":"100000437342461"}],"count":3},"comments":{"data":[{"id":"303471226332209_307822012563797_307822775897054","from":{"name":"Satvin Sato","id":"1241400157"},"message":"Duty of Board of Director... :D","created_time":"2011-11-15T15:43:21+0000","likes":1},{"id":"303471226332209_307822012563797_307829429229722","from":{"name":"Prince Mishra","id":"1107033555"},"message":"yaar ye khatta mujhe samajh nahi aaya :( :(","created_time":"2011-11-15T15:57:19+0000","likes":1},{"id":"303471226332209_307822012563797_307830012562997","from":{"name":"Virendra Singh Bhalothia","id":"626924343"},"message":"Check the background! ;)","created_time":"2011-11-15T15:58:44+0000","likes":1},{"id":"303471226332209_307822012563797_307831422562856","from":{"name":"Prince Mishra","id":"1107033555"},"message":"hahaha mai to kahi aur hi \"GodZilla\" dhoondh raha tha ;)","created_time":"2011-11-15T16:02:07+0000"},{"id":"303471226332209_307822012563797_307846835894648","from":{"name":"Satvin Sato","id":"1241400157"},"message":"Chod ho yaar tum log.... haste haste lag gyi meri,....:D","created_time":"2011-11-15T16:34:11+0000"}],"count":5},"subscribed":true},{"id":"303471226332209_307817262564272","from":{"name":"Sanjay Khan","id":"624923774"},"to":{"data":[{"name":"Jagmohan Vishwakarma","id":"626648844"},{"name":"Ankur Yadav","id":"650749607"},{"name":"Satvin Sato","id":"1241400157"},{"name":"Prince Mishra","id":"1107033555"},{"name":"Dinesh Saini","id":"1430345706"},{"name":"Kaustubh Dharmadhikari","id":"737370555"},{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"CPF Jagmohan Vishwakarma joined as Security Officer. \n\ncc- Ankur Yadav Satvin Sato Prince Mishra Dinesh Saini Kaustubh Dharmadhikari","message_tags":{"4":[{"id":"626648844","name":"Jagmohan Vishwakarma","type":"user","offset":4,"length":20}],"59":[{"id":"650749607","name":"Ankur Yadav","type":"user","offset":59,"length":11}],"71":[{"id":"1241400157","name":"Satvin Sato","type":"user","offset":71,"length":11}],"83":[{"id":"1107033555","name":"Prince Mishra","type":"user","offset":83,"length":13}],"97":[{"id":"1430345706","name":"Dinesh Saini","type":"user","offset":97,"length":12}],"110":[{"id":"737370555","name":"Kaustubh Dharmadhikari","type":"user","offset":110,"length":22}]},"actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307817262564272"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307817262564272"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T15:30:59+0000","updated_time":"2011-11-15T16:23:40+0000","likes":{"data":[{"name":"Prince Mishra","id":"1107033555"},{"name":"Ankur Yadav","id":"650749607"},{"name":"Pawan Sihag","id":"1111364038"},{"name":"Kaustubh Dharmadhikari","id":"737370555"},{"name":"Satvin Sato","id":"1241400157"}],"count":6},"comments":{"data":[{"id":"303471226332209_307817262564272_307829099229755","from":{"name":"Prince Mishra","id":"1107033555"},"message":"So whats the roadmap on security front?","created_time":"2011-11-15T15:56:44+0000"},{"id":"303471226332209_307817262564272_307830505896281","from":{"name":"Jagmohan Vishwakarma","id":"626648844"},"message":"all infiltration vll b cut off...only employees n chinkis allowed..","created_time":"2011-11-15T15:59:57+0000","likes":5},{"id":"303471226332209_307817262564272_307831972562801","from":{"name":"Prince Mishra","id":"1107033555"},"message":"Then you ready to take charge Capt. Jagmohan Vishwakarma","message_tags":[{"id":"626648844","name":"Jagmohan Vishwakarma","type":"user","offset":36,"length":20}],"created_time":"2011-11-15T16:03:10+0000","likes":1},{"id":"303471226332209_307817262564272_307841459228519","from":{"name":"Satvin Sato","id":"1241400157"},"message":"chinkies.... ha ha.... feeeeeeeeeeeeeeeeeeeeeeeeel","created_time":"2011-11-15T16:22:51+0000","likes":1},{"id":"303471226332209_307817262564272_307841792561819","from":{"name":"Prince Mishra","id":"1107033555"},"message":"hahahaha","created_time":"2011-11-15T16:23:40+0000"}],"count":5},"subscribed":true},{"id":"303471226332209_307831015896230","from":{"name":"Pradeep Paliwal","id":"100000109252637"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Bolaa dukaan-daar, ke kyaa chahiye tumhain\nJo bhii kaho ge merii dukaan per wo paoge\nmaine kahaa ke kutte ke khaane kaa cake hai\nbolaa yahiin pe khaaoge yaa leke jaaoge\u2026","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307831015896230"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307831015896230"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T16:01:04+0000","updated_time":"2011-11-15T16:01:04+0000","likes":{"data":[{"name":"Omvir Singh","id":"100000890803498"}],"count":1},"comments":{"count":0}},{"id":"303471226332209_307761819236483","from":{"name":"Mukesh Yadav","id":"100000841872126"},"to":{"data":[{"version":1,"name":"KHATTA CORP.","id":"303471226332209"}]},"message":"Ek Ladki Ka Rape Ka Case Court Mein Chal Raha Thha\n\nJudge: \u201cJab Tumhara Rape Ho Raha Thha Tab Tum Kesa Mehsus Kar Rahi Thi?\u201d\n\nLadki: \u201cLaddu Agar Jabardasti Se Bhi Khilaya Jaye To Meetha Hi Lagta Hai.\u201c","actions":[{"name":"Comment","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307761819236483"},{"name":"Like","link":"http:\/\/www.facebook.com\/303471226332209\/posts\/307761819236483"}],"privacy":{"value":""},"type":"status","created_time":"2011-11-15T13:16:51+0000","updated_time":"2011-11-15T15:53:48+0000","likes":{"data":[{"name":"Ankur Yadav","id":"650749607"},{"name":"Vinit Sharma","id":"1399563388"},{"name":"Satvin Sato","id":"1241400157"},{"name":"Omvir Singh","id":"100000890803498"}],"count":5},"comments":{"data":[{"id":"303471226332209_307761819236483_307762529236412","from":{"name":"Ankur Yadav","id":"650749607"},"message":"hehehe kha the be caddo bhai ...missed ur khattas...WELCOME :)","created_time":"2011-11-15T13:18:44+0000"},{"id":"303471226332209_307761819236483_307763455902986","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"KHUB SUNENGE KHATTE LOG JB MIL BETHENGE 3 YAAR,M CADO SIR ND KHATTA CORP...","created_time":"2011-11-15T13:21:24+0000","likes":2},{"id":"303471226332209_307761819236483_307796945899637","from":{"name":"Mukesh Yadav","id":"100000841872126"},"message":"bhoola bhatka aa gaya... ghar waapis","created_time":"2011-11-15T14:43:13+0000"},{"id":"303471226332209_307761819236483_307797089232956","from":{"name":"Mukesh Yadav","id":"100000841872126"},"message":"Omvir Singh ne raasta dikhaaya","message_tags":[{"id":"100000890803498","name":"Omvir Singh","type":"user","offset":0,"length":11}],"created_time":"2011-11-15T14:43:32+0000","likes":1},{"id":"303471226332209_307761819236483_307827629229902","from":{"name":"Omvir Singh","id":"100000890803498"},"message":"raste banane balo ko hm kya rasta dikhayenge sir....","created_time":"2011-11-15T15:53:48+0000","likes":1}],"count":5}}],"paging":{"previous":"https:\/\/graph.facebook.com\/303471226332209\/feed?limit=25&__paging_token=303471226332209_307942435885088&access_token=AAABbziNJcXsBAILBJhHCFIzXloOTUNuXatsmTrkToBNKH3Uj5JjndoTvdfZC3Yj8wZC9Pk5NoVgYWtBolmiObi1b7rZCX2r1ZBvDy6RtSwZDZD&since=1321386747&__previous=1","next":"https:\/\/graph.facebook.com\/303471226332209\/feed?limit=25&access_token=AAABbziNJcXsBAILBJhHCFIzXloOTUNuXatsmTrkToBNKH3Uj5JjndoTvdfZC3Yj8wZC9Pk5NoVgYWtBolmiObi1b7rZCX2r1ZBvDy6RtSwZDZD&until=1321372428&__paging_token=303471226332209_307761819236483"}} |
{
"require": {
"slim/slim": "^3.12",
"symfony/dotenv": "^4.2",
"slim/twig-view": "^2.4",
"predis/predis": "^1.1",
"illuminate/database": "^5.8",
"symfony/process": "^4.2",
"dragonmantank/cron-expression": "^2.3",
"fengqi/hanzi": "^0.1.2",
"chumper/zipper": "^1.0"
},
"autoload": {
"psr-4": {
"App\\": "app/",
"Command\\": "command/"
}
},
"scripts": {
"post-install-cmd": [
"@php -r \"file_exists('.env') || copy('.env.example', '.env');\""
]
}
}
|
{"cardClass":"NEUTRAL","cost":2,"dbfId":36721,"id":"BRMA09_2_TB","name":"Open the Gates","playRequirements":{"REQ_NUM_MINION_SLOTS":1},"set":"TB","text":"<b>Hero Power</b>\nSummon three 1/1 Whelps.","type":"HERO_POWER"} |
{
"id": 924636255,
"type": "Feature",
"properties": {
"addr:full":"150 Tunnel Avenue London Greater London SE10 0PL",
"addr:housenumber":"150",
"addr:postcode":"se10 0pl",
"addr:street":"Tunnel Avenue",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"0.0099569997,51.4896850586,0.0099569997,51.4896850586",
"geom:latitude":51.489685,
"geom:longitude":0.009957,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"150 Tunnel Avenue",
"sg:city":"London",
"sg:classifiers":[
{
"category":"Manufacturing",
"subcategory":"Signs",
"type":"Manufacturing & Wholesale Goods"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 20 8305 0485",
"sg:postcode":"SE10 0PL",
"sg:province":"Greater London",
"sg:tags":[
"writer"
],
"src:geom":"simplegeo",
"wof:belongsto":[
1158865877,
102191581,
85633159,
404227469,
101750367,
1158857295
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_5qay0la5oB3g1L9e3jDDMj_51.489685_0.009957@1300740843"
},
"wof:country":"GB",
"wof:created":1471972149,
"wof:geomhash":"5439a2192837de2963b62557dd1be805",
"wof:hierarchy":[
{
"borough_id":1158857295,
"continent_id":102191581,
"country_id":85633159,
"locality_id":101750367,
"macroregion_id":404227469,
"neighbourhood_id":1158865877,
"region_id":-1,
"venue_id":924636255
}
],
"wof:id":924636255,
"wof:lastmodified":1513973959,
"wof:name":"Ted Baker",
"wof:parent_id":1158865877,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"writer"
]
},
"bbox": [
0.0099569997,
51.4896850586,
0.0099569997,
51.4896850586
],
"geometry": {"coordinates":[0.0099569997,51.4896850586],"type":"Point"}
} |
{
"patthana16.35:1.1": "sc1, ms40P16_203, msdiv77",
"patthana16.35:2.1": "sc2, ms40P16_204"
} |
{
"interface": ":4984",
"adminInterface": ":4985",
"log": [
"*"
],
"databases": {
"sync_gateway": {
"server": "http://139.59.180.64:8091",
"users": {
"GUEST": {
"disabled": true
}
},
"sync": "function (newDoc, oldDoc) {\n\n if (oldDoc && oldDoc._deleted) {\n throw({forbidden: \"Attempt to edit a deleted doc\"});\n }\n\n if (oldDoc) {\n var previousOwners = [];\n\n if (oldDoc.owners) {\n for (var i in oldDoc.owners) {\n previousOwners.push(oldDoc.owners[i]);\n }\n } else if (oldDoc.owner) {\n previousOwners.push(oldDoc.owner)\n }\n\n if (previousOwners.length > 0) {\n previousOwners.push('admin');\n requireUser(previousOwners);\n }\n }\n\n function assignToChannels(doc) {\n if (doc.owner) {\n channel('ch_private_' + doc.owner);\n }\n\n if (!doc.type) {\n return;\n }\n\n if (doc.recipients) {\n for (var rec in doc.recipients) {\n channel('ch_private_' + doc.recipients[rec]);\n }\n }\n\n if (doc.type === 'chat-message') {\n if (doc.delivery_receipts_by_owner) {\n for (var userId in doc.delivery_receipts_by_owner) {\n channel('ch_private_' + userId);\n }\n }\n\n if (doc.read_receipts_by_owner) {\n for (var userId in doc.read_receipts_by_owner) {\n channel('ch_private_' + userId);\n }\n }\n } else if (doc.type === 'delivery-receipt' || doc.type === 'read-receipt') {\n // doc.recipients... handled above\n } else if (doc.type === 'live-chat') {\n // doc.recipients... handled above\n } else if (doc.type === 'attachment') {\n if (doc.readers) {\n for (var r in doc.readers) {\n channel('ch_private_' + doc.readers[r]);\n }\n }\n } else if (doc.type === 'chat-group') {\n if (doc.owners) {\n for (var o in doc.owners) {\n channel('ch_private_' + doc.owners[o]);\n }\n }\n\n if (doc.members) {\n for (var m in doc.members) {\n channel('ch_private_' + doc.members[m]);\n }\n }\n\n if (doc.readers) {\n for (var re in doc.readers) {\n channel('ch_private_' + doc.readers[re]);\n }\n }\n } else if (doc.type === 'account') {\n channel('ch_private_' + doc.user_id);\n } else if (doc.type === 'public-profile') {\n channel('ch_public_' + doc.owner);\n }\n }\n\n if (newDoc._deleted) {\n assignToChannels(oldDoc);\n } else {\n // cannot do validation checks on deleted docs\n // http://developer.couchbase.com/documentation/mobile/1.2/develop/guides/sync-gateway/sync-function-api-guide/validation/index.html\n\n if (newDoc.state && newDoc.state === 'archived') {\n return;\n }\n\n if (!newDoc.type) {\n throw({forbidden: \"All docs must have a type\"});\n }\n\n if (!newDoc.owner) {\n var hasAtLeastOneOwner = false;\n\n for (o in newDoc.owners) {\n if (newDoc.owners[o]) {\n hasAtLeastOneOwner = true;\n break;\n }\n }\n\n if (!hasAtLeastOneOwner) {\n throw({forbidden: \"Doc '\" + newDoc.type + \"' does not have any owners\"});\n }\n }\n\n if (newDoc.type === 'chat-message') {\n\n if (!newDoc.chat_group_id) {\n throw {forbidden: \"Doc '\" + newDoc.type + \"' must have a chat_group_id\"}\n }\n\n if (!newDoc.client_timestamp_utc) {\n throw {forbidden: \"Doc '\" + newDoc.type + \"' must have a client_timestamp_utc\"}\n }\n\n requireRole(['admin', \"contrib_\" + newDoc.chat_group_id]);\n } else if (newDoc.type === 'attachment') {\n\n if (!newDoc.client_timestamp_utc) {\n throw {forbidden: \"Doc '\" + newDoc.type + \"' must have a client_timestamp_utc\"}\n }\n\n if (!newDoc.chat_group_id) {\n throw {forbidden: \"Doc '\" + newDoc.type + \"' must have a chat_group_id\"}\n }\n } else if (newDoc.type === 'delivery-receipt' || newDoc.type === 'read-receipt') {\n\n if (!newDoc.chat_group_id) {\n throw {forbidden: \"Doc '\" + newDoc.type + \"' must have a chat_group_id\"}\n }\n\n } else if (newDoc.type === 'live-chat') {\n\n if (!newDoc.chat_group_id) {\n throw {forbidden: \"Doc '\" + newDoc.type + \"' must have a chat_group_id\"}\n }\n\n requireRole(['admin', \"contrib_\" + newDoc.chat_group_id]);\n } else if (newDoc.type === 'chat-group') {\n var chatGroupId = newDoc._id;\n\n if (newDoc.owners) {\n for (var o in newDoc.owners) {\n role(newDoc.owners[o], \"role:contrib_\" + chatGroupId);\n }\n }\n\n if (newDoc.members) {\n for (var m in newDoc.members) {\n role(newDoc.members[m], \"role:contrib_\" + chatGroupId);\n }\n }\n } else if (newDoc.type === 'account') {\n if (!newDoc.user_id) {\n throw({forbidden: \"Doc '\" + newDoc.type + \"' has no user_id\"});\n }\n } else if (newDoc.type === 'public-profile') {\n if (!newDoc.account_id) {\n throw({forbidden: \"Doc '\" + newDoc.type + \"' has no account_id\"});\n }\n }\n\n assignToChannels(newDoc);\n }\n\n}",
"bucket": "sync_gateway",
"event_handlers": {
"max_processes": 500,
"wait_for_process": "50",
"document_changed": [
{
"handler": "webhook",
"url": "http://139.59.180.64:8080/pomochat/webhook",
"timeout": 1
}
]
}
}
}
} |
{"title":"V.A. - Everydjur and Every Mir (2004)","uid":5491735,"size":74837952,"categoryP":"audio","categoryS":"music","magnet":"?xt=urn:btih:b8df544ca1d8358bed79a5508a7ab8bc493e8f8f&dn=V.A.+-+Everydjur+and+Every+Mir+%282004%29&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":0,"leechers":1,"uploader":"zhmuplik","files":29,"time":1271064302,"description":"The very first Djur And MIR compilation where every track is like a meatball of gold. The only release ever featuring SMKs "Efter Doktorn". Released April 2004.\n\n01 - Slagsmålsklubben - Efter Doktorn (4:22) \t\n02 - Frej The Man - Dumma Gris (2:15) \t\n03 - 50 Hertz - Välkommen På Café (3:10) \t\n04 - Trollske Ramón - Does The Bergakungen Dance (1:59) \t\t\n05 - Nu Ska Du Få På Käften - Burre Räv & The Sup Dig Full Trio (2:22) \n06 - Convoy Roadstar - Youth Of The Japanese Shoes (2:59) \t\t\n07 - Offerprästers Orkester - Mockasiner & Tvåhandssvärd (2:14) \t\n08 - Din Stalker - Lidilidi (4:11) \t\n09 - DJ Upperkurt - Satan Är Här Med Prostatabesvär (3:09) \t\t\n10 - DJ Billig - Väskryckarnas Bal (3:08) \t\n11 - Enuk - Castillo De Anna (3:17) \t\n12 - Joni - Nu Ska Vi Valsa (1:04) \t\n13 - Juno Brothers - Festfistaren (1:40) \t\n14 - Terror Flynn - Kills The Will (2:51) \t\n15 - Mondo Stereo - Natriumpentathol (3:45) \t\n16 - The Solbrillers - Färg (2:14) \t\n17 - Djävfulen & Påvfen - Vlad Tepez Mental Hospital (1:26)","torrent":{"xt":"urn:btih:b8df544ca1d8358bed79a5508a7ab8bc493e8f8f","amp;dn":"V.A.+-+Everydjur+and+Every+Mir+%282004%29","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"b8df544ca1d8358bed79a5508a7ab8bc493e8f8f","infoHashBuffer":{"type":"Buffer","data":[184,223,84,76,161,216,53,139,237,121,165,80,138,122,184,188,73,62,143,143]},"announce":[],"urlList":[]}} |
{
"block.wood.creak": {
"category": "block",
"sounds": [
"betterwithprime:wood/wood_creak_0",
"betterwithprime:wood/wood_creak_1",
"betterwithprime:wood/wood_creak_2",
"betterwithprime:wood/wood_creak_3"
],
"subtitle": "subtitles.bwm.wood.creak"
},
"block.stone.grind": {
"category": "block",
"sounds": [
"betterwithprime:stone/stone_grind_0",
"betterwithprime:stone/stone_grind_1",
"betterwithprime:stone/stone_grind_2",
"betterwithprime:stone/stone_grind_3"
],
"subtitle": "subtitles.bwm.stone.grind"
}
} |
{"poster":"colinturnup01","date":"2016-05-29T12:31:01.747+0000","title":"Wieviel LP verliere ich,wenn ich eine Promo verliere?","subforum":"Ligen & gewertete Spiele","up_votes":1,"down_votes":1,"body":"Wenn man eine Promo verliert,hat man dann wieder 0 LP?","replies":[{"poster":"Nycaria","date":"2016-05-29T12:34:25.076+0000","up_votes":1,"down_votes":0,"body":"Du verlierst nicht alle LP,wie viele genau weiß ich allerdings nicht. Denke mal so zwischen 20-40 je nachdem wie deine MMR ist (<-ist einfach nur geschätzt)\n\nEdit: \n> Die Anzahl der LP, die von 100 abgezogen wird, entspricht dem Nettoverlust an LP aus deinen Aufstiegsspielen. Wenn du beispielsweise eines von drei Spielen gewinnst, werden 1 LP Zugewinn und 2 LP Verlust verbucht. Die Nettodifferenz ist das, was von 100 abgezogen wird.","replies":[]}]} |
{
"micrownet" : [
"agglomeration"
],
"duck" : [
"\n<a href=\"http://duckduckgo.com/c/Urban_geography\">Urban geography</a>",
"\n<a href=\"http://duckduckgo.com/c/Urban_studies_and_planning_terminology\">Urban studies and planning terminology</a>",
"Urban agglomeration",
"\n<a href=\"http://duckduckgo.com/c/Demography\">Demography</a>",
"\n<a href=\"http://duckduckgo.com/World's_largest_cities\">World's largest cities</a> - A list of the largest municipalities (cities) areas by population",
"https://i.duckduckgo.com/i/a0ffa359.jpg",
"\n<a href=\"http://duckduckgo.com/Megacity\">Megacity</a> - A megacity is usually defined as a metropolitan area with a total population in excess of 10 million people.",
"\n<a href=\"http://duckduckgo.com/Metropolis\">Metropolis</a> - A metropolis is a very large city or urban area which is a significant economic, political, and cultural center for a country or region, and an important hub for regional or international connec...",
"\n<a href=\"http://duckduckgo.com/Agglomeration_communities_in_France\">Agglomeration communities in France</a> - An agglomeration community is a metropolitan government structure in France, created by the Chevènement Law of 1999.",
"\n<a href=\"http://duckduckgo.com/City\">City</a> - A city is a relatively large and permanent settlement.",
"\n<a href=\"http://duckduckgo.com/c/Economic_geography\">Economic geography</a>",
"In the study of human settlements, an urban agglomeration is an extended city or town area comprising the built-up area of a central place (usually a municipality) and any suburbs linked by continuous urban area.",
"\n<a href=\"http://duckduckgo.com/Metropolitan_area\">Metropolitan area</a> - A metropolitan area, metro area or metro is a region consisting of a densely populated urban core and its less-populated surrounding territories, sharing industry, infrastructu...",
"\n<a href=\"http://duckduckgo.com/c/Population\">Population</a>"
],
"common" : {
"milestones" : [
"<a target=\"_new\" href=\"http://en.wikinews.org/wiki/2008_Google_Developer_Day_makes_its_inception_in_Taiwan\" title=\"2008 Google Developer Day makes its inception in Taiwan\">2008 Google Developer Day makes its inception in Taiwan</a>",
"<a target=\"_new\" href=\"http://en.wikinews.org/wiki/Listening_to_you_at_last:_EU_plans_to_tap_cell_phones\" title=\"Listening to you at last: EU plans to tap cell phones\">Listening to you at last: EU plans to tap cell phones</a>"
],
"image" : [
[],
[]
]
},
"Lists" : [],
"created" : 1373470310,
"book" : [],
"micro-www" : {
"agglomeration" : [
""
]
},
"wiki" : {
"cat" : [
"Urban studies and planning terminology",
"Urban geography",
"Economic geography",
"Demography",
"Population",
"Types of populated places"
],
"text" : "\n\n, the world's largest urban agglomeration, with 38.4 million people.]] urban\nagglomeration, viewed at dusk.]] In the study of human settlements, an ' urban\nagglomeration' is an extended city or town area comprising the built-up area of\na central place (usually a [[municipality]]) and any suburbs linked by\ncontinuous urban area. INSEE, the French Statistical Institute, uses the term\n[[unité urbaine]], which means continuous urbanized area. However, because of\ndifferences in definitions of what does and does not constitute an\n\"agglomeration\", as well as variations and limitations in statistical or\ngeographical methodology, it can be problematic to compare different\nagglomerations around the world. It may not be clear, for instance, whether an\narea should be considered to be a satellite and part of an agglomeration, or a\ndistinct entity in itself.\n\nThe term \"agglomeration\" is also linked to \"conurbation\", which is a more\nspecific term for large urban clusters where the built-up zones of influence of\ndistinct cities or towns are connected by continuous built-up development\n([[Essen]] - [[Dortmund]] and others in the Rhine-Ruhr district), even in\ndifferent regions, states or countries, ([[Lille]] - [[Kortrijk]] in France and\nBelgium). Each city or town in a conurbation may nevertheless continue to act as\nan independent focus for a substantial part of the area. urban agglomeration is\nthe San Diego-Tijuana metropolitan area, creating a trans-border conurbation\nbetween the western United States and Mexico, respectively.]]\n",
"title" : "Urban%20agglomeration",
"headings" : [
"Largest occurrences",
"Specific legal definition",
"See also",
"External links",
"References"
]
},
"micro-relation" : [
"2: City",
"2: Mexico_City",
"2: Delhi",
"1: Town",
"1: Suburb",
"1: Urban_area",
"1: INSEE",
"1: Conurbation",
"1: San_Diego-Tijuana_metropolitan_area",
"1: United_States",
"1: Mexico",
"1: Tokyo_metropolitan_area",
"1: New_York_metropolitan_area",
"1: Seoul_Metropolitan_Area",
"1: Tokyo",
"1: Seoul",
"1: Jakarta",
"1: Metro_Manila",
"1: Mumbai",
"1: New_York_City",
"1: São_Paulo",
"1: Shanghai",
"1: Administrative_subdivisions_of_Quebec",
"1: Quebec",
"1: Municipal_reorganization_in_Quebec",
"1: Montreal",
"1: Quebec_City",
"1: Longueuil,_Quebec",
"1: Metropolitan_communities_of_Quebec",
"1: Regional_county_municipality",
"1: World's_largest_cities",
"1: Megacity",
"1: Metropolis",
"1: Metropolitan_area",
"1: Transborder_agglomeration",
"1: Urban_sprawl",
"1: Global_city"
]
}
|
{"title":"Tuxguitar 1.2 unofficial Release with drum fix LInux","uid":7057537,"size":6834824,"categoryP":"applications","categoryS":"unix","magnet":"?xt=urn:btih:0dc0ddc3702d7790070e1847a75544469ffaf393&dn=Tuxguitar+1.2+unofficial+Release+with+drum+fix+LInux&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":0,"leechers":0,"uploader":"mr.p3n15","files":1408,"time":1330191966,"description":"Tuxguitar for linux with a drum Patch for a correct drum rendering,you also can get it from \n#sudo add-apt-repository ppa:simpoir","torrent":{"xt":"urn:btih:0dc0ddc3702d7790070e1847a75544469ffaf393","amp;dn":"Tuxguitar+1.2+unofficial+Release+with+drum+fix+LInux","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"0dc0ddc3702d7790070e1847a75544469ffaf393","infoHashBuffer":{"type":"Buffer","data":[13,192,221,195,112,45,119,144,7,14,24,71,167,85,68,70,159,250,243,147]},"announce":[],"urlList":[]}} |
{
"id": 908022751,
"type": "Feature",
"properties": {
"addr:full":"Unit 1 Lion Works, Station Road, Whittlesford Cambridge Cambridgeshire CB2 4NL",
"addr:housenumber":"unit 1",
"addr:postcode":"cb2 4nl",
"addr:street":"Lion Works Station Road",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"0.1479309946,52.1121940613,0.1479309946,52.1121940613",
"geom:latitude":52.112194,
"geom:longitude":0.147931,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"Unit 1 Lion Works, Station Road, Whittlesford",
"sg:city":"Cambridge",
"sg:classifiers":[
{
"category":"Home & Garden",
"subcategory":"Household Appliances",
"type":"Retail Goods"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 1223 839900",
"sg:postcode":"CB2 4NL",
"sg:province":"Cambridgeshire",
"sg:tags":[
"refrigeration",
"air conditioning"
],
"src:geom":"simplegeo",
"wof:belongsto":[],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_2TncqDCw50rSiBWMsJG0QJ_52.112194_0.147931@1300740505"
},
"wof:country":"GB",
"wof:created":1471928834,
"wof:geomhash":"889d771f0d08e2ad444dc3b70a932429",
"wof:hierarchy":[],
"wof:id":908022751,
"wof:lastmodified":1496942423,
"wof:name":"R M J Engineering Ltd.",
"wof:parent_id":-1,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"refrigeration",
"air conditioning"
]
},
"bbox": [
0.1479309946,
52.1121940613,
0.1479309946,
52.1121940613
],
"geometry": {"coordinates":[0.1479309946,52.1121940613],"type":"Point"}
} |
["ca45f9ea98c357aae36a7ee1f6ff04b196404831","f6959721ed0e6ca62b143cc860f643381619827c"] |
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1326, 0.1326], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7348, 0.7348], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1403, 0.1403], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 0.8445360660552979, "u_mse": [0.1326, 0.1326], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1337, 0.1337], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7326, 0.7326], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1443, 0.1443], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 0.7952370643615723, "u_mse": [0.1337, 0.1337], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1439, 0.1439], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7122, 0.7122], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1607, 0.1607], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 0.9253699779510498, "u_mse": [0.1439, 0.1439], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1423, 0.1423], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7154, 0.7154], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1554, 0.1554], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 0.945655107498169, "u_mse": [0.1423, 0.1423], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1348, 0.1348], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7304, 0.7304], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1389, 0.1389], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.0356171131134033, "u_mse": [0.1348, 0.1348], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1378, 0.1378], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7244, 0.7244], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1443, 0.1443], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.1968629360198975, "u_mse": [0.1378, 0.1378], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1883, 0.1883], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6234, 0.6234], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2026, 0.2026], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.2759380340576172, "u_mse": [0.1883, 0.1883], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2376, 0.2376], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5248, 0.5248], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2586, 0.2586], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.3125438690185547, "u_mse": [0.2376, 0.2376], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2965, 0.2965], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.407, 0.407], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.3131, 0.3131], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.4283418655395508, "u_mse": [0.2965, 0.2965], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1308, 0.1308], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7384, 0.7384], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.138, 0.138], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.509531021118164, "u_mse": [0.1308, 0.1308], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0795, 0.0795], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.841, 0.841], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.0922, 0.0922], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.7073209285736084, "u_mse": [0.0795, 0.0795], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0952, 0.0952], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8096, 0.8096], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1162, 0.1162], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 1.927682876586914, "u_mse": [0.0952, 0.0952], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0858, 0.0858], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8284, 0.8284], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1024, 0.1024], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.0329530239105225, "u_mse": [0.0858, 0.0858], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1037, 0.1037], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7926, 0.7926], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1305, 0.1305], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.2442569732666016, "u_mse": [0.1037, 0.1037], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0952, 0.0952], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8096, 0.8096], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1158, 0.1158], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.283374071121216, "u_mse": [0.0952, 0.0952], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0981, 0.0981], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8038, 0.8038], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1186, 0.1186], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.3773698806762695, "u_mse": [0.0981, 0.0981], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1568, 0.1568], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6864, 0.6864], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1815, 0.1815], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.349848985671997, "u_mse": [0.1568, 0.1568], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2193, 0.2193], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5614, 0.5614], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2623, 0.2623], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.555294990539551, "u_mse": [0.2193, 0.2193], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2768, 0.2768], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4464, 0.4464], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.31, 0.31], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.669796943664551, "u_mse": [0.2768, 0.2768], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1, 0.1], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8, 0.8], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1144, 0.1144], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.829320192337036, "u_mse": [0.1, 0.1], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1229, 0.1229], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7542, 0.7542], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1433, 0.1433], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.012618064880371, "u_mse": [0.1229, 0.1229], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1204, 0.1204], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7592, 0.7592], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1449, 0.1449], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.017448902130127, "u_mse": [0.1204, 0.1204], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1007, 0.1007], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7986, 0.7986], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1099, 0.1099], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 2.963031053543091, "u_mse": [0.1007, 0.1007], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1385, 0.1385], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.723, 0.723], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1648, 0.1648], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.089711904525757, "u_mse": [0.1385, 0.1385], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1074, 0.1074], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7852, 0.7852], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1248, 0.1248], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.2711639404296875, "u_mse": [0.1074, 0.1074], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1147, 0.1147], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7706, 0.7706], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1291, 0.1291], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.76944899559021, "u_mse": [0.1147, 0.1147], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1715, 0.1715], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.657, 0.657], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.188, 0.188], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.7537460327148438, "u_mse": [0.1715, 0.1715], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2353, 0.2353], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5294, 0.5294], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2543, 0.2543], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.729403018951416, "u_mse": [0.2353, 0.2353], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3011, 0.3011], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3978, 0.3978], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3394, 0.3394], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.9517600536346436, "u_mse": [0.3011, 0.3011], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0965, 0.0965], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.807, 0.807], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1034, 0.1034], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 3.950561046600342, "u_mse": [0.0965, 0.0965], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0961, 0.0961], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8078, 0.8078], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.105, 0.105], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.111112117767334, "u_mse": [0.0961, 0.0961], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0955, 0.0955], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.809, 0.809], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1027, 0.1027], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.2358238697052, "u_mse": [0.0955, 0.0955], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1367, 0.1367], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7266, 0.7266], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.162, 0.162], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.268258094787598, "u_mse": [0.1367, 0.1367], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1213, 0.1213], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7574, 0.7574], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1516, 0.1516], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.383378028869629, "u_mse": [0.1213, 0.1213], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1064, 0.1064], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7872, 0.7872], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1154, 0.1154], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.435999155044556, "u_mse": [0.1064, 0.1064], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1119, 0.1119], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7762, 0.7762], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.122, 0.122], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.719007968902588, "u_mse": [0.1119, 0.1119], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1648, 0.1648], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6704, 0.6704], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2015, 0.2015], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.543416976928711, "u_mse": [0.1648, 0.1648], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2304, 0.2304], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5392, 0.5392], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.264, 0.264], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.9401938915252686, "u_mse": [0.2304, 0.2304], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2982, 0.2982], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4036, 0.4036], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3349, 0.3349], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.868683099746704, "u_mse": [0.2982, 0.2982], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1048, 0.1048], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7904, 0.7904], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1194, 0.1194], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.178483009338379, "u_mse": [0.1048, 0.1048], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1065, 0.1065], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.787, 0.787], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1169, 0.1169], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 4.974800109863281, "u_mse": [0.1065, 0.1065], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.11, 0.11], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.78, 0.78], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1163, 0.1163], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.041435956954956, "u_mse": [0.11, 0.11], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1022, 0.1022], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7956, 0.7956], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1149, 0.1149], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.285120010375977, "u_mse": [0.1022, 0.1022], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1012, 0.1012], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7976, 0.7976], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1232, 0.1232], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.729942083358765, "u_mse": [0.1012, 0.1012], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1097, 0.1097], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7806, 0.7806], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1294, 0.1294], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.771255970001221, "u_mse": [0.1097, 0.1097], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1151, 0.1151], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7698, 0.7698], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1273, 0.1273], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.814877986907959, "u_mse": [0.1151, 0.1151], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1698, 0.1698], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6604, 0.6604], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2039, 0.2039], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.930548191070557, "u_mse": [0.1698, 0.1698], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2394, 0.2394], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5212, 0.5212], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2889, 0.2889], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.951865196228027, "u_mse": [0.2394, 0.2394], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.313, 0.313], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.374, 0.374], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3456, 0.3456], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 5.903395891189575, "u_mse": [0.313, 0.313], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.115, 0.115], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.77, 0.77], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1269, 0.1269], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 6.167448043823242, "u_mse": [0.115, 0.115], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0949, 0.0949], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8102, 0.8102], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1109, 0.1109], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 6.39896297454834, "u_mse": [0.0949, 0.0949], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0854, 0.0854], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8292, 0.8292], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1019, 0.1019], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 6.846654891967773, "u_mse": [0.0854, 0.0854], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0953, 0.0953], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8094, 0.8094], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1106, 0.1106], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 6.725265979766846, "u_mse": [0.0953, 0.0953], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1053, 0.1053], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7894, 0.7894], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1265, 0.1265], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.0685999393463135, "u_mse": [0.1053, 0.1053], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0806, 0.0806], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8388, 0.8388], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.093, 0.093], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.122910976409912, "u_mse": [0.0806, 0.0806], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1215, 0.1215], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.757, 0.757], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1399, 0.1399], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.113933086395264, "u_mse": [0.1215, 0.1215], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1435, 0.1435], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.713, 0.713], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1724, 0.1724], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.540473937988281, "u_mse": [0.1435, 0.1435], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1998, 0.1998], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6004, 0.6004], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.23, 0.23], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.557607889175415, "u_mse": [0.1998, 0.1998], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2539, 0.2539], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4922, 0.4922], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.311, 0.311], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.665582895278931, "u_mse": [0.2539, 0.2539], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1181, 0.1181], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7638, 0.7638], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1473, 0.1473], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.697792053222656, "u_mse": [0.1181, 0.1181], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1037, 0.1037], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7926, 0.7926], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1175, 0.1175], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.535099029541016, "u_mse": [0.1037, 0.1037], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1106, 0.1106], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7788, 0.7788], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1187, 0.1187], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.211509943008423, "u_mse": [0.1106, 0.1106], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1234, 0.1234], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7532, 0.7532], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1471, 0.1471], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 7.904773950576782, "u_mse": [0.1234, 0.1234], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1131, 0.1131], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7738, 0.7738], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1304, 0.1304], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.064028978347778, "u_mse": [0.1131, 0.1131], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1091, 0.1091], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7818, 0.7818], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1293, 0.1293], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.012028932571411, "u_mse": [0.1091, 0.1091], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1092, 0.1092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7816, 0.7816], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1197, 0.1197], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.002921104431152, "u_mse": [0.1092, 0.1092], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1786, 0.1786], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6428, 0.6428], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.207, 0.207], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.587603092193604, "u_mse": [0.1786, 0.1786], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.254, 0.254], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.492, 0.492], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2829, 0.2829], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.833055973052979, "u_mse": [0.254, 0.254], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3294, 0.3294], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3412, 0.3412], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.3682, 0.3682], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.504533052444458, "u_mse": [0.3294, 0.3294], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0981, 0.0981], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8038, 0.8038], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1101, 0.1101], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.752591133117676, "u_mse": [0.0981, 0.0981], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0993, 0.0993], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8014, 0.8014], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1114, 0.1114], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 8.725587129592896, "u_mse": [0.0993, 0.0993], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1101, 0.1101], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7798, 0.7798], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1371, 0.1371], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.24540901184082, "u_mse": [0.1101, 0.1101], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1004, 0.1004], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7992, 0.7992], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1272, 0.1272], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.02315092086792, "u_mse": [0.1004, 0.1004], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1019, 0.1019], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7962, 0.7962], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1187, 0.1187], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.585767984390259, "u_mse": [0.1019, 0.1019], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1418, 0.1418], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7164, 0.7164], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1776, 0.1776], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.381526947021484, "u_mse": [0.1418, 0.1418], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1217, 0.1217], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7566, 0.7566], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1476, 0.1476], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.493074893951416, "u_mse": [0.1217, 0.1217], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1829, 0.1829], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6342, 0.6342], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.207, 0.207], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.720732927322388, "u_mse": [0.1829, 0.1829], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2584, 0.2584], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4832, 0.4832], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2958, 0.2958], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.970982074737549, "u_mse": [0.2584, 0.2584], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3352, 0.3352], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3296, 0.3296], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3853, 0.3853], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.570306062698364, "u_mse": [0.3352, 0.3352], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0878, 0.0878], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8244, 0.8244], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.0992, 0.0992], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.237839937210083, "u_mse": [0.0878, 0.0878], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1286, 0.1286], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7428, 0.7428], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1563, 0.1563], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.826671838760376, "u_mse": [0.1286, 0.1286], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1279, 0.1279], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7442, 0.7442], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1835, 0.1835], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 9.946172952651978, "u_mse": [0.1279, 0.1279], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0959, 0.0959], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8082, 0.8082], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1217, 0.1217], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.087353229522705, "u_mse": [0.0959, 0.0959], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1344, 0.1344], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7312, 0.7312], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1786, 0.1786], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.248219013214111, "u_mse": [0.1344, 0.1344], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1091, 0.1091], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7818, 0.7818], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1285, 0.1285], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.340963125228882, "u_mse": [0.1091, 0.1091], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1247, 0.1247], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7506, 0.7506], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1481, 0.1481], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.849039077758789, "u_mse": [0.1247, 0.1247], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1852, 0.1852], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6296, 0.6296], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2081, 0.2081], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.428406000137329, "u_mse": [0.1852, 0.1852], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2658, 0.2658], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4684, 0.4684], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3012, 0.3012], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.506904125213623, "u_mse": [0.2658, 0.2658], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3463, 0.3463], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3074, 0.3074], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3822, 0.3822], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.017897844314575, "u_mse": [0.3463, 0.3463], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0934, 0.0934], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8132, 0.8132], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.108, 0.108], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.733111143112183, "u_mse": [0.0934, 0.0934], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0934, 0.0934], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8132, 0.8132], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1057, 0.1057], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 10.931992053985596, "u_mse": [0.0934, 0.0934], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0926, 0.0926], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8148, 0.8148], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1059, 0.1059], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.14695405960083, "u_mse": [0.0926, 0.0926], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.096, 0.096], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.808, 0.808], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1067, 0.1067], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.099402904510498, "u_mse": [0.096, 0.096], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.116, 0.116], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.768, 0.768], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1469, 0.1469], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.401163101196289, "u_mse": [0.116, 0.116], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1056, 0.1056], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7888, 0.7888], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1216, 0.1216], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.648262977600098, "u_mse": [0.1056, 0.1056], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1142, 0.1142], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7716, 0.7716], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1359, 0.1359], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.026839017868042, "u_mse": [0.1142, 0.1142], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1853, 0.1853], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6294, 0.6294], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2138, 0.2138], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.8901948928833, "u_mse": [0.1853, 0.1853], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2667, 0.2667], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4666, 0.4666], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2913, 0.2913], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.65482497215271, "u_mse": [0.2667, 0.2667], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3488, 0.3488], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3024, 0.3024], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3909, 0.3909], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.874377965927124, "u_mse": [0.3488, 0.3488], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1108, 0.1108], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7784, 0.7784], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.142, 0.142], "realization": 0, "beta_mse": [0.0, 0.0], "runtime": 11.637110233306885, "u_mse": [0.1108, 0.1108], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0949, 0.0949], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8102, 0.8102], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1075, 0.1075], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.153764963150024, "u_mse": [0.0949, 0.0949], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1045, 0.1045], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.791, 0.791], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1263, 0.1263], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.24887490272522, "u_mse": [0.1045, 0.1045], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0971, 0.0971], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8058, 0.8058], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.111, 0.111], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.214479923248291, "u_mse": [0.0971, 0.0971], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0964, 0.0964], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8072, 0.8072], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.114, 0.114], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 11.663172960281372, "u_mse": [0.0964, 0.0964], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1059, 0.1059], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7882, 0.7882], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1248, 0.1248], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 11.74250316619873, "u_mse": [0.1059, 0.1059], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0998, 0.0998], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8004, 0.8004], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1164, 0.1164], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.673000812530518, "u_mse": [0.0998, 0.0998], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1165, 0.1165], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.767, 0.767], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1324, 0.1324], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.025599956512451, "u_mse": [0.1165, 0.1165], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1327, 0.1327], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7346, 0.7346], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1514, 0.1514], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 11.890017986297607, "u_mse": [0.1327, 0.1327], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1534, 0.1534], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6932, 0.6932], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1703, 0.1703], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.00980806350708, "u_mse": [0.1534, 0.1534], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0924, 0.0924], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8152, 0.8152], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1006, 0.1006], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.323914051055908, "u_mse": [0.0924, 0.0924], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1123, 0.1123], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7754, 0.7754], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1219, 0.1219], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.259402990341187, "u_mse": [0.1123, 0.1123], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1096, 0.1096], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7808, 0.7808], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1209, 0.1209], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.521114110946655, "u_mse": [0.1096, 0.1096], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1017, 0.1017], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7966, 0.7966], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1116, 0.1116], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.305890083312988, "u_mse": [0.1017, 0.1017], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1073, 0.1073], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7854, 0.7854], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1366, 0.1366], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.690444946289062, "u_mse": [0.1073, 0.1073], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1149, 0.1149], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7702, 0.7702], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1399, 0.1399], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.554874897003174, "u_mse": [0.1149, 0.1149], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1061, 0.1061], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7878, 0.7878], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1119, 0.1119], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.83353304862976, "u_mse": [0.1061, 0.1061], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1607, 0.1607], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6786, 0.6786], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1814, 0.1814], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.950394868850708, "u_mse": [0.1607, 0.1607], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2164, 0.2164], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5672, 0.5672], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2451, 0.2451], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.195300102233887, "u_mse": [0.2164, 0.2164], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2833, 0.2833], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4334, 0.4334], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.3153, 0.3153], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.042155027389526, "u_mse": [0.2833, 0.2833], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1034, 0.1034], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7932, 0.7932], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1098, 0.1098], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 12.819463014602661, "u_mse": [0.1034, 0.1034], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1175, 0.1175], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.765, 0.765], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1299, 0.1299], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.425010919570923, "u_mse": [0.1175, 0.1175], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1057, 0.1057], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7886, 0.7886], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1121, 0.1121], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.416738033294678, "u_mse": [0.1057, 0.1057], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1173, 0.1173], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7654, 0.7654], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1362, 0.1362], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.138510942459106, "u_mse": [0.1173, 0.1173], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.159, 0.159], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.682, 0.682], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2007, 0.2007], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.212191104888916, "u_mse": [0.159, 0.159], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1241, 0.1241], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7518, 0.7518], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1478, 0.1478], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.248687982559204, "u_mse": [0.1241, 0.1241], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1238, 0.1238], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7524, 0.7524], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1468, 0.1468], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.285772800445557, "u_mse": [0.1238, 0.1238], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.151, 0.151], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.698, 0.698], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1689, 0.1689], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.641290187835693, "u_mse": [0.151, 0.151], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1971, 0.1971], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6058, 0.6058], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2225, 0.2225], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 13.854856967926025, "u_mse": [0.1971, 0.1971], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2437, 0.2437], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5126, 0.5126], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2763, 0.2763], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.082974910736084, "u_mse": [0.2437, 0.2437], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1033, 0.1033], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7934, 0.7934], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1101, 0.1101], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.936171054840088, "u_mse": [0.1033, 0.1033], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1047, 0.1047], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7906, 0.7906], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1125, 0.1125], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.232997179031372, "u_mse": [0.1047, 0.1047], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1012, 0.1012], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7976, 0.7976], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1134, 0.1134], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.02047610282898, "u_mse": [0.1012, 0.1012], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1203, 0.1203], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7594, 0.7594], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1309, 0.1309], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.479566097259521, "u_mse": [0.1203, 0.1203], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1092, 0.1092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7816, 0.7816], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.124, 0.124], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.456102132797241, "u_mse": [0.1092, 0.1092], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1053, 0.1053], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7894, 0.7894], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.124, 0.124], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.977491855621338, "u_mse": [0.1053, 0.1053], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.115, 0.115], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.77, 0.77], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1257, 0.1257], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.543437004089355, "u_mse": [0.115, 0.115], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1762, 0.1762], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6476, 0.6476], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2049, 0.2049], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 14.555056095123291, "u_mse": [0.1762, 0.1762], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2413, 0.2413], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5174, 0.5174], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2728, 0.2728], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 16.477862119674683, "u_mse": [0.2413, 0.2413], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3109, 0.3109], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3782, 0.3782], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.361, 0.361], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 16.6317138671875, "u_mse": [0.3109, 0.3109], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1187, 0.1187], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7626, 0.7626], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1469, 0.1469], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.016581058502197, "u_mse": [0.1187, 0.1187], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1157, 0.1157], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7686, 0.7686], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1289, 0.1289], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.127179145812988, "u_mse": [0.1157, 0.1157], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1198, 0.1198], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7604, 0.7604], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1397, 0.1397], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 16.893628120422363, "u_mse": [0.1198, 0.1198], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1312, 0.1312], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7376, 0.7376], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1558, 0.1558], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 17.26709818840027, "u_mse": [0.1312, 0.1312], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1365, 0.1365], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.727, 0.727], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.161, 0.161], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 16.87982201576233, "u_mse": [0.1365, 0.1365], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1217, 0.1217], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7566, 0.7566], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1428, 0.1428], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.980957984924316, "u_mse": [0.1217, 0.1217], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1339, 0.1339], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7322, 0.7322], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1693, 0.1693], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.8069429397583, "u_mse": [0.1339, 0.1339], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1962, 0.1962], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6076, 0.6076], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2375, 0.2375], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.805267095565796, "u_mse": [0.1962, 0.1962], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2586, 0.2586], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4828, 0.4828], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2907, 0.2907], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 15.586181163787842, "u_mse": [0.2586, 0.2586], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3233, 0.3233], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3534, 0.3534], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3541, 0.3541], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 16.340664863586426, "u_mse": [0.3233, 0.3233], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1181, 0.1181], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7638, 0.7638], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1375, 0.1375], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 16.28997278213501, "u_mse": [0.1181, 0.1181], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0926, 0.0926], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8148, 0.8148], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1131, 0.1131], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 18.384954929351807, "u_mse": [0.0926, 0.0926], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1007, 0.1007], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7986, 0.7986], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1172, 0.1172], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 18.365702867507935, "u_mse": [0.1007, 0.1007], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1091, 0.1091], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7818, 0.7818], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1363, 0.1363], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 18.551594018936157, "u_mse": [0.1091, 0.1091], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.116, 0.116], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.768, 0.768], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1381, 0.1381], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 18.69440793991089, "u_mse": [0.116, 0.116], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1084, 0.1084], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7832, 0.7832], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1362, 0.1362], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.39239501953125, "u_mse": [0.1084, 0.1084], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1077, 0.1077], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7846, 0.7846], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1202, 0.1202], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.48855686187744, "u_mse": [0.1077, 0.1077], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1644, 0.1644], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6712, 0.6712], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1927, 0.1927], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.231418132781982, "u_mse": [0.1644, 0.1644], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2372, 0.2372], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5256, 0.5256], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2742, 0.2742], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.269665956497192, "u_mse": [0.2372, 0.2372], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3098, 0.3098], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3804, 0.3804], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.331, 0.331], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.416446924209595, "u_mse": [0.3098, 0.3098], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0999, 0.0999], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8002, 0.8002], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1148, 0.1148], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.64606499671936, "u_mse": [0.0999, 0.0999], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1483, 0.1483], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7034, 0.7034], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.194, 0.194], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.715795040130615, "u_mse": [0.1483, 0.1483], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1034, 0.1034], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7932, 0.7932], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1194, 0.1194], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 19.775980949401855, "u_mse": [0.1034, 0.1034], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1079, 0.1079], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7842, 0.7842], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1264, 0.1264], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 20.036272048950195, "u_mse": [0.1079, 0.1079], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1081, 0.1081], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7838, 0.7838], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1283, 0.1283], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 20.62271499633789, "u_mse": [0.1081, 0.1081], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1385, 0.1385], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.723, 0.723], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1692, 0.1692], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 20.10879683494568, "u_mse": [0.1385, 0.1385], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1179, 0.1179], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7642, 0.7642], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1306, 0.1306], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 20.21991801261902, "u_mse": [0.1179, 0.1179], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.181, 0.181], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.638, 0.638], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2029, 0.2029], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 21.756173133850098, "u_mse": [0.181, 0.181], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2606, 0.2606], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4788, 0.4788], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2946, 0.2946], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.222694158554077, "u_mse": [0.2606, 0.2606], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3434, 0.3434], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3132, 0.3132], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.3742, 0.3742], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.20840096473694, "u_mse": [0.3434, 0.3434], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1268, 0.1268], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7464, 0.7464], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1474, 0.1474], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.392117977142334, "u_mse": [0.1268, 0.1268], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1056, 0.1056], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7888, 0.7888], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1272, 0.1272], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.20919919013977, "u_mse": [0.1056, 0.1056], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0923, 0.0923], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8154, 0.8154], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1063, 0.1063], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 21.58897590637207, "u_mse": [0.0923, 0.0923], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1113, 0.1113], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7774, 0.7774], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1277, 0.1277], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.852913856506348, "u_mse": [0.1113, 0.1113], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1094, 0.1094], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7812, 0.7812], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1212, 0.1212], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.652583837509155, "u_mse": [0.1094, 0.1094], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1081, 0.1081], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7838, 0.7838], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1247, 0.1247], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 22.715957164764404, "u_mse": [0.1081, 0.1081], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1156, 0.1156], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7688, 0.7688], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1355, 0.1355], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.141170978546143, "u_mse": [0.1156, 0.1156], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.181, 0.181], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.638, 0.638], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2175, 0.2175], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.980189085006714, "u_mse": [0.181, 0.181], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2604, 0.2604], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4792, 0.4792], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3047, 0.3047], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.169250965118408, "u_mse": [0.2604, 0.2604], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.336, 0.336], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.328, 0.328], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3926, 0.3926], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.560702085494995, "u_mse": [0.336, 0.336], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1124, 0.1124], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7752, 0.7752], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1378, 0.1378], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.543640851974487, "u_mse": [0.1124, 0.1124], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0928, 0.0928], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8144, 0.8144], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1115, 0.1115], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.63485097885132, "u_mse": [0.0928, 0.0928], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1127, 0.1127], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7746, 0.7746], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1427, 0.1427], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 24.312094926834106, "u_mse": [0.1127, 0.1127], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1187, 0.1187], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7626, 0.7626], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.151, 0.151], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.42016100883484, "u_mse": [0.1187, 0.1187], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1095, 0.1095], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.781, 0.781], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1292, 0.1292], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.967468976974487, "u_mse": [0.1095, 0.1095], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0956, 0.0956], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8088, 0.8088], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1177, 0.1177], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.00559711456299, "u_mse": [0.0956, 0.0956], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1086, 0.1086], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7828, 0.7828], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1304, 0.1304], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 24.709015130996704, "u_mse": [0.1086, 0.1086], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1841, 0.1841], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6318, 0.6318], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2125, 0.2125], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 23.827069997787476, "u_mse": [0.1841, 0.1841], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2681, 0.2681], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4638, 0.4638], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2933, 0.2933], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 24.39574694633484, "u_mse": [0.2681, 0.2681], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3496, 0.3496], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3008, 0.3008], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3821, 0.3821], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.077104806900024, "u_mse": [0.3496, 0.3496], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.117, 0.117], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.766, 0.766], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1422, 0.1422], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 24.894055128097534, "u_mse": [0.117, 0.117], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.097, 0.097], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.806, 0.806], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1059, 0.1059], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 26.63107991218567, "u_mse": [0.097, 0.097], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0979, 0.0979], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8042, 0.8042], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.119, 0.119], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.339864015579224, "u_mse": [0.0979, 0.0979], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1174, 0.1174], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7652, 0.7652], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1469, 0.1469], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 24.873539209365845, "u_mse": [0.1174, 0.1174], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1026, 0.1026], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7948, 0.7948], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.117, 0.117], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.295557975769043, "u_mse": [0.1026, 0.1026], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1199, 0.1199], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7602, 0.7602], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1365, 0.1365], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.33984088897705, "u_mse": [0.1199, 0.1199], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1165, 0.1165], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.767, 0.767], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1413, 0.1413], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.616888999938965, "u_mse": [0.1165, 0.1165], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1865, 0.1865], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.627, 0.627], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2146, 0.2146], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.013252019882202, "u_mse": [0.1865, 0.1865], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2674, 0.2674], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4652, 0.4652], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.288, 0.288], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.92367386817932, "u_mse": [0.2674, 0.2674], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3476, 0.3476], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3048, 0.3048], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3891, 0.3891], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 25.62819004058838, "u_mse": [0.3476, 0.3476], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1021, 0.1021], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7958, 0.7958], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1228, 0.1228], "realization": 1, "beta_mse": [0.0, 0.0], "runtime": 26.168931007385254, "u_mse": [0.1021, 0.1021], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1168, 0.1168], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7664, 0.7664], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1296, 0.1296], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 25.131177186965942, "u_mse": [0.1168, 0.1168], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1047, 0.1047], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7906, 0.7906], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1081, 0.1081], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 25.08471918106079, "u_mse": [0.1047, 0.1047], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1216, 0.1216], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7568, 0.7568], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.139, 0.139], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.016646146774292, "u_mse": [0.1216, 0.1216], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1212, 0.1212], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7576, 0.7576], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1354, 0.1354], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.523795127868652, "u_mse": [0.1212, 0.1212], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1336, 0.1336], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7328, 0.7328], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1588, 0.1588], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.545861959457397, "u_mse": [0.1336, 0.1336], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1167, 0.1167], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7666, 0.7666], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1335, 0.1335], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.778254985809326, "u_mse": [0.1167, 0.1167], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1647, 0.1647], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6706, 0.6706], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.186, 0.186], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.15216612815857, "u_mse": [0.1647, 0.1647], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2121, 0.2121], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5758, 0.5758], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2486, 0.2486], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.3890118598938, "u_mse": [0.2121, 0.2121], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2626, 0.2626], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4748, 0.4748], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2932, 0.2932], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.556342124938965, "u_mse": [0.2626, 0.2626], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.115, 0.115], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.77, 0.77], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1221, 0.1221], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.73229217529297, "u_mse": [0.115, 0.115], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1016, 0.1016], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7968, 0.7968], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.105, 0.105], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.217872142791748, "u_mse": [0.1016, 0.1016], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1285, 0.1285], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.743, 0.743], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1469, 0.1469], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.527209997177124, "u_mse": [0.1285, 0.1285], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1051, 0.1051], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7898, 0.7898], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1182, 0.1182], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 26.799764156341553, "u_mse": [0.1051, 0.1051], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.125, 0.125], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.75, 0.75], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1491, 0.1491], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.174274921417236, "u_mse": [0.125, 0.125], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1121, 0.1121], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7758, 0.7758], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1203, 0.1203], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.96218514442444, "u_mse": [0.1121, 0.1121], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.129, 0.129], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.742, 0.742], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1417, 0.1417], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.04051685333252, "u_mse": [0.129, 0.129], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1715, 0.1715], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.657, 0.657], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1956, 0.1956], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.95165991783142, "u_mse": [0.1715, 0.1715], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2293, 0.2293], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5414, 0.5414], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.254, 0.254], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.890788078308105, "u_mse": [0.2293, 0.2293], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2872, 0.2872], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4256, 0.4256], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.31, 0.31], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 27.1825430393219, "u_mse": [0.2872, 0.2872], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1092, 0.1092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7816, 0.7816], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1166, 0.1166], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.23301410675049, "u_mse": [0.1092, 0.1092], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1085, 0.1085], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.783, 0.783], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.117, 0.117], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.416320085525513, "u_mse": [0.1085, 0.1085], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0965, 0.0965], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.807, 0.807], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1014, 0.1014], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.788729190826416, "u_mse": [0.0965, 0.0965], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1037, 0.1037], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7926, 0.7926], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1146, 0.1146], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.50594997406006, "u_mse": [0.1037, 0.1037], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1142, 0.1142], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7716, 0.7716], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1269, 0.1269], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.962743997573853, "u_mse": [0.1142, 0.1142], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1041, 0.1041], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7918, 0.7918], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1148, 0.1148], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.553087949752808, "u_mse": [0.1041, 0.1041], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1112, 0.1112], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7776, 0.7776], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1264, 0.1264], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 28.371809005737305, "u_mse": [0.1112, 0.1112], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1713, 0.1713], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6574, 0.6574], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1991, 0.1991], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.702502012252808, "u_mse": [0.1713, 0.1713], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2342, 0.2342], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5316, 0.5316], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2646, 0.2646], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.364555835723877, "u_mse": [0.2342, 0.2342], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3005, 0.3005], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.399, 0.399], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3502, 0.3502], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.884448051452637, "u_mse": [0.3005, 0.3005], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0992, 0.0992], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8016, 0.8016], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1095, 0.1095], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.34825110435486, "u_mse": [0.0992, 0.0992], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1037, 0.1037], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7926, 0.7926], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1184, 0.1184], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.2410991191864, "u_mse": [0.1037, 0.1037], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1195, 0.1195], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.761, 0.761], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1449, 0.1449], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.838311910629272, "u_mse": [0.1195, 0.1195], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1044, 0.1044], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7912, 0.7912], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1186, 0.1186], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 31.03287100791931, "u_mse": [0.1044, 0.1044], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1094, 0.1094], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7812, 0.7812], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1237, 0.1237], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.320533990859985, "u_mse": [0.1094, 0.1094], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1182, 0.1182], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7636, 0.7636], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1387, 0.1387], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 31.187369108200073, "u_mse": [0.1182, 0.1182], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.119, 0.119], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.762, 0.762], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1445, 0.1445], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.177582025527954, "u_mse": [0.119, 0.119], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1739, 0.1739], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6522, 0.6522], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2077, 0.2077], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.314789056777954, "u_mse": [0.1739, 0.1739], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2434, 0.2434], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5132, 0.5132], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2837, 0.2837], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.612457036972046, "u_mse": [0.2434, 0.2434], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3171, 0.3171], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3658, 0.3658], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.342, 0.342], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 31.719416856765747, "u_mse": [0.3171, 0.3171], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1056, 0.1056], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7888, 0.7888], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1254, 0.1254], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.7361159324646, "u_mse": [0.1056, 0.1056], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1057, 0.1057], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7886, 0.7886], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1237, 0.1237], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 31.0901780128479, "u_mse": [0.1057, 0.1057], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1059, 0.1059], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7882, 0.7882], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1175, 0.1175], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 31.60305690765381, "u_mse": [0.1059, 0.1059], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1191, 0.1191], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7618, 0.7618], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1341, 0.1341], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 31.548768997192383, "u_mse": [0.1191, 0.1191], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1281, 0.1281], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7438, 0.7438], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1435, 0.1435], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.609067916870117, "u_mse": [0.1281, 0.1281], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1279, 0.1279], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7442, 0.7442], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1433, 0.1433], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.032812118530273, "u_mse": [0.1279, 0.1279], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1154, 0.1154], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7692, 0.7692], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1358, 0.1358], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.42377805709839, "u_mse": [0.1154, 0.1154], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1774, 0.1774], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6452, 0.6452], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2038, 0.2038], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.886157989501953, "u_mse": [0.1774, 0.1774], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2401, 0.2401], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5198, 0.5198], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2844, 0.2844], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.48852801322937, "u_mse": [0.2401, 0.2401], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.303, 0.303], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.394, 0.394], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3437, 0.3437], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.985930919647217, "u_mse": [0.303, 0.303], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1104, 0.1104], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7792, 0.7792], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1271, 0.1271], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 29.331531047821045, "u_mse": [0.1104, 0.1104], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1176, 0.1176], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7648, 0.7648], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1437, 0.1437], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.207277059555054, "u_mse": [0.1176, 0.1176], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0867, 0.0867], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8266, 0.8266], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1057, 0.1057], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.523509979248047, "u_mse": [0.0867, 0.0867], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0952, 0.0952], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8096, 0.8096], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1093, 0.1093], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 30.729981184005737, "u_mse": [0.0952, 0.0952], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.092, 0.092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.816, 0.816], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1123, 0.1123], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 32.62380599975586, "u_mse": [0.092, 0.092], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0923, 0.0923], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8154, 0.8154], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1097, 0.1097], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.115434885025024, "u_mse": [0.0923, 0.0923], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0872, 0.0872], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8256, 0.8256], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1047, 0.1047], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 32.26972413063049, "u_mse": [0.0872, 0.0872], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1624, 0.1624], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6752, 0.6752], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1781, 0.1781], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 32.80227780342102, "u_mse": [0.1624, 0.1624], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2347, 0.2347], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5306, 0.5306], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2751, 0.2751], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.325942039489746, "u_mse": [0.2347, 0.2347], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3069, 0.3069], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3862, 0.3862], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.341, 0.341], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.26302695274353, "u_mse": [0.3069, 0.3069], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.109, 0.109], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.782, 0.782], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1306, 0.1306], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.87038588523865, "u_mse": [0.109, 0.109], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0878, 0.0878], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8244, 0.8244], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1048, 0.1048], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 32.68151497840881, "u_mse": [0.0878, 0.0878], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.09, 0.09], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.82, 0.82], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1001, 0.1001], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.002809047698975, "u_mse": [0.09, 0.09], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0981, 0.0981], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8038, 0.8038], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1209, 0.1209], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.033940076828, "u_mse": [0.0981, 0.0981], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1086, 0.1086], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7828, 0.7828], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1232, 0.1232], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.7298469543457, "u_mse": [0.1086, 0.1086], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.119, 0.119], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.762, 0.762], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1413, 0.1413], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 32.510315895080566, "u_mse": [0.119, 0.119], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1607, 0.1607], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6786, 0.6786], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1916, 0.1916], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.22179293632507, "u_mse": [0.1607, 0.1607], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1823, 0.1823], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6354, 0.6354], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2109, 0.2109], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.82098388671875, "u_mse": [0.1823, 0.1823], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2642, 0.2642], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4716, 0.4716], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2954, 0.2954], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.060189962387085, "u_mse": [0.2642, 0.2642], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3448, 0.3448], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3104, 0.3104], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.3688, 0.3688], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.8531551361084, "u_mse": [0.3448, 0.3448], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1111, 0.1111], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7778, 0.7778], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.129, 0.129], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.672301054000854, "u_mse": [0.1111, 0.1111], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0984, 0.0984], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8032, 0.8032], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1147, 0.1147], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.70152807235718, "u_mse": [0.0984, 0.0984], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1184, 0.1184], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7632, 0.7632], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1435, 0.1435], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.85423493385315, "u_mse": [0.1184, 0.1184], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0967, 0.0967], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8066, 0.8066], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1194, 0.1194], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.257993936538696, "u_mse": [0.0967, 0.0967], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0883, 0.0883], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8234, 0.8234], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.0983, 0.0983], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.3862361907959, "u_mse": [0.0883, 0.0883], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1116, 0.1116], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7768, 0.7768], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1417, 0.1417], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.14002203941345, "u_mse": [0.1116, 0.1116], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0988, 0.0988], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8024, 0.8024], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1148, 0.1148], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.156697034835815, "u_mse": [0.0988, 0.0988], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1806, 0.1806], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6388, 0.6388], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2113, 0.2113], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.34005689620972, "u_mse": [0.1806, 0.1806], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2619, 0.2619], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4762, 0.4762], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2963, 0.2963], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.154685974121094, "u_mse": [0.2619, 0.2619], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3459, 0.3459], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3082, 0.3082], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3669, 0.3669], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.61988401412964, "u_mse": [0.3459, 0.3459], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1069, 0.1069], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7862, 0.7862], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1202, 0.1202], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.899230003356934, "u_mse": [0.1069, 0.1069], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0986, 0.0986], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8028, 0.8028], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1092, 0.1092], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.996788024902344, "u_mse": [0.0986, 0.0986], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1275, 0.1275], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.745, 0.745], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1489, 0.1489], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 36.114339113235474, "u_mse": [0.1275, 0.1275], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1032, 0.1032], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7936, 0.7936], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1234, 0.1234], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 36.7103910446167, "u_mse": [0.1032, 0.1032], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.122, 0.122], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.756, 0.756], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1443, 0.1443], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.175124168395996, "u_mse": [0.122, 0.122], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1188, 0.1188], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7624, 0.7624], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1424, 0.1424], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 33.731252908706665, "u_mse": [0.1188, 0.1188], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1264, 0.1264], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7472, 0.7472], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1569, 0.1569], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.442033767700195, "u_mse": [0.1264, 0.1264], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1807, 0.1807], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6386, 0.6386], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2027, 0.2027], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.258378982543945, "u_mse": [0.1807, 0.1807], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2622, 0.2622], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4756, 0.4756], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2959, 0.2959], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.40838098526001, "u_mse": [0.2622, 0.2622], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3466, 0.3466], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3068, 0.3068], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3784, 0.3784], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.60374116897583, "u_mse": [0.3466, 0.3466], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0919, 0.0919], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8162, 0.8162], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1068, 0.1068], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.30075788497925, "u_mse": [0.0919, 0.0919], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1079, 0.1079], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7842, 0.7842], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1288, 0.1288], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.66147804260254, "u_mse": [0.1079, 0.1079], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1059, 0.1059], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7882, 0.7882], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1153, 0.1153], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.616771936416626, "u_mse": [0.1059, 0.1059], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1171, 0.1171], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7658, 0.7658], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1367, 0.1367], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.61845111846924, "u_mse": [0.1171, 0.1171], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1318, 0.1318], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7364, 0.7364], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1593, 0.1593], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.70568799972534, "u_mse": [0.1318, 0.1318], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.115, 0.115], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.77, 0.77], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.136, 0.136], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.41475510597229, "u_mse": [0.115, 0.115], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1092, 0.1092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7816, 0.7816], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1209, 0.1209], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 36.077332973480225, "u_mse": [0.1092, 0.1092], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1844, 0.1844], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6312, 0.6312], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2133, 0.2133], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 34.657965898513794, "u_mse": [0.1844, 0.1844], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2615, 0.2615], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.477, 0.477], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2996, 0.2996], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 36.36656594276428, "u_mse": [0.2615, 0.2615], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3422, 0.3422], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3156, 0.3156], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3851, 0.3851], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 36.431029081344604, "u_mse": [0.3422, 0.3422], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0962, 0.0962], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8076, 0.8076], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1113, 0.1113], "realization": 2, "beta_mse": [0.0, 0.0], "runtime": 35.87707591056824, "u_mse": [0.0962, 0.0962], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1097, 0.1097], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7806, 0.7806], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1206, 0.1206], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 34.944743156433105, "u_mse": [0.1097, 0.1097], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1083, 0.1083], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7834, 0.7834], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1145, 0.1145], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.97112202644348, "u_mse": [0.1083, 0.1083], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1092, 0.1092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7816, 0.7816], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1203, 0.1203], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.04010891914368, "u_mse": [0.1092, 0.1092], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.108, 0.108], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.784, 0.784], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1152, 0.1152], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.2615921497345, "u_mse": [0.108, 0.108], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1086, 0.1086], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7828, 0.7828], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1179, 0.1179], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.09241199493408, "u_mse": [0.1086, 0.1086], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1176, 0.1176], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7648, 0.7648], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1324, 0.1324], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 35.865506172180176, "u_mse": [0.1176, 0.1176], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1352, 0.1352], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7296, 0.7296], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1422, 0.1422], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 35.975646018981934, "u_mse": [0.1352, 0.1352], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1582, 0.1582], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6836, 0.6836], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1746, 0.1746], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.34769010543823, "u_mse": [0.1582, 0.1582], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1895, 0.1895], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.621, 0.621], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2122, 0.2122], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 35.69535803794861, "u_mse": [0.1895, 0.1895], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1002, 0.1002], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7996, 0.7996], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1055, 0.1055], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 35.56574988365173, "u_mse": [0.1002, 0.1002], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0986, 0.0986], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8028, 0.8028], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1051, 0.1051], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 35.34516501426697, "u_mse": [0.0986, 0.0986], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0931, 0.0931], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8138, 0.8138], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.0938, 0.0938], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 37.838634967803955, "u_mse": [0.0931, 0.0931], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1002, 0.1002], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7996, 0.7996], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1073, 0.1073], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.23058605194092, "u_mse": [0.1002, 0.1002], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1095, 0.1095], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.781, 0.781], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.13, 0.13], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 38.600579023361206, "u_mse": [0.1095, 0.1095], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.109, 0.109], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.782, 0.782], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1174, 0.1174], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.54100489616394, "u_mse": [0.109, 0.109], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1104, 0.1104], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7792, 0.7792], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1255, 0.1255], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 36.73756003379822, "u_mse": [0.1104, 0.1104], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1526, 0.1526], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6948, 0.6948], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1831, 0.1831], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 37.32617783546448, "u_mse": [0.1526, 0.1526], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2128, 0.2128], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5744, 0.5744], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2525, 0.2525], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 38.92560696601868, "u_mse": [0.2128, 0.2128], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2777, 0.2777], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4446, 0.4446], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.302, 0.302], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.31680703163147, "u_mse": [0.2777, 0.2777], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0944, 0.0944], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8112, 0.8112], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.098, 0.098], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.334458112716675, "u_mse": [0.0944, 0.0944], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1239, 0.1239], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7522, 0.7522], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1509, 0.1509], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.52756404876709, "u_mse": [0.1239, 0.1239], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0955, 0.0955], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.809, 0.809], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1079, 0.1079], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.28611516952515, "u_mse": [0.0955, 0.0955], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1047, 0.1047], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7906, 0.7906], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1137, 0.1137], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.23969602584839, "u_mse": [0.1047, 0.1047], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1213, 0.1213], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7574, 0.7574], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1333, 0.1333], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.35051918029785, "u_mse": [0.1213, 0.1213], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1187, 0.1187], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7626, 0.7626], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1366, 0.1366], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.11706900596619, "u_mse": [0.1187, 0.1187], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1189, 0.1189], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7622, 0.7622], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1354, 0.1354], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.55886793136597, "u_mse": [0.1189, 0.1189], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1696, 0.1696], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6608, 0.6608], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1937, 0.1937], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.54756188392639, "u_mse": [0.1696, 0.1696], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2286, 0.2286], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5428, 0.5428], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2478, 0.2478], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.79788589477539, "u_mse": [0.2286, 0.2286], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2879, 0.2879], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4242, 0.4242], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3147, 0.3147], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 38.92602300643921, "u_mse": [0.2879, 0.2879], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1224, 0.1224], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7552, 0.7552], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.139, 0.139], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.54637002944946, "u_mse": [0.1224, 0.1224], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1216, 0.1216], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7568, 0.7568], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1422, 0.1422], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.97662019729614, "u_mse": [0.1216, 0.1216], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1016, 0.1016], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7968, 0.7968], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1158, 0.1158], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.665778160095215, "u_mse": [0.1016, 0.1016], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1096, 0.1096], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7808, 0.7808], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1313, 0.1313], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 41.268285036087036, "u_mse": [0.1096, 0.1096], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1234, 0.1234], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7532, 0.7532], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1477, 0.1477], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.52362108230591, "u_mse": [0.1234, 0.1234], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1291, 0.1291], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7418, 0.7418], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1581, 0.1581], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.61662292480469, "u_mse": [0.1291, 0.1291], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1164, 0.1164], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7672, 0.7672], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1451, 0.1451], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.76931810379028, "u_mse": [0.1164, 0.1164], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1831, 0.1831], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6338, 0.6338], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2093, 0.2093], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.93989896774292, "u_mse": [0.1831, 0.1831], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2465, 0.2465], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.507, 0.507], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2745, 0.2745], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 41.785324811935425, "u_mse": [0.2465, 0.2465], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3122, 0.3122], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3756, 0.3756], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3339, 0.3339], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 41.45005989074707, "u_mse": [0.3122, 0.3122], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1093, 0.1093], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7814, 0.7814], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1325, 0.1325], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.14226293563843, "u_mse": [0.1093, 0.1093], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1141, 0.1141], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7718, 0.7718], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1375, 0.1375], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 39.88040208816528, "u_mse": [0.1141, 0.1141], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1174, 0.1174], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7652, 0.7652], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1386, 0.1386], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 42.62358283996582, "u_mse": [0.1174, 0.1174], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1183, 0.1183], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7634, 0.7634], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1327, 0.1327], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 42.60345506668091, "u_mse": [0.1183, 0.1183], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1091, 0.1091], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7818, 0.7818], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1273, 0.1273], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.48053598403931, "u_mse": [0.1091, 0.1091], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.136, 0.136], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.728, 0.728], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1681, 0.1681], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.960488080978394, "u_mse": [0.136, 0.136], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1213, 0.1213], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7574, 0.7574], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1437, 0.1437], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.70694899559021, "u_mse": [0.1213, 0.1213], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1877, 0.1877], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6246, 0.6246], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2149, 0.2149], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 40.76693415641785, "u_mse": [0.1877, 0.1877], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2519, 0.2519], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4962, 0.4962], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2919, 0.2919], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 43.42676496505737, "u_mse": [0.2519, 0.2519], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3143, 0.3143], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3714, 0.3714], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3361, 0.3361], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 43.77262210845947, "u_mse": [0.3143, 0.3143], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1195, 0.1195], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.761, 0.761], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1363, 0.1363], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 41.68635392189026, "u_mse": [0.1195, 0.1195], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0986, 0.0986], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8028, 0.8028], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1095, 0.1095], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 43.52352595329285, "u_mse": [0.0986, 0.0986], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0892, 0.0892], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8216, 0.8216], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.106, 0.106], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 43.542783975601196, "u_mse": [0.0892, 0.0892], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0933, 0.0933], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8134, 0.8134], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.111, 0.111], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.48005795478821, "u_mse": [0.0933, 0.0933], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1012, 0.1012], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7976, 0.7976], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1189, 0.1189], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.1085250377655, "u_mse": [0.1012, 0.1012], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.146, 0.146], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.708, 0.708], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1738, 0.1738], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.80068516731262, "u_mse": [0.146, 0.146], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1004, 0.1004], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7992, 0.7992], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1097, 0.1097], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.725565910339355, "u_mse": [0.1004, 0.1004], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1726, 0.1726], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6548, 0.6548], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.195, 0.195], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 42.9004909992218, "u_mse": [0.1726, 0.1726], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2487, 0.2487], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5026, 0.5026], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.275, 0.275], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 45.85892701148987, "u_mse": [0.2487, 0.2487], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3282, 0.3282], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3436, 0.3436], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.3558, 0.3558], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 45.55390119552612, "u_mse": [0.3282, 0.3282], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1017, 0.1017], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7966, 0.7966], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1266, 0.1266], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 45.719143867492676, "u_mse": [0.1017, 0.1017], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0999, 0.0999], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8002, 0.8002], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1187, 0.1187], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.08757400512695, "u_mse": [0.0999, 0.0999], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0934, 0.0934], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8132, 0.8132], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1037, 0.1037], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 45.842069149017334, "u_mse": [0.0934, 0.0934], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0967, 0.0967], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8066, 0.8066], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1214, 0.1214], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.88861298561096, "u_mse": [0.0967, 0.0967], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1056, 0.1056], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7888, 0.7888], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1346, 0.1346], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.25214195251465, "u_mse": [0.1056, 0.1056], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0971, 0.0971], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8058, 0.8058], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1212, 0.1212], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.12279510498047, "u_mse": [0.0971, 0.0971], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0942, 0.0942], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8116, 0.8116], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.102, 0.102], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.26887106895447, "u_mse": [0.0942, 0.0942], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1761, 0.1761], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6478, 0.6478], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1945, 0.1945], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.356919050216675, "u_mse": [0.1761, 0.1761], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2566, 0.2566], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4868, 0.4868], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2795, 0.2795], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.052298069000244, "u_mse": [0.2566, 0.2566], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3354, 0.3354], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3292, 0.3292], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.3751, 0.3751], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.12567901611328, "u_mse": [0.3354, 0.3354], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1031, 0.1031], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7938, 0.7938], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1301, 0.1301], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 46.43235802650452, "u_mse": [0.1031, 0.1031], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1155, 0.1155], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.769, 0.769], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1414, 0.1414], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 43.97149705886841, "u_mse": [0.1155, 0.1155], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1036, 0.1036], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7928, 0.7928], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1295, 0.1295], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 43.81551194190979, "u_mse": [0.1036, 0.1036], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1062, 0.1062], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7876, 0.7876], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1347, 0.1347], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.81182098388672, "u_mse": [0.1062, 0.1062], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1172, 0.1172], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7656, 0.7656], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1347, 0.1347], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.858104944229126, "u_mse": [0.1172, 0.1172], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1102, 0.1102], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7796, 0.7796], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1272, 0.1272], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.08285212516785, "u_mse": [0.1102, 0.1102], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1143, 0.1143], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7714, 0.7714], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1341, 0.1341], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.96419310569763, "u_mse": [0.1143, 0.1143], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1792, 0.1792], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6416, 0.6416], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2015, 0.2015], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.53045701980591, "u_mse": [0.1792, 0.1792], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2585, 0.2585], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.483, 0.483], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2995, 0.2995], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 48.80639100074768, "u_mse": [0.2585, 0.2585], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3418, 0.3418], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3164, 0.3164], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3671, 0.3671], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.397562980651855, "u_mse": [0.3418, 0.3418], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1301, 0.1301], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7398, 0.7398], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1671, 0.1671], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.016607999801636, "u_mse": [0.1301, 0.1301], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1027, 0.1027], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7946, 0.7946], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1355, 0.1355], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.33094501495361, "u_mse": [0.1027, 0.1027], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0912, 0.0912], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8176, 0.8176], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1085, 0.1085], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 48.414867877960205, "u_mse": [0.0912, 0.0912], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.0929, 0.0929], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8142, 0.8142], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.109, 0.109], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.19843006134033, "u_mse": [0.0929, 0.0929], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1203, 0.1203], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7594, 0.7594], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1542, 0.1542], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 47.501709938049316, "u_mse": [0.1203, 0.1203], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1266, 0.1266], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7468, 0.7468], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1615, 0.1615], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 48.45507788658142, "u_mse": [0.1266, 0.1266], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.127, 0.127], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.746, 0.746], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1659, 0.1659], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 48.669793128967285, "u_mse": [0.127, 0.127], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.183, 0.183], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.634, 0.634], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2119, 0.2119], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 44.834941148757935, "u_mse": [0.183, 0.183], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2654, 0.2654], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4692, 0.4692], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2914, 0.2914], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 51.37683391571045, "u_mse": [0.2654, 0.2654], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3476, 0.3476], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3048, 0.3048], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3834, 0.3834], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 45.31998801231384, "u_mse": [0.3476, 0.3476], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1207, 0.1207], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7586, 0.7586], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.15, 0.15], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 49.444992780685425, "u_mse": [0.1207, 0.1207], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1509, 0.1509], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6982, 0.6982], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.206, 0.206], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 54.22992491722107, "u_mse": [0.1509, 0.1509], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1061, 0.1061], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7878, 0.7878], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1189, 0.1189], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 54.11450982093811, "u_mse": [0.1061, 0.1061], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1078, 0.1078], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7844, 0.7844], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1337, 0.1337], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 48.703964948654175, "u_mse": [0.1078, 0.1078], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1069, 0.1069], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7862, 0.7862], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1247, 0.1247], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 51.991535902023315, "u_mse": [0.1069, 0.1069], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1096, 0.1096], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7808, 0.7808], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.127, 0.127], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 54.82482314109802, "u_mse": [0.1096, 0.1096], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1181, 0.1181], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7638, 0.7638], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1441, 0.1441], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 55.448811054229736, "u_mse": [0.1181, 0.1181], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1844, 0.1844], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6312, 0.6312], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2051, 0.2051], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 54.22621297836304, "u_mse": [0.1844, 0.1844], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.2619, 0.2619], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4762, 0.4762], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.2936, 0.2936], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 54.37297320365906, "u_mse": [0.2619, 0.2619], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.3432, 0.3432], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3136, 0.3136], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.3692, 0.3692], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 54.938589096069336, "u_mse": [0.3432, 0.3432], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 1878, "b_mse": [0.1054, 0.1054], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7892, 0.7892], "adv_type": "random_pgd_csl", "dataset": "dc", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1298, 0.1298], "realization": 3, "beta_mse": [0.0, 0.0], "runtime": 56.46660399436951, "u_mse": [0.1054, 0.1054], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.143, 0.143], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.714, 0.714], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1652, 0.1652], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 55.683839082717896, "u_mse": [0.143, 0.143], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1162, 0.1162], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7676, 0.7676], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1214, 0.1214], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 54.56529092788696, "u_mse": [0.1162, 0.1162], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1221, 0.1221], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7558, 0.7558], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1261, 0.1261], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 54.83159112930298, "u_mse": [0.1221, 0.1221], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1328, 0.1328], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7344, 0.7344], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1559, 0.1559], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.46217393875122, "u_mse": [0.1328, 0.1328], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1169, 0.1169], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7662, 0.7662], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1265, 0.1265], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.69306802749634, "u_mse": [0.1169, 0.1169], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1271, 0.1271], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7458, 0.7458], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1357, 0.1357], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.314093828201294, "u_mse": [0.1271, 0.1271], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.153, 0.153], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.694, 0.694], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.171, 0.171], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 55.84867787361145, "u_mse": [0.153, 0.153], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.206, 0.206], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.588, 0.588], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2293, 0.2293], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.54369521141052, "u_mse": [0.206, 0.206], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2541, 0.2541], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4918, 0.4918], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.2779, 0.2779], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.30519700050354, "u_mse": [0.2541, 0.2541], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1211, 0.1211], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7578, 0.7578], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.1, "weekday": 0, "prob_mse": [0.1263, 0.1263], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 57.84864592552185, "u_mse": [0.1211, 0.1211], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.121, 0.121], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.758, 0.758], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1407, 0.1407], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 52.72708082199097, "u_mse": [0.121, 0.121], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.092, 0.092], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.816, 0.816], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.0987, 0.0987], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.42150592803955, "u_mse": [0.092, 0.092], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0959, 0.0959], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8082, 0.8082], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1083, 0.1083], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 57.59073305130005, "u_mse": [0.0959, 0.0959], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0961, 0.0961], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8078, 0.8078], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1082, 0.1082], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 57.18826699256897, "u_mse": [0.0961, 0.0961], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0982, 0.0982], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8036, 0.8036], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1082, 0.1082], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.933013916015625, "u_mse": [0.0982, 0.0982], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1043, 0.1043], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7914, 0.7914], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1235, 0.1235], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 58.37161707878113, "u_mse": [0.1043, 0.1043], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1563, 0.1563], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6874, 0.6874], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1759, 0.1759], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 77.44582509994507, "u_mse": [0.1563, 0.1563], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2194, 0.2194], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5612, 0.5612], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.2442, 0.2442], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 74.05872702598572, "u_mse": [0.2194, 0.2194], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2829, 0.2829], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.4342, 0.4342], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.3121, 0.3121], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 62.10067391395569, "u_mse": [0.2829, 0.2829], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0978, 0.0978], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8044, 0.8044], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.2, "weekday": 0, "prob_mse": [0.1106, 0.1106], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 78.60844707489014, "u_mse": [0.0978, 0.0978], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0953, 0.0953], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.8094, 0.8094], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1144, 0.1144], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 75.15533995628357, "u_mse": [0.0953, 0.0953], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.0915, 0.0915], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.817, 0.817], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1004, 0.1004], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 80.07157301902771, "u_mse": [0.0915, 0.0915], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1013, 0.1013], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7974, 0.7974], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1095, 0.1095], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 88.52084112167358, "u_mse": [0.1013, 0.1013], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1041, 0.1041], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7918, 0.7918], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1234, 0.1234], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 94.88036298751831, "u_mse": [0.1041, 0.1041], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1186, 0.1186], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7628, 0.7628], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1428, 0.1428], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 81.0738799571991, "u_mse": [0.1186, 0.1186], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.111, 0.111], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.778, 0.778], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1295, 0.1295], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 82.19837498664856, "u_mse": [0.111, 0.111], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.179, 0.179], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.642, 0.642], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1999, 0.1999], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 102.28875398635864, "u_mse": [0.179, 0.179], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2453, 0.2453], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5094, 0.5094], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.2915, 0.2915], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.92406892776489, "u_mse": [0.2453, 0.2453], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3203, 0.3203], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.3594, 0.3594], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.3533, 0.3533], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 73.46965312957764, "u_mse": [0.3203, 0.3203], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1057, 0.1057], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7886, 0.7886], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.3, "weekday": 0, "prob_mse": [0.1186, 0.1186], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 77.36216306686401, "u_mse": [0.1057, 0.1057], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1055, 0.1055], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.789, 0.789], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1189, 0.1189], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 84.61357188224792, "u_mse": [0.1055, 0.1055], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1172, 0.1172], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7656, 0.7656], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1295, 0.1295], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 67.47100901603699, "u_mse": [0.1172, 0.1172], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1115, 0.1115], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.777, 0.777], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1217, 0.1217], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 80.10649800300598, "u_mse": [0.1115, 0.1115], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1355, 0.1355], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.729, 0.729], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1583, 0.1583], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 79.20898485183716, "u_mse": [0.1355, 0.1355], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1107, 0.1107], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7786, 0.7786], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1258, 0.1258], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 65.92608690261841, "u_mse": [0.1107, 0.1107], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1106, 0.1106], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7788, 0.7788], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1378, 0.1378], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 79.42851686477661, "u_mse": [0.1106, 0.1106], "gamma": 0.09, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1786, 0.1786], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.6428, 0.6428], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1972, 0.1972], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 74.64290404319763, "u_mse": [0.1786, 0.1786], "gamma": 0.2, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.2413, 0.2413], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.5174, 0.5174], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.2931, 0.2931], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 78.40927481651306, "u_mse": [0.2413, 0.2413], "gamma": 0.3, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.3085, 0.3085], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.383, 0.383], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.3408, 0.3408], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 61.102388858795166, "u_mse": [0.3085, 0.3085], "gamma": 0.4, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1117, 0.1117], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7766, 0.7766], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.4, "weekday": 0, "prob_mse": [0.1249, 0.1249], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 56.78765392303467, "u_mse": [0.1117, 0.1117], "gamma": 0.5, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1173, 0.1173], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7654, 0.7654], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1344, 0.1344], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 55.37762808799744, "u_mse": [0.1173, 0.1173], "gamma": 0.0, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1149, 0.1149], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7702, 0.7702], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1306, 0.1306], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 82.1156120300293, "u_mse": [0.1149, 0.1149], "gamma": 0.01, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1076, 0.1076], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7848, 0.7848], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1122, 0.1122], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 79.51535701751709, "u_mse": [0.1076, 0.1076], "gamma": 0.03, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1086, 0.1086], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7828, 0.7828], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1283, 0.1283], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 57.339686155319214, "u_mse": [0.1086, 0.1086], "gamma": 0.05, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1258, 0.1258], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7484, 0.7484], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1448, 0.1448], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 59.1885130405426, "u_mse": [0.1258, 0.1258], "gamma": 0.07, "T": 43}
{"acc": [0.0, 0.0], "network_size": 708, "b_mse": [0.1362, 0.1362], "ref_ratio": 0.6, "hour": 8, "alpha_mse": [0.0, 0.0], "d_mse": [0.7276, 0.7276], "adv_type": "random_pgd_csl", "dataset": "philly", "sample_size": 1, "test_ratio": 0.5, "weekday": 0, "prob_mse": [0.1556, 0.1556], "realization": 4, "beta_mse": [0.0, 0.0], "runtime": 58.91400194168091, "u_mse": [0.1362, 0.1362], "gamma": 0.09, "T": 43}
|
{
"protocol": "sftp",
"host": "phyzx92.55555.io",
"port":1022,
"username": "lin",
"password": "hyperv3154",
"remotePath": "/home/lin/lab/pythonScripts"
}
|
{"title":"Thick as Thieves - Kortney Kane HD720p","uid":7674669,"size":2468642598,"categoryP":"porn","categoryS":"hd___movies","magnet":"?xt=urn:btih:9a03a132149043f1b76e025e22f82c8b5b7174ed&dn=Thick+as+Thieves+-+Kortney+Kane+HD720p+&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":0,"leechers":2,"uploader":"R0thschild","files":-1,"time":1348666823,"description":"Screenshots: \n <a href="\nhttp://imagelunex.com/?v=2rda9n45hh.jpg" rel="nofollow" target="_NEW">\nhttp://imagelunex.com/?v=2rda9n45hh.jpg</a>","torrent":{"xt":"urn:btih:9a03a132149043f1b76e025e22f82c8b5b7174ed","amp;dn":"Thick+as+Thieves+-+Kortney+Kane+HD720p+","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"9a03a132149043f1b76e025e22f82c8b5b7174ed","infoHashBuffer":{"type":"Buffer","data":[154,3,161,50,20,144,67,241,183,110,2,94,34,248,44,139,91,113,116,237]},"announce":[],"urlList":[]}} |
{
"id": 119235803,
"type": "Feature",
"properties": {
"addr:full":"Flemington Rd Glenrothes Fife KY7 5QJ",
"addr:housenumber":"",
"addr:postcode":"ky7 5qj",
"addr:street":"Flemington Rd",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"-3.159884,56.198158,-3.159884,56.198158",
"geom:latitude":56.198158,
"geom:longitude":-3.159884,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"Flemington Rd",
"sg:city":"Glenrothes",
"sg:classifiers":[
{
"category":"Wholesale",
"subcategory":"Machinery & Supplies",
"type":"Manufacturing & Wholesale Goods"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 1592 753745",
"sg:postcode":"KY7 5QJ",
"sg:province":"Fife",
"sg:tags":[
"equipment",
"handling",
"lifting"
],
"src:geom":"simplegeo",
"wof:belongsto":[
85633159,
85684569
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_0JKSC2CHrXCQP2dcUJaZz0_56.198158_-3.159884@1300740094"
},
"wof:country":"GB",
"wof:geomhash":"a92771eefdf2e12b5e0cf0e41b0d68fe",
"wof:hierarchy":[
{
"country_id":85633159,
"region_id":85684569,
"venue_id":119235803
}
],
"wof:id":119235803,
"wof:lastmodified":1496968042,
"wof:name":"Bekaert Ltd",
"wof:parent_id":85684569,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[]
},
"bbox": [
-3.159884,
56.198158,
-3.159884,
56.198158
],
"geometry": {"coordinates":[-3.159884,56.198158],"type":"Point"}
} |
{"poster":"ErCabrero","date":"2017-12-09T15:04:34.469+0000","title":"Let us upload a pictures","subforum":"[ARCHIVED] Help & Support","up_votes":1,"down_votes":0,"body":"It would be easier for us if we can upload a picture or a screenshot hereon forums an on the report a bug pages instead of having to upload on a server and adding here the link","replies":[{"poster":"Porocles","date":"2017-12-09T16:07:26.132+0000","up_votes":1,"down_votes":0,"body":"You can! While attachments aren't available on the boards itself, posting an image link like from imgur will auto upload to your thread!\n\nhttps://i.imgur.com/2Y4oZtj.gif\n\nThere's also a solid [formatting guide ](https://boards.na.leagueoflegends.com/en/c/community-moderation/atIUBQPe-boards-users-guide)here to help!","replies":[{"poster":"ErCabrero","date":"2017-12-11T01:22:30.398+0000","up_votes":1,"down_votes":0,"body":"instead of having to upload on a server and adding here the link","replies":[]}]}]} |
{"code":"1301142001","province_code":"13","regency_code":"1301","district_code":"130114","name":"SUNGAI GAMBIR SAKO TAPAN"} |
{"uuid": "c60361da-a875-490f-bd36-f5b9794701ce", "children": ["3a7eeeb3-7764-4704-ab29-134d6a6e872d"], "befores": [{"name": "_skip_sensitive", "status": "passed", "start": 1594187732339, "stop": 1594187732339}], "start": 1594187732339, "stop": 1594187835187} |
{"code":"8208012010","province_code":"82","regency_code":"8208","district_code":"820801","name":"LOHO BUBBA"} |
{"response": {"status": "ok", "userTier": "developer", "total": 1, "content": {"id": "sport/2004/jul/21/cycling.cycling", "type": "article", "sectionId": "sport", "sectionName": "Sport", "webPublicationDate": "2004-07-20T23:17:25Z", "webTitle": "Cycling: Millar's tale of life on the edge makes sad reading", "webUrl": "https://www.theguardian.com/sport/2004/jul/21/cycling.cycling", "apiUrl": "https://content.guardianapis.com/sport/2004/jul/21/cycling.cycling", "fields": {"headline": "Millar's tale of life on the edge makes sad reading", "bodyText": "David Millar's alleged testimony to Judge Richard Pallain is required reading for idealistic young cyclists and seasoned sports officials alike. It is a cautionary tale, of his transformation from a young rider adamant that he would never use drugs to an embittered professional athlete disgusted with his use of banned substances. For reasons known only to a mole inside the French legal team which since March last year has been investigating alleged drug-taking and dealing within the Cofidis squad, the transcripts were leaked to L'Equipe's investigative reporter Damien Ressiot, as was a wealth of other material published in the sports daily just before Easter in which Millar was first named. The picture that emerges is a remarkably dignified one. It shows a man under immense pressure to perform in the face of crises in his personal life, and receiving little support or guidance in a situation where he was tempted to use drugs in a sport that was making unnatural demands on him. At the start of his career, Millar said, he had to feign illness and abandon races in order to get rest. \"I said to Cofidis that I needed to race less, but there were always riders who were injured or ill.\" According to his lawyer Paul-Albert Iweins, Millar \"explained that he was caught in a sort of maximum pressure as leader of the team, faced with the obligation to get results\". Iweins claimed Cofidis had had a \"policy of generally hiding their head in the sand\". Millar also relates the personal crisis that almost led him to quit the sport in 2000, after finishing the Tour de France, when he found himself alone in his flat in Biarritz. \"I had been living in a privileged, cosseted milieu. I found the sudden transition hard to deal with and I wondered if all these sacrifices were worth the bother, just to be alone after the race. I was asking questions about my life and the direction I wanted it to go in.\" He relates how, instead of going to the world championships in 2000, he \"disappeared\". He added: \"My parents were worried; they could see I was changing, I was becoming unstable.\" Millar says he was introduced to the idea of using the blood booster erythropoietin (EPO) by another rider during the 2001 Tour de France when he crashed in the prologue time-trial and continued in spite of his injuries \"because, in cycling, you only stop if you've broken something. I had 10 days of atrocious suffering on a physical level, and most of all on a mental level. \"During that Tour de France and while I was going badly [another rider] said to me that we would do a good job of preparing for the Tour of Spain. He saw that I was unhappy with the team and myself. \"I understood what that meant . . . I knew what was going on.\" In August 2001, Millar said, he paid the rider \u00a3250 per syringe for EPO and was shown how to inject it in his shoulder. \"I took EPO because I knew that the Cofidis team was going to the Tour of Spain on condition that I started and got a result. No one had any need to put pressure on me, but I felt it. As I was not happy in my life, I had based everything on my sporting career and I only saw myself as a cyclist and I thought people only saw me like that. \"After taking EPO for the Tour of Spain in 2001 I did not feel good. For me I was a cheat; I had crossed the line and I felt bad. I doped because my job was to arrive highly placed at the finish. There were magazines, sports newspapers, television stations waiting for my results.\" In 2002, in the midst of an intense personal crisis, Millar began working with a Spanish doctor. \"At the start of 2002 I did not want to touch my bike any more. I had gone to Australia with my girlfriend at the end of 2001. It had gone badly. I had destroyed everything I had built with her . . . I was very bad mentally, and I came back with glandular fever. \"I contacted [the doctor], who put me on a course of vitamin B12, Prefolic and [a product to detoxify his liver].\" Millar said he won his 2002 Tour stage in B\u00e9ziers without recourse to EPO but kept the two empty syringes after winning the world time-trial title in Canada following a course of the drug as \"testimony to the shame I felt at doping myself. I was not proud of doping; I was not happy about it.\" His conclusion is telling: \"You dope because you are a prisoner of yourself, of glory, of money. I was a prisoner of the person that I had become.\""}, "isHosted": false, "pillarId": "pillar/sport", "pillarName": "Sport"}}} |
{"ygPerms":{"resourceCapabilityList":[{"resourceType":"GROUP","capabilities":[{"name":"READ"},{"name":"WELCOME_MSG"}]},{"resourceType":"PHOTO","capabilities":[{"name":"READ"},{"name":"UPLOAD"},{"name":"UPLOADTEMP"}]},{"resourceType":"FILE","capabilities":[{"name":"READ"}]},{"resourceType":"MEMBER","capabilities":[{"name":"READ"}]},{"resourceType":"LINK","capabilities":[{"name":"CREATE"},{"name":"READ"}]},{"resourceType":"CALENDAR","capabilities":[{"name":"READ"}]},{"resourceType":"DATABASE","capabilities":[{"name":"READ_DATA"},{"name":"READ"}]},{"resourceType":"POLL","capabilities":[]},{"resourceType":"MESSAGE","capabilities":[{"name":"CREATE"},{"name":"READ"}]},{"resourceType":"PENDING_MESSAGE","capabilities":[]},{"resourceType":"ATTACHMENTS","capabilities":[]},{"resourceType":"PHOTOMATIC_ALBUMS","capabilities":[{"name":"UPLOAD"}]},{"resourceType":"MEMBERSHIP_TYPE","capabilities":[{"name":"READ"}]},{"resourceType":"POST","capabilities":[{"name":"CREATE"},{"name":"READ"}]},{"resourceType":"PIN","capabilities":[{"name":"DELETE"},{"name":"UPDATE"},{"name":"CREATE"},{"name":"READ"}]}],"subStatus":"NORMAL","groupUrl":"groups.yahoo.com","intlCode":"us"},"comscore":"pageview_candidate","ygData":{"totalMsgInTopic":5,"messages":[{"userId":247566989,"authorName":"Sandra Dodd","from":"Sandra Dodd <Sandra@...>","profile":"sandralynndodd","replyTo":"LIST","spamInfo":{"isSpam":false,"reason":"0"},"subject":"Re: [AlwaysLearning] Re: Help with unschooling application","postDate":"1459723825","msgId":76920,"canDelete":false,"contentTrasformed":false,"systemMessage":false,"headers":{"messageIdInHeader":"PEE0RUNENTU1LUVFRDMtNDEzMy04MjQyLTg1QUU2MjBCM0ZCQUBTYW5kcmFkb2RkLmNvbT4=","inReplyToHeader":"PG5kcjVycisxYXRhdjJsQFlhaG9vR3JvdXBzLmNvbT4=","referencesHeader":"PG5kcGdyOSsxczVyZjg4QFlhaG9vR3JvdXBzLmNvbT4gPG5kcjVycisxYXRhdjJsQFlhaG9vR3JvdXBzLmNvbT4="},"prevInTopic":76919,"nextInTopic":0,"prevInTime":76919,"nextInTime":76921,"topicId":76914,"numMessagesInTopic":0,"msgSnippet":"If ANYONE here is not already in contact with other unschoolers where you live, please do take care of that. This discussion isn’t intended to help anyone","messageBody":"<div id=\"ygrps-yiv-2089274706\">If ANYONE here is not already in contact with other unschoolers where you live, please do take care of that. This discussion isn’t intended to help anyone with local laws or requirements.<br/>\n<br/>\nAlso, I just saw the subject line clearly: "Re: Help with unschooling application”<br/>\nDon’t apply to unschool. Don’t even mention the word. <br/>\n<br/>\nApply to homeschool (or to home educate, whatever the term is).<br/>\nThere are lots of ideas at the link given the other day (and in case anyone missed it, here:<br/>\n<a rel=\"nofollow\" target=\"_blank\" href=\"http://sandradodd.com/unschoolingcurrriculum)\">http://sandradodd.com/unschoolingcurrriculum)</a> for describing a less-structured learning plan. <br/>\n<br/>\nGood luck but I hope you’re not applying to unschool. :-) The response will probably be no.<br/>\n<br/>\nAnd check with the locals. If it’s not too risky to fail to apply, you might try forgetting it next year, or doing it late, and seeing how that goes. Holly was never registered to homeschool. I was going to register her as soon as someone contacted me and said “We can’t find your forms (about Kirby or Marty, who were registered four times and once, respectively). Nobody ever contacted me, and I was not hiding out—I was pretty blatantly out there.<br/>\n<br/>\nOne aspect of why a school district or a state might not track down every homeschooling family is that it will make them look worse, if it’s known how many they are. And they’re government workers. If they’re required to keep track of and file those applications, they get paid to do that. They won’t get paid extra to go to more work to double check whether there is MORE work they should be doing. If they file what comes in, they’ve done their job. <br/>\n<br/>\nOne thing some of us did in New Mexico, when forms needed to be notarized and mailed on paper (which is not the case anymore) was we filled out the forms, got them notarized, made photocopies for us (and enough to give anyone who asked) and the original was…. I sent mine to the government by putting it in the county landfill via the trash, but others did differently. <br/>\n<br/>\nThen time passed, and the plan was that if someone said “We don’t have your papers,” we could say “Oh, Really? Do you want a copy?”<br/>\n<br/>\nThey would totally assume they had lost them, or the mail had, and we could send a photocopy of it, dated and notarized that it had been filled in before the deadline and all. <br/>\n<br/>\nBut never once did any of us ever get a request, and so my clever plan, as clever as it was, never played all the way out. You’re welcome to use it. :-)<br/>\n<br/>\n<br/>\nSandra</div>","specialLinks":[]},{"userId":359707411,"authorName":"Robyn Coburn","from":"Robyn Coburn <dezignarob@...>","profile":"dezignarob","replyTo":"LIST","spamInfo":{"isSpam":false,"reason":"0"},"subject":"Re: Help with unschooling application","postDate":"1459710743","msgId":76917,"canDelete":false,"contentTrasformed":false,"systemMessage":false,"headers":{"messageIdInHeader":"PDQxQjQ5REJBLTQ4NDgtNEM2Mi1CRkMwLUMzODI1RDcyOTI5RkBnbWFpbC5jb20+"},"prevInTopic":76915,"nextInTopic":76919,"prevInTime":76916,"nextInTime":76918,"topicId":76914,"numMessagesInTopic":0,"msgSnippet":"Make sure you use the phrase heuristic confidence as a goal. It is eduspeak for they are confidently able to see how to use new learning tools like","messageBody":"<div id=\"ygrps-yiv-1337969250\">Make sure you use the phrase "heuristic confidence" as a goal. <br/>\n<br/>\nIt is eduspeak for "they are confidently able to see how to use new learning tools" like cuisinaire rods or other hands on tools.<br/>\n<br/>\nPut in something about "suitable for different learning styles" and "vocational". <br/>\n<br/>\nRobyn L Coburn<br/>\nHttp://WorkInProduction.com<br/>\nHttp://IggyJingles.com</div>","specialLinks":[]},{"userId":471246740,"authorName":"","from":"settledwater@...","profile":"katiesloat","replyTo":"LIST","spamInfo":{"isSpam":false,"reason":"0"},"subject":"Re: Help with unschooling application","postDate":"1459690171","msgId":76919,"canDelete":false,"contentTrasformed":false,"systemMessage":false,"headers":{"messageIdInHeader":"PG5kcjVycisxYXRhdjJsQFlhaG9vR3JvdXBzLmNvbT4=","inReplyToHeader":"PG5kcGdyOSsxczVyZjg4QFlhaG9vR3JvdXBzLmNvbT4=","referencesHeader":"PG5kcGdyOSsxczVyZjg4QFlhaG9vR3JvdXBzLmNvbT4="},"prevInTopic":76917,"nextInTopic":76920,"prevInTime":76918,"nextInTime":76920,"topicId":76914,"numMessagesInTopic":0,"msgSnippet":"There is a Facebook group called Unschool Australia. It is a closed group so you need to apply for membership but it is worth it just for their files section.","messageBody":"<div id=\"ygrps-yiv-111476971\">There is a Facebook group called Unschool Australia. It is a closed group so you need to apply for membership but it is worth it just for their files section. Various people have posted their successful Board of Studies (BoS) applications. <br/>\n<br/>\nYour other option is the Homeschool Education Association. (www.hea.edu.au). <br/>\n<br/>\nI would recommend getting in contact with both groups.<br/>\n<br/>\nAs for my personal experience (NSW), I felt like I needed to play the BoS game when they came audit my home. <br/>\n<br/>\nKeep in mind that somewhere it is stated in BoS documents that no one can make a child learn, all you can do is provide the opportunities eg. resources in your home and outings/experiences and ideas/plans. This applies to school teachers and to home educators. <br/>\n<br/>\nI hope this helps.<br/>\nKatie</div>","specialLinks":[]},{"userId":247566989,"authorName":"Sandra Dodd","from":"Sandra Dodd <Sandra@...>","profile":"sandralynndodd","replyTo":"LIST","spamInfo":{"isSpam":false,"reason":"0"},"subject":"Re: [AlwaysLearning] Help with unschooling application","postDate":"1459644892","msgId":76915,"canDelete":false,"contentTrasformed":false,"systemMessage":false,"headers":{"messageIdInHeader":"PDYxOTUzNzI2LTA1MEEtNDhBRS05M0UxLTEzNDNBMzA3QkVCNEBTYW5kcmFkb2RkLmNvbT4=","inReplyToHeader":"PG5kcGdyOSsxczVyZjg4QFlhaG9vR3JvdXBzLmNvbT4=","referencesHeader":"PG5kcGdyOSsxczVyZjg4QFlhaG9vR3JvdXBzLmNvbT4="},"prevInTopic":76914,"nextInTopic":76917,"prevInTime":76914,"nextInTime":76916,"topicId":76914,"numMessagesInTopic":0,"msgSnippet":"-=-We are enrolled in Correspondence School-=- See whether you need to un-enroll from that, so that there’s not “failure” looming (if there are reports","messageBody":"<div id=\"ygrps-yiv-21964767\">-=-We are enrolled in Correspondence School-=-<br/>\n<br/>\nSee whether you need to un-enroll from that, so that there’s not “failure” looming (if there are reports or tests or whatnot).<br/>\n<br/>\nThe best thing to do is get hooked up with Australian resources and people. Find a discussion. (I hope someone will come through and send you a link on the side or something.)<br/>\n<br/>\nBE SURE to find unschoolers.<br/>\n<br/>\nThen, lift some educational jargon from some of this:<br/>\n<br/>\n<a rel=\"nofollow\" target=\"_blank\" href=\"http://sandradodd.com/unschoolingcurriculum.html\">http://sandradodd.com/unschoolingcurriculum.html</a><br/>\n<br/>\nThere are several examples.<br/>\n<br/>\nYou might also find examples on one of the Australian websites that’s more particular to the local terminology.<br/>\n<br/>\n“Authentic assessment” should be in there a couple of times to say how you will know if they can read or do math. <br/>\n“Integrated curriculum” needs to be there, too. :-)<br/>\n<br/>\nThis discussion isn’t set up to discuss this further, but the resources are there the examples. <br/>\nThere might be an Australian link or two here:<br/>\n<a rel=\"nofollow\" target=\"_blank\" href=\"http://sandradodd.com/world\">http://sandradodd.com/world</a><br/>\n<br/>\nSandra</div>","specialLinks":[]},{"userId":241339464,"authorName":"","from":"jendakini@...","profile":"jendakini","replyTo":"LIST","spamInfo":{"isSpam":false,"reason":"0"},"subject":"Help with unschooling application","postDate":"1459635881","msgId":76914,"canDelete":false,"contentTrasformed":false,"systemMessage":false,"headers":{"messageIdInHeader":"PG5kcGdyOSsxczVyZjg4QFlhaG9vR3JvdXBzLmNvbT4="},"prevInTopic":0,"nextInTopic":76915,"prevInTime":76913,"nextInTime":76915,"topicId":76914,"numMessagesInTopic":0,"msgSnippet":"We are so new to unschooling we are not even officially doing it. We have an application into the Ministry of Ed in New Zealand but are supposedly enrolled","messageBody":"<div id=\"ygrps-yiv-1853202160\">We are so new to unschooling we are not even "officially" doing it. We have an application into the Ministry of Ed in New Zealand but are supposedly "enrolled and attending school" while they take their 6 weeks to process it. We are enrolled in Correspondence School (which is government run home schooling) but like my middle child succinctly stated "This is the same stupid stuff we have to do at school but at home I don't even have my mates around. If I have to do this work at home I'd rather be in school" and fair enough. So we have made the Correspondence curriculum optional and it pretty much just sits in its boxes.<br><br>I have spoken with 2 other unschooling families on the West Coast where we live and they said they didn't have any issues with their applications but unfortunately we weren't so lucky. This is what the Ministry has asked us for:<br><p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\"><span style=\"\">-<span style=\"font:7.0pt;\"> \n</span></span>A detailed project/topic plan which includes aims, \nobjectives, strategies, activities, assessment components (Rufus and \nArlo should each have an individualised plan)</p> \n<p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\"><span style=\"\">-<span style=\"font:7.0pt;\"> \n</span></span>How literacy and numeracy components will be included in the learning cycle</p> \n<p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\"><span style=\"\">-<span style=\"font:7.0pt;\"> \n</span></span>More detailed information about the different curriculum \nstrands (English, Maths, Learning Languages, Health and PE, Social \nSciences, The Arts, Technology and Science)in terms of what strategies \nand content will be offered</p> \n<p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\"><span style=\"\">-<span style=\"font:7.0pt;\"> \n</span></span>Assessment components and learning goals</p><p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\"><br></p><p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\">The "edubabble" makes my head swim. I have sorted the "curriculum" question, point 3. But how do I make the other points (especially the FIRST one) fit into our goal of unschooling. Do I just go fictional? <br></p><p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\"><br></p><p class=\"ygrps-yiv-1853202160yiv5818418659MsoListParagraph\" style=\"\">I'd appreciate some Always Learning wisdom. Cheers, Aroha<br></p><br><p><span><br class=\"ygrps-yiv-1853202160yui-cursor\"></span></p></div>","specialLinks":[]}],"prevTopicId":76883,"nextTopicId":76889}} |
{"poster":"withmyshield","date":"2017-06-13T00:52:50.773+0000","title":"Click...Nothing","subforum":"[ARCHIVED] Help & Support","up_votes":1,"down_votes":0,"body":"I cannot Launch League at all!!!!!!!!!","replies":[{"poster":"Porocles","date":"2017-06-13T17:48:47.938+0000","up_votes":1,"down_votes":0,"body":"I can help with this! Firstly, give your PC a restart in case something has crashed in the background. Otherwise, your OS settings could be blocking your patcher from launching so nothing is coming up. You'll want to go through the steps found in the [permissions](https://support.riotgames.com/hc/en-us/articles/204134104-Setting-up-your-Permissions) guide to resolve this. Let me know if you have any questions!","replies":[]}]} |
{
"forum_title": "Coins - Rafmynt",
"id": "58749",
"title": "Höftin stöðva viðskipti með Bitcoin",
"url": "https://spjall.vaktin.is/viewtopic.php?f=86&t=58749",
"posts": [
{
"user_name": "Output",
"text": "http://www.mbl.is/vidskipti/frettir/201 ... d_bitcoin/\nHvað finnst fólk um þetta?",
"date": "2013-12-19 21:23:00",
"post_id": "543180",
"reply_to_id": false
},
{
"user_name": "GuðjónR",
"text": "Verð nú að segja að þetta kemur mér ekkert sérstaklega á óvart.",
"date": "2013-12-19 21:35:00",
"post_id": "543182",
"reply_to_id": "543180"
},
{
"user_name": "magnusgu87",
"text": "Las gott komment á pírataspjallinu, hvort seðlabankinn væri ekki með þessari yfirlýsingu að staðfesta að bitcoin væri í raunverulegur gjaldmiðill í þeirra augum. Fannst þetta athyglisverður punktur.",
"date": "2013-12-19 21:53:00",
"post_id": "543184",
"reply_to_id": "543182"
},
{
"user_name": "appel",
"text": "Það er full langt gengið að túlka ummæli SÍ sem þannig.\nÞað er engin viðurkenning þarna frá SÍ um að bitcoin sé \"raunverulegur gjaldmiðill\". Hinsvegar skiptir sú viðurkenning eiginlega engu máli.",
"date": "2013-12-19 21:58:00",
"post_id": "543185",
"reply_to_id": "543184"
},
{
"user_name": "Monk",
"text": "Seðlabankar út um allan heim hafa verið að keppast við að vara við bitcoin síðustu vikurnar. Þeir eru skíthræddir um að missa tökin á litlu lokuðu hagkerfunum sínum í hendurnar á neytendaöflum sem þeir hafa litla sem enga stjórn á.",
"date": "2013-12-19 22:05:00",
"post_id": "543187",
"reply_to_id": "543185"
},
{
"user_name": "blitz",
"text": "Það er einmitt ástæðan.",
"date": "2013-12-19 22:05:00",
"post_id": "543188",
"reply_to_id": "543187"
},
{
"user_name": "appel",
"text": "Tja, þeir fylgjast með honum út frá akademískum áhuga. Ben Bernanke er mjög \"áhugasamur\" um þróun Bitcoins, enda er Bitcoin mjög óvenjulegur gjaldmiðill. Þeir hafa varað venjulegt fólk við honum útaf því hve hverfull hann er.",
"date": "2013-12-19 22:10:00",
"post_id": "543189",
"reply_to_id": "543188"
},
{
"user_name": "rapport",
"text": "WUT?\nFyrir mér er bitcoin einhverskonar crowdsourcing á skorti á almennri skynsemi og rétt að vara fólk við því að eyða alvöru peningum í þetta.",
"date": "2013-12-19 22:44:00",
"post_id": "543195",
"reply_to_id": "543189"
},
{
"user_name": "Sallarólegur",
"text": "Er það ekki frekar vegna þess hversu óstöðugur og ófyrirsjáanlegur?",
"date": "2013-12-20 00:08:00",
"post_id": "543205",
"reply_to_id": "543195"
},
{
"user_name": "Monk",
"text": "Auðvitað segja þeir það en sama má segja um ýmis önnur viðskipti sem þykja fullkomlega eðlileg. Verðbréf eru til dæmis afskaplega óstöðug og ófyrirsjáanleg fjárfesting. Munurinn er að þau eru undir stjórn og lúta reglum allskonar ríkisbákna sem bitcoin gerir ekki. Fyrir mér er það nákvæmlega þetta regluleysi sem er svo heillandi við þessa tilraun sem Bitcoin er.",
"date": "2013-12-20 00:15:00",
"post_id": "543209",
"reply_to_id": "543205"
},
{
"user_name": "GuðjónR",
"text": "http://www.dv.is/frettir/2013/12/23/pau ... durnesjum/",
"date": "2013-12-23 15:00:00",
"post_id": "543617",
"reply_to_id": "543209"
},
{
"user_name": "tlord",
"text": "Þeir segja bara að Bitcoin sé hvorki vara né þjónusta.\nÞess vegna vilja þeir meina að ekki sé heimilt að kaupa Bitcoin í gegnum höftin.\nMenn þurfa að vera rólegir í að túlka....",
"date": "2013-12-23 15:21:00",
"post_id": "543618",
"reply_to_id": "543617"
},
{
"user_name": "Hargo",
"text": "Mér finnst nú bara frábært að erlend fyrirtæki ákveða að koma hingað og hýsa sína netþjóna hjá Verne Holdings á Suðurnesjum, hvort sem þeir ætla að nota þá til að framleiða Bitcoin \"gervipeninga\" eða eitthvað annað.\nEn það verður áhugavert að fylgjast með þróun Bitcoin, það er á hreinu. Vonandi koma bara fleiri fyrirtæki með sína netþjóna til landsins og vilja hýsa sinn búnað á Íslandi.",
"date": "2013-12-23 16:39:00",
"post_id": "543621",
"reply_to_id": "543618"
},
{
"user_name": "tlord",
"text": "hvað þýðir orð í gæsalöppum sem byrjar á \"gervi\" ? er það gervigervi eða ekkigervi",
"date": "2013-12-23 16:48:00",
"post_id": "543622",
"reply_to_id": "543621"
},
{
"user_name": "Hargo",
"text": "Hehe það var aðallega vegna þess að þessi Paul Krugman í linknum sem GuðjónR linkaði á talaði um Bitcoin sem hálfgert gervigull og gervifyrirbæri.",
"date": "2013-12-23 16:51:00",
"post_id": "543623",
"reply_to_id": "543622"
},
{
"user_name": "tlord",
"text": "Krugman er \njólasveinn\n dude eins og flestir hagfræðingar. Hagfræði er ekki vísindi, þetta er frekar einhverskonar speki (sbr stjörnuspeki).\nHagfræði er reyndar líka undirgrein í félagsfræði. Möo allt er byggt á einhverjum túlkunum, mati, áliti og skoðunum..",
"date": "2013-12-23 16:57:00",
"post_id": "543624",
"reply_to_id": "543623"
},
{
"user_name": "blitz",
"text": "Það er nokkuð augljóst að Bitcoin fellur ekki undir viðskiptajöfnuð. Augljósast er að flokka BC undir fjármagnsjöfnuð og þ.al. undir höftin.",
"date": "2013-12-23 18:16:00",
"post_id": "543636",
"reply_to_id": "543624"
}
],
"date": "2013-12-19 21:23:00"
} |
{"poster":"Isaak001","date":"2018-06-23T11:50:59.096+0000","title":"finalmente posso dire \"dopo questa mi tolgo la vita\"","subforum":"Discussioni generali","embed":{"description":"ho iniziato tempo fa a prendere in considerazione seriamente il \"se sei bravo sali\" cominciando a pensare: in effetti è vero, faccio cagare, devo migliorare, darò il massimo e bla bla bla. da adc mi sono spostato top e ogni volta mi adeguavo pickando ciò che serviva al","url":"https://boards.euw.leagueoflegends.com/it/c/discussioni-generali-it/muxMfFhV-non-capisco-sono-davvero-io-il-problema","image":"https://cdn.leagueoflegends.com/apollo/assets/vb/boards-wallpaper.jpg"},"up_votes":2,"down_votes":6,"body":"il post linkato lo ho scritto guardacaso ieri e oggi signore e signore ne ho avuto la conferma\r\nil "SE SEI BRAVO SCALI" è ufficialmente diventato un meme per me\r\normai è ufficiale, il low elo ha bisognio o di uno che smurfa con lui o dei boost perchè altrimenti non sale più\r\ninsomma, non mi puoi dire "devi solo giocare per te e non pensare a quello che fanno gli altri se mi trovo un tryndamere 10/0 in 20 min perchè lo yasuo non aveva tp e flash ma ign e tp, la morgana che non riusciva a prendere una q manco quando stavano fermi ( e non lo dico per dire, è successo veramente ) la kai'sa che quando fai le call va afk (solo ed esclusivamente quando fai le call.......strano) e, tanto per cambiare, anche la bot avversaria era fiddata come lo schifo, io sono il jng ok, ti dico "arrivo bot" e nel momento in cui lo dico......niente......non ci sono reazioni....\r\nnon provano a fare bait..... non fanno manco la cazzata che fanno molti in questo elo ovvero ingaggare prima che arrivo......loro li... a prendere farm, io ripingo che vado ma loro niente, allora sul momento me ne vado e 5 secondi dopo sento "doppia uccisione nemica" \r\nva bhe, almeno il mid è bravo, lo ho fatto arrivare 10/0\r\nabbiamo giocato in 2 la partita e io non capivo che succedeva, non ho detto niente in tutta la partita per evitare flame a caso che dicevo "saranno tiltati, non dire niente, amgari andando avanti si riprendono" non avrei mai pensato che in realtà io stavo giocando con 1 unranked e uno che si era appena classificato in b3 corrispettivamente di lv 63 e 47, in pratica hanno iniziato ieri a giocare, come mai erano in partita con me?? per quale motivo eravamo TUTTI silver dal S3 a S1 tranne loro due?? senza contare che la kai'sa a fine game ha detto apertamente\r\n"non sopporto quelli che se la prendono così tanto per una partita persa in NORMAL"\r\nriepilogo della situazione:\r\nstavo giocando con 1 che non sapeva che stavamo rankando, due che non sarebbero dovuti stare in quella partita (tengo a precisare che erano premade) e un kayn e un veigar che continuavano ad invadermi e cercare di uccidermi(senza ricordare che io con zac avevo la passiva) per poi dare una doppia a lux ogni sacrosanta volta............sono io a non essere bravo, non è il matchmeaking che è sfasato","replies":[{"poster":"Scico1996","date":"2018-06-23T16:47:45.681+0000","up_votes":5,"down_votes":1,"body":"Così è esagerato, cioè, matematicamente parlando la frase \"se sei forte sali\" è proprio sbagliata, l'affermazione giusta sarebbe \"da un certo punto in poi la tua skill è sufficiente a farti vincere un game da solo\", motivo per cui se metti un challenger in bronzo questo ti torna challenger. In bronzo e silver questo vale poco, nel senso che tu dovrai ugualmente migliorarti (\"il dire non salgo per colpa del mio team\" è sbagliato tanto quanto dire \"se sei forte sali\") ma non pensare che se giochi da gold in silver arriverai immediatamente in gold. Questo perché per quanto tu possa essere migliore degli altri non hai ugualmente una conoscenza sufficiente del gioco per poter carryare il game da solo, ovvio che il giocare da gold è la condizione necessaria, poi serve fortuna nel beccare n game influenzabili dalla tua skill. Discorso analogo lo si può fare nel matchmaking, che sia fatto male è facilmente notabile, come è anche facile notare che gli smurf non vengono \"bloccati\" non per impossibilità ma perché non lo si vuole fare, personalmente ritengo che uno smurf non dovrebbe essere presente nella promo di qualcuno sia a favore che contro, diciamo che se il matchmaking venisse migliorato si potrebbero tranquillamente togliere tutti i supporti promozione che ci sono in bronzo-silver-gold (cosa che a me farebbe molto piacere) e vi sarebbe molta più meritocrazia. \nPer farti un esempio, io sono main adc e la season scorsa l'ho finita g5 per poi droppare in pre-season e ritornarci sempre nello stesso periodo. Ora sono bloccato in silver 2/1 con tipo 700 game, ammetto che il mio tiltare pesantemente abbia influito ma di promo per gold già ne ho perse e di imputabile avevo ben poco, l'unica cosa che posso fare è non tiltare tipo oggi ed aspettare i famosi \"n\" game per poi tornare gold ed avere voce in capitolo sulla perdita di un game. Quindi, consiglio personale, gioca e se fai qualche errore ragionaci su ma nulla di più, prima o poi inizierai a salire","replies":[{"poster":"Isaak001","date":"2018-06-23T18:00:27.190+0000","up_votes":1,"down_votes":1,"body":"posso dire che ti amo?? hai perfettamente ragione su tutto, ma a me ha fatto parecchio tiltare il fattore b3 e unranked, non altro.\nse leggi il post che ho linkato (scritto più chiaramente e in modo sensato e pacato) ti renderai conto del fatto che il mio problema necessariamente non è neanche il fatto che continuo a perdere, so di fare errori come ogni essere umano fa, senza contare che per me è anche un periodaccio in generale","replies":[]}]},{"poster":"Linkimaru","date":"2018-06-24T09:19:19.304+0000","up_votes":3,"down_votes":0,"body":"E va bene, meriti di sapere la verità. La riot è un associazione scientifica che studia il comportamento umano, Lol non è un gioco bensì un evoluto sistema di profilazione e analisi comportamentale; i pro player, gli eventi i tornei... tutto falso... studiato a tavolino per rendere la cosa più credibile. Tu, insieme a molti altri siete stati scelti ed usati come cavie per raccogliere informazioni ma non solo, le tue partite vengono trasmesse in diretta streaming ai giocatori di alto elo (che in realtà hanno finanziato il progetto e gli è stato dato il rank che desideravano, nessuno è in grado di arrivare ad un elo alto con le proprie forze come avrai già capito) i quali si divertono e si passano il tempo ridendo alle spalle delle povere cavie di elo basso che smadonnano contro il sistema palesemente truccato. nelle promo in particolare sono tutti attori pagati che seguono le linee guida dei piani alti per farti perdere e rimanere bloccato a vita.\nSpero sia tutto chiaro.\n\"Buongiorno... e casomai non vi rivedessi, buon pomeriggio, buonasera e buonanotte!\" cit.","replies":[]},{"poster":"Ringiel","date":"2018-06-23T14:50:42.335+0000","up_votes":2,"down_votes":1,"body":"se per te\" il low elo ha bisognio o di uno che smurfa con lui o dei boost perchè altrimenti non sale più\" perchè continui a fare ranked? tanto sono una presa in giro no?","replies":[{"poster":"Isaak001","date":"2018-06-23T15:29:27.623+0000","up_votes":1,"down_votes":1,"body":"perchè ci spero ancora, il fattore \"scalare è complicato\" non implica il fatto che devo mollare perchè non soddisfa i miei requisiti, non sono nessuno per volere che le partite siano perfettamente bilanciate o vincere tutte le partite, ma almeno PRETENDO, non solo per me ma anche per altri, che il matchmeaking non ti metta in squadra gente di quel calibro, insomma che ci stavano a fare li, da dove sono sbucati quei 2??? io di questo mi sono lamentato, che il matchmeaking mette persone a caso in game, capivo se lo yas era s4 e morg b1 ma uno era addirittura unranked dai","replies":[{"poster":"Trauja","date":"2018-06-23T19:31:11.581+0000","up_votes":3,"down_votes":2,"body":"Ma non gli rispondere nemmeno.. Ringiel è uno che si legge tutte ste tipi di discussioni in cui gente si lamenta solo per sentirsi giusto un poco meglio con se stesso e più forte senza aggiungere nulla di costruttivo alla discussione.\nRispondendo alla discussione, è vero.. il fattore randomness ha un suo ruolo ( una volta m'è capitato un unranked fizz top, fortuna abbiamo fatto remake, e ti giuro, lui non ci vedeva nulla di strano ovviamente ), l'unica cosa è grindare piano piano, vincendo quel 1-2% di partite in più se effettivamente porti un vantaggio di qualsiasi genere al team.\nL'hard carry in cui uno teoricamente dovrebbe carriarsi da solo e vincere l'80% dei game esiste solo per gente plat - dia + per i comuni mortali come noi serve pazienza e impegno.","replies":[{"poster":"Isaak001","date":"2018-06-23T19:37:51.158+0000","up_votes":2,"down_votes":1,"body":"oltre il resto a me ha fatto tiltare il fattore del duo unranked, non altro","replies":[{"poster":"Trauja","date":"2018-06-23T19:49:43.578+0000","up_votes":1,"down_votes":2,"body":"Ma infatti è inaccettabile avere in ranked gente unranked first promo.\nCapirei già se uno è unranked ma magari gioca da altre stagioni e si deve ripiazzare, e allora l'mmr almeno un pò di verità la porterebbe ( ma nemmeno ).. ma un nuovo account livello 30 non può andare in piazzamento ed avere lo stesso mmr di un mid silver, è davvero strano che la riot si aspetti che un nuovo account sia già a quel livello.\nSecondo me i nuovi account dovrebbero iniziare TUTTI aventi mmr mid bronzo, perchè così toglie anche \" prestigio \" alla gente che come me il silver se l'è preso scalando (piazzato b2) perchè vedi gente che nelle 10 promo si è fatta carriare 2-3 game vincendoli e viene piazzata in mid silver quando chiaramente non è ancora a quel livello (anche se non grandioso) di gioco, e il silver diventa la classica fiesta di : smurf, gente normale, hard stuck, scarsi che ci si sono ritrovati piazzati etc","replies":[{"poster":"FireM1nd","date":"2018-06-25T00:30:50.395+0000","up_votes":3,"down_votes":0,"body":"L'mmr delle first placement di un account è un caso molto particolare. Dato che il sistema elo non ha abbastanza dati per analizzare il giocatore, per comodità ne prende di esterni, conformi alla \"moda\" di abilità dei giocatori. In parole povere: sono appena arrivato al 30, faccio le mie prime placement 5/5 una vinta e una persa ogni volta. Il sistema vede un 50/50 senza alcuna streak. Ovvio, non sa che pesci prendere, dato che la tua performance in game non viene contata. Quindi, cosa fa? Guarda qual'è l'elo più comune in quella regione, in quel momento, e ti schiaffa in mezzo alla massa. È matematico. Se il 60% dei giocatori è s1, se ti schiaffo s1 riduco solo al 40% le possibilità di errore. Il sistema funziona così da un bel po, ma non è neanche questa la parte bella. La parte bella, è che la riot ha visto un lento ma costante miglioramento dei giocatori. Ogni anno, il grosso dei players scala generalmente una divisione. È il caso di fine s7, quando la maggior parte dei giocatori di lol era effettivamente s1. In teoria questo sarebve un gran bel dato, segno del fatto che la community sta migliorando (almeno a livello di abilità) ma c'è un problema di mezzo: con l'andare delle cose, hanno notato che se questo tasso di crescita si mantenesse costante, il sistema, in un paio di season, avrebbe piazzato direttamente in Gold i nuovi giocatori nel qual caso avessero fatto delle placement anche solo al limite della sufficienza. In poche parole: ne vinci 3 su dieci -> Gold V a mani basse. È il motivo per il quale hanno deciso che il sistema di ranked IN TOTO necessita di un pesante restauro, perché rischia di creare grossi divari nei giudizi tra vecchi e nuovi giocatori. Ecco quindi il perché di nuove leghe, e di leghe a ruoli.","replies":[]}]}]}]}]}]},{"poster":"Athìs","date":"2018-06-23T12:52:37.191+0000","up_votes":4,"down_votes":2,"body":"\"sono io a non essere bravo, non è il matchmeaking che è sfasato\" ti sei risposto da solo.\nSe non sai abbastanza forte da carryare il team è inutile che ti lamenti, perchè di partite equilibrate ne troverai pochi.\nGiusto un consiglio, eviterei di giocare tank se vuoi puntare al carry, un yi o jax fanno al caso tuo anche se puoi carryare con chiunque.\nPoi se si becca quella partita dove tutti sono fedati pace.","replies":[{"poster":"Isaak001","date":"2018-06-23T14:29:59.851+0000","up_votes":1,"down_votes":1,"body":"sei passato dal dire che il problema sono io perchè non so carriare al \"troverai poche partite bilanciate\"\n{{sticker:zombie-brand-clap}} mi rifiuto di rispondere","replies":[{"poster":"Athìs","date":"2018-06-23T15:50:39.200+0000","up_votes":1,"down_votes":0,"body":"Liberissimo di farlo, ma è un dato di fatto che in ogni partita (salvo alcuni casi) troverai almeno uno che feeda, pure tu.\nSe in quei casi non sai portare la tua squadra alla vittoria è ovvio che non sali; Come può benissimo accadere il contrario dove tu fai schifo per l'intero game ma vieni carryato dalla tua squadra. Se invece vuoi vincere solo grazie alle tue capacità allora lol non fa per te.","replies":[{"poster":"Isaak001","date":"2018-06-23T16:29:16.161+0000","up_votes":2,"down_votes":0,"body":"ma tu da dove vieni fuori con ste cagate?? chi ha mai detto che voglio vincere solo grazie alle mie capacità, qui si tratta del fatto appunto che un team fatto bene (in quanto a persone, non a champion) è raro trovarlo, il problema sta nel fatto che\n1: se non è in meta ti flammo perchè non serve a niente\n2: tu devi prevedere che io sto per fare una cosa, senza che io te la dica o senza che magari ti abbia spiegato prima, non lo fai?? ti flammo\n3: stai giocando male, magari neanche tanto, ma cmq non stai andando bene, non mi interessa, tu sei zed tu devi essere 30/0, sei nabbo ti flammo\n4: il jiungler non ganka e quello avversario è sempre bot/mid/top, anche se noi stiamo pushiando come degli stronzi e cmq colpa del jngler\netcc....\nio non mi sto lamentando del fatto che non ne vinco mezza da un po (come detto nel post che ho linkato, spero che almeno UNA delle persone che abbia letto questo abbia anche leto l'altro) ma solo del metchmeaking che funziona in modo PARECCHIO STRANO e che ogni tanto fa sti scherzi, ok forse come post è stato molto aggressivo ma il senso bene o male si dovrebbe capire","replies":[]}]}]},{"poster":"SuraKnight","date":"2018-06-23T13:17:26.858+0000","up_votes":2,"down_votes":1,"body":"> [{quoted}](name=Athìs,realm=EUW,application-id=MJVj50Jw,discussion-id=073ZZIqF,comment-id=0000,timestamp=2018-06-23T12:52:37.191+0000)\n>\n> Giusto un consiglio, eviterei di giocare tank se vuoi puntare al carry, un yi o jax fanno al caso tuo anche se puoi carryare con chiunque.\n\n{{sticker:zombie-brand-mindblown}}","replies":[]}]}]} |
[{"jsonrpc": "2.0", "result": [{"blockTime": 1631980513, "confirmationStatus": "finalized", "err": null, "memo": null, "signature": "29ZBF9BbG4UZFLpWPwchdbARGeqyZBP2AUVXjEyjRCDUd4644i5YgTCD8TYvTc1zEARKs26gxxgJDidoN6aux6ct", "slot": 97147210}], "id": "533de89c-e083-4930-b91e-c1a1e4921e06"}] |
{"tag":"de-","count":532,"results":[{"word":"de","explain":"(前缀)离开；除去；否定；倒转"},{"word":"deacon","explain":"n. 副主祭,执事,公会会长"},{"word":"deactivate","explain":"vt. 释放，去活化"},{"word":"deactive","explain":"使不活动"},{"word":"dead","explain":"adj.死的"},{"word":"deaden","explain":"vt. 使减弱,消除"},{"word":"deadline","explain":"n. 期限；最后付款期限；最后期限，截止交稿日期，监牢周围的死线"},{"word":"deadlock","explain":"n. 僵局，僵持"},{"word":"deadly","explain":"adj. 致死的；致命的；死一般的；非常，极度；危险的，极有害的；ad. 死一般地；死一样地，非常，很"},{"word":"deadweight","explain":"n.(船的最大)载重量"},{"word":"deaf","explain":"adj.聋的"},{"word":"deafen","explain":"vt.使聋；使隔音"},{"word":"deafness","explain":"n. 聋,不听"},{"word":"deal","explain":"vi.做买卖；对付"},{"word":"dealer","explain":"n. 商人；贩子；发牌者；经记人，证券商；商人贩子"},{"word":"dealing","explain":"n. 行为,交易"},{"word":"dealt","explain":"deal的过去式(分词)"},{"word":"dean","explain":"n.(大学)院长，系主任"},{"word":"dear","explain":"adj.昂贵的，高价的"},{"word":"dearly","explain":"adv. 深爱地；热切地；亲爱地；深深地(爱等)；昂贵；极，非常，昂贵地"},{"word":"dearness","explain":"n. 高价,亲爱"},{"word":"dearth","explain":"n. 缺乏,粮食不足,饥谨"},{"word":"deary","explain":"n. 可爱的,爱人"},{"word":"death","explain":"n.死，死亡；灭亡"},{"word":"deathbed","explain":"n. 临死所卧之床,临终之时"},{"word":"deathless","explain":" adj. 不死的,不灭的,永恒的"},{"word":"deathlike","explain":"a. 死了一样的,象死人的"},{"word":"deathly","explain":"a. 死一般的"},{"word":"debacle","explain":"n. 解冻，崩溃."},{"word":"debar","explain":"vt. 排除,防止,禁止"},{"word":"debase","explain":"v. 贬低，贬损"},{"word":"debasement","explain":" n. (品格、地位、品质的) 降低; (货币"},{"word":"debatable","explain":"a. 可争论的,成问题的,未决定的"},{"word":"debate","explain":"n.,vt.&vi.争论；辩论"},{"word":"debater","explain":"n. 讨论家,讨论者"},{"word":"debauch","explain":"v. 使放荡，堕落"},{"word":"debauchery","explain":"n. 放荡，沉缅酒色"},{"word":"debenture","explain":"n. 公司债,公司债券,退税证明书"},{"word":"debilitate","explain":"v. 使衰弱"},{"word":"debility","explain":"n. 衰弱，虚弱"},{"word":"debit","explain":"n. 借方,借"},{"word":"debonair","explain":"adj. 殷勤的，快活的，温雅的"},{"word":"debouch","explain":"v. 流出，进入(开阔地区)"},{"word":"debrief","explain":"v. 向...询问情况，汇报情况"},{"word":"debris","explain":"n. 废墟，残骸"},{"word":"debt","explain":"n.债，债务，欠债"},{"word":"debtee","explain":"n.债权人"},{"word":"debtor","explain":"n. 负债者"},{"word":"debug","explain":"vt. 调试"},{"word":"debugger","explain":"n. 调试程序"},{"word":"debunk","explain":"v. 揭穿真相，暴露"},{"word":"debut","explain":"n. 初次登台，初次露面"},{"word":"debutante","explain":"n. 初次参加社交活动的少女"},{"word":"decade","explain":"n.十年，十年期"},{"word":"decadence","explain":"n. 衰落，颓废"},{"word":"decadent","explain":"adj. 衰落的；颓废的，衰退的"},{"word":"decagon","explain":"n. 十边形,十角形"},{"word":"decagram","explain":"十克"},{"word":"decameter","explain":"分米"},{"word":"decamp","explain":"v. （士兵）离营，匆忙秘密地离开"},{"word":"decant","explain":"vt. 轻轻倒出,移入其他容器"},{"word":"decanter","explain":"n. 有塞子的玻璃瓶"},{"word":"decathlon","explain":"n. 十项运动"},{"word":"decay","explain":"vt.使腐朽，使腐烂"},{"word":"decease","explain":"n. 死亡"},{"word":"deceased","explain":"adj. 已死的"},{"word":"deceit","explain":"n.欺骗，欺诈"},{"word":"deceitful","explain":"a. 欺诈的"},{"word":"deceive","explain":"vt.欺骗，蒙蔽，行骗"},{"word":"deceiver","explain":"n. 欺人者,欺诈者"},{"word":"decelerate","explain":"v. 使减速，降低速度；减速；减缓"},{"word":"December","explain":"n.十二月"},{"word":"decency","explain":"n. 得体,礼貌,正派"},{"word":"decent","explain":"a.正派的；体面的"},{"word":"decentralization","explain":"分权管理"},{"word":"decentralize","explain":"v.给予更多权"},{"word":"deception","explain":"n. 欺骗"},{"word":"deceptive","explain":"a. 迷惑的,虚伪的,欺诈的"},{"word":"decertifacation","explain":"否认代表资格"},{"word":"decibel","explain":"n. 分贝(音量的单位)"},{"word":"decide","explain":"vi.&vt.下决心；决定"},{"word":"decided","explain":"a. 确定的,坚决的"},{"word":"decidedly","explain":"ad.明确地，坚决地"},{"word":"deciduous","explain":"adj. 脱落的，落叶的"},{"word":"decimal","explain":"a.小数的，十进制的"},{"word":"decimate","explain":"v. 毁掉大部分，大量杀死"},{"word":"decipher","explain":"v. 解开（疑团），破译（密码）"},{"word":"decision","explain":"n. 决定，决心；果断；判决，决议；判定，决策；决断"},{"word":"decisive","explain":"a.决定性的；果断的"},{"word":"deck","explain":"n. 甲板，舱面；层面；覆盖物；复盖"},{"word":"declaim","explain":"v. 高谈阔论，雄辩，大声说"},{"word":"declamation","explain":"n. 高声说话，高调"},{"word":"declaration","explain":"n.宣布，宣言；申诉"},{"word":"declare","explain":"vt.断言；声明；表明"},{"word":"declared","explain":"a. 承认的，申报的"},{"word":"declassify","explain":"v. 撤销，保密"},{"word":"declension","explain":"n. 词尾变化,格变化,倾斜,衰退"},{"word":"declination","explain":"n. 倾斜，衰微"},{"word":"decline","explain":"vt.下倾；偏斜；衰退"},{"word":"declining","explain":"adj. 下降的，衰落的"},{"word":"declivity","explain":"n. 倾斜面，斜坡"},{"word":"decode","explain":"v. 译解(密码)；vt. 破译；译(电报)；解码，译码"},{"word":"decollete","explain":" adj. <女装> 露出颈部和肩部的,袒胸露"},{"word":"decompose","explain":"vt.&vi.腐败；分解"},{"word":"decomposition","explain":"n. 腐烂，崩溃"},{"word":"decorate","explain":"vt.装饰，装璜，修饰"},{"word":"decoration","explain":"n. 装饰,装饰品"},{"word":"decorative","explain":"a.装饰的；可作装饰的"},{"word":"decorator","explain":"n.装饰者"},{"word":"decorous","explain":"adj. 符合礼节的，相称的"},{"word":"decorum","explain":"n. 礼节，礼貌"},{"word":"decoy","explain":"v. 欺骗，引诱"},{"word":"decrease","explain":"v. 减少，降低，缩短；减小；减弱；n. 减少，减小"},{"word":"decreasing","explain":"递减"},{"word":"decree","explain":"v. & n. (专制君主的)命令；vi. 规定；n. 法令，政令；教令；公告；天命；命令，v. 判决；颁布命令，公告；命令；法令 vt. 颁布(法令、政令)"},{"word":"decrement","explain":"减值，减量，赤字，贬值"},{"word":"decrepit","explain":"adj. 衰老的，破旧的"},{"word":"decrepitude","explain":"n. 衰老、破旧"},{"word":"decry","explain":"v. 责难，贬低（价值）"},{"word":"dedicate","explain":"vt.奉献；献身"},{"word":"dedicated","explain":"a. 潜心的"},{"word":"dedication","explain":"n. 奉献,致力,献辞"},{"word":"deduce","explain":"vt.演绎，推论，推断"},{"word":"deducible","explain":"a. 可推论的"},{"word":"deduct","explain":"vt. 扣除,演绎"},{"word":"deduction","explain":"n. 减除,扣除,减除额,推论"},{"word":"deed","explain":"n.行为；事迹"},{"word":"deem","explain":"vt.认为，相信 vi.想"},{"word":"deemphasize","explain":"降低重要性"},{"word":"deep","explain":"adj.深的"},{"word":"deepen","explain":"vt.加深 vi.深化"},{"word":"deeply","explain":"adv. 深深地；深切地"},{"word":"deer","explain":"n.鹿"},{"word":"deerskin","explain":"n. 鹿皮,鹿皮制衣服"},{"word":"deface","explain":"vt. 损伤外观"},{"word":"defalcate","explain":"v. 侵吞公款"},{"word":"defalcation","explain":"挪用，盗"},{"word":"defamation","explain":"n. 破坏名誉,中伤,诽谤"},{"word":"defamatory","explain":"adj. 诽谤的"},{"word":"defame","explain":"v. 诽谤，中伤"},{"word":"default","explain":"n.&vi.不履行；缺席"},{"word":"defeat","explain":"n.击败；失败"},{"word":"defeatist","explain":"n. 失败主义者"},{"word":"defecation","explain":"n. 通便，排粪"},{"word":"defect","explain":"n.缺点，缺陷，欠缺"},{"word":"defection","explain":"n. 脱党，变节"},{"word":"defective","explain":"a. 有缺陷的,欠缺的"},{"word":"defector","explain":"n. 背叛者,叛离者"},{"word":"defects","explain":"n.不合格品"},{"word":"defence","explain":"n.防御；防务；辩护"},{"word":"defenceless","explain":"a.未设防的"},{"word":"defend","explain":"vt.保卫，防守"},{"word":"defendant","explain":"n. 被告"},{"word":"defender","explain":"n.防御者"},{"word":"defense","explain":"n. 防卫,防卫物"},{"word":"defenseless","explain":"a. 无防备的"},{"word":"defensible","explain":"a. 可防卫的,可拥护的,可辩护的"},{"word":"defensive","explain":"a. 防卫的,防备用的,辩护的"},{"word":"defer","explain":"vt. 拖延；推迟，vi. 服从；拖延，延期"},{"word":"deference","explain":"n. 敬意，尊重"},{"word":"deferential","explain":"a. 恭敬的"},{"word":"deferred","explain":"a. 延期的，递延的"},{"word":"defferent","explain":"a. 各种的，差异的"},{"word":"defferential","explain":"a. 微分的"},{"word":"defiance","explain":"n. 蔑视,挑衅"},{"word":"defiant","explain":"a. 挑衅的,目中无人的"},{"word":"deficiency","explain":"n.缺乏；不足之数"},{"word":"deficient","explain":"adj. 缺乏的，欠缺的；不足的；(in)缺乏的"},{"word":"deficit","explain":"n. 不足，赤字"},{"word":"defile","explain":"v. 弄污，弄脏n. （山间）小道"},{"word":"defilement","explain":"n. 弄脏,污秽,污辱"},{"word":"definable","explain":"a. 可定义的，可确定的"},{"word":"define","explain":"vt.给…下定义；限定"},{"word":"definite","explain":"a.明确的；肯定的"},{"word":"definitely","explain":" adv. 一定，肯定地；一定地，明确地；绝对"},{"word":"definition","explain":"n. 定义，解说；释义；定界；解释；限定；明确；确实，清晰度；界限；(轮廓等)清晰，规定"},{"word":"definitive","explain":"n. 限定词"},{"word":"deflate","explain":"v. 收缩，紧缩"},{"word":"deflated","explain":"adj. 灰心丧气的"},{"word":"deflation","explain":"n. 紧缩通货"},{"word":"deflator","explain":"减缩指数，消除通胀因素指数"},{"word":"deflect","explain":"vt.&vi.(使)偏斜"},{"word":"deflection","explain":"n. 偏斜，歪斜；偏差；偏转，转向"},{"word":"deforestation","explain":"n.砍伐森林"},{"word":"deform","explain":"vt.损坏…的形状"},{"word":"deformation","explain":"n. 损坏；变形；畸形；毁坏"},{"word":"deformity","explain":"n.畸形；残废"},{"word":"defraud","explain":"vt. 欺骗"},{"word":"defray","explain":"v. 支付，支出"},{"word":"deft","explain":"adj. 灵巧的，熟练的(adeft)"},{"word":"deftly","explain":"ad. 灵巧地，敏捷地"},{"word":"defunct","explain":"adj. 死亡的，过时的"},{"word":"defy","explain":"vt.向…挑战；蔑视"},{"word":"degeneracy","explain":"n. 退步,退化,堕落"},{"word":"degenerate","explain":"vi. 衰败；腐化，堕落；vt. 退步"},{"word":"degeneration","explain":"n. 退化,恶化,堕落"},{"word":"degenerative","explain":"a.(疾病)变性的"},{"word":"degradation","explain":"n.降级；退化；衰变"},{"word":"degrade","explain":"vt. 降低品格；堕落；使降给；使堕落；降级；使降级；使堕落；贬黜"},{"word":"degraded","explain":"adj. 被贬低的；堕落的"},{"word":"degree","explain":"n.程度；度；学位"},{"word":"dehumanize","explain":"v. 使失掉人性"},{"word":"dehydrate","explain":"v. 除去水份，脱水"},{"word":"deification","explain":"n. 神化，崇拜"},{"word":"deify","explain":"v. 奉为神，崇拜"},{"word":"deign","explain":"v. 屈尊，惠允（做某事）"},{"word":"deist","explain":"n. 自然神论信仰者"},{"word":"deition","explain":"版本"},{"word":"deity","explain":"n. 神，神性"},{"word":"dejected","explain":" adj.垂头丧气的；沮丧的，失望的"},{"word":"dejection","explain":"n. 沮丧，颓丧"},{"word":"del","explain":"n. 保付"},{"word":"delay","explain":"vt.推迟；耽搁；延误"},{"word":"delecate","explain":"a. 微妙的，棘手的"},{"word":"delectable","explain":"adj. 赏心悦目的，愉悦的"},{"word":"delectation","explain":"n. 享受，愉快"},{"word":"delegate","explain":"n.代表，委员，特派员"},{"word":"delegation","explain":"n.代表团"},{"word":"delete","explain":"vt.删除；擦掉"},{"word":"deleterious","explain":"adj. （对身心）有害的，有毒的"},{"word":"deletion","explain":"n. 删除；取消"},{"word":"deleville","explain":"德勒维尔(地名)"},{"word":"delft","explain":" n. (荷兰) 戴夫特 (彩色) 陶器"},{"word":"deliberate","explain":"a.深思熟虑的；审慎的"},{"word":"deliberately","explain":" adv.仔细考虑地，故意地；审慎地，慎重地；从容地；有意地"},{"word":"deliberation","explain":"n. 熟虑,熟思,协议"},{"word":"delicacy","explain":"n. 细软,精致,精美的食品,娇气,"},{"word":"delicate","explain":"a.纤细的；易碎的"},{"word":"delicately","explain":"ad. 精美地；微妙地"},{"word":"delicatessen","explain":"n.熟食"},{"word":"delicious","explain":"adj. 美味的；可口的；怡人的；精美的；美妙的"},{"word":"delight","explain":"n.快乐"},{"word":"delighted","explain":"adj. 大喜；高兴的，欣喜的；喜欢的"},{"word":"delightful","explain":"a. 令人愉快的,可喜的"},{"word":"delightfully","explain":"ad. 大喜，欣然"},{"word":"delimit","explain":"vt. 定界，定义；v. 定界，划界"},{"word":"delimiter","explain":"n. 定界符，分界符"},{"word":"delineate","explain":"v. 描画"},{"word":"delineation","explain":"n. 画轮廓,略图,图形"},{"word":"delinquency","explain":"n. 失职，罪行"},{"word":"delinquent","explain":"adj. 疏忽职务的，有过失的"},{"word":"deliquesce","explain":"v. 融解；潮解"},{"word":"deliquescent","explain":"a. 溶解的,溶解性的"},{"word":"delirious","explain":"adj. 精神错乱的"},{"word":"delirium","explain":"n. 精神错乱，发狂"},{"word":"deliver","explain":"vt.投递，送交；发表"},{"word":"deliverance","explain":"n. 救出,救助,释放,判决"},{"word":"deliverer","explain":"n. 救助者,引渡人,交付者"},{"word":"delivery","explain":"n. 投递；交付；分娩；递送，讲述；送交；交货；传送；交货，发送，传递"},{"word":"dell","explain":"n. 小谷,小溪谷"},{"word":"della","explain":"德拉(女名)"},{"word":"Delphic","explain":"adj.神谕(似)的; 暧昧的,含混的"},{"word":"delta","explain":"n. （河流的）三角洲"},{"word":"delude","explain":"v. 欺骗，哄骗"},{"word":"deluge","explain":" vt. 泛滥；n. 大洪水，豪雨；大水灾"},{"word":"delusion","explain":"n. 欺骗，幻想"},{"word":"delusive","explain":"a. 困惑的,迷惑的,欺瞒的"},{"word":"deluxe","explain":"a.昂贵的；奢侈的"},{"word":"delve","explain":"vt.vi.探究,查考n.坑,穴"},{"word":"demagogue","explain":"n. 煽动者,群众煽动者"},{"word":"demand","explain":"vt. 要求；需要；询问；查问；v. 请求；查问；需要；n. 需要；请求，需求"},{"word":"demanding","explain":"adj. 对人要求严格的；过分要求的"},{"word":"demarcate","explain":"v. 划分，划界"},{"word":"demarcation","explain":"n. 定界线；分开"},{"word":"demean","explain":"v. 贬抑，降低"},{"word":"demeanor","explain":"n. 举止，行为"},{"word":"demeanour","explain":"n. 行为,举止"},{"word":"demerit","explain":"n. 缺点,短处,过失"},{"word":"demesne","explain":"n. 领地，范围"},{"word":"demise","explain":"n. 崩,薨,死亡"},{"word":"demobilize","explain":"v. 遣散(军人)，复员"},{"word":"democracy","explain":"n.民主，民主制"},{"word":"democrat","explain":"n. 民主正体论者,民主主义者,民主党员"},{"word":"democratic","explain":"a.民主的，民主政体的"},{"word":"demography","explain":"n. 人口统计；人口学"},{"word":"demolish","explain":"v. 摧毁，拆除"},{"word":"demolition","explain":"n. 破坏，拆除"},{"word":"demon","explain":"n. 魔鬼"},{"word":"demonetize","explain":"非货币化"},{"word":"demoniac","explain":"a. 着魔的,恶魔的"},{"word":"demonstrable","explain":"a. 可论证的"},{"word":"demonstrate","explain":"vt.说明；论证；表露"},{"word":"demonstration","explain":"n. 示范,实证"},{"word":"demonstrative","explain":"a. 说明的,指示的"},{"word":"demonstrator","explain":"n.示威"},{"word":"demoralization","explain":"n. 道德颓废,堕落,士气沮丧"},{"word":"demote","explain":"v. 降级，降职"},{"word":"demotic","explain":"a. 人民的,民众的,通俗文体的"},{"word":"demur","explain":"v. 表示异议，反对"},{"word":"demure","explain":"adj. 严肃的，矜持的"},{"word":"demurrage","explain":"n. 滞期费；滞期"},{"word":"den","explain":"n.窝，兽穴"},{"word":"denial","explain":"n.否定；拒绝相信"},{"word":"denigrate","explain":"v. 污蔑，诽谤"},{"word":"denizen","explain":"n. 居民，外籍居民"},{"word":"denmark","explain":" n. 丹麦(欧洲)"},{"word":"denominate","explain":"v. 命名，取名"},{"word":"denomination","explain":"n. 命名，（长度，币值的）单位"},{"word":"denominator","explain":"n. 分母,命名者"},{"word":"denotation","explain":"n.意义"},{"word":"denote","explain":"vt.指示，意味着"},{"word":"denouement","explain":"n. （小说的）结尾，结局"},{"word":"denounce","explain":"vt.谴责，声讨；告发"},{"word":"dense","explain":"adj. 稠密的，密集的；愚钝的；(烟等)浓厚的；(烟、雾等)浓密的"},{"word":"densely","explain":"ad. 密集地；浓厚地"},{"word":"density","explain":"n.密集，稠密；密度"},{"word":"dent","explain":"n. 缺口，凹痕，v. 弄凹"},{"word":"dental","explain":"a. 牙齿的"},{"word":"dentist","explain":"n.牙科医生"},{"word":"dentistry","explain":"n.牙医"},{"word":"denture","explain":"n. 假牙"},{"word":"denude","explain":"v. 脱去，剥蚀，剥夺"},{"word":"denunciation","explain":"n. 谴责,告发"},{"word":"denver","explain":"n. 丹佛(市)(美国)"},{"word":"deny","explain":"vt.否定；拒绝相信"},{"word":"deoxygenate","explain":"vt. 除去氧气"},{"word":"depart","explain":"vi.离开，起程；出发"},{"word":"departed","explain":"a. 已往的；已故的"},{"word":"department","explain":"n.部，司，局，处，系"},{"word":"departure","explain":"n.离开，出发，起程"},{"word":"depend","explain":"vi.依靠"},{"word":"dependability","explain":"n. 可依赖性；可靠性"},{"word":"dependable","explain":"a. 可依靠的"},{"word":"dependant","explain":"n. 受赡养者；侍从；依赖别人"},{"word":"dependence","explain":"n. 依赖,依存,信赖"},{"word":"dependency","explain":"n. 属国,保护地,从属物"},{"word":"dependent","explain":"a.依靠的，依赖的"},{"word":"depict","explain":"vt. 描写"},{"word":"depiction","explain":"n. 描述"},{"word":"depilate","explain":"vt. 拔毛,脱毛"},{"word":"deplete","explain":"v. 倒空，耗尽"},{"word":"depletion","explain":"n. 耗尽，枯竭"},{"word":"deplorable","explain":"a. 可叹的,悲惨的,凄惨的"},{"word":"deplore","explain":"vt. 悲悼,哀叹,悔恨"},{"word":"deploy","explain":"vt.vi. 展开,配置"},{"word":"depopulate","explain":"vt.vi. (使)人口减少"},{"word":"depopulation","explain":"n. 人口减少"},{"word":"deport","explain":"vt. 持,举止,驱逐"},{"word":"deportation","explain":"n. 驱逐,放逐"},{"word":"deportment","explain":"n. （尤指少女的）风度，举止"},{"word":"depose","explain":"v. 免职，废黜"},{"word":"deposit","explain":"vt.使沉淀；存放"},{"word":"deposition","explain":"n.免职，罢免；口供"},{"word":"depositor","explain":"存款人，储户"},{"word":"depository","explain":"n. 存储处,贮藏所,受托者"},{"word":"depot","explain":"n. 货栈；仓库"},{"word":"deprave","explain":"vt. 使堕落,使恶化,使腐败"},{"word":"depravity","explain":"n. 堕落，恶习"},{"word":"deprecate","explain":"vt. 声明不赞成,抨击,反对"},{"word":"deprecatory","explain":"a. 不赞成的,反对的,恳求的"},{"word":"depreciate","explain":"v. 贬低，贬值"},{"word":"depreciation","explain":"n. 贬值；折旧；跌价"},{"word":"depredation","explain":"n. 劫掠，蹂躏"},{"word":"depress","explain":"vt.使沮丧；按下"},{"word":"depressed","explain":"adj. 郁郁不乐；情绪低落的；消沉的；萧条的"},{"word":"depression","explain":"n.沮丧；不景气，萧条"},{"word":"deprivation","explain":"n. 剥夺，丧失"},{"word":"deprive","explain":"vt.夺去；使(人)失去"},{"word":"depth","explain":"n.深度；深厚；深处"},{"word":"deputation","explain":"n. 代理任命,代表,代表团"},{"word":"depute","explain":"v. 派…为代表或代理"},{"word":"deputy","explain":"n.代理人 a.副的"},{"word":"derail","explain":"vt. 使出轨"},{"word":"derange","explain":"vt. 扰乱,使发狂"},{"word":"deranged","explain":"adj. 疯狂的"},{"word":"derangement","explain":"n. 精神错乱"},{"word":"deregulation","explain":"n.撤消管制规定"},{"word":"derelict","explain":"adj. 荒废的，被弃置的"},{"word":"dereliction","explain":"n. 无主,抛弃物"},{"word":"deride","explain":"v. 嘲弄，愚弄"},{"word":"derision","explain":"n. 嘲笑"},{"word":"derisive","explain":"adj. 嘲弄的"},{"word":"derivation","explain":"n.引出；起源；衍生"},{"word":"derivative","explain":"adj. 派生的，无创意的"},{"word":"derive","explain":"vt. 取得；得到；来自；由来；(from)导出；起源于；追寻…的起源，派生，引出；vi. 起源；由来，衍生"},{"word":"derma","explain":"n. 真皮,皮肤"},{"word":"dermatologist","explain":"n. 皮肤学者,皮肤科医生"},{"word":"dermatology","explain":"n. 皮肤(病)学"},{"word":"derogate","explain":"v. 贬低，诽谤"},{"word":"derogation","explain":"n. 毁损,减损,堕落"},{"word":"derogatory","explain":"adj. 不敬的，诽谤的"},{"word":"derrick","explain":"n. 铁架塔"},{"word":"dervish","explain":"n. 回教的托钵僧"},{"word":"desalinize","explain":"v. 除去盐份"},{"word":"descant","explain":"n. 合唱，详述"},{"word":"descend","explain":"vi.下来，下降；下倾"},{"word":"descendant","explain":"n.子孙，后裔；弟子"},{"word":"descent","explain":"n.下降；出身；斜坡"},{"word":"describe","explain":"vt.形容；描写，描绘"},{"word":"described","explain":"a. 被看到的，被发现的"},{"word":"description","explain":"n.描写，形容；种类"},{"word":"descriptive","explain":"a. 描述的,叙述的"},{"word":"descry","explain":"v. 远远看到，望见"},{"word":"desecrate","explain":"v. 玷辱，亵渎"},{"word":"desecration","explain":"n. 亵渎神圣,污辱"},{"word":"desert","explain":"vt.遗弃；擅离(职守)"},{"word":"deserted","explain":"adj. 被人遗弃的；废弃的，荒无的；无人的；无人居住的"},{"word":"deserter","explain":"n. 背弃者,脱党者,逃亡者"},{"word":"desertion","explain":"n. 丢掉,遗弃,逃亡"},{"word":"deserve","explain":"vt.应受，值得"},{"word":"deservedly","explain":"ad. 应得报酬地,当然地"},{"word":"desiccant","explain":"n. 干燥剂"},{"word":"desiccate","explain":"v. （使）完全干涸，脱水。"},{"word":"desideratum","explain":"n. 必需品，要求"},{"word":"design","explain":"vt.设计 n.设计；图样"},{"word":"designate","explain":"vt.指出，指示；指定"},{"word":"designated","explain":"a. 指定的，特指的"},{"word":"designation","explain":"n. 指定，名称，称呼"},{"word":"designedly","explain":"ad. 故意地,有计划地,特意地"},{"word":"designer","explain":"n. 设计家 "},{"word":"designing","explain":"n. 设计; 构思,图案"},{"word":"desirability","explain":"n. 称心如意的人(东西)"},{"word":"desirable","explain":"a.值得相望的；可取的"},{"word":"desire","explain":"vt.相望；要求 n.愿望"},{"word":"desirous","explain":"adj. 渴望的"},{"word":"desist","explain":"v. 停止，中止"},{"word":"desk","explain":"n.书桌，办公桌"},{"word":"desks","explain":"书桌"},{"word":"desktop","explain":"a. 台式的"},{"word":"desolate","explain":" adj.荒凉的，荒芜的；孤独的；凄凉的；不幸的，被遗弃的；孤寂的vt. 使悲惨"},{"word":"desolation","explain":"n. 荒芜,荒废,荒凉"},{"word":"despair","explain":"n.绝望；失望"},{"word":"despairing","explain":"a. 感到绝望的"},{"word":"despatch","explain":"vt.vi.n. 派遣"},{"word":"desperado","explain":"n. 亡命之徒"},{"word":"desperate","explain":"adj. 什么也不顾的；拼死的；绝望的；不顾一切的；危急的；令人绝望的"},{"word":"desperately","explain":" adv.绝望地，孤注一掷地；拼命地；令人绝望地"},{"word":"desperation","explain":"n. 绝望"},{"word":"despicable","explain":"adj. 可鄙的，卑劣的"},{"word":"despise","explain":"vt.鄙视，蔑视"},{"word":"despite","explain":"prep. 尽管；不管，不顾；任凭；n. 轻蔑"},{"word":"despiteful","explain":"a. 故意为难的,有恶意的"},{"word":"despoil","explain":"v. 夺取，抢夺"},{"word":"despond","explain":"vi. 沮丧,失去勇气"},{"word":"despondence","explain":"n. 失去勇气,失望"},{"word":"despondency","explain":"n. 失去勇气,失望"},{"word":"despondent","explain":"adj. 失望的，意气消沉的"},{"word":"desposal","explain":"n. 布置，安排；处理，处置"},{"word":"despot","explain":"n. 专制君主,暴君"},{"word":"despotic","explain":"adj. 专横的、暴虐的"},{"word":"despotism","explain":"n. 专制，暴政"},{"word":"dessert","explain":"n.甜点心"},{"word":"destination","explain":"n.目的地，终点；目标"},{"word":"destine","explain":"vt. 命定，注定；指定，(常用被动态)(to)预定，v. 注定，预定"},{"word":"destiny","explain":"n.命运，天数"},{"word":"destitute","explain":"adj. 贫乏的，穷困的"},{"word":"destitution","explain":"n. 穷困,贫穷,缺乏"},{"word":"destroy","explain":"vt.破坏；消灭；打破"},{"word":"destroyer","explain":"n. 破坏者,驱除者,作破坏的事物,"},{"word":"destruct","explain":"vi. 破坏"},{"word":"destruction","explain":"n.破坏，毁灭，消灭"},{"word":"destructive","explain":"a.破坏(性)的，危害的"},{"word":"desturbance","explain":"n. 骚动，动乱；打扰，干扰"},{"word":"desuetude","explain":"n. 废止，不用"},{"word":"desultory","explain":"adj. 不连贯的，散漫的"},{"word":"detach","explain":"vt.分开；派遣(军队)"},{"word":"detachable","explain":"a. 可分离的"},{"word":"detached","explain":"adj. 分开的，超然公平的"},{"word":"detachment","explain":"n. 分离、超然、公平"},{"word":"detail","explain":"n. 细节；枝节；零件；琐碎，小事；详细情况；元件，详情；详述；琐事vt. 详述，细说；详细说明"},{"word":"detailed","explain":" adj. 详细的，详尽的；说细的"},{"word":"details","explain":"细节"},{"word":"detain","explain":"vt. 耽搁；扣押，拘留；使延迟，留住；阻止"},{"word":"detect","explain":"vt.察觉，发觉；侦察"},{"word":"detectable","explain":"adj. 可觉察的"},{"word":"detection","explain":"n.察觉，发觉；侦察"},{"word":"detective","explain":"n.侦探，密探"},{"word":"detector","explain":"n.发觉者，探测器"},{"word":"detemination","explain":"n. 决定"},{"word":"detention","explain":"n. 挽留,延迟,拘留"},{"word":"deter","explain":"v. 威慑，吓住"},{"word":"detergent","explain":"adj. 净化的，n. 清洁剂"},{"word":"deteriorate","explain":"v. （使）变坏，恶化"},{"word":"deterioration","explain":"n. 恶化,降低,退化"},{"word":"determinant","explain":"n. 判定"},{"word":"determinate","explain":"a. 确定的,一定的,决定的"},{"word":"determination","explain":"n.决心；决定；确定"},{"word":"determine","explain":"vt.决定；查明；决心"},{"word":"determined","explain":"adj. 坚决的，有决心的；确定的；毅然的"},{"word":"deterrent","explain":"n. 制止物；adj. 威慑的，制止的"},{"word":"detest","explain":"v. 深恶，憎恶"},{"word":"detestable","explain":"a. 嫌恶的,可憎的,可厌恶的"},{"word":"detestation","explain":"n. 憎恶,嫌恶,厌恶的人"},{"word":"dethrone","explain":"vt. 废黜,废位赶出"},{"word":"dethronement","explain":" n. 废立,废位; 权威地位的推翻"},{"word":"detonate","explain":"v. (使)爆炸，引爆"},{"word":"detonation","explain":"n. 爆炸（声）"},{"word":"detour","explain":"n. 弯路，绕行之路"},{"word":"detoxicate","explain":"vt. 除毒，解毒"},{"word":"detoxify","explain":"v. 除去…的毒物"},{"word":"detract","explain":"vt.vi. 减去,贬低"},{"word":"detraction","explain":"n.恶言,诽谤,贬低,降低"},{"word":"detractor","explain":"v. 贬低者"},{"word":"detriment","explain":"n. 损害，伤害"},{"word":"detrimental","explain":"adj. 损害的，造成伤害的"},{"word":"detritus","explain":"n. 岩屑，碎石"},{"word":"detroit","explain":"n. 底特律(美国城市)"},{"word":"deuce","explain":"n. 两点,平手,平分"},{"word":"devaluation","explain":"n. 贬值"},{"word":"devalue","explain":"v. 贬值"},{"word":"devastate","explain":"v. 摧毁，破坏"},{"word":"devastating","explain":"adj. 破坏性的"},{"word":"devastation","explain":"n. 毁坏"},{"word":"develop","explain":"vt.使(颜色等)显现"},{"word":"developer","explain":"n. 开发者，显影剂"},{"word":"developing","explain":"adj. 发展中的"},{"word":"development","explain":"n. 发展；开发；生长；进展，显影；研制"},{"word":"deviant","explain":"adj. 越出常规的，反常的"},{"word":"deviate","explain":"vt.(使)背离，偏离"},{"word":"deviation","explain":"n.背离，偏离；偏差数"},{"word":"device","explain":"n.器械，装置；设计"},{"word":"devil","explain":"n.魔鬼，恶魔"},{"word":"devilish","explain":"adj. 如恶魔的"},{"word":"devious","explain":"adj. 不正直的，弯曲的"},{"word":"devise","explain":"vt.设计，发明"},{"word":"devoid","explain":"adj. 缺乏的；无…的，缺…的；空的，缺少的"},{"word":"devoir","explain":"n. 本分,义务,敬意"},{"word":"devolution","explain":"n. 责任转移；权利下放；退化"},{"word":"devolve","explain":"vt. 转移,传下,委托"},{"word":"devote","explain":"vt.将…奉献，致力于"},{"word":"devoted","explain":"adj. 忠实的；热心的；献身于…的"},{"word":"devotee","explain":"n. 热爱家,献身者,皈依者"},{"word":"devotion","explain":"n.献身；忠诚；专心"},{"word":"devotional","explain":"a. 信仰的,虔诚的,祷告的"},{"word":"devour","explain":"vt.吞食；吞灭，毁灭"},{"word":"devout","explain":"a. 虔诚的,虔敬的,衷心的"},{"word":"dew","explain":"n.露，露水"},{"word":"dewberry","explain":"n. 悬钩子之类,其果实"},{"word":"dewdrop","explain":"n.露珠,露滴"},{"word":"dewy","explain":"a. 露湿的,带露水的,如露的"},{"word":"dexterity","explain":"n. 纯熟、灵巧"},{"word":"dexterous","explain":"adj. 灵巧的，熟练的"}]} |
["a22dee78ec323b81ffedc3d8c144d88cafd23f26"] |
{"poster":"Commander Ronin","date":"2019-07-24T10:24:38.459+0000","title":"Suche jmd der mir Riven beibringt","subforum":"Level Up: Guides & Tipps","up_votes":1,"down_votes":0,"body":"Ich möchte mit Riven besser werden und dazu brauch ich etwas unterstützung.Befinde mich gerade in silber und wollte meinen championpool um 1 ausbauen und habe mich für riven entschieden.\r\nMomentan sind meine mains Darius,Nasus und renekton vor allem\r\n\r\nMein IGN bei Interesse anschreiben : Commander Ronin","replies":[{"poster":"PHUB Silphi","date":"2019-07-24T14:12:37.998+0000","up_votes":1,"down_votes":0,"body":"wenn du aus fun spielst tu das \nwenn du besser werden willst fass riven nicht an","replies":[{"poster":"Commander Ronin","date":"2019-07-25T10:05:10.612+0000","up_votes":1,"down_votes":0,"body":"der echte silphi ?","replies":[{"poster":"PHUB Silphi","date":"2019-07-25T14:06:15.943+0000","up_votes":1,"down_votes":0,"body":"bin fanboi pinguin!","replies":[]}]}]},{"poster":"00 M4ddin 00","date":"2019-07-24T10:57:32.727+0000","up_votes":1,"down_votes":0,"body":"Lass es lieber :) \n\nAusser du möchtest auf der Straß angespuckt werden :D \n\n\nFrag mal Necromant wie das Leben eines Riven Spieler ist.... Wenn man es den leben nennen kann","replies":[{"poster":"Necromant","date":"2019-07-24T11:13:36.711+0000","up_votes":1,"down_votes":0,"body":"Wenn man n Fick auf die Meinung anderer gibt, macht der Champ ganz gut Laune tbh :)","replies":[{"poster":"Commander Ronin","date":"2019-07-24T11:56:26.957+0000","up_votes":1,"down_votes":0,"body":"hab dich mal geaddet","replies":[{"poster":"Necromant","date":"2019-07-24T12:13:55.309+0000","up_votes":1,"down_votes":0,"body":"Geht klar, schreib mich einfach mal dann an wenn ich Online bin ^^ sonst vergesse ich sowas gern mal","replies":[{"poster":"Commander Ronin","date":"2019-07-24T12:53:01.104+0000","up_votes":1,"down_votes":0,"body":"mach ich :)","replies":[]}]}]}]},{"poster":"Commander Ronin","date":"2019-07-24T11:56:50.540+0000","up_votes":1,"down_votes":0,"body":"was ist so schlimm daran riven zu spielen ?","replies":[{"poster":"Necromant","date":"2019-07-24T12:29:07.955+0000","up_votes":1,"down_votes":0,"body":"Manche Leute mögen sie einfach nicht ^^ es gibt halt für jeden Champion Leute, die wer nicht mag ^^","replies":[]},{"poster":"łΛKUMΛ Wulfł","date":"2019-07-24T12:28:22.498+0000","up_votes":1,"down_votes":0,"body":"Prinzipiell wenig.\nwenn man es aus einem \"improvement\" Standpunkt sieht, dann hat jemand der in Gold/Silber dümpelt nicht die fähigkeiten, um das maximum aus Riven zu holen.\nDa sollte man sich eher auf einfache Champions stürzen und Basics verbessern^^","replies":[]}]}]},{"poster":"Necromant","date":"2019-07-24T11:27:25.023+0000","up_votes":1,"down_votes":0,"body":":)","replies":[]}]} |
{"category": "Criminal Appeal", "status": "Remittitur Issued/Case Closed", "case_url": "http://caseinfo.nvsupremecourt.us/public/caseView.do?csIID=5562", "caption": "RIVERA (MICHAEL) VS. WARDEN", "type": "Fast Track", "case_no": "39673", "subtype": "Post-Conviction", "parties": [{"Represented By": "Matthew J. Stermitz", "Role": "Appellant", "Party Name": "Michael T. Rivera"}, {"Represented By": "Frankie Sue Del Papa (Attorney General/Carson City)", "Role": "Respondent", "Party Name": "Warden, Lovelock Correctional Center, Craig Farwell"}], "docket": [{"Date": "05/22/2002", "Type/Subtype": "Filing Fee - Filing Fee Waived", "Description": "Filing Fee Waived."}, {"Type/Subtype": "Notice of Appeal Documents - Notice of Appeal/Proper Person", "Description": "Filed Certified Copy of Notice of Appeal/Proper Person. Appeal docketed in the Supreme Court this day.", "Document Number": "02-09045", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=69530&sireDocumentNumber=02-09045", "Date": "05/22/2002", "Pending?": "NA"}, {"Type/Subtype": "Notice of Appeal Documents - Notice of Appeal/Fast Track", "Description": "Filed Certified Copy of Notice of Appeal/Fast Track. (Fast Track Notice mailed to all counsel.)", "Document Number": "02-10337", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=70619&sireDocumentNumber=02-10337", "Date": "06/13/2002", "Pending?": "NA"}, {"Type/Subtype": "Order/Procedural - Order Fast Track", "Description": "Filed Order/Fast Track. This appeal is governed by NRAP 3C. Mr. Stermitz is ordered to comply with NRAP 3C. Transcript request form due: 10 days. Fast track statement (and appendix) due: 40 days. Thereafter, this appeal shall proceed in accordance with the provisions of NRAP 3C.", "Document Number": "02-10388", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=295136&sireDocumentNumber=02-10388", "Date": "06/14/2002", "Pending?": "NA"}, {"Type/Subtype": "Fast Track Brief - Fast Track Statement", "Description": "Filed Fast Track Statement.", "Document Number": "02-10701", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=178611&sireDocumentNumber=02-10701", "Date": "06/20/2002", "Pending?": "NA"}, {"Type/Subtype": "Appendix - Appendix to Fast Track Statement", "Description": "Filed Appendix to Fast Track Statement.", "Document Number": "02-10702", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=178612&sireDocumentNumber=02-10702", "Date": "06/20/2002", "Pending?": "NA"}, {"Type/Subtype": "Transcript Request - Certificate of No Transcript Request", "Description": "Filed Certificate of No Transcript Request.", "Document Number": "02-10704", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=178614&sireDocumentNumber=02-10704", "Date": "06/20/2002", "Pending?": "NA"}, {"Type/Subtype": "Fast Track Brief - Fast Track Response", "Description": "Filed Fast Track Response.", "Document Number": "02-11807", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=197365&sireDocumentNumber=02-11807", "Date": "07/10/2002", "Pending?": "NA"}, {"Type/Subtype": "Order/Dispositional - Order of Affirmance", "Description": "Filed Order of Affirmance. \"ORDER the judgment of the district court AFFIRMED.\" SNP03-MS/ML/NB.", "Document Number": "02-17869", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=76480&sireDocumentNumber=02-17869", "Date": "10/15/2002", "Pending?": "NA"}, {"Type/Subtype": "Remittitur - Remittitur", "Description": "Issued Remittitur.", "Document Number": "02-17969", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=77415&sireDocumentNumber=02-17969", "Date": "11/12/2002", "Pending?": "NA"}, {"Date": "11/12/2002", "Type/Subtype": "Case Status Update - Remittitur Issued/Case Closed", "Description": "Remittitur Issued/Case Closed."}, {"Type/Subtype": "Remittitur - Remittitur", "Description": "Filed Remittitur. Received by County Clerk on November 14, 2002.", "Document Number": "02-17969", "Document URL": "/document/view.do?csNameID=5562&csIID=5562&deLinkID=79493&sireDocumentNumber=02-17969", "Date": "12/23/2002", "Pending?": "NA"}], "filed.date": "05/22/2002", "metadata": {"To SP/Judge:": "NA", "Lower Court Case(s):": "Elko Co. - Fourth Judicial District - CVHC987051", "Submission Date:": "NA", "Panel Assigned:": "Panel", "Case Status:": "Remittitur Issued/Case Closed", "Replacement:": "NA", "Oral Argument Location:": "NA", "Classification:": "Criminal Appeal - Fast Track - Post-Conviction", "Oral Argument:": "NA", "Disqualifications:": "NA", "SP Status:": "NA", "Short Caption:": "RIVERA (MICHAEL) VS. WARDEN", "How Submitted:": "NA"}} |
{"title":"�mõri�ðn Dðydrõðms - Sðrðh Shõvþn","uid":8014211,"size":1474228607,"categoryP":"porn","categoryS":"hd___movies","magnet":"?xt=urn:btih:a5ed917155c2483c82eb9dd284dc02ad1f68c19b&dn=%D0%90m%D0%B5ri%D1%81%D0%B0n+D%D0%B0ydr%D0%B5%D0%B0ms+-+S%D0%B0r%D0%B0h+Sh%D0%B5v%D0%BEn&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":0,"leechers":0,"uploader":"Bakelitko","files":1,"time":1358072365,"description":"Screens:\n <a href="\nhttp://www.pixdir.net/show/20008_74022dcf28468b3738a55f78d14dfe340.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20008_74022dcf28468b3738a55f78d14dfe340.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20009_74022dcf28468b3738a55f78d14dfe341.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20009_74022dcf28468b3738a55f78d14dfe341.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20010_74022dcf28468b3738a55f78d14dfe342.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20010_74022dcf28468b3738a55f78d14dfe342.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20011_74022dcf28468b3738a55f78d14dfe343.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20011_74022dcf28468b3738a55f78d14dfe343.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20012_74022dcf28468b3738a55f78d14dfe344.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20012_74022dcf28468b3738a55f78d14dfe344.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20013_74022dcf28468b3738a55f78d14dfe345.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20013_74022dcf28468b3738a55f78d14dfe345.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20014_74022dcf28468b3738a55f78d14dfe346.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20014_74022dcf28468b3738a55f78d14dfe346.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20015_74022dcf28468b3738a55f78d14dfe347.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20015_74022dcf28468b3738a55f78d14dfe347.jpg</a>\n <a href="\nhttp://www.pixdir.net/show/20016_74022dcf28468b3738a55f78d14dfe348.jpg" rel="nofollow" target="_NEW">\nhttp://www.pixdir.net/show/20016_74022dcf28468b3738a55f78d14dfe348.jpg</a>\n\n","torrent":{"xt":"urn:btih:a5ed917155c2483c82eb9dd284dc02ad1f68c19b","amp;dn":"%D0%90m%D0%B5ri%D1%81%D0%B0n+D%D0%B0ydr%D0%B5%D0%B0ms+-+S%D0%B0r%D0%B0h+Sh%D0%B5v%D0%BEn","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"a5ed917155c2483c82eb9dd284dc02ad1f68c19b","infoHashBuffer":{"type":"Buffer","data":[165,237,145,113,85,194,72,60,130,235,157,210,132,220,2,173,31,104,193,155]},"announce":[],"urlList":[]}} |
{"latitude":43.0774,"longitude":-70.7563,"zipcode":"03802","msa":"6450","dma":"506","state":"NH"} |
[{"lineNumber": 59, "col_offset": 0, "nodeName": "_is_new_style_class", "type": "Callable[[Any], Any]"}, {"lineNumber": 64, "col_offset": 0, "nodeName": "_compile_and_register_class", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 239, "col_offset": 0, "nodeName": "script_method", "type": "Callable[[Any], Any]"}, {"lineNumber": 747, "col_offset": 0, "nodeName": "call_prepare_scriptable_func_impl", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 781, "col_offset": 0, "nodeName": "call_prepare_scriptable_func", "type": "Callable[[Any], Any]"}, {"lineNumber": 785, "col_offset": 0, "nodeName": "script", "type": "Callable[..., Any]"}, {"lineNumber": 1002, "col_offset": 0, "nodeName": "_check_overload_defaults", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 1013, "col_offset": 0, "nodeName": "_compile_function_with_overload", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 1036, "col_offset": 0, "nodeName": "_get_overloads", "type": "Callable[[Any], Any]"}, {"lineNumber": 1059, "col_offset": 0, "nodeName": "_check_directly_compile_overloaded", "type": "Callable[[Any], Any]"}, {"lineNumber": 1069, "col_offset": 0, "nodeName": "interface", "type": "Callable[[Any], Any]"}, {"lineNumber": 1100, "col_offset": 0, "nodeName": "_recursive_compile_class", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 1111, "col_offset": 0, "nodeName": "_unwrap_optional", "type": "Callable[[Any], Any]"}, {"lineNumber": 39, "col_offset": 0, "nodeName": "graph_for", "type": "Callable[..., Any]"}, {"lineNumber": 39, "col_offset": 34, "nodeName": "_graph_for", "type": "Callable[..., Any]"}, {"lineNumber": 40, "col_offset": 0, "nodeName": "graph_for", "type": "Callable[..., Any]"}, {"lineNumber": 40, "col_offset": 36, "nodeName": "_graph_for", "type": "Callable[..., Any]"}, {"lineNumber": 41, "col_offset": 0, "nodeName": "ScriptFunction", "type": "Any"}, {"lineNumber": 41, "col_offset": 17, "nodeName": "ScriptFunction", "type": "Any"}, {"lineNumber": 49, "col_offset": 3, "nodeName": "_enabled", "type": "torch.jit._state.EnabledProxy"}, {"lineNumber": 53, "col_offset": 4, "nodeName": "Attribute", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 86, "col_offset": 25, "nodeName": "object", "type": "Type[object]"}, {"lineNumber": 122, "col_offset": 24, "nodeName": "OrderedDictWrapper", "type": "Type[OrderedDictWrapper]"}, {"lineNumber": 171, "col_offset": 17, "nodeName": "type", "type": "Type[Type[Any]]"}, {"lineNumber": 230, "col_offset": 21, "nodeName": "object", "type": "Type[object]"}, {"lineNumber": 235, "col_offset": 20, "nodeName": "Warning", "type": "Type[Warning]"}, {"lineNumber": 267, "col_offset": 3, "nodeName": "_enabled", "type": "torch.jit._state.EnabledProxy"}, {"lineNumber": 676, "col_offset": 4, "nodeName": "_get_methods", "type": "Callable[[Any], Any]"}, {"lineNumber": 721, "col_offset": 4, "nodeName": "_make_fail", "type": "Callable[[Any], Any]"}, {"lineNumber": 1108, "col_offset": 0, "nodeName": "CompilationUnit", "type": "Any"}, {"lineNumber": 1108, "col_offset": 18, "nodeName": "CompilationUnit", "type": "Any"}, {"lineNumber": 39, "col_offset": 0, "nodeName": "ScriptMethod", "type": "Any"}, {"lineNumber": 40, "col_offset": 0, "nodeName": "ScriptFunction", "type": "Any"}, {"lineNumber": 41, "col_offset": 17, "nodeName": "_C", "type": "Any"}, {"lineNumber": 46, "col_offset": 0, "nodeName": "set_module", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 46, "col_offset": 11, "nodeName": "ScriptFunction", "type": "Any"}, {"lineNumber": 50, "col_offset": 4, "nodeName": "Attribute", "type": "Type[`namedtuple-Attribute-value-type`]"}, {"lineNumber": 65, "col_offset": 4, "nodeName": "ast", "type": "Any"}, {"lineNumber": 66, "col_offset": 4, "nodeName": "defaults", "type": "Any"}, {"lineNumber": 254, "col_offset": 4, "nodeName": "_rcb", "type": "Any"}, {"lineNumber": 339, "col_offset": 32, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 739, "col_offset": 23, "nodeName": "Module", "type": "Any"}, {"lineNumber": 743, "col_offset": 32, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 751, "col_offset": 4, "nodeName": "obj_id", "type": "int"}, {"lineNumber": 758, "col_offset": 4, "nodeName": "obj", "type": "Any"}, {"lineNumber": 761, "col_offset": 19, "nodeName": "obj", "type": "Any"}, {"lineNumber": 763, "col_offset": 4, "nodeName": "new_obj_dict", "type": "Dict[Any, Any]"}, {"lineNumber": 765, "col_offset": 8, "nodeName": "name", "type": "Any"}, {"lineNumber": 765, "col_offset": 16, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 779, "col_offset": 11, "nodeName": "obj", "type": "Any"}, {"lineNumber": 782, "col_offset": 4, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 946, "col_offset": 4, "nodeName": "qualified_name", "type": "Any"}, {"lineNumber": 1038, "col_offset": 4, "nodeName": "existing_compiled_fns", "type": "Any"}, {"lineNumber": 1039, "col_offset": 4, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1040, "col_offset": 4, "nodeName": "uncompiled_overloads", "type": "Optional[List[Any]]"}, {"lineNumber": 1044, "col_offset": 4, "nodeName": "compiled_fns", "type": "List[nothing]"}, {"lineNumber": 1045, "col_offset": 8, "nodeName": "overload_fn", "type": "Any"}, {"lineNumber": 1045, "col_offset": 23, "nodeName": "uncompiled_overloads", "type": "Optional[List[Any]]"}, {"lineNumber": 1050, "col_offset": 7, "nodeName": "existing_compiled_fns", "type": "Any"}, {"lineNumber": 1056, "col_offset": 11, "nodeName": "compiled_fns", "type": "Any"}, {"lineNumber": 1060, "col_offset": 4, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1079, "col_offset": 4, "nodeName": "is_module_interface", "type": "bool"}, {"lineNumber": 1087, "col_offset": 4, "nodeName": "qualified_name", "type": "Any"}, {"lineNumber": 1088, "col_offset": 4, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 1092, "col_offset": 4, "nodeName": "ast", "type": "Any"}, {"lineNumber": 1096, "col_offset": 4, "nodeName": "__torch_script_interface__", "type": "bool"}, {"lineNumber": 1097, "col_offset": 11, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1101, "col_offset": 4, "nodeName": "_qual_name", "type": "Any"}, {"lineNumber": 1104, "col_offset": 4, "nodeName": "error_stack", "type": "Any"}, {"lineNumber": 1105, "col_offset": 4, "nodeName": "rcb", "type": "Callable[[Any], Any]"}, {"lineNumber": 1108, "col_offset": 18, "nodeName": "_C", "type": "Any"}, {"lineNumber": 1109, "col_offset": 0, "nodeName": "set_module", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1109, "col_offset": 11, "nodeName": "CompilationUnit", "type": "Any"}, {"lineNumber": 1113, "col_offset": 11, "nodeName": "x", "type": "Any"}, {"lineNumber": 1116, "col_offset": 0, "nodeName": "_register_builtin", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1116, "col_offset": 18, "nodeName": "_unwrap_optional", "type": "Callable[[Any], Any]"}, {"lineNumber": 1117, "col_offset": 0, "nodeName": "_register_builtin", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1117, "col_offset": 18, "nodeName": "is_scripting", "type": "Callable[[], bool]"}, {"lineNumber": 1118, "col_offset": 0, "nodeName": "_register_builtin", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1118, "col_offset": 18, "nodeName": "has_torch_function", "type": "Any"}, {"lineNumber": 1119, "col_offset": 0, "nodeName": "_register_builtin", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1119, "col_offset": 18, "nodeName": "has_torch_function_unary", "type": "Any"}, {"lineNumber": 1120, "col_offset": 0, "nodeName": "_register_builtin", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1120, "col_offset": 18, "nodeName": "has_torch_function_variadic", "type": "Any"}, {"lineNumber": 39, "col_offset": 0, "nodeName": "_C", "type": "Any"}, {"lineNumber": 40, "col_offset": 0, "nodeName": "_C", "type": "Any"}, {"lineNumber": 41, "col_offset": 17, "nodeName": "torch", "type": "module"}, {"lineNumber": 50, "col_offset": 16, "nodeName": "namedtuple", "type": "Callable[..., Type[Any]]"}, {"lineNumber": 54, "col_offset": 15, "nodeName": "value", "type": "Any"}, {"lineNumber": 60, "col_offset": 7, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 60, "col_offset": 15, "nodeName": "cls", "type": "Any"}, {"lineNumber": 65, "col_offset": 10, "nodeName": "get_jit_class_def", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 65, "col_offset": 28, "nodeName": "obj", "type": "Any"}, {"lineNumber": 65, "col_offset": 33, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 66, "col_offset": 15, "nodeName": "get_default_args_for_class", "type": "Callable[[Any], Any]"}, {"lineNumber": 66, "col_offset": 61, "nodeName": "obj", "type": "Any"}, {"lineNumber": 67, "col_offset": 4, "nodeName": "_jit_script_class_compile", "type": "Any"}, {"lineNumber": 67, "col_offset": 39, "nodeName": "qualified_name", "type": "Any"}, {"lineNumber": 67, "col_offset": 55, "nodeName": "ast", "type": "Any"}, {"lineNumber": 67, "col_offset": 60, "nodeName": "defaults", "type": "Any"}, {"lineNumber": 67, "col_offset": 70, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 68, "col_offset": 4, "nodeName": "_add_script_class", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 68, "col_offset": 39, "nodeName": "obj", "type": "Any"}, {"lineNumber": 68, "col_offset": 44, "nodeName": "qualified_name", "type": "Any"}, {"lineNumber": 88, "col_offset": 8, "nodeName": "_c", "type": "Any"}, {"lineNumber": 88, "col_offset": 18, "nodeName": "_c", "type": "Any"}, {"lineNumber": 131, "col_offset": 8, "nodeName": "_python_modules", "type": "Any"}, {"lineNumber": 131, "col_offset": 31, "nodeName": "python_dict", "type": "Any"}, {"lineNumber": 134, "col_offset": 8, "nodeName": "r", "type": "Any"}, {"lineNumber": 135, "col_offset": 15, "nodeName": "r", "type": "Any"}, {"lineNumber": 174, "col_offset": 8, "nodeName": "_methods", "type": "Dict[Any, Any]"}, {"lineNumber": 175, "col_offset": 8, "nodeName": "_constants_set", "type": "Set[Any]"}, {"lineNumber": 176, "col_offset": 12, "nodeName": "base", "type": "Any"}, {"lineNumber": 194, "col_offset": 8, "nodeName": "original_init", "type": "Any"}, {"lineNumber": 226, "col_offset": 8, "nodeName": "__init__", "type": "Callable[..., Any]"}, {"lineNumber": 226, "col_offset": 23, "nodeName": "init_then_script", "type": "Callable[..., Any]"}, {"lineNumber": 240, "col_offset": 11, "nodeName": "_enabled", "type": "torch.jit._state.EnabledProxy"}, {"lineNumber": 241, "col_offset": 15, "nodeName": "fn", "type": "Any"}, {"lineNumber": 254, "col_offset": 11, "nodeName": "createResolutionCallbackFromFrame", "type": "Callable[..., Any]"}, {"lineNumber": 255, "col_offset": 10, "nodeName": "get_jit_def", "type": "Callable[..., Any]"}, {"lineNumber": 255, "col_offset": 22, "nodeName": "fn", "type": "Any"}, {"lineNumber": 255, "col_offset": 26, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 261, "col_offset": 8, "nodeName": "const_mapping", "type": "Any"}, {"lineNumber": 261, "col_offset": 29, "nodeName": "const_mapping", "type": "Any"}, {"lineNumber": 276, "col_offset": 23, "nodeName": "with_metaclass", "type": "Callable[..., Type[Any]]"}, {"lineNumber": 276, "col_offset": 38, "nodeName": "ScriptMeta", "type": "Type[ScriptMeta]"}, {"lineNumber": 276, "col_offset": 50, "nodeName": "Module", "type": "Any"}, {"lineNumber": 282, "col_offset": 8, "nodeName": "__jit_unused_properties__", "type": "List[str]"}, {"lineNumber": 287, "col_offset": 8, "nodeName": "forward", "type": "_CachedForward"}, {"lineNumber": 365, "col_offset": 8, "nodeName": "_disable_script_meta", "type": "bool"}, {"lineNumber": 666, "col_offset": 8, "nodeName": "name", "type": "Any"}, {"lineNumber": 666, "col_offset": 14, "nodeName": "item", "type": "Any"}, {"lineNumber": 666, "col_offset": 22, "nodeName": "items", "type": "Any"}, {"lineNumber": 725, "col_offset": 15, "nodeName": "fail", "type": "Callable[..., Any]"}, {"lineNumber": 727, "col_offset": 8, "nodeName": "name", "type": "str"}, {"lineNumber": 727, "col_offset": 14, "nodeName": "method", "type": "Any"}, {"lineNumber": 727, "col_offset": 24, "nodeName": "_get_methods", "type": "Callable[[Any], Any]"}, {"lineNumber": 727, "col_offset": 37, "nodeName": "Module", "type": "Any"}, {"lineNumber": 739, "col_offset": 23, "nodeName": "nn", "type": "module"}, {"lineNumber": 749, "col_offset": 15, "nodeName": "obj", "type": "Any"}, {"lineNumber": 751, "col_offset": 13, "nodeName": "id", "type": "Callable[[Any], int]"}, {"lineNumber": 751, "col_offset": 16, "nodeName": "obj", "type": "Any"}, {"lineNumber": 755, "col_offset": 7, "nodeName": "obj_id", "type": "int"}, {"lineNumber": 755, "col_offset": 17, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 758, "col_offset": 86, "nodeName": "obj", "type": "Any"}, {"lineNumber": 761, "col_offset": 4, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 765, "col_offset": 16, "nodeName": "obj", "type": "Any"}, {"lineNumber": 766, "col_offset": 8, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 776, "col_offset": 8, "nodeName": "k", "type": "Any"}, {"lineNumber": 776, "col_offset": 11, "nodeName": "v", "type": "Any"}, {"lineNumber": 776, "col_offset": 16, "nodeName": "items", "type": "Callable"}, {"lineNumber": 777, "col_offset": 29, "nodeName": "v", "type": "Any"}, {"lineNumber": 783, "col_offset": 11, "nodeName": "call_prepare_scriptable_func_impl", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 783, "col_offset": 45, "nodeName": "obj", "type": "Any"}, {"lineNumber": 783, "col_offset": 50, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 926, "col_offset": 11, "nodeName": "_enabled", "type": "torch.jit._state.EnabledProxy"}, {"lineNumber": 927, "col_offset": 15, "nodeName": "obj", "type": "Any"}, {"lineNumber": 929, "col_offset": 7, "nodeName": "optimize", "type": "Any"}, {"lineNumber": 935, "col_offset": 7, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 935, "col_offset": 18, "nodeName": "obj", "type": "Any"}, {"lineNumber": 935, "col_offset": 23, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 936, "col_offset": 15, "nodeName": "obj", "type": "Any"}, {"lineNumber": 937, "col_offset": 7, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 937, "col_offset": 18, "nodeName": "obj", "type": "Any"}, {"lineNumber": 937, "col_offset": 23, "nodeName": "ScriptFunction", "type": "Any"}, {"lineNumber": 938, "col_offset": 15, "nodeName": "obj", "type": "Any"}, {"lineNumber": 940, "col_offset": 7, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 940, "col_offset": 18, "nodeName": "obj", "type": "Any"}, {"lineNumber": 940, "col_offset": 23, "nodeName": "Module", "type": "Any"}, {"lineNumber": 941, "col_offset": 8, "nodeName": "obj", "type": "Any"}, {"lineNumber": 946, "col_offset": 21, "nodeName": "_qualified_name", "type": "Callable[[Any], Any]"}, {"lineNumber": 946, "col_offset": 37, "nodeName": "obj", "type": "Any"}, {"lineNumber": 947, "col_offset": 7, "nodeName": "isclass", "type": "Callable[[object], bool]"}, {"lineNumber": 947, "col_offset": 23, "nodeName": "obj", "type": "Any"}, {"lineNumber": 975, "col_offset": 15, "nodeName": "obj", "type": "Any"}, {"lineNumber": 983, "col_offset": 8, "nodeName": "maybe_already_compiled_fn", "type": "Any"}, {"lineNumber": 984, "col_offset": 11, "nodeName": "maybe_already_compiled_fn", "type": "Any"}, {"lineNumber": 1003, "col_offset": 8, "nodeName": "name", "type": "Any"}, {"lineNumber": 1003, "col_offset": 14, "nodeName": "overload_value", "type": "Any"}, {"lineNumber": 1003, "col_offset": 32, "nodeName": "items", "type": "Any"}, {"lineNumber": 1038, "col_offset": 28, "nodeName": "_try_get_jit_cached_overloads", "type": "Callable[[Any], Any]"}, {"lineNumber": 1038, "col_offset": 58, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1039, "col_offset": 16, "nodeName": "_qualified_name", "type": "Callable[[Any], Any]"}, {"lineNumber": 1039, "col_offset": 32, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1040, "col_offset": 27, "nodeName": "_get_fn_overloads", "type": "Callable[[Any], Optional[List[Any]]]"}, {"lineNumber": 1040, "col_offset": 59, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1041, "col_offset": 7, "nodeName": "uncompiled_overloads", "type": "Optional[List[Any]]"}, {"lineNumber": 1042, "col_offset": 15, "nodeName": "existing_compiled_fns", "type": "Any"}, {"lineNumber": 1051, "col_offset": 8, "nodeName": "compiled_fns", "type": "Any"}, {"lineNumber": 1054, "col_offset": 4, "nodeName": "_set_jit_overload_cache", "type": "Callable[[Any, Any], None]"}, {"lineNumber": 1054, "col_offset": 28, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1054, "col_offset": 33, "nodeName": "compiled_fns", "type": "Any"}, {"lineNumber": 1055, "col_offset": 4, "nodeName": "_clear_fn_overloads", "type": "Callable[[Any], None]"}, {"lineNumber": 1055, "col_offset": 38, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1060, "col_offset": 16, "nodeName": "_qualified_name", "type": "Callable[[Any], Any]"}, {"lineNumber": 1060, "col_offset": 32, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1087, "col_offset": 21, "nodeName": "_qualified_name", "type": "Callable[[Any], Any]"}, {"lineNumber": 1087, "col_offset": 37, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1088, "col_offset": 10, "nodeName": "createResolutionCallbackFromFrame", "type": "Callable[..., Any]"}, {"lineNumber": 1092, "col_offset": 10, "nodeName": "get_jit_class_def", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 1092, "col_offset": 28, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1092, "col_offset": 33, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 1093, "col_offset": 4, "nodeName": "_jit_script_interface_compile", "type": "Any"}, {"lineNumber": 1094, "col_offset": 8, "nodeName": "qualified_name", "type": "Any"}, {"lineNumber": 1094, "col_offset": 24, "nodeName": "ast", "type": "Any"}, {"lineNumber": 1094, "col_offset": 29, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 1094, "col_offset": 34, "nodeName": "is_module_interface", "type": "bool"}, {"lineNumber": 1096, "col_offset": 4, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1101, "col_offset": 17, "nodeName": "_qualified_name", "type": "Callable[[Any], Any]"}, {"lineNumber": 1101, "col_offset": 33, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1104, "col_offset": 18, "nodeName": "CallStack", "type": "Any"}, {"lineNumber": 1104, "col_offset": 37, "nodeName": "_qual_name", "type": "Any"}, {"lineNumber": 1104, "col_offset": 49, "nodeName": "loc", "type": "Any"}, {"lineNumber": 1105, "col_offset": 10, "nodeName": "createResolutionCallbackForClassMethods", "type": "Callable[[Any], Callable[[Any], Any]]"}, {"lineNumber": 1105, "col_offset": 64, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1106, "col_offset": 4, "nodeName": "_compile_and_register_class", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 1106, "col_offset": 32, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1106, "col_offset": 37, "nodeName": "rcb", "type": "Callable[[Any], Any]"}, {"lineNumber": 1106, "col_offset": 42, "nodeName": "_qual_name", "type": "Any"}, {"lineNumber": 1108, "col_offset": 18, "nodeName": "torch", "type": "module"}, {"lineNumber": 1112, "col_offset": 11, "nodeName": "x", "type": "Any"}, {"lineNumber": 1117, "col_offset": 18, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 39, "col_offset": 0, "nodeName": "torch", "type": "module"}, {"lineNumber": 40, "col_offset": 0, "nodeName": "torch", "type": "module"}, {"lineNumber": 50, "col_offset": 16, "nodeName": "collections", "type": "module"}, {"lineNumber": 65, "col_offset": 33, "nodeName": "obj", "type": "Any"}, {"lineNumber": 66, "col_offset": 15, "nodeName": "frontend", "type": "module"}, {"lineNumber": 67, "col_offset": 4, "nodeName": "_C", "type": "Any"}, {"lineNumber": 68, "col_offset": 4, "nodeName": "_state", "type": "module"}, {"lineNumber": 88, "col_offset": 8, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 91, "col_offset": 16, "nodeName": "k", "type": "Any"}, {"lineNumber": 94, "col_offset": 16, "nodeName": "v", "type": "Any"}, {"lineNumber": 97, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 100, "col_offset": 14, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 103, "col_offset": 15, "nodeName": "items", "type": "Any"}, {"lineNumber": 106, "col_offset": 11, "nodeName": "k", "type": "Any"}, {"lineNumber": 106, "col_offset": 20, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 111, "col_offset": 8, "nodeName": "setattr", "type": "Any"}, {"lineNumber": 111, "col_offset": 24, "nodeName": "k", "type": "Any"}, {"lineNumber": 111, "col_offset": 27, "nodeName": "v", "type": "Any"}, {"lineNumber": 114, "col_offset": 15, "nodeName": "contains", "type": "Any"}, {"lineNumber": 114, "col_offset": 32, "nodeName": "k", "type": "Any"}, {"lineNumber": 117, "col_offset": 11, "nodeName": "k", "type": "Any"}, {"lineNumber": 117, "col_offset": 20, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 119, "col_offset": 15, "nodeName": "getattr", "type": "Any"}, {"lineNumber": 119, "col_offset": 31, "nodeName": "k", "type": "Any"}, {"lineNumber": 124, "col_offset": 8, "nodeName": "__init__", "type": "Callable[[Any], Any]"}, {"lineNumber": 131, "col_offset": 8, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 134, "col_offset": 12, "nodeName": "items", "type": "Any"}, {"lineNumber": 138, "col_offset": 15, "nodeName": "k", "type": "Any"}, {"lineNumber": 138, "col_offset": 20, "nodeName": "_python_modules", "type": "Any"}, {"lineNumber": 150, "col_offset": 11, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 150, "col_offset": 22, "nodeName": "v", "type": "Any"}, {"lineNumber": 150, "col_offset": 25, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 152, "col_offset": 38, "nodeName": "v", "type": "Any"}, {"lineNumber": 160, "col_offset": 15, "nodeName": "_python_modules", "type": "Any"}, {"lineNumber": 174, "col_offset": 8, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 175, "col_offset": 8, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 175, "col_offset": 29, "nodeName": "set", "type": "Type[Set[Any]]"}, {"lineNumber": 176, "col_offset": 20, "nodeName": "reversed", "type": "Type[reversed[Any]]"}, {"lineNumber": 176, "col_offset": 29, "nodeName": "bases", "type": "Any"}, {"lineNumber": 179, "col_offset": 12, "nodeName": "base_constants", "type": "Any"}, {"lineNumber": 180, "col_offset": 12, "nodeName": "_constants_set", "type": "Set[Any]"}, {"lineNumber": 183, "col_offset": 12, "nodeName": "k", "type": "Any"}, {"lineNumber": 183, "col_offset": 15, "nodeName": "v", "type": "Any"}, {"lineNumber": 183, "col_offset": 20, "nodeName": "sorted", "type": "Callable"}, {"lineNumber": 188, "col_offset": 11, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 188, "col_offset": 19, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 194, "col_offset": 24, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 194, "col_offset": 32, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 198, "col_offset": 12, "nodeName": "num_methods", "type": "int"}, {"lineNumber": 200, "col_offset": 12, "nodeName": "added_methods_in_init", "type": "bool"}, {"lineNumber": 226, "col_offset": 8, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 227, "col_offset": 15, "nodeName": "__init__", "type": "Callable"}, {"lineNumber": 227, "col_offset": 47, "nodeName": "name", "type": "Any"}, {"lineNumber": 227, "col_offset": 53, "nodeName": "bases", "type": "Any"}, {"lineNumber": 227, "col_offset": 60, "nodeName": "attrs", "type": "Any"}, {"lineNumber": 232, "col_offset": 15, "nodeName": "__getattr__", "type": "Any"}, {"lineNumber": 254, "col_offset": 11, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 255, "col_offset": 26, "nodeName": "fn", "type": "Any"}, {"lineNumber": 261, "col_offset": 8, "nodeName": "self", "type": "ConstMap"}, {"lineNumber": 264, "col_offset": 15, "nodeName": "const_mapping", "type": "Any"}, {"lineNumber": 287, "col_offset": 18, "nodeName": "_CachedForward", "type": "Type[_CachedForward]"}, {"lineNumber": 332, "col_offset": 12, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 333, "col_offset": 12, "nodeName": "ast", "type": "Any"}, {"lineNumber": 369, "col_offset": 12, "nodeName": "_c", "type": "Any"}, {"lineNumber": 369, "col_offset": 22, "nodeName": "cpp_module", "type": "Any"}, {"lineNumber": 390, "col_offset": 12, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 396, "col_offset": 19, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 409, "col_offset": 12, "nodeName": "_initializing", "type": "bool"}, {"lineNumber": 426, "col_offset": 12, "nodeName": "modules", "type": "Dict[Any, Any]"}, {"lineNumber": 429, "col_offset": 12, "nodeName": "_modules", "type": "OrderedModuleDict"}, {"lineNumber": 432, "col_offset": 12, "nodeName": "_parameters", "type": "OrderedDictWrapper"}, {"lineNumber": 433, "col_offset": 12, "nodeName": "_buffers", "type": "OrderedDictWrapper"}, {"lineNumber": 449, "col_offset": 19, "nodeName": "graph", "type": "Any"}, {"lineNumber": 458, "col_offset": 19, "nodeName": "inlined_graph", "type": "Any"}, {"lineNumber": 467, "col_offset": 19, "nodeName": "code", "type": "Any"}, {"lineNumber": 481, "col_offset": 12, "nodeName": "r", "type": "Any"}, {"lineNumber": 481, "col_offset": 16, "nodeName": "code_with_constants", "type": "Any"}, {"lineNumber": 536, "col_offset": 12, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 545, "col_offset": 15, "nodeName": "_initializing", "type": "Any"}, {"lineNumber": 564, "col_offset": 15, "nodeName": "_initializing", "type": "Any"}, {"lineNumber": 610, "col_offset": 12, "nodeName": "self_method", "type": "Any"}, {"lineNumber": 632, "col_offset": 12, "nodeName": "self_method", "type": "Callable[[], Any]"}, {"lineNumber": 632, "col_offset": 26, "nodeName": "__dir__", "type": "Callable[[], Any]"}, {"lineNumber": 643, "col_offset": 12, "nodeName": "self_method", "type": "Callable[[], Any]"}, {"lineNumber": 643, "col_offset": 26, "nodeName": "__bool__", "type": "Callable[[], Any]"}, {"lineNumber": 666, "col_offset": 22, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 674, "col_offset": 8, "nodeName": "setattr", "type": "Callable[[Any, str, Any], None]"}, {"lineNumber": 674, "col_offset": 16, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 674, "col_offset": 30, "nodeName": "name", "type": "Any"}, {"lineNumber": 674, "col_offset": 36, "nodeName": "item", "type": "Any"}, {"lineNumber": 680, "col_offset": 15, "nodeName": "getmembers", "type": "Callable[..., List[Tuple[str, Any]]]"}, {"lineNumber": 681, "col_offset": 12, "nodeName": "cls", "type": "Any"}, {"lineNumber": 727, "col_offset": 37, "nodeName": "nn", "type": "module"}, {"lineNumber": 728, "col_offset": 11, "nodeName": "startswith", "type": "Callable[..., bool]"}, {"lineNumber": 739, "col_offset": 23, "nodeName": "torch", "type": "module"}, {"lineNumber": 748, "col_offset": 11, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 748, "col_offset": 22, "nodeName": "obj", "type": "Any"}, {"lineNumber": 748, "col_offset": 27, "nodeName": "Module", "type": "Any"}, {"lineNumber": 756, "col_offset": 15, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 758, "col_offset": 42, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 758, "col_offset": 50, "nodeName": "obj", "type": "Any"}, {"lineNumber": 758, "col_offset": 10, "nodeName": "__prepare_scriptable__", "type": "Any"}, {"lineNumber": 761, "col_offset": 9, "nodeName": "obj_id", "type": "int"}, {"lineNumber": 766, "col_offset": 21, "nodeName": "get", "type": "Any"}, {"lineNumber": 766, "col_offset": 38, "nodeName": "name", "type": "Any"}, {"lineNumber": 767, "col_offset": 11, "nodeName": "name", "type": "Any"}, {"lineNumber": 770, "col_offset": 33, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 776, "col_offset": 16, "nodeName": "new_obj_dict", "type": "Dict[Any, Any]"}, {"lineNumber": 777, "col_offset": 8, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 930, "col_offset": 8, "nodeName": "warn", "type": "Callable"}, {"lineNumber": 940, "col_offset": 23, "nodeName": "nn", "type": "module"}, {"lineNumber": 941, "col_offset": 14, "nodeName": "call_prepare_scriptable_func", "type": "Callable[[Any], Any]"}, {"lineNumber": 941, "col_offset": 43, "nodeName": "obj", "type": "Any"}, {"lineNumber": 942, "col_offset": 15, "nodeName": "create_script_module", "type": "Callable[..., Any]"}, {"lineNumber": 943, "col_offset": 12, "nodeName": "obj", "type": "Any"}, {"lineNumber": 943, "col_offset": 17, "nodeName": "infer_methods_to_compile", "type": "Callable[[Any], Any]"}, {"lineNumber": 947, "col_offset": 7, "nodeName": "inspect", "type": "module"}, {"lineNumber": 950, "col_offset": 11, "nodeName": "issubclass", "type": "Callable[[Type[Any], Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 950, "col_offset": 22, "nodeName": "obj", "type": "Any"}, {"lineNumber": 950, "col_offset": 27, "nodeName": "Module", "type": "Any"}, {"lineNumber": 959, "col_offset": 11, "nodeName": "issubclass", "type": "Callable[[Type[Any], Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 959, "col_offset": 22, "nodeName": "obj", "type": "Any"}, {"lineNumber": 959, "col_offset": 27, "nodeName": "Enum", "type": "Type[enum.Enum]"}, {"lineNumber": 960, "col_offset": 19, "nodeName": "obj", "type": "Any"}, {"lineNumber": 972, "col_offset": 11, "nodeName": "_rcb", "type": "Any"}, {"lineNumber": 973, "col_offset": 12, "nodeName": "_rcb", "type": "Any"}, {"lineNumber": 974, "col_offset": 8, "nodeName": "_compile_and_register_class", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 974, "col_offset": 36, "nodeName": "obj", "type": "Any"}, {"lineNumber": 974, "col_offset": 41, "nodeName": "_rcb", "type": "Any"}, {"lineNumber": 974, "col_offset": 47, "nodeName": "qualified_name", "type": "Any"}, {"lineNumber": 978, "col_offset": 11, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 978, "col_offset": 19, "nodeName": "obj", "type": "Any"}, {"lineNumber": 979, "col_offset": 12, "nodeName": "obj", "type": "Any"}, {"lineNumber": 979, "col_offset": 18, "nodeName": "__original_fn", "type": "Any"}, {"lineNumber": 980, "col_offset": 12, "nodeName": "_rcb", "type": "Any"}, {"lineNumber": 982, "col_offset": 8, "nodeName": "_check_directly_compile_overloaded", "type": "Callable[[Any], Any]"}, {"lineNumber": 982, "col_offset": 43, "nodeName": "obj", "type": "Any"}, {"lineNumber": 983, "col_offset": 36, "nodeName": "_try_get_jit_cached_function", "type": "Callable[[Any], Any]"}, {"lineNumber": 983, "col_offset": 65, "nodeName": "obj", "type": "Any"}, {"lineNumber": 985, "col_offset": 19, "nodeName": "maybe_already_compiled_fn", "type": "Any"}, {"lineNumber": 986, "col_offset": 14, "nodeName": "get_jit_def", "type": "Callable[..., Any]"}, {"lineNumber": 986, "col_offset": 26, "nodeName": "obj", "type": "Any"}, {"lineNumber": 986, "col_offset": 31, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 1003, "col_offset": 32, "nodeName": "overload_defaults", "type": "Any"}, {"lineNumber": 1040, "col_offset": 27, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 1046, "col_offset": 8, "nodeName": "append", "type": "Callable"}, {"lineNumber": 1051, "col_offset": 23, "nodeName": "existing_compiled_fns", "type": "Any"}, {"lineNumber": 1051, "col_offset": 47, "nodeName": "compiled_fns", "type": "List[nothing]"}, {"lineNumber": 1055, "col_offset": 4, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 1061, "col_offset": 7, "nodeName": "_get_fn_overloads", "type": "Callable[[Any], Optional[List[Any]]]"}, {"lineNumber": 1061, "col_offset": 39, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1061, "col_offset": 53, "nodeName": "_try_get_jit_cached_overloads", "type": "Callable[[Any], Any]"}, {"lineNumber": 1061, "col_offset": 83, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1062, "col_offset": 14, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 1070, "col_offset": 11, "nodeName": "isclass", "type": "Callable[[object], bool]"}, {"lineNumber": 1070, "col_offset": 27, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1071, "col_offset": 14, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 1072, "col_offset": 11, "nodeName": "_is_new_style_class", "type": "Callable[[Any], Any]"}, {"lineNumber": 1072, "col_offset": 31, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1073, "col_offset": 14, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 1079, "col_offset": 26, "nodeName": "issubclass", "type": "Callable[[Type[Any], Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 1079, "col_offset": 37, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1079, "col_offset": 42, "nodeName": "Module", "type": "Any"}, {"lineNumber": 1081, "col_offset": 11, "nodeName": "is_module_interface", "type": "bool"}, {"lineNumber": 1082, "col_offset": 14, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 1088, "col_offset": 10, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 1092, "col_offset": 33, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1093, "col_offset": 4, "nodeName": "_C", "type": "Any"}, {"lineNumber": 1104, "col_offset": 18, "nodeName": "_C", "type": "Any"}, {"lineNumber": 1105, "col_offset": 10, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 61, "col_offset": 41, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 61, "col_offset": 49, "nodeName": "cls", "type": "Any"}, {"lineNumber": 66, "col_offset": 15, "nodeName": "jit", "type": "module"}, {"lineNumber": 67, "col_offset": 4, "nodeName": "torch", "type": "module"}, {"lineNumber": 68, "col_offset": 4, "nodeName": "jit", "type": "module"}, {"lineNumber": 97, "col_offset": 19, "nodeName": "values", "type": "Callable[[], Any]"}, {"lineNumber": 103, "col_offset": 15, "nodeName": "_c", "type": "Any"}, {"lineNumber": 107, "col_offset": 18, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 111, "col_offset": 8, "nodeName": "_c", "type": "Any"}, {"lineNumber": 114, "col_offset": 15, "nodeName": "_c", "type": "Any"}, {"lineNumber": 118, "col_offset": 18, "nodeName": "KeyError", "type": "Type[KeyError]"}, {"lineNumber": 118, "col_offset": 27, "nodeName": "k", "type": "Any"}, {"lineNumber": 119, "col_offset": 15, "nodeName": "_c", "type": "Any"}, {"lineNumber": 124, "col_offset": 48, "nodeName": "ModuleDict", "type": "Any"}, {"lineNumber": 124, "col_offset": 68, "nodeName": "module", "type": "Any"}, {"lineNumber": 134, "col_offset": 12, "nodeName": "_python_modules", "type": "Any"}, {"lineNumber": 138, "col_offset": 20, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 151, "col_offset": 12, "nodeName": "setattr", "type": "Any"}, {"lineNumber": 151, "col_offset": 28, "nodeName": "k", "type": "Any"}, {"lineNumber": 151, "col_offset": 31, "nodeName": "v", "type": "Any"}, {"lineNumber": 152, "col_offset": 12, "nodeName": "_python_modules", "type": "Any"}, {"lineNumber": 154, "col_offset": 18, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 160, "col_offset": 15, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 160, "col_offset": 36, "nodeName": "k", "type": "Any"}, {"lineNumber": 175, "col_offset": 33, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 175, "col_offset": 41, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 177, "col_offset": 16, "nodeName": "k", "type": "Any"}, {"lineNumber": 177, "col_offset": 19, "nodeName": "v", "type": "Any"}, {"lineNumber": 177, "col_offset": 24, "nodeName": "items", "type": "Any"}, {"lineNumber": 178, "col_offset": 34, "nodeName": "v", "type": "Any"}, {"lineNumber": 179, "col_offset": 29, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 179, "col_offset": 37, "nodeName": "base", "type": "Any"}, {"lineNumber": 180, "col_offset": 12, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 180, "col_offset": 33, "nodeName": "union", "type": "Callable[..., Set[Any]]"}, {"lineNumber": 180, "col_offset": 58, "nodeName": "base_constants", "type": "Any"}, {"lineNumber": 183, "col_offset": 27, "nodeName": "items", "type": "Any"}, {"lineNumber": 184, "col_offset": 15, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 184, "col_offset": 26, "nodeName": "v", "type": "Any"}, {"lineNumber": 184, "col_offset": 29, "nodeName": "ScriptMethodStub", "type": "Type[torch.jit._recursive.`namedtuple-ScriptMethodStub-resolution_callback-def_-original_method`]"}, {"lineNumber": 186, "col_offset": 59, "nodeName": "v", "type": "Any"}, {"lineNumber": 192, "col_offset": 19, "nodeName": "__init__", "type": "Callable"}, {"lineNumber": 192, "col_offset": 51, "nodeName": "name", "type": "Any"}, {"lineNumber": 192, "col_offset": 57, "nodeName": "bases", "type": "Any"}, {"lineNumber": 192, "col_offset": 64, "nodeName": "attrs", "type": "Any"}, {"lineNumber": 198, "col_offset": 26, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 198, "col_offset": 30, "nodeName": "_methods", "type": "Dict[Any, Any]"}, {"lineNumber": 199, "col_offset": 12, "nodeName": "original_init", "type": "Any"}, {"lineNumber": 199, "col_offset": 26, "nodeName": "self", "type": "Any"}, {"lineNumber": 200, "col_offset": 56, "nodeName": "num_methods", "type": "int"}, {"lineNumber": 202, "col_offset": 29, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 218, "col_offset": 16, "nodeName": "concrete_type", "type": "Any"}, {"lineNumber": 218, "col_offset": 32, "nodeName": "_concrete_type", "type": "Any"}, {"lineNumber": 219, "col_offset": 20, "nodeName": "name", "type": "Any"}, {"lineNumber": 223, "col_offset": 20, "nodeName": "name", "type": "str"}, {"lineNumber": 232, "col_offset": 15, "nodeName": "self", "type": "_CachedForward"}, {"lineNumber": 264, "col_offset": 15, "nodeName": "self", "type": "ConstMap"}, {"lineNumber": 264, "col_offset": 34, "nodeName": "attr", "type": "Any"}, {"lineNumber": 285, "col_offset": 12, "nodeName": "__init__", "type": "Any"}, {"lineNumber": 290, "col_offset": 46, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 292, "col_offset": 19, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 292, "col_offset": 27, "nodeName": "_actual_script_module", "type": "Any"}, {"lineNumber": 292, "col_offset": 55, "nodeName": "attr", "type": "Any"}, {"lineNumber": 295, "col_offset": 46, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 312, "col_offset": 12, "nodeName": "setattr", "type": "Callable[[Any, str, Any], None]"}, {"lineNumber": 312, "col_offset": 20, "nodeName": "_actual_script_module", "type": "Any"}, {"lineNumber": 312, "col_offset": 48, "nodeName": "attr", "type": "Any"}, {"lineNumber": 312, "col_offset": 54, "nodeName": "value", "type": "Any"}, {"lineNumber": 315, "col_offset": 42, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 332, "col_offset": 18, "nodeName": "createResolutionCallbackFromFrame", "type": "Callable[..., Any]"}, {"lineNumber": 333, "col_offset": 18, "nodeName": "_parse_source_def", "type": "Any"}, {"lineNumber": 333, "col_offset": 45, "nodeName": "src", "type": "Any"}, {"lineNumber": 334, "col_offset": 12, "nodeName": "_methods", "type": "Any"}, {"lineNumber": 334, "col_offset": 45, "nodeName": "ScriptMethodStub", "type": "Type[torch.jit._recursive.`namedtuple-ScriptMethodStub-resolution_callback-def_-original_method`]"}, {"lineNumber": 334, "col_offset": 62, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 334, "col_offset": 67, "nodeName": "ast", "type": "Any"}, {"lineNumber": 337, "col_offset": 19, "nodeName": "_replicate_for_data_parallel", "type": "Any"}, {"lineNumber": 368, "col_offset": 12, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 369, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 370, "col_offset": 12, "nodeName": "__init__", "type": "Callable[[], Any]"}, {"lineNumber": 374, "col_offset": 12, "nodeName": "delattr", "type": "Callable[[Any, Union[bytearray, bytes, str]], None]"}, {"lineNumber": 374, "col_offset": 20, "nodeName": "self", "type": "Any"}, {"lineNumber": 390, "col_offset": 28, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 390, "col_offset": 50, "nodeName": "cpp_module", "type": "Any"}, {"lineNumber": 391, "col_offset": 12, "nodeName": "init_fn", "type": "Callable[[Any], Any]"}, {"lineNumber": 391, "col_offset": 20, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 395, "col_offset": 12, "nodeName": "_finalize_scriptmodule", "type": "Any"}, {"lineNumber": 395, "col_offset": 57, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 400, "col_offset": 40, "nodeName": "OrderedDictWrapper", "type": "Type[OrderedDictWrapper]"}, {"lineNumber": 403, "col_offset": 37, "nodeName": "OrderedDictWrapper", "type": "Type[OrderedDictWrapper]"}, {"lineNumber": 406, "col_offset": 37, "nodeName": "OrderedModuleDict", "type": "Type[OrderedModuleDict]"}, {"lineNumber": 407, "col_offset": 16, "nodeName": "_c", "type": "Any"}, {"lineNumber": 407, "col_offset": 34, "nodeName": "_modules", "type": "Any"}, {"lineNumber": 409, "col_offset": 12, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 418, "col_offset": 12, "nodeName": "__init__", "type": "Callable[[Any], Any]"}, {"lineNumber": 418, "col_offset": 26, "nodeName": "cpp_module", "type": "Any"}, {"lineNumber": 421, "col_offset": 34, "nodeName": "from_jit_type", "type": "Any"}, {"lineNumber": 427, "col_offset": 16, "nodeName": "name", "type": "Any"}, {"lineNumber": 427, "col_offset": 22, "nodeName": "cpp_module", "type": "Any"}, {"lineNumber": 427, "col_offset": 36, "nodeName": "items", "type": "Any"}, {"lineNumber": 429, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 429, "col_offset": 28, "nodeName": "OrderedModuleDict", "type": "Type[OrderedModuleDict]"}, {"lineNumber": 429, "col_offset": 46, "nodeName": "_c", "type": "Any"}, {"lineNumber": 429, "col_offset": 55, "nodeName": "modules", "type": "Dict[Any, Any]"}, {"lineNumber": 432, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 432, "col_offset": 31, "nodeName": "OrderedDictWrapper", "type": "Type[OrderedDictWrapper]"}, {"lineNumber": 433, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 433, "col_offset": 28, "nodeName": "OrderedDictWrapper", "type": "Type[OrderedDictWrapper]"}, {"lineNumber": 441, "col_offset": 12, "nodeName": "__dict__", "type": "Dict[Any, Any]"}, {"lineNumber": 458, "col_offset": 19, "nodeName": "forward", "type": "Any"}, {"lineNumber": 467, "col_offset": 19, "nodeName": "forward", "type": "Any"}, {"lineNumber": 481, "col_offset": 16, "nodeName": "forward", "type": "Any"}, {"lineNumber": 490, "col_offset": 19, "nodeName": "save", "type": "Any"}, {"lineNumber": 504, "col_offset": 19, "nodeName": "_save_for_mobile", "type": "Any"}, {"lineNumber": 507, "col_offset": 19, "nodeName": "_save_to_buffer_for_mobile", "type": "Any"}, {"lineNumber": 510, "col_offset": 19, "nodeName": "save_to_buffer", "type": "Any"}, {"lineNumber": 513, "col_offset": 19, "nodeName": "get_debug_state", "type": "Any"}, {"lineNumber": 516, "col_offset": 19, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 516, "col_offset": 45, "nodeName": "original_name", "type": "str"}, {"lineNumber": 519, "col_offset": 19, "nodeName": "graph_for", "type": "Any"}, {"lineNumber": 525, "col_offset": 19, "nodeName": "str", "type": "Type[str]"}, {"lineNumber": 536, "col_offset": 18, "nodeName": "createResolutionCallbackFromFrame", "type": "Callable[..., Any]"}, {"lineNumber": 537, "col_offset": 12, "nodeName": "_define", "type": "Any"}, {"lineNumber": 537, "col_offset": 28, "nodeName": "_concrete_type", "type": "Any"}, {"lineNumber": 537, "col_offset": 49, "nodeName": "src", "type": "Any"}, {"lineNumber": 537, "col_offset": 54, "nodeName": "rcb", "type": "Any"}, {"lineNumber": 540, "col_offset": 38, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 545, "col_offset": 15, "nodeName": "self", "type": "Any"}, {"lineNumber": 550, "col_offset": 15, "nodeName": "attr", "type": "Any"}, {"lineNumber": 550, "col_offset": 23, "nodeName": "_modules", "type": "Any"}, {"lineNumber": 561, "col_offset": 19, "nodeName": "__getattr__", "type": "Callable[[Any], Any]"}, {"lineNumber": 561, "col_offset": 66, "nodeName": "attr", "type": "Any"}, {"lineNumber": 564, "col_offset": 15, "nodeName": "self", "type": "Any"}, {"lineNumber": 567, "col_offset": 15, "nodeName": "attr", "type": "Any"}, {"lineNumber": 567, "col_offset": 23, "nodeName": "_modules", "type": "Any"}, {"lineNumber": 568, "col_offset": 38, "nodeName": "value", "type": "Any"}, {"lineNumber": 593, "col_offset": 18, "nodeName": "PickleError", "type": "Type[pickle.PickleError]"}, {"lineNumber": 600, "col_offset": 19, "nodeName": "wrap_cpp_module", "type": "Callable[[Any], Any]"}, {"lineNumber": 603, "col_offset": 19, "nodeName": "wrap_cpp_module", "type": "Callable[[Any], Any]"}, {"lineNumber": 610, "col_offset": 26, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 610, "col_offset": 34, "nodeName": "self", "type": "Any"}, {"lineNumber": 610, "col_offset": 40, "nodeName": "method_name", "type": "str"}, {"lineNumber": 615, "col_offset": 19, "nodeName": "self_method", "type": "Any"}, {"lineNumber": 618, "col_offset": 19, "nodeName": "forward_magic_method", "type": "Callable[..., Any]"}, {"lineNumber": 621, "col_offset": 19, "nodeName": "forward_magic_method", "type": "Callable[..., Any]"}, {"lineNumber": 621, "col_offset": 60, "nodeName": "idx", "type": "Any"}, {"lineNumber": 624, "col_offset": 19, "nodeName": "forward_magic_method", "type": "Callable[..., Any]"}, {"lineNumber": 627, "col_offset": 19, "nodeName": "forward_magic_method", "type": "Callable[..., Any]"}, {"lineNumber": 627, "col_offset": 61, "nodeName": "key", "type": "Any"}, {"lineNumber": 632, "col_offset": 26, "nodeName": "self", "type": "Any"}, {"lineNumber": 633, "col_offset": 15, "nodeName": "__func__", "type": "Any"}, {"lineNumber": 637, "col_offset": 19, "nodeName": "self_method", "type": "Callable[[], Any]"}, {"lineNumber": 643, "col_offset": 26, "nodeName": "self", "type": "Any"}, {"lineNumber": 644, "col_offset": 15, "nodeName": "__func__", "type": "Any"}, {"lineNumber": 648, "col_offset": 19, "nodeName": "self_method", "type": "Callable[[], Any]"}, {"lineNumber": 657, "col_offset": 19, "nodeName": "_construct", "type": "Any"}, {"lineNumber": 658, "col_offset": 56, "nodeName": "init_fn", "type": "Callable[[Any], Any]"}, {"lineNumber": 666, "col_offset": 22, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 669, "col_offset": 11, "nodeName": "startswith", "type": "Any"}, {"lineNumber": 669, "col_offset": 36, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 669, "col_offset": 44, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 669, "col_offset": 58, "nodeName": "name", "type": "Any"}, {"lineNumber": 680, "col_offset": 15, "nodeName": "inspect", "type": "module"}, {"lineNumber": 723, "col_offset": 18, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 727, "col_offset": 37, "nodeName": "torch", "type": "module"}, {"lineNumber": 728, "col_offset": 11, "nodeName": "name", "type": "str"}, {"lineNumber": 731, "col_offset": 12, "nodeName": "name", "type": "str"}, {"lineNumber": 731, "col_offset": 24, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 732, "col_offset": 16, "nodeName": "name", "type": "str"}, {"lineNumber": 732, "col_offset": 28, "nodeName": "_compiled_methods_allowlist", "type": "Set[str]"}, {"lineNumber": 734, "col_offset": 12, "nodeName": "setattr", "type": "Callable[[Any, str, Any], None]"}, {"lineNumber": 734, "col_offset": 20, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 734, "col_offset": 43, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 741, "col_offset": 12, "nodeName": "__init__", "type": "Any"}, {"lineNumber": 745, "col_offset": 12, "nodeName": "__init__", "type": "Callable[..., Any]"}, {"lineNumber": 748, "col_offset": 27, "nodeName": "nn", "type": "module"}, {"lineNumber": 758, "col_offset": 10, "nodeName": "obj", "type": "Any"}, {"lineNumber": 766, "col_offset": 21, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 768, "col_offset": 16, "nodeName": "k", "type": "Any"}, {"lineNumber": 768, "col_offset": 19, "nodeName": "v", "type": "Any"}, {"lineNumber": 768, "col_offset": 24, "nodeName": "items", "type": "Any"}, {"lineNumber": 770, "col_offset": 12, "nodeName": "new_obj_dict", "type": "Dict[Any, Any]"}, {"lineNumber": 774, "col_offset": 33, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 777, "col_offset": 8, "nodeName": "obj", "type": "Any"}, {"lineNumber": 777, "col_offset": 21, "nodeName": "name", "type": "Any"}, {"lineNumber": 930, "col_offset": 8, "nodeName": "warnings", "type": "module"}, {"lineNumber": 940, "col_offset": 23, "nodeName": "torch", "type": "module"}, {"lineNumber": 942, "col_offset": 15, "nodeName": "_recursive", "type": "module"}, {"lineNumber": 943, "col_offset": 17, "nodeName": "_recursive", "type": "module"}, {"lineNumber": 950, "col_offset": 27, "nodeName": "nn", "type": "module"}, {"lineNumber": 951, "col_offset": 18, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 959, "col_offset": 27, "nodeName": "enum", "type": "module"}, {"lineNumber": 962, "col_offset": 15, "nodeName": "_is_new_style_class", "type": "Callable[[Any], Any]"}, {"lineNumber": 962, "col_offset": 35, "nodeName": "obj", "type": "Any"}, {"lineNumber": 963, "col_offset": 18, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 967, "col_offset": 11, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 968, "col_offset": 18, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 973, "col_offset": 19, "nodeName": "createResolutionCallbackFromFrame", "type": "Callable[..., Any]"}, {"lineNumber": 979, "col_offset": 18, "nodeName": "obj", "type": "Any"}, {"lineNumber": 980, "col_offset": 19, "nodeName": "createResolutionCallbackFromClosure", "type": "Callable[[Any], Any]"}, {"lineNumber": 980, "col_offset": 69, "nodeName": "obj", "type": "Any"}, {"lineNumber": 986, "col_offset": 31, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1004, "col_offset": 11, "nodeName": "name", "type": "Any"}, {"lineNumber": 1004, "col_offset": 23, "nodeName": "impl_defaults", "type": "Any"}, {"lineNumber": 1004, "col_offset": 63, "nodeName": "overload_value", "type": "Any"}, {"lineNumber": 1005, "col_offset": 18, "nodeName": "FrontendError", "type": "Type[torch.jit.frontend.FrontendError]"}, {"lineNumber": 1006, "col_offset": 16, "nodeName": "loc", "type": "Any"}, {"lineNumber": 1014, "col_offset": 20, "nodeName": "get_jit_def", "type": "Callable[..., Any]"}, {"lineNumber": 1014, "col_offset": 32, "nodeName": "overload_fn", "type": "Any"}, {"lineNumber": 1014, "col_offset": 45, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 1046, "col_offset": 8, "nodeName": "compiled_fns", "type": "List[nothing]"}, {"lineNumber": 1047, "col_offset": 12, "nodeName": "_compile_function_with_overload", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 1047, "col_offset": 44, "nodeName": "overload_fn", "type": "Any"}, {"lineNumber": 1047, "col_offset": 57, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1047, "col_offset": 68, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1061, "col_offset": 7, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 1063, "col_offset": 12, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 1065, "col_offset": 77, "nodeName": "qual_name", "type": "Any"}, {"lineNumber": 1070, "col_offset": 11, "nodeName": "inspect", "type": "module"}, {"lineNumber": 1079, "col_offset": 42, "nodeName": "nn", "type": "module"}, {"lineNumber": 1079, "col_offset": 63, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 1081, "col_offset": 35, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 1093, "col_offset": 4, "nodeName": "torch", "type": "module"}, {"lineNumber": 1104, "col_offset": 18, "nodeName": "torch", "type": "module"}, {"lineNumber": 61, "col_offset": 29, "nodeName": "dir", "type": "Callable[..., List[str]]"}, {"lineNumber": 61, "col_offset": 33, "nodeName": "cls", "type": "Any"}, {"lineNumber": 66, "col_offset": 15, "nodeName": "torch", "type": "module"}, {"lineNumber": 68, "col_offset": 4, "nodeName": "torch", "type": "module"}, {"lineNumber": 91, "col_offset": 22, "nodeName": "k", "type": "Any"}, {"lineNumber": 91, "col_offset": 25, "nodeName": "v", "type": "Any"}, {"lineNumber": 91, "col_offset": 30, "nodeName": "items", "type": "Callable[[], Any]"}, {"lineNumber": 94, "col_offset": 22, "nodeName": "k", "type": "Any"}, {"lineNumber": 94, "col_offset": 25, "nodeName": "v", "type": "Any"}, {"lineNumber": 94, "col_offset": 30, "nodeName": "items", "type": "Callable[[], Any]"}, {"lineNumber": 97, "col_offset": 19, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 103, "col_offset": 15, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 108, "col_offset": 16, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 109, "col_offset": 43, "nodeName": "k", "type": "Any"}, {"lineNumber": 111, "col_offset": 8, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 114, "col_offset": 15, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 119, "col_offset": 15, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 124, "col_offset": 8, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 124, "col_offset": 14, "nodeName": "OrderedModuleDict", "type": "Type[OrderedModuleDict]"}, {"lineNumber": 124, "col_offset": 33, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 124, "col_offset": 48, "nodeName": "_C", "type": "Any"}, {"lineNumber": 134, "col_offset": 12, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 151, "col_offset": 12, "nodeName": "_c", "type": "Any"}, {"lineNumber": 152, "col_offset": 12, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 152, "col_offset": 33, "nodeName": "k", "type": "Any"}, {"lineNumber": 155, "col_offset": 16, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 156, "col_offset": 75, "nodeName": "k", "type": "Any"}, {"lineNumber": 156, "col_offset": 78, "nodeName": "v", "type": "Any"}, {"lineNumber": 178, "col_offset": 16, "nodeName": "_methods", "type": "Dict[Any, Any]"}, {"lineNumber": 179, "col_offset": 61, "nodeName": "set", "type": "Type[Set[Any]]"}, {"lineNumber": 180, "col_offset": 33, "nodeName": "_constants_set", "type": "Set[Any]"}, {"lineNumber": 183, "col_offset": 27, "nodeName": "attrs", "type": "Any"}, {"lineNumber": 185, "col_offset": 16, "nodeName": "delattr", "type": "Callable[[Any, Union[bytearray, bytes, str]], None]"}, {"lineNumber": 185, "col_offset": 24, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 185, "col_offset": 29, "nodeName": "k", "type": "Any"}, {"lineNumber": 186, "col_offset": 16, "nodeName": "_methods", "type": "Dict[Any, Any]"}, {"lineNumber": 198, "col_offset": 30, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 199, "col_offset": 33, "nodeName": "args", "type": "Tuple[Any, ...]"}, {"lineNumber": 199, "col_offset": 41, "nodeName": "kwargs", "type": "Dict[str, Any]"}, {"lineNumber": 200, "col_offset": 36, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 200, "col_offset": 40, "nodeName": "_methods", "type": "Dict[Any, Any]"}, {"lineNumber": 202, "col_offset": 15, "nodeName": "type", "type": "Type[Type[Any]]"}, {"lineNumber": 202, "col_offset": 20, "nodeName": "self", "type": "Any"}, {"lineNumber": 205, "col_offset": 20, "nodeName": "cls", "type": "Any"}, {"lineNumber": 213, "col_offset": 20, "nodeName": "create_script_module", "type": "Callable[..., Any]"}, {"lineNumber": 213, "col_offset": 62, "nodeName": "self", "type": "Any"}, {"lineNumber": 213, "col_offset": 68, "nodeName": "make_stubs", "type": "Callable[[Any], Any]"}, {"lineNumber": 218, "col_offset": 32, "nodeName": "_actual_script_module", "type": "Any"}, {"lineNumber": 219, "col_offset": 28, "nodeName": "get_attributes", "type": "Any"}, {"lineNumber": 221, "col_offset": 20, "nodeName": "name", "type": "Any"}, {"lineNumber": 221, "col_offset": 26, "nodeName": "_", "type": "Any"}, {"lineNumber": 221, "col_offset": 31, "nodeName": "get_modules", "type": "Any"}, {"lineNumber": 227, "col_offset": 15, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 227, "col_offset": 21, "nodeName": "ScriptMeta", "type": "Type[ScriptMeta]"}, {"lineNumber": 227, "col_offset": 33, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 290, "col_offset": 46, "nodeName": "self", "type": "Any"}, {"lineNumber": 291, "col_offset": 23, "nodeName": "__getattr__", "type": "Any"}, {"lineNumber": 291, "col_offset": 61, "nodeName": "attr", "type": "Any"}, {"lineNumber": 292, "col_offset": 27, "nodeName": "self", "type": "Any"}, {"lineNumber": 295, "col_offset": 46, "nodeName": "self", "type": "Any"}, {"lineNumber": 301, "col_offset": 19, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 301, "col_offset": 30, "nodeName": "value", "type": "Any"}, {"lineNumber": 301, "col_offset": 37, "nodeName": "Attribute", "type": "Union[Type[`namedtuple-Attribute-value-type`], Callable[[Any, Any], Any]]"}, {"lineNumber": 308, "col_offset": 49, "nodeName": "type", "type": "Any"}, {"lineNumber": 309, "col_offset": 20, "nodeName": "value", "type": "Any"}, {"lineNumber": 309, "col_offset": 28, "nodeName": "value", "type": "Any"}, {"lineNumber": 310, "col_offset": 23, "nodeName": "__setattr__", "type": "Any"}, {"lineNumber": 310, "col_offset": 61, "nodeName": "attr", "type": "Any"}, {"lineNumber": 310, "col_offset": 67, "nodeName": "value", "type": "Any"}, {"lineNumber": 312, "col_offset": 20, "nodeName": "self", "type": "Any"}, {"lineNumber": 315, "col_offset": 42, "nodeName": "self", "type": "Any"}, {"lineNumber": 319, "col_offset": 23, "nodeName": "define", "type": "Any"}, {"lineNumber": 319, "col_offset": 57, "nodeName": "src", "type": "Any"}, {"lineNumber": 332, "col_offset": 18, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 333, "col_offset": 18, "nodeName": "_C", "type": "Any"}, {"lineNumber": 334, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 334, "col_offset": 26, "nodeName": "name", "type": "Any"}, {"lineNumber": 337, "col_offset": 19, "nodeName": "_actual_script_module", "type": "Any"}, {"lineNumber": 368, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 395, "col_offset": 12, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 401, "col_offset": 16, "nodeName": "ParameterDict", "type": "Any"}, {"lineNumber": 401, "col_offset": 39, "nodeName": "_c", "type": "Any"}, {"lineNumber": 404, "col_offset": 16, "nodeName": "BufferDict", "type": "Any"}, {"lineNumber": 404, "col_offset": 36, "nodeName": "_c", "type": "Any"}, {"lineNumber": 407, "col_offset": 16, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 407, "col_offset": 34, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 418, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 421, "col_offset": 34, "nodeName": "ConcreteModuleType", "type": "Any"}, {"lineNumber": 422, "col_offset": 16, "nodeName": "_type", "type": "Any"}, {"lineNumber": 428, "col_offset": 16, "nodeName": "modules", "type": "Dict[Any, Any]"}, {"lineNumber": 428, "col_offset": 32, "nodeName": "wrap_cpp_module", "type": "Callable[[Any], Any]"}, {"lineNumber": 428, "col_offset": 48, "nodeName": "cpp_module", "type": "Any"}, {"lineNumber": 429, "col_offset": 46, "nodeName": "self", "type": "Any"}, {"lineNumber": 432, "col_offset": 50, "nodeName": "ParameterDict", "type": "Any"}, {"lineNumber": 432, "col_offset": 73, "nodeName": "_c", "type": "Any"}, {"lineNumber": 433, "col_offset": 47, "nodeName": "BufferDict", "type": "Any"}, {"lineNumber": 433, "col_offset": 67, "nodeName": "_c", "type": "Any"}, {"lineNumber": 441, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 449, "col_offset": 19, "nodeName": "_get_method", "type": "Any"}, {"lineNumber": 458, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 467, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 481, "col_offset": 16, "nodeName": "self", "type": "Any"}, {"lineNumber": 482, "col_offset": 20, "nodeName": "r", "type": "Any"}, {"lineNumber": 482, "col_offset": 26, "nodeName": "ConstMap", "type": "Type[ConstMap]"}, {"lineNumber": 490, "col_offset": 19, "nodeName": "_c", "type": "Any"}, {"lineNumber": 490, "col_offset": 32, "nodeName": "str", "type": "Type[str]"}, {"lineNumber": 490, "col_offset": 36, "nodeName": "f", "type": "Any"}, {"lineNumber": 490, "col_offset": 42, "nodeName": "kwargs", "type": "Dict[str, Any]"}, {"lineNumber": 504, "col_offset": 19, "nodeName": "_c", "type": "Any"}, {"lineNumber": 504, "col_offset": 45, "nodeName": "args", "type": "Tuple[Any, ...]"}, {"lineNumber": 504, "col_offset": 53, "nodeName": "kwargs", "type": "Dict[str, Any]"}, {"lineNumber": 507, "col_offset": 19, "nodeName": "_c", "type": "Any"}, {"lineNumber": 507, "col_offset": 55, "nodeName": "args", "type": "Tuple[Any, ...]"}, {"lineNumber": 507, "col_offset": 63, "nodeName": "kwargs", "type": "Dict[str, Any]"}, {"lineNumber": 510, "col_offset": 19, "nodeName": "_c", "type": "Any"}, {"lineNumber": 510, "col_offset": 43, "nodeName": "args", "type": "Tuple[Any, ...]"}, {"lineNumber": 510, "col_offset": 51, "nodeName": "kwargs", "type": "Dict[str, Any]"}, {"lineNumber": 513, "col_offset": 19, "nodeName": "_c", "type": "Any"}, {"lineNumber": 516, "col_offset": 45, "nodeName": "self", "type": "Any"}, {"lineNumber": 519, "col_offset": 19, "nodeName": "forward", "type": "Any"}, {"lineNumber": 519, "col_offset": 43, "nodeName": "args", "type": "Tuple[Any, ...]"}, {"lineNumber": 519, "col_offset": 51, "nodeName": "kwargs", "type": "Dict[str, Any]"}, {"lineNumber": 523, "col_offset": 15, "nodeName": "type", "type": "Type[Type[Any]]"}, {"lineNumber": 523, "col_offset": 20, "nodeName": "self", "type": "Any"}, {"lineNumber": 523, "col_offset": 29, "nodeName": "str", "type": "Type[str]"}, {"lineNumber": 525, "col_offset": 23, "nodeName": "name", "type": "Any"}, {"lineNumber": 536, "col_offset": 18, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 537, "col_offset": 12, "nodeName": "_c", "type": "Any"}, {"lineNumber": 537, "col_offset": 28, "nodeName": "self", "type": "Any"}, {"lineNumber": 540, "col_offset": 38, "nodeName": "self", "type": "Any"}, {"lineNumber": 541, "col_offset": 22, "nodeName": "RuntimeError", "type": "Type[RuntimeError]"}, {"lineNumber": 546, "col_offset": 23, "nodeName": "__getattr__", "type": "Callable[[Any], Any]"}, {"lineNumber": 546, "col_offset": 70, "nodeName": "attr", "type": "Any"}, {"lineNumber": 550, "col_offset": 23, "nodeName": "self", "type": "Any"}, {"lineNumber": 551, "col_offset": 23, "nodeName": "_modules", "type": "Any"}, {"lineNumber": 552, "col_offset": 17, "nodeName": "hasattr", "type": "Any"}, {"lineNumber": 552, "col_offset": 33, "nodeName": "attr", "type": "Any"}, {"lineNumber": 565, "col_offset": 23, "nodeName": "__setattr__", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 565, "col_offset": 70, "nodeName": "attr", "type": "Any"}, {"lineNumber": 565, "col_offset": 76, "nodeName": "value", "type": "Any"}, {"lineNumber": 567, "col_offset": 23, "nodeName": "self", "type": "Any"}, {"lineNumber": 568, "col_offset": 16, "nodeName": "_modules", "type": "Any"}, {"lineNumber": 569, "col_offset": 17, "nodeName": "hasattr", "type": "Any"}, {"lineNumber": 569, "col_offset": 33, "nodeName": "attr", "type": "Any"}, {"lineNumber": 593, "col_offset": 18, "nodeName": "pickle", "type": "module"}, {"lineNumber": 600, "col_offset": 19, "nodeName": "_recursive", "type": "module"}, {"lineNumber": 600, "col_offset": 56, "nodeName": "copy", "type": "Callable"}, {"lineNumber": 600, "col_offset": 66, "nodeName": "_c", "type": "Any"}, {"lineNumber": 603, "col_offset": 19, "nodeName": "_recursive", "type": "module"}, {"lineNumber": 603, "col_offset": 56, "nodeName": "deepcopy", "type": "Callable"}, {"lineNumber": 603, "col_offset": 70, "nodeName": "_c", "type": "Any"}, {"lineNumber": 603, "col_offset": 79, "nodeName": "memo", "type": "Any"}, {"lineNumber": 611, "col_offset": 15, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 611, "col_offset": 23, "nodeName": "self_method", "type": "Any"}, {"lineNumber": 611, "col_offset": 57, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 612, "col_offset": 16, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 612, "col_offset": 39, "nodeName": "method_name", "type": "str"}, {"lineNumber": 614, "col_offset": 22, "nodeName": "NotImplementedError", "type": "Type[NotImplementedError]"}, {"lineNumber": 615, "col_offset": 32, "nodeName": "args", "type": "Tuple[Any]"}, {"lineNumber": 615, "col_offset": 40, "nodeName": "kwargs", "type": "Dict[nothing, nothing]"}, {"lineNumber": 618, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 621, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 624, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 627, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 633, "col_offset": 15, "nodeName": "self_method", "type": "Callable[[], Any]"}, {"lineNumber": 633, "col_offset": 39, "nodeName": "get_function_from_type", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 634, "col_offset": 16, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 636, "col_offset": 23, "nodeName": "__dir__", "type": "Any"}, {"lineNumber": 644, "col_offset": 15, "nodeName": "self_method", "type": "Callable[[], Any]"}, {"lineNumber": 644, "col_offset": 39, "nodeName": "get_function_from_type", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 645, "col_offset": 16, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 657, "col_offset": 19, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 658, "col_offset": 16, "nodeName": "_replicate_for_data_parallel", "type": "Any"}, {"lineNumber": 667, "col_offset": 15, "nodeName": "callable", "type": "Callable[[Any], bool]"}, {"lineNumber": 667, "col_offset": 24, "nodeName": "item", "type": "Any"}, {"lineNumber": 667, "col_offset": 38, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 667, "col_offset": 49, "nodeName": "item", "type": "Any"}, {"lineNumber": 667, "col_offset": 55, "nodeName": "property", "type": "Type[property]"}, {"lineNumber": 669, "col_offset": 11, "nodeName": "name", "type": "Any"}, {"lineNumber": 723, "col_offset": 31, "nodeName": "name", "type": "str"}, {"lineNumber": 731, "col_offset": 24, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 734, "col_offset": 43, "nodeName": "method", "type": "Any"}, {"lineNumber": 734, "col_offset": 60, "nodeName": "_make_fail", "type": "Callable[[Any], Any]"}, {"lineNumber": 734, "col_offset": 71, "nodeName": "name", "type": "str"}, {"lineNumber": 748, "col_offset": 27, "nodeName": "torch", "type": "module"}, {"lineNumber": 756, "col_offset": 20, "nodeName": "id", "type": "Callable[[Any], int]"}, {"lineNumber": 756, "col_offset": 23, "nodeName": "obj", "type": "Any"}, {"lineNumber": 766, "col_offset": 21, "nodeName": "obj", "type": "Any"}, {"lineNumber": 768, "col_offset": 24, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 769, "col_offset": 16, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 769, "col_offset": 32, "nodeName": "call_prepare_scriptable_func_impl", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 769, "col_offset": 66, "nodeName": "v", "type": "Any"}, {"lineNumber": 769, "col_offset": 69, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 770, "col_offset": 25, "nodeName": "name", "type": "Any"}, {"lineNumber": 771, "col_offset": 13, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 771, "col_offset": 24, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 771, "col_offset": 36, "nodeName": "Module", "type": "Any"}, {"lineNumber": 772, "col_offset": 12, "nodeName": "new_obj_dict", "type": "Dict[Any, Any]"}, {"lineNumber": 772, "col_offset": 33, "nodeName": "call_prepare_scriptable_func_impl", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 772, "col_offset": 67, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 772, "col_offset": 79, "nodeName": "memo", "type": "Dict[int, Any]"}, {"lineNumber": 774, "col_offset": 12, "nodeName": "new_obj_dict", "type": "Dict[Any, Any]"}, {"lineNumber": 942, "col_offset": 15, "nodeName": "jit", "type": "module"}, {"lineNumber": 943, "col_offset": 17, "nodeName": "jit", "type": "module"}, {"lineNumber": 950, "col_offset": 27, "nodeName": "torch", "type": "module"}, {"lineNumber": 952, "col_offset": 16, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 954, "col_offset": 51, "nodeName": "obj", "type": "Any"}, {"lineNumber": 967, "col_offset": 15, "nodeName": "mro", "type": "Any"}, {"lineNumber": 973, "col_offset": 19, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 973, "col_offset": 67, "nodeName": "_frames_up", "type": "Any"}, {"lineNumber": 980, "col_offset": 19, "nodeName": "_jit_internal", "type": "module"}, {"lineNumber": 1004, "col_offset": 40, "nodeName": "impl_defaults", "type": "Any"}, {"lineNumber": 1005, "col_offset": 18, "nodeName": "frontend", "type": "module"}, {"lineNumber": 1007, "col_offset": 16, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 1014, "col_offset": 45, "nodeName": "overload_fn", "type": "Any"}, {"lineNumber": 1079, "col_offset": 42, "nodeName": "torch", "type": "module"}, {"lineNumber": 1079, "col_offset": 67, "nodeName": "mro", "type": "Any"}, {"lineNumber": 1081, "col_offset": 39, "nodeName": "mro", "type": "Any"}, {"lineNumber": 91, "col_offset": 30, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 94, "col_offset": 30, "nodeName": "self", "type": "OrderedDictWrapper"}, {"lineNumber": 124, "col_offset": 48, "nodeName": "torch", "type": "module"}, {"lineNumber": 151, "col_offset": 12, "nodeName": "self", "type": "OrderedModuleDict"}, {"lineNumber": 177, "col_offset": 24, "nodeName": "getattr", "type": "Callable[..., Any]"}, {"lineNumber": 177, "col_offset": 32, "nodeName": "base", "type": "Any"}, {"lineNumber": 178, "col_offset": 16, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 178, "col_offset": 29, "nodeName": "k", "type": "Any"}, {"lineNumber": 180, "col_offset": 33, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 186, "col_offset": 16, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 186, "col_offset": 29, "nodeName": "__name__", "type": "Any"}, {"lineNumber": 192, "col_offset": 19, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 192, "col_offset": 25, "nodeName": "ScriptMeta", "type": "Type[ScriptMeta]"}, {"lineNumber": 192, "col_offset": 37, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 200, "col_offset": 40, "nodeName": "cls", "type": "ScriptMeta"}, {"lineNumber": 205, "col_offset": 26, "nodeName": "type", "type": "Type[Type[Any]]"}, {"lineNumber": 205, "col_offset": 31, "nodeName": "module", "type": "Any"}, {"lineNumber": 206, "col_offset": 23, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 206, "col_offset": 31, "nodeName": "cls", "type": "Any"}, {"lineNumber": 213, "col_offset": 20, "nodeName": "_recursive", "type": "module"}, {"lineNumber": 218, "col_offset": 32, "nodeName": "self", "type": "Any"}, {"lineNumber": 219, "col_offset": 28, "nodeName": "concrete_type", "type": "Any"}, {"lineNumber": 220, "col_offset": 20, "nodeName": "delattr", "type": "Callable[[Any, Union[bytearray, bytes, str]], None]"}, {"lineNumber": 220, "col_offset": 28, "nodeName": "self", "type": "Any"}, {"lineNumber": 220, "col_offset": 34, "nodeName": "name", "type": "Any"}, {"lineNumber": 221, "col_offset": 31, "nodeName": "concrete_type", "type": "Any"}, {"lineNumber": 222, "col_offset": 20, "nodeName": "delattr", "type": "Callable[[Any, Union[bytearray, bytes, str]], None]"}, {"lineNumber": 222, "col_offset": 28, "nodeName": "self", "type": "Any"}, {"lineNumber": 222, "col_offset": 34, "nodeName": "name", "type": "Any"}, {"lineNumber": 224, "col_offset": 20, "nodeName": "delattr", "type": "Callable[[Any, Union[bytearray, bytes, str]], None]"}, {"lineNumber": 224, "col_offset": 28, "nodeName": "self", "type": "Any"}, {"lineNumber": 224, "col_offset": 34, "nodeName": "name", "type": "str"}, {"lineNumber": 285, "col_offset": 12, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 285, "col_offset": 18, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 285, "col_offset": 32, "nodeName": "self", "type": "Any"}, {"lineNumber": 306, "col_offset": 48, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 307, "col_offset": 24, "nodeName": "__annotations__", "type": "Dict[Any, Any]"}, {"lineNumber": 308, "col_offset": 20, "nodeName": "__annotations__", "type": "Dict[Any, Any]"}, {"lineNumber": 308, "col_offset": 49, "nodeName": "value", "type": "Any"}, {"lineNumber": 309, "col_offset": 28, "nodeName": "value", "type": "Any"}, {"lineNumber": 319, "col_offset": 23, "nodeName": "_actual_script_module", "type": "Any"}, {"lineNumber": 333, "col_offset": 18, "nodeName": "torch", "type": "module"}, {"lineNumber": 337, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 370, "col_offset": 12, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 370, "col_offset": 18, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 370, "col_offset": 41, "nodeName": "self", "type": "Any"}, {"lineNumber": 401, "col_offset": 16, "nodeName": "_C", "type": "Any"}, {"lineNumber": 401, "col_offset": 39, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 404, "col_offset": 16, "nodeName": "_C", "type": "Any"}, {"lineNumber": 404, "col_offset": 36, "nodeName": "script_module", "type": "Any"}, {"lineNumber": 421, "col_offset": 34, "nodeName": "_C", "type": "Any"}, {"lineNumber": 422, "col_offset": 16, "nodeName": "_c", "type": "Any"}, {"lineNumber": 427, "col_offset": 36, "nodeName": "ModuleDict", "type": "Any"}, {"lineNumber": 427, "col_offset": 56, "nodeName": "_c", "type": "Any"}, {"lineNumber": 428, "col_offset": 24, "nodeName": "name", "type": "Any"}, {"lineNumber": 432, "col_offset": 50, "nodeName": "_C", "type": "Any"}, {"lineNumber": 432, "col_offset": 73, "nodeName": "self", "type": "Any"}, {"lineNumber": 433, "col_offset": 47, "nodeName": "_C", "type": "Any"}, {"lineNumber": 433, "col_offset": 67, "nodeName": "self", "type": "Any"}, {"lineNumber": 438, "col_offset": 20, "nodeName": "k", "type": "Any"}, {"lineNumber": 438, "col_offset": 23, "nodeName": "v", "type": "Any"}, {"lineNumber": 438, "col_offset": 28, "nodeName": "items", "type": "Any"}, {"lineNumber": 449, "col_offset": 19, "nodeName": "_c", "type": "Any"}, {"lineNumber": 482, "col_offset": 35, "nodeName": "r", "type": "Any"}, {"lineNumber": 490, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 504, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 507, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 510, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 513, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 519, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 523, "col_offset": 33, "nodeName": "name", "type": "Any"}, {"lineNumber": 537, "col_offset": 12, "nodeName": "self", "type": "Any"}, {"lineNumber": 551, "col_offset": 23, "nodeName": "self", "type": "Any"}, {"lineNumber": 551, "col_offset": 37, "nodeName": "attr", "type": "Any"}, {"lineNumber": 552, "col_offset": 17, "nodeName": "_c", "type": "Any"}, {"lineNumber": 553, "col_offset": 23, "nodeName": "getattr", "type": "Any"}, {"lineNumber": 553, "col_offset": 39, "nodeName": "attr", "type": "Any"}, {"lineNumber": 554, "col_offset": 17, "nodeName": "_has_method", "type": "Any"}, {"lineNumber": 554, "col_offset": 37, "nodeName": "attr", "type": "Any"}, {"lineNumber": 555, "col_offset": 16, "nodeName": "script_method", "type": "Any"}, {"lineNumber": 558, "col_offset": 38, "nodeName": "script_method", "type": "Any"}, {"lineNumber": 559, "col_offset": 23, "nodeName": "script_method", "type": "Any"}, {"lineNumber": 561, "col_offset": 19, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 561, "col_offset": 25, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 561, "col_offset": 48, "nodeName": "self", "type": "Any"}, {"lineNumber": 568, "col_offset": 16, "nodeName": "self", "type": "Any"}, {"lineNumber": 568, "col_offset": 30, "nodeName": "attr", "type": "Any"}, {"lineNumber": 569, "col_offset": 17, "nodeName": "_c", "type": "Any"}, {"lineNumber": 570, "col_offset": 16, "nodeName": "setattr", "type": "Any"}, {"lineNumber": 570, "col_offset": 32, "nodeName": "attr", "type": "Any"}, {"lineNumber": 570, "col_offset": 38, "nodeName": "value", "type": "Any"}, {"lineNumber": 600, "col_offset": 19, "nodeName": "jit", "type": "module"}, {"lineNumber": 600, "col_offset": 56, "nodeName": "copy", "type": "module"}, {"lineNumber": 600, "col_offset": 66, "nodeName": "self", "type": "Any"}, {"lineNumber": 603, "col_offset": 19, "nodeName": "jit", "type": "module"}, {"lineNumber": 603, "col_offset": 56, "nodeName": "copy", "type": "module"}, {"lineNumber": 603, "col_offset": 70, "nodeName": "self", "type": "Any"}, {"lineNumber": 658, "col_offset": 16, "nodeName": "_c", "type": "Any"}, {"lineNumber": 741, "col_offset": 12, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 745, "col_offset": 12, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 769, "col_offset": 27, "nodeName": "k", "type": "Any"}, {"lineNumber": 771, "col_offset": 36, "nodeName": "nn", "type": "module"}, {"lineNumber": 771, "col_offset": 61, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 771, "col_offset": 72, "nodeName": "sub_module", "type": "Any"}, {"lineNumber": 771, "col_offset": 84, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 772, "col_offset": 25, "nodeName": "name", "type": "Any"}, {"lineNumber": 774, "col_offset": 25, "nodeName": "name", "type": "Any"}, {"lineNumber": 942, "col_offset": 15, "nodeName": "torch", "type": "module"}, {"lineNumber": 943, "col_offset": 17, "nodeName": "torch", "type": "module"}, {"lineNumber": 967, "col_offset": 15, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1004, "col_offset": 54, "nodeName": "name", "type": "Any"}, {"lineNumber": 1005, "col_offset": 18, "nodeName": "jit", "type": "module"}, {"lineNumber": 1009, "col_offset": 47, "nodeName": "name", "type": "Any"}, {"lineNumber": 1079, "col_offset": 67, "nodeName": "obj", "type": "Any"}, {"lineNumber": 1081, "col_offset": 39, "nodeName": "obj", "type": "Any"}, {"lineNumber": 186, "col_offset": 29, "nodeName": "original_method", "type": "Any"}, {"lineNumber": 207, "col_offset": 32, "nodeName": "v", "type": "Any"}, {"lineNumber": 209, "col_offset": 31, "nodeName": "infer_methods_to_compile", "type": "Callable[[Any], Any]"}, {"lineNumber": 209, "col_offset": 56, "nodeName": "module", "type": "Any"}, {"lineNumber": 213, "col_offset": 20, "nodeName": "jit", "type": "module"}, {"lineNumber": 213, "col_offset": 96, "nodeName": "added_methods_in_init", "type": "bool"}, {"lineNumber": 291, "col_offset": 23, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 291, "col_offset": 29, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 291, "col_offset": 43, "nodeName": "self", "type": "Any"}, {"lineNumber": 306, "col_offset": 48, "nodeName": "__class__", "type": "Type[Any]"}, {"lineNumber": 307, "col_offset": 24, "nodeName": "__class__", "type": "Type[Any]"}, {"lineNumber": 308, "col_offset": 20, "nodeName": "self", "type": "Any"}, {"lineNumber": 308, "col_offset": 41, "nodeName": "attr", "type": "Any"}, {"lineNumber": 310, "col_offset": 23, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 310, "col_offset": 29, "nodeName": "ScriptModule", "type": "Type[Any]"}, {"lineNumber": 310, "col_offset": 43, "nodeName": "self", "type": "Any"}, {"lineNumber": 319, "col_offset": 23, "nodeName": "self", "type": "Any"}, {"lineNumber": 334, "col_offset": 26, "nodeName": "name", "type": "Any"}, {"lineNumber": 401, "col_offset": 16, "nodeName": "torch", "type": "module"}, {"lineNumber": 404, "col_offset": 16, "nodeName": "torch", "type": "module"}, {"lineNumber": 421, "col_offset": 34, "nodeName": "torch", "type": "module"}, {"lineNumber": 422, "col_offset": 16, "nodeName": "self", "type": "Any"}, {"lineNumber": 427, "col_offset": 36, "nodeName": "_C", "type": "Any"}, {"lineNumber": 427, "col_offset": 56, "nodeName": "self", "type": "Any"}, {"lineNumber": 432, "col_offset": 50, "nodeName": "torch", "type": "module"}, {"lineNumber": 433, "col_offset": 47, "nodeName": "torch", "type": "module"}, {"lineNumber": 438, "col_offset": 28, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 439, "col_offset": 23, "nodeName": "isinstance", "type": "Callable[[Any, Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 439, "col_offset": 34, "nodeName": "v", "type": "Any"}, {"lineNumber": 439, "col_offset": 37, "nodeName": "ScriptMethod", "type": "Any"}, {"lineNumber": 449, "col_offset": 19, "nodeName": "self", "type": "Any"}, {"lineNumber": 525, "col_offset": 23, "nodeName": "_type", "type": "Any"}, {"lineNumber": 546, "col_offset": 23, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 546, "col_offset": 29, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 546, "col_offset": 52, "nodeName": "self", "type": "Any"}, {"lineNumber": 552, "col_offset": 17, "nodeName": "self", "type": "Any"}, {"lineNumber": 553, "col_offset": 23, "nodeName": "_c", "type": "Any"}, {"lineNumber": 554, "col_offset": 17, "nodeName": "_c", "type": "Any"}, {"lineNumber": 555, "col_offset": 32, "nodeName": "_get_method", "type": "Any"}, {"lineNumber": 555, "col_offset": 52, "nodeName": "attr", "type": "Any"}, {"lineNumber": 558, "col_offset": 16, "nodeName": "__dict__", "type": "Any"}, {"lineNumber": 565, "col_offset": 23, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 565, "col_offset": 29, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 565, "col_offset": 52, "nodeName": "self", "type": "Any"}, {"lineNumber": 569, "col_offset": 17, "nodeName": "self", "type": "Any"}, {"lineNumber": 570, "col_offset": 16, "nodeName": "_c", "type": "Any"}, {"lineNumber": 572, "col_offset": 16, "nodeName": "hasattr", "type": "Callable[[Any, str], bool]"}, {"lineNumber": 572, "col_offset": 24, "nodeName": "self", "type": "Any"}, {"lineNumber": 573, "col_offset": 20, "nodeName": "attr", "type": "Any"}, {"lineNumber": 577, "col_offset": 22, "nodeName": "AttributeError", "type": "Type[AttributeError]"}, {"lineNumber": 590, "col_offset": 23, "nodeName": "__setattr__", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 590, "col_offset": 70, "nodeName": "attr", "type": "Any"}, {"lineNumber": 590, "col_offset": 76, "nodeName": "value", "type": "Any"}, {"lineNumber": 600, "col_offset": 19, "nodeName": "torch", "type": "module"}, {"lineNumber": 603, "col_offset": 19, "nodeName": "torch", "type": "module"}, {"lineNumber": 636, "col_offset": 23, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 636, "col_offset": 29, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 636, "col_offset": 52, "nodeName": "self", "type": "Any"}, {"lineNumber": 658, "col_offset": 16, "nodeName": "self", "type": "Any"}, {"lineNumber": 681, "col_offset": 37, "nodeName": "isfunction", "type": "Callable[[object], bool]"}, {"lineNumber": 681, "col_offset": 56, "nodeName": "x", "type": "Any"}, {"lineNumber": 681, "col_offset": 62, "nodeName": "ismethod", "type": "Callable[[object], bool]"}, {"lineNumber": 681, "col_offset": 79, "nodeName": "x", "type": "Any"}, {"lineNumber": 771, "col_offset": 36, "nodeName": "torch", "type": "module"}, {"lineNumber": 1005, "col_offset": 18, "nodeName": "torch", "type": "module"}, {"lineNumber": 186, "col_offset": 29, "nodeName": "v", "type": "Any"}, {"lineNumber": 213, "col_offset": 20, "nodeName": "torch", "type": "module"}, {"lineNumber": 306, "col_offset": 48, "nodeName": "self", "type": "Any"}, {"lineNumber": 307, "col_offset": 24, "nodeName": "self", "type": "Any"}, {"lineNumber": 334, "col_offset": 26, "nodeName": "ast", "type": "Any"}, {"lineNumber": 427, "col_offset": 36, "nodeName": "torch", "type": "module"}, {"lineNumber": 438, "col_offset": 28, "nodeName": "self", "type": "Any"}, {"lineNumber": 439, "col_offset": 37, "nodeName": "_C", "type": "Any"}, {"lineNumber": 523, "col_offset": 33, "nodeName": "_type", "type": "Any"}, {"lineNumber": 525, "col_offset": 23, "nodeName": "_c", "type": "Any"}, {"lineNumber": 553, "col_offset": 23, "nodeName": "self", "type": "Any"}, {"lineNumber": 554, "col_offset": 17, "nodeName": "self", "type": "Any"}, {"lineNumber": 555, "col_offset": 32, "nodeName": "_c", "type": "Any"}, {"lineNumber": 558, "col_offset": 16, "nodeName": "self", "type": "Any"}, {"lineNumber": 558, "col_offset": 30, "nodeName": "attr", "type": "Any"}, {"lineNumber": 570, "col_offset": 16, "nodeName": "self", "type": "Any"}, {"lineNumber": 573, "col_offset": 28, "nodeName": "keys", "type": "Any"}, {"lineNumber": 578, "col_offset": 20, "nodeName": "format", "type": "Callable[..., str]"}, {"lineNumber": 579, "col_offset": 24, "nodeName": "attr", "type": "Any"}, {"lineNumber": 579, "col_offset": 30, "nodeName": "value", "type": "Any"}, {"lineNumber": 681, "col_offset": 37, "nodeName": "inspect", "type": "module"}, {"lineNumber": 681, "col_offset": 62, "nodeName": "inspect", "type": "module"}, {"lineNumber": 207, "col_offset": 38, "nodeName": "k", "type": "Any"}, {"lineNumber": 207, "col_offset": 41, "nodeName": "v", "type": "Any"}, {"lineNumber": 207, "col_offset": 46, "nodeName": "sorted", "type": "Callable"}, {"lineNumber": 439, "col_offset": 37, "nodeName": "torch", "type": "module"}, {"lineNumber": 523, "col_offset": 33, "nodeName": "_c", "type": "Any"}, {"lineNumber": 525, "col_offset": 23, "nodeName": "self", "type": "Any"}, {"lineNumber": 555, "col_offset": 32, "nodeName": "self", "type": "Any"}, {"lineNumber": 590, "col_offset": 23, "nodeName": "super", "type": "Type[super]"}, {"lineNumber": 590, "col_offset": 29, "nodeName": "RecursiveScriptModule", "type": "Type[Any]"}, {"lineNumber": 590, "col_offset": 52, "nodeName": "self", "type": "Any"}, {"lineNumber": 207, "col_offset": 53, "nodeName": "items", "type": "Any"}, {"lineNumber": 523, "col_offset": 33, "nodeName": "self", "type": "Any"}, {"lineNumber": 573, "col_offset": 28, "nodeName": "get_constants", "type": "Any"}, {"lineNumber": 207, "col_offset": 53, "nodeName": "_methods", "type": "Any"}, {"lineNumber": 573, "col_offset": 28, "nodeName": "_concrete_type", "type": "Any"}, {"lineNumber": 207, "col_offset": 53, "nodeName": "cls", "type": "Any"}, {"lineNumber": 573, "col_offset": 28, "nodeName": "self", "type": "Any"}] |
{
"id": 924470425,
"type": "Feature",
"properties": {
"addr:full":"309 Collier Row Lane Romford Essex RM5 3ND",
"addr:housenumber":"309",
"addr:postcode":"rm5 3nd",
"addr:street":"Collier Row Lane",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"0.1631550044,51.5987968445,0.1631550044,51.5987968445",
"geom:latitude":51.598797,
"geom:longitude":0.163155,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"309 Collier Row Lane",
"sg:city":"Romford",
"sg:classifiers":[
{
"category":"Retail",
"subcategory":"Funeral Services",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 1708 724064",
"sg:postcode":"RM5 3ND",
"sg:province":"Essex",
"src:geom":"simplegeo",
"wof:belongsto":[
85786741,
102191581,
85633159,
404227469,
101750367,
1158857313
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_0ETIkfrVa7Qc8U0QrnDItH_51.598797_0.163155@1300740806"
},
"wof:country":"GB",
"wof:created":1471964191,
"wof:geomhash":"ea23724e5f804f73c43d375e89907173",
"wof:hierarchy":[
{
"borough_id":1158857313,
"continent_id":102191581,
"country_id":85633159,
"locality_id":101750367,
"macroregion_id":404227469,
"neighbourhood_id":85786741,
"region_id":-1,
"venue_id":924470425
}
],
"wof:id":924470425,
"wof:lastmodified":1513993867,
"wof:name":"Harold Wood Funeral Services Ltd.",
"wof:parent_id":85786741,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[]
},
"bbox": [
0.1631550044,
51.5987968445,
0.1631550044,
51.5987968445
],
"geometry": {"coordinates":[0.1631550044,51.5987968445],"type":"Point"}
} |
["794bf3c9c03642a21abbfa6225da2efc1d262d32"] |
{"id":42235,"title":"BNM a fost chemat la răspundere. „Partidul Nostru” a solicitat de la Banca Națională să publice raportul Kroll privind „furtul secolului”","content":"Astfel, printre întrebările adresate pe numele guvernatorului BNM, Sergiu Cioclea, figurează următoarele:\n- Din care motive nu s-au făcut publice rezultatele fazei a II-a a raportului de investigație a fraudei bancare efectuat de către Consortium Kroll, Steptoe & Johnson în decembrie 2017?\n- Există sau nu acordul Consortiumului Kroll, Steptoe & Johnson de a face publice rezultatele fazei a II-a a raportului de investigare a fraudei bancare, în contextul scrisorii de însoțire expediată pe numele Guvernatorului BNM pe 20.12.2017?\n- Dacă au parvenit sau nu, față de dumneavoastră în calitate de Guvernator al BNM sau față de alți membri ai organelor de conducere ale BNM, solicitări scrise sau verbale, din partea persoanelor oficiale din RM sau a liderilor de partide politice, cu cerința de a nu face publice rezultatele fazei a II-a a investigației fraudei bancare, efectuată de Consonium Kroll, Steptoe & Johnson în decembrie 2017? Dacă da, cine sunt solicitanții?\n- Ce carențe din sistem au fost identificate de BNM și ce măsuri de rigoare au fost întreprinse drept rezultat al analizei rezultatelor fazei a II-a a raportului de investigație a fraudei bancare?\n- Dacă Consonium Kroll, Steptoe & Johnson a furnizat în ianuarie 2018 careva informații obținute în rezultatul investigației fraudei bancare pentru a fi transmise organelor Procuraturii? Dacă da, cînd aceste materiale au fost transmise către organele Procuraturii?\n- Care sînt persoanele fizice identificate prin faza a II-a a investigației fraudei bancare efectuat de către Consonium Kroll, Steptoe & Johnson în decembrie 2017 care au contribuit la devalizarea sistemului bancar din Republica Moldova, precum și la delapidarea mijloacelor bănești din bănci?\n„Partidul Nostru” indică că informația solicitată reprezintă un interes sporit pentru cetățenii Republicii Moldova, dat fiind faptul că, potrivit legii, pentru furtul miliardului urmează să se răsplătească cetățenii Moldovei.\n"} |
{"test":{"dirs":["_0mt8GMbKrr0JGnQ0KfBkooaPJ-B2Cs6q1IIuRvV5B7f","7GFg3cmw5w7cHy4As-6qieCxS4re4AKmBKjMJZt_pr3f"]}}
|
{
"id": 924356963,
"type": "Feature",
"properties": {
"addr:full":"61 Marion Rd, Hanham Bristol City of Bristol BS15 3LE",
"addr:housenumber":"61",
"addr:postcode":"bs15 3le",
"addr:street":"Marion Rd",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"-2.5241088867,51.4408226013,-2.5241088867,51.4408226013",
"geom:latitude":51.440823,
"geom:longitude":-2.524109,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"61 Marion Rd, Hanham",
"sg:city":"Bristol",
"sg:classifiers":[
{
"category":"Building & Trades",
"subcategory":"Construction",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 117 967 4446",
"sg:postcode":"BS15 3LE",
"sg:province":"City of Bristol",
"sg:tags":[
"builder"
],
"src:geom":"simplegeo",
"wof:belongsto":[
85787903,
"102191581",
404447367,
"85633159",
404227469,
101914025,
85684423
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_3OS6jRXU32iDOaI22axON0_51.440823_-2.524109@1300740781"
},
"wof:country":"GB",
"wof:created":1471957964,
"wof:geomhash":"b70e8cff8b490bf4c5856fc2b54e3407",
"wof:hierarchy":[
{
"continent_id":"102191581",
"country_id":"85633159",
"localadmin_id":404447367,
"locality_id":101914025,
"macroregion_id":404227469,
"neighbourhood_id":85787903,
"region_id":85684423,
"venue_id":924356963
}
],
"wof:id":924356963,
"wof:lastmodified":1496944455,
"wof:name":"Cleo Roberts Ltd",
"wof:parent_id":85787903,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"builder"
]
},
"bbox": [
-2.5241088867,
51.4408226013,
-2.5241088867,
51.4408226013
],
"geometry": {"coordinates":[-2.5241088867,51.4408226013],"type":"Point"}
} |
[{"name": "JD", "zip": "83686", "created": 1355191628, "time": "21:07:08", "date": "12-12-10", "id": "50c6954cc988d4747b00000b"}, {"name": "AB", "zip": "34684", "created": 1355185663, "time": "19:27:43", "date": "12-12-10", "id": "50c67dff00e5793113000004"}, {"name": "CH", "zip": "46804", "created": 1355181737, "time": "18:22:17", "date": "12-12-10", "id": "50c66ea98cce3f5113000008"}, {"name": "MS", "zip": "21157", "created": 1355178341, "time": "17:25:41", "date": "12-12-10", "id": "50c66165c988d4de6a000003"}, {"name": "LO", "zip": "06482", "created": 1355176516, "time": "16:55:16", "date": "12-12-10", "id": "50c65a442bcfa3385f000010"}, {"name": "NL", "zip": "07044-2239", "created": 1355175647, "time": "16:40:47", "date": "12-12-10", "id": "50c656dfee140fe04e00000e"}, {"name": "NL", "zip": "92886", "created": 1355170241, "time": "15:10:41", "date": "12-12-10", "id": "50c641c1cde5b8361b000014"}, {"name": "JT", "zip": "29485", "created": 1355168534, "time": "14:42:14", "date": "12-12-10", "id": "50c63b16688938db64000001"}, {"name": "ph", "zip": "43140", "created": 1355127720, "time": "03:22:00", "date": "12-12-10", "id": "50c59ba8cde5b8e178000001"}] |
{"file": "rotated_2796d0100ef341959b9b097105764b92_15.jpg", "image_size": [{"width": 2165, "height": 1656, "depth": 3}], "annotations": [{"class_id": 1, "top": 500, "left": 895, "width": 147, "height": 168}, {"class_id": 1, "top": 388, "left": 1208, "width": 171, "height": 167}, {"class_id": 0, "top": 374, "left": 1001, "width": 132, "height": 118}, {"class_id": 0, "top": 618, "left": 875, "width": 69, "height": 64}, {"class_id": 0, "top": 455, "left": 1009, "width": 66, "height": 63}], "categories": [{"class_id": 0, "name": "3clover"}, {"class_id": 1, "name": "4clover"}]} |
{"poster":"KitingViper","date":"2018-06-28T19:26:54.934+0000","title":"Midlaner [19] sucht Team zum tryharden. (trainingszeiten,tuniere,''scrims'',clash ect.)","subforum":"Clans & Teams","embed":{"description":"KitingViper / Silver 3 49LP / 17W 12L Win Ratio 59% / Cassiopeia - 18W 11L Win Ratio 62%, Ryze - 0W 1L Win Ratio 0%","url":"http://euw.op.gg/summoner/userName=KitingViper","image":"http://opgg-static.akamaized.net/images/logo/2015/reverse.rectangle.png"},"up_votes":1,"down_votes":0,"body":"Hey,\r\nmeine Name ist Justin bin 19 jahre alt und bin Midlaner seit Season 4 allerdings habe ich Anfang Season 6 mit League of Legends aufgehört und spiele jetzt seit ca. 3 Monaten wieder lol. Damalige Elo war im Platinbereich.\r\nWie man im Titel schwer erkennen kann suche ich ein Team mit ambitionen sich zu verbessern und als Team leistung zu bringen. Dazu gehören für mich Trainingszeiten die so ca. bei 2-3 mal die woche liegen sollten und vielleicht auch ein bisschen variabel sein können da sich mein Arbeitsplan jeden Monat ändert und ich meistens von 17:00 bis 21:30 arbeite.\r\nAußerdem gehört es sich für mich dazu sich eigene schlechte Spiele anzusehen und Fehler zu finden die man dann zukünftig verhindert und sich Taktiken, Teamcomps ect, zu überlegen die Sinn machen und gut sind (sprich man spielt nicht einfach Blind irgendwas und hofft das es klappt sondern hat einen ''Gameplan'').\r\nIch denke was ich mir so ungefähr vorstelle ist klar geworden ^^\r\n\r\nJetzt noch etwas zu meiner Spielart und Elo.\r\nElomäßig befinde ich momentan Silber III was allerdings nicht wirklich viel aussagt da ich nicht wirklich viele ranked games gespielt habe und die, die ich gespielt habe sind größtenteils gut verlaufen insbesondere auf Cassio. Generell würde ich nicht jeden Spieler nach der Elo abstempeln. Generell würde ich mich vorsichtig gesagt ohne arrogant zu klingen eher in sagen wir mal Gold 3 wiederfinden stand jetzt. Aber wie schon erwähnt sagt Elo teilweise nicht immer was über die Qualität des Spielers aus also ist es mir dementsprechend auch egal welche Elo ihr seid ^^. zu meiner Spielweise würde ich sagen das ich mich in den letzten Monaten sehr stark auf Teamfights fokussiert hab und stark an meinen positionspiel gearbeitet habe was denk ich mal eine gute Spielweise gerade fürs Teamplay ist. Ich weiß auch des öfteren sehr gut wo meine Grenzen sind was bedeutet das ich an sich eher selten sterbe bzw selten unnötig. Und meine persönliche Faustregel heißt sterb nicht im 1v1 oder sagen wir mal sehr sehr selten klar kann man auch mal das 1v1 annehmen und sich verschätzen was missplayen oder Pech haben ect. aber wer jedes 3 Game 2-3 mal alleine im 1v1 stirbt gegen seinen laner der macht meiner Meinung nach etwas falsch. Ich würde also meine Stärken auch im direkten 1v1 sehen ( was nicht heißt das ich den Gegner 3-4 mal Solo kille aber alleine das 0/0 aus der laningphase gehen gegen ein offensichtlich schwieriges Machtup sehe ich auch als 1v1 stärke an.)\r\nAn sich beherrsche ich auch teilweise die Lanemanipulation womit man gegen schwächere Spieler die diese nicht verstehen oder zu agressiv spielen ohne Vision auch einen ziemlichen Vorteil raus holen kann wenn man dies ausnutzt und mit ganks bestraft. All in All ist meine Spielweise zwar auch offensiv aber auch nicht mit zu viel Risiko da ich auch immer auf mein Scaling vertraue da ich bevorzugt lategame Champions spiele und wie gesagt auch weiß wo die Grenzen des möglichen sind.\r\nDas ist meine Selbsteinschätzung was meine Spielweise betrifft ich hoffe das kommt nicht Arrogant oder ähnliches rüber. Man sollte nur vorher wissen welche ''Art'' Midlaner sich man sich in sein Team holen will ^^.\r\n\r\npuh... so jetzt kommt der Punkt der bei Teams nicht so beliebt ist. Wenn ihr euch meine gespielten Champions in Ranked Games anseht oder auch generell werdet ihr sehen das ich Cassio OTP bin und das heißt nicht das ich mich fürs Team nur auf einen Champion konzentriere oder keinen Championpool hätte oder aufbauen könnte. Mein Championpool ist nur etwas ''eingerostet'' da viele Champions die ich damals gespielt habe einfach nicht mehr Spielbar sind in der jetztigen Meta und ich auf diesen seit meiner Pause nicht mehr gespielt habe. Logischerweise werde ich wieder einen Championpool anlegen und paar meiner alten Mains die heute viable sind wieder spielen und mir vielleicht auch neue Metachamps die ich noch nicht gespielt habe anschauen. Allerdings würde es mich freuen wenn man auch versucht mich meine Cassiopeia spielen zu lassen ich habe damals grade auch einen Champion wie Cassio ausgesucht weil sie eigentlich so gut wie fast immer in eine Teamcomp passt und blüht grade in Teamfights auf. Einer der wichtigsten Sachen im Teamranked. Also würde es mich freuen wenn man es einfach mal versucht Cassiopeia ins Team zu integrieren nicht komplett einen otp ablehnt :).\r\nUnd wie gesagt ich werde mir trotzdem einen kleineren Championpool noch antrainieren (gerade weil dieser Champion auch mal gerade in Clash oder Teams die nicht gegen spielen wollen gebannt wird bzw man auch mal eine Teamcomp spielen will in der ein anderer Midlaner besser aufgehoben ist ) jedoch meine präferenzen doch ganz gerne weiterhin auf Cassiopeia legen :) Andere Vorstellungen kann man auch gerne im Teamspeak oder so bereden und mal schauen ob man auf einen Nenner kommt. Ich bin auch Anpassungsfähig die Sache mit Cassiopeia is lediglich eine bitte ^^.\r\n\r\nNaja falls ihr also einen Midlaner sucht und wir die gleichen Intressen haben bezüglich meiner Erwartungen im ersten Abschnitt\r\nund ich euer Intresse geweckt haben sollte dann addet mich einfach auf lol oder schreibt mir einen Kommentar ^^.\r\n\r\nmfg Justin ^^\r\n\r\nIGN: KitingViper","replies":[]} |
{"title":"UFC.163.Aldo.vs.Korean.Zombie.720p.HDTV.x264-RUDOS","uid":8779611,"size":6202301378,"categoryP":"video","categoryS":"hd___tv_shows","magnet":"?xt=urn:btih:dce948e710f97ed8da879ec068a607ee5f707f02&dn=UFC.163.Aldo.vs.Korean.Zombie.720p.HDTV.x264-RUDOS&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":1,"leechers":1,"uploader":"TvTeam","files":3,"time":1375647840,"description":"::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::\n <a href="\nhttp://www.torrenting.com" rel="nofollow" target="_NEW">\nhttp://www.torrenting.com</a>\n\n::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::\n\n(= Enjoy No RAR releases =)","torrent":{"xt":"urn:btih:dce948e710f97ed8da879ec068a607ee5f707f02","amp;dn":"UFC.163.Aldo.vs.Korean.Zombie.720p.HDTV.x264-RUDOS","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"dce948e710f97ed8da879ec068a607ee5f707f02","infoHashBuffer":{"type":"Buffer","data":[220,233,72,231,16,249,126,216,218,135,158,192,104,166,7,238,95,112,127,2]},"announce":[],"urlList":[]}} |
{
"Version": "2.1.47.86",
"Names": {
"ace": "achenés",
"ady": "adigeo",
"alt": "altái del sur",
"arp": "arapajó",
"ars": "árabe de Néyed",
"bla": "siksiká",
"ccp": "chakma",
"eu": "vasco",
"goh": "alemán de la alta edad antigua",
"grc": "griego clásico",
"gu": "gujarati",
"ht": "haitiano",
"kbd": "cabardiano",
"krc": "karachái-bálkaro",
"lo": "laosiano",
"luo": "luo",
"nr": "ndebele del sur",
"nso": "sesotho del norte",
"prg": "prusiano antiguo",
"rm": "retorrománico",
"shu": "árabe (Chad)",
"sma": "sami del sur",
"st": "sesotho del sur",
"sw": "swahili",
"syr": "siríaco",
"tet": "tetun",
"tyv": "tuvano",
"tzm": "tamazight del Marruecos Central",
"vai": "vai",
"wal": "walamo",
"wuu": "wu",
"xal": "calmuco",
"zun": "zuni"
},
"LocalizedNames": {
"sw_CD": "swahili (Congo)"
}
}
|
{
"database" : {
"host": "ds036709.mlab.com",
"port": "36709",
"collection": "biores",
"username": "readonly",
"password": "biores.2016"
}
} |
{"poster":"Doo ","date":"2015-12-09T21:47:57.886+0000","title":"Zac","subforum":"Champions & Gameplay","up_votes":1,"down_votes":2,"body":"Spielt den noch jemand? Habe den schon jahrelang nicht mehr gesehen. Vor paar Monaten noch selbst ein wenig gespielt, aber der ist ja wirklich komplett von der Bildfläche verschwunden. {{champion:154}} \r\n\r\nVor einigen Monaten war er noch gut zu spielen, top eigentlich meistens gewonnen und meines Erachtens war er auch relativ stark, warum sieht man Zac nicht mehr?","replies":[{"poster":"ü ö","date":"2015-12-10T03:47:30.227+0000","up_votes":2,"down_votes":0,"body":"Ich spiele ihn. Allerdings nur im Jungle. Die Ganks sind der Hammer mit der wahnsinnigen Range, dazu super zum engagen.","replies":[]},{"poster":"shibal thresh","date":"2015-12-09T23:24:31.801+0000","up_votes":1,"down_votes":0,"body":"Zac ist seitdem ich angefangen habe mein main gewesen :D (erstmal in der top lane, bin aber dann in den jgl gewechselt)","replies":[{"poster":"Alustus","date":"2015-12-10T13:22:54.672+0000","up_votes":1,"down_votes":0,"body":"Ich habe ihn lange auf der Support postion gespielt (season 3)","replies":[]}]},{"poster":"SK Benteke","date":"2015-12-09T22:29:06.778+0000","up_votes":1,"down_votes":0,"body":"top eher schwach, da die tank items ( bzw. ad-items) ihm nicht besonders gut stehen, im jungle ist er alright","replies":[{"poster":"Stealth Cat","date":"2015-12-10T07:44:59.940+0000","up_votes":1,"down_votes":0,"body":"Frage mich wieso der im Teamranked nicht wirklich abused wird. Aber zurzeit ist wohl einfach die Lee/Elise/Kindred/Nidalee Meta.","replies":[]}]},{"poster":"Shacoláde","date":"2015-12-10T07:40:45.453+0000","up_votes":1,"down_votes":0,"body":"Da realtiv selten Zac gespielt wird, spiele ich ihn selbst eigentlich sehr gerne im Jungle, da die Leute nicht wissen wie man gegen ihne spielt bzw. warded und ihn dadurch sehr gut in die Karten spielen. Wie die Leute vor mir schon gesagt habe, dass er sehr sehr stark im ganken ist durch die Große Range, dadurch ergeben sich Wege die andere Jungle nicht haben. \nWas ich aber umso stärker finde ist das Ulty im Teamfight durch seinen W kannst du die Leute (ähnlich Malphite Ulty) upkicken während du Laufe des Teamfight die Leute durchs Ulty spliten kannst was es deinem Team leichter macht einzelne Ziele isloiert zu fokusen. \nIch mag ihn echt gern im Jungle. Warscheinlich weil er in der LCS oder in der WM nicht gepickt wird, spielt ihn so selten einer.","replies":[]},{"poster":"blackriderr","date":"2015-12-09T22:30:43.317+0000","up_votes":1,"down_votes":0,"body":"Ich spiele zac noch auf der top (:","replies":[]}]} |
{"geometry":{"type":"Polygon","coordinates":[[[92.404007,53.264664],[92.389442,53.264004],[92.388054,53.263939],[92.379288,53.266747],[92.378319,53.272198],[92.376610,53.281754],[92.384033,53.283466],[92.385735,53.288128],[92.400764,53.290188],[92.408714,53.293758],[92.415146,53.291298],[92.415398,53.289333],[92.415611,53.287651],[92.406013,53.278965],[92.404007,53.264664]]]},"properties":{"qs:a1r":"*","geom:longitude":92.394941,"qs:level":"localadmin","lbl:latitude":53.275450,"wof:hierarchy":[{"locality_id":102014229,"country_id":85632685,"region_id":85688139}],"iso:country":"RU","src:geom_alt":[],"wof:geomhash":"397887254689d6ed9cc128557c6a17a2","qs:a1_lc":"2401400000100","wof:breaches":[],"wof:name":"YERMAKOVSKOYE","src:geom":"quattroshapes","qs:adm0":"Russia","wof:supersedes":[],"qs:loc_alt":"\u0415\u0440\u043c\u0430\u043a\u043e\u0432\u0441\u043a\u043e\u0435","qs:la_lc":"*","qs:loc":"YERMAKOVSKOYE","wof:id":102014229,"qs:pop":0,"lbl:longitude":92.400210,"geom:area":0.000726,"qs:id":0,"geom:latitude":53.278133,"qs:source":"SUnknown","src:centroid_lbl":"geonames","wof:belongsto":[85632685,85688139],"qs:type":"\u0441","wof:placetype":"locality","gn:population":8910,"qs:a0":"\u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0430\u044f \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u044f","qs:a1":"*\u041a\u0440\u0430\u0441\u043d\u043e\u044f\u0440\u0441\u043a\u0438\u0439 \u043a\u0440\u0430\u0439","qs:a2":"\u0415\u0440\u043c\u0430\u043a\u043e\u0432\u0441\u043a\u0438\u0439","wof:lastmodified":1439923970,"wof:superseded_by":[],"wof:concordances":{"gn:id":1485904},"wof:parent_id":85688139},"type":"Feature","bbox":[92.376610,53.263939,92.415611,53.293758],"id":102014229} |
{"title":"Whitney.S01E06.HDTV.subtitulada.Esp.sc.avi","uid":7139726,"size":182133066,"categoryP":"video","categoryS":"tv_shows","magnet":"?xt=urn:btih:d35cc17721493710be6956634e727d453b99a70c&dn=Whitney.S01E06.HDTV.subtitulada.Esp.sc.avi&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":0,"leechers":1,"uploader":"sediartv","files":1,"time":1332961389,"description":"Whitney.S01E06.HDTV.subtitulado.Esp.sc.\n","torrent":{"xt":"urn:btih:d35cc17721493710be6956634e727d453b99a70c","amp;dn":"Whitney.S01E06.HDTV.subtitulada.Esp.sc.avi","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"d35cc17721493710be6956634e727d453b99a70c","infoHashBuffer":{"type":"Buffer","data":[211,92,193,119,33,73,55,16,190,105,86,99,78,114,125,69,59,153,167,12]},"announce":[],"urlList":[]}} |
{"uuid":"3f6a7f08-4277-4cb4-ba71-bc0c6263b17f","name":"Default Suite","start":1520772881568,"stop":1520772913836,"children":["7310a1be-20e3-4a80-8326-da86a343318b"],"befores":[{"name":"initTestSuite","status":"passed","stage":"finished","description":"","start":1520772881630,"stop":1520772884045}],"afters":[{"name":"tearDown","status":"passed","stage":"finished","description":"","start":1520772912943,"stop":1520772913836,"steps":[{"name":"Browser cleanup","status":"passed","stage":"finished","start":1520772912944,"stop":1520772913836}]}]} |
{
"name": "manager",
"behaviors": ["ChangeMode"]
}
|
{
"name": "local-storage",
"version": "1.0.1",
"description": "local-storage micro library",
"directories": {
"lib": "./lib"
},
"main": "lib/index.js",
"repository": {
"type": "git",
"url": "https://github.com/grammarly/local-storage"
},
"keywords": [
"es6",
"micro",
"microjs",
"local-storage"
],
"author": {
"name": "Grammarly",
"email": "engineering@grammarly.com",
"url": "https://github.com/grammarly"
},
"license": "MIT",
"homepage": "https://github.com/grammarly/local-storage",
"scripts": {
"watch": "gulp watch",
"prepublish": "gulp prepublish",
"test": "gulp test",
"test-node": "gulp test-node",
"test-browser": "gulp test-browser"
},
"devDependencies": {
"es6-lib": "^0.0.9",
"babel-polyfill": "git://github.com/grammarly/babel-polyfill.git#1.0.1",
"chai": "^2.2.0"
}
}
|
{
"name": "VarrowAccountingPortal",
"version": "0.1.0",
"description": "",
"main": "app.js",
"dependencies": {
"express": ">= 4.*",
"connect-busboy": "*",
"node-xlsx": "*",
"xmlbuilder": "*",
"jade": "*",
"body-parser": "*",
"xml2js": "*",
"jsforce": "*",
"xlsx": "*"
},
"repository": {
"type": "git",
"url": "https://github.com/Varrow/VarrowAccountingPortal.git"
},
"keywords": [
"Node",
"Express",
"Busboy"
],
"author": "csunley@varrow.com",
"license": "MIT",
"bugs": {
"url": "https://github.com/Varrow/VarrowAccountingPortal/issues"
},
"homepage": "https://github.com/Varrow/VarrowAccountingPortal"
}
|
[{"lineNumber": 15, "col_offset": 0, "nodeName": "prepare_widget", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 22, "col_offset": 0, "nodeName": "file_list_widget", "type": "Callable[[Any], Any]"}, {"lineNumber": 27, "col_offset": 0, "nodeName": "batch_role_widget", "type": "Callable[[Any], Any]"}, {"lineNumber": 32, "col_offset": 0, "nodeName": "role_names", "type": "Callable[[], Any]"}, {"lineNumber": 37, "col_offset": 0, "nodeName": "parent_applet", "type": "Callable[[Any], Any]"}, {"lineNumber": 46, "col_offset": 0, "nodeName": "batch_processing_gui", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 51, "col_offset": 0, "nodeName": "filenames", "type": "Callable[[], Any]"}, {"lineNumber": 56, "col_offset": 0, "nodeName": "drag_n_drop_event", "type": "Callable[[Any], Any]"}, {"lineNumber": 67, "col_offset": 0, "nodeName": "test_FileListWidget_drag_n_drop", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 81, "col_offset": 0, "nodeName": "test_BatchRoleWidget", "type": "Callable[[Any, Any, Any], Any]"}, {"lineNumber": 94, "col_offset": 0, "nodeName": "RequestMock", "type": "Callable[..., Any]"}, {"lineNumber": 105, "col_offset": 0, "nodeName": "assert_gui_in_init_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 110, "col_offset": 0, "nodeName": "assert_gui_in_running_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 115, "col_offset": 0, "nodeName": "test_BatchProcessingGui_execution", "type": "Callable[[Any, Any, Any, Any], Any]"}, {"lineNumber": 179, "col_offset": 0, "nodeName": "test_BatchProcessingGui_clear", "type": "Callable[[Any, Any, Any, Any], Any]"}, {"lineNumber": 194, "col_offset": 0, "nodeName": "assert_raises", "type": "Callable[[Any], Any]"}, {"lineNumber": 191, "col_offset": 0, "nodeName": "does_not_raise", "type": "ContextManager[None]"}, {"lineNumber": 19, "col_offset": 11, "nodeName": "widget", "type": "Any"}, {"lineNumber": 39, "col_offset": 4, "nodeName": "parent_applet", "type": "unittest.mock.Mock"}, {"lineNumber": 40, "col_offset": 4, "nodeName": "value", "type": "Any"}, {"lineNumber": 40, "col_offset": 76, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 41, "col_offset": 4, "nodeName": "run_export", "type": "unittest.mock.Mock"}, {"lineNumber": 43, "col_offset": 11, "nodeName": "parent_applet", "type": "unittest.mock.Mock"}, {"lineNumber": 58, "col_offset": 4, "nodeName": "dndevent", "type": "unittest.mock.Mock"}, {"lineNumber": 59, "col_offset": 4, "nodeName": "urls", "type": "List[Any]"}, {"lineNumber": 60, "col_offset": 8, "nodeName": "url", "type": "Any"}, {"lineNumber": 60, "col_offset": 15, "nodeName": "urls", "type": "List[Any]"}, {"lineNumber": 62, "col_offset": 4, "nodeName": "return_value", "type": "bool"}, {"lineNumber": 63, "col_offset": 4, "nodeName": "return_value", "type": "List[Any]"}, {"lineNumber": 63, "col_offset": 55, "nodeName": "urls", "type": "List[Any]"}, {"lineNumber": 64, "col_offset": 11, "nodeName": "dndevent", "type": "unittest.mock.Mock"}, {"lineNumber": 76, "col_offset": 8, "nodeName": "url", "type": "Any"}, {"lineNumber": 76, "col_offset": 15, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 102, "col_offset": 11, "nodeName": "inner", "type": "Callable[..., Any]"}, {"lineNumber": 106, "col_offset": 4, "nodeName": "gui", "type": "Any"}, {"lineNumber": 106, "col_offset": 10, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 111, "col_offset": 4, "nodeName": "gui", "type": "Any"}, {"lineNumber": 111, "col_offset": 10, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 119, "col_offset": 4, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 120, "col_offset": 4, "nodeName": "request_mock", "type": "Callable[..., Any]"}, {"lineNumber": 180, "col_offset": 8, "nodeName": "role_name", "type": "Any"}, {"lineNumber": 180, "col_offset": 21, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 185, "col_offset": 8, "nodeName": "role_name", "type": "Any"}, {"lineNumber": 185, "col_offset": 21, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 191, "col_offset": 17, "nodeName": "nullcontext", "type": "Callable"}, {"lineNumber": 220, "col_offset": 4, "nodeName": "secondary_data_role", "type": "Any"}, {"lineNumber": 16, "col_offset": 4, "nodeName": "addWidget", "type": "Any"}, {"lineNumber": 16, "col_offset": 20, "nodeName": "widget", "type": "Any"}, {"lineNumber": 17, "col_offset": 4, "nodeName": "show", "type": "Any"}, {"lineNumber": 18, "col_offset": 4, "nodeName": "waitForWindowShown", "type": "Any"}, {"lineNumber": 18, "col_offset": 29, "nodeName": "widget", "type": "Any"}, {"lineNumber": 24, "col_offset": 11, "nodeName": "prepare_widget", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 24, "col_offset": 26, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 29, "col_offset": 11, "nodeName": "prepare_widget", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 29, "col_offset": 26, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 39, "col_offset": 20, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 40, "col_offset": 4, "nodeName": "DatasetRoles", "type": "Any"}, {"lineNumber": 41, "col_offset": 4, "nodeName": "parent_applet", "type": "unittest.mock.Mock"}, {"lineNumber": 41, "col_offset": 31, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 48, "col_offset": 11, "nodeName": "prepare_widget", "type": "Callable[[Any, Any], Any]"}, {"lineNumber": 48, "col_offset": 26, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 58, "col_offset": 15, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 61, "col_offset": 8, "nodeName": "isLocalFile", "type": "unittest.mock.Mock"}, {"lineNumber": 62, "col_offset": 4, "nodeName": "has_urls", "type": "Any"}, {"lineNumber": 63, "col_offset": 4, "nodeName": "urls", "type": "Any"}, {"lineNumber": 70, "col_offset": 4, "nodeName": "dragEnterEvent", "type": "Any"}, {"lineNumber": 70, "col_offset": 36, "nodeName": "drag_n_drop_event", "type": "Any"}, {"lineNumber": 71, "col_offset": 11, "nodeName": "called_once", "type": "Any"}, {"lineNumber": 74, "col_offset": 4, "nodeName": "dropEvent", "type": "Any"}, {"lineNumber": 74, "col_offset": 31, "nodeName": "drag_n_drop_event", "type": "Any"}, {"lineNumber": 77, "col_offset": 8, "nodeName": "items", "type": "Any"}, {"lineNumber": 87, "col_offset": 4, "nodeName": "addItems", "type": "Any"}, {"lineNumber": 87, "col_offset": 43, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 88, "col_offset": 11, "nodeName": "filepaths", "type": "Any"}, {"lineNumber": 88, "col_offset": 42, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 90, "col_offset": 4, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 90, "col_offset": 21, "nodeName": "clear_button", "type": "Any"}, {"lineNumber": 90, "col_offset": 53, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 95, "col_offset": 7, "nodeName": "instances", "type": "Any"}, {"lineNumber": 96, "col_offset": 8, "nodeName": "instances", "type": "List[unittest.mock.Mock]"}, {"lineNumber": 117, "col_offset": 4, "nodeName": "assert_gui_in_init_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 117, "col_offset": 29, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 120, "col_offset": 19, "nodeName": "RequestMock", "type": "Callable[..., Any]"}, {"lineNumber": 120, "col_offset": 31, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 133, "col_offset": 8, "nodeName": "req", "type": "nothing"}, {"lineNumber": 134, "col_offset": 39, "nodeName": "call_args", "type": "nothing"}, {"lineNumber": 146, "col_offset": 8, "nodeName": "req", "type": "nothing"}, {"lineNumber": 148, "col_offset": 37, "nodeName": "call_args", "type": "nothing"}, {"lineNumber": 151, "col_offset": 8, "nodeName": "info_messagebox", "type": "unittest.mock.Mock"}, {"lineNumber": 165, "col_offset": 8, "nodeName": "req", "type": "nothing"}, {"lineNumber": 166, "col_offset": 37, "nodeName": "call_args", "type": "nothing"}, {"lineNumber": 169, "col_offset": 8, "nodeName": "critical_messagebox", "type": "unittest.mock.Mock"}, {"lineNumber": 181, "col_offset": 8, "nodeName": "current_data_role_widget", "type": "Any"}, {"lineNumber": 186, "col_offset": 8, "nodeName": "current_data_role_widget", "type": "Any"}, {"lineNumber": 196, "col_offset": 33, "nodeName": "exp", "type": "Any"}, {"lineNumber": 219, "col_offset": 4, "nodeName": "addItems", "type": "Any"}, {"lineNumber": 219, "col_offset": 80, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 220, "col_offset": 26, "nodeName": "_data_role_widgets", "type": "Any"}, {"lineNumber": 16, "col_offset": 4, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 17, "col_offset": 4, "nodeName": "widget", "type": "Any"}, {"lineNumber": 18, "col_offset": 4, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 24, "col_offset": 33, "nodeName": "FileListWidget", "type": "Type[ilastik.applets.batchProcessing.batchProcessingGui.FileListWidget]"}, {"lineNumber": 29, "col_offset": 33, "nodeName": "BatchRoleWidget", "type": "Type[ilastik.applets.batchProcessing.batchProcessingGui.BatchRoleWidget]"}, {"lineNumber": 39, "col_offset": 20, "nodeName": "mock", "type": "module"}, {"lineNumber": 40, "col_offset": 4, "nodeName": "topLevelOperator", "type": "Any"}, {"lineNumber": 41, "col_offset": 31, "nodeName": "mock", "type": "module"}, {"lineNumber": 48, "col_offset": 33, "nodeName": "BatchProcessingGui", "type": "Type[ilastik.applets.batchProcessing.batchProcessingGui.BatchProcessingGui]"}, {"lineNumber": 48, "col_offset": 52, "nodeName": "parent_applet", "type": "Any"}, {"lineNumber": 58, "col_offset": 15, "nodeName": "mock", "type": "module"}, {"lineNumber": 59, "col_offset": 12, "nodeName": "QUrl", "type": "Any"}, {"lineNumber": 59, "col_offset": 17, "nodeName": "x", "type": "Any"}, {"lineNumber": 59, "col_offset": 24, "nodeName": "x", "type": "Any"}, {"lineNumber": 59, "col_offset": 29, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 61, "col_offset": 8, "nodeName": "url", "type": "Any"}, {"lineNumber": 61, "col_offset": 26, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 62, "col_offset": 4, "nodeName": "return_value", "type": "Any"}, {"lineNumber": 63, "col_offset": 4, "nodeName": "return_value", "type": "Any"}, {"lineNumber": 68, "col_offset": 11, "nodeName": "count", "type": "Any"}, {"lineNumber": 70, "col_offset": 4, "nodeName": "file_list_widget", "type": "Any"}, {"lineNumber": 71, "col_offset": 11, "nodeName": "acceptProposedAction", "type": "Any"}, {"lineNumber": 72, "col_offset": 11, "nodeName": "count", "type": "Any"}, {"lineNumber": 74, "col_offset": 4, "nodeName": "file_list_widget", "type": "Any"}, {"lineNumber": 75, "col_offset": 11, "nodeName": "count", "type": "Any"}, {"lineNumber": 75, "col_offset": 39, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 75, "col_offset": 43, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 77, "col_offset": 16, "nodeName": "findItems", "type": "Any"}, {"lineNumber": 77, "col_offset": 43, "nodeName": "url", "type": "Any"}, {"lineNumber": 77, "col_offset": 48, "nodeName": "MatchFixedString", "type": "Any"}, {"lineNumber": 82, "col_offset": 29, "nodeName": "text", "type": "Any"}, {"lineNumber": 83, "col_offset": 29, "nodeName": "text", "type": "Any"}, {"lineNumber": 85, "col_offset": 11, "nodeName": "count", "type": "Any"}, {"lineNumber": 87, "col_offset": 4, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 88, "col_offset": 11, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 90, "col_offset": 4, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 90, "col_offset": 21, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 90, "col_offset": 53, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 91, "col_offset": 11, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 91, "col_offset": 15, "nodeName": "filepaths", "type": "Any"}, {"lineNumber": 99, "col_offset": 8, "nodeName": "append", "type": "Union[Any, Callable]"}, {"lineNumber": 100, "col_offset": 15, "nodeName": "instances", "type": "Union[Any, List[unittest.mock.Mock]]"}, {"lineNumber": 107, "col_offset": 11, "nodeName": "isEnabled", "type": "Any"}, {"lineNumber": 112, "col_offset": 46, "nodeName": "isVisible", "type": "Any"}, {"lineNumber": 112, "col_offset": 80, "nodeName": "isEnabled", "type": "Any"}, {"lineNumber": 116, "col_offset": 11, "nodeName": "count", "type": "Any"}, {"lineNumber": 116, "col_offset": 43, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 116, "col_offset": 47, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 121, "col_offset": 9, "nodeName": "patch", "type": "unittest.mock._patcher"}, {"lineNumber": 122, "col_offset": 8, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 122, "col_offset": 25, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 122, "col_offset": 58, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 126, "col_offset": 8, "nodeName": "addItems", "type": "Any"}, {"lineNumber": 126, "col_offset": 84, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 128, "col_offset": 8, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 128, "col_offset": 25, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 128, "col_offset": 58, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 130, "col_offset": 8, "nodeName": "assert_gui_in_running_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 130, "col_offset": 36, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 133, "col_offset": 14, "nodeName": "pop", "type": "Callable"}, {"lineNumber": 134, "col_offset": 8, "nodeName": "success_args", "type": "nothing"}, {"lineNumber": 134, "col_offset": 22, "nodeName": "success_kwargs", "type": "nothing"}, {"lineNumber": 134, "col_offset": 39, "nodeName": "notify_finished", "type": "nothing"}, {"lineNumber": 138, "col_offset": 8, "nodeName": "assert_gui_in_init_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 138, "col_offset": 33, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 140, "col_offset": 8, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 140, "col_offset": 25, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 140, "col_offset": 58, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 142, "col_offset": 8, "nodeName": "assert_gui_in_running_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 142, "col_offset": 36, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 145, "col_offset": 8, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 145, "col_offset": 25, "nodeName": "cancel_button", "type": "Any"}, {"lineNumber": 145, "col_offset": 61, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 146, "col_offset": 14, "nodeName": "pop", "type": "Callable"}, {"lineNumber": 147, "col_offset": 8, "nodeName": "assert_called_once", "type": "nothing"}, {"lineNumber": 148, "col_offset": 8, "nodeName": "cancel_args", "type": "nothing"}, {"lineNumber": 148, "col_offset": 21, "nodeName": "cancel_kwargs", "type": "nothing"}, {"lineNumber": 148, "col_offset": 37, "nodeName": "notify_cancelled", "type": "nothing"}, {"lineNumber": 151, "col_offset": 26, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 158, "col_offset": 8, "nodeName": "assert_gui_in_init_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 158, "col_offset": 33, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 161, "col_offset": 8, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 161, "col_offset": 25, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 161, "col_offset": 58, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 163, "col_offset": 8, "nodeName": "assert_gui_in_running_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 163, "col_offset": 36, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 165, "col_offset": 14, "nodeName": "pop", "type": "Callable"}, {"lineNumber": 166, "col_offset": 8, "nodeName": "failed_args", "type": "nothing"}, {"lineNumber": 166, "col_offset": 21, "nodeName": "failed_kwargs", "type": "nothing"}, {"lineNumber": 166, "col_offset": 37, "nodeName": "notify_failed", "type": "nothing"}, {"lineNumber": 169, "col_offset": 30, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 176, "col_offset": 8, "nodeName": "assert_gui_in_init_state", "type": "Callable[[Any], Any]"}, {"lineNumber": 176, "col_offset": 33, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 181, "col_offset": 35, "nodeName": "_data_role_widgets", "type": "Any"}, {"lineNumber": 182, "col_offset": 8, "nodeName": "addItems", "type": "Any"}, {"lineNumber": 182, "col_offset": 54, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 186, "col_offset": 35, "nodeName": "_data_role_widgets", "type": "Any"}, {"lineNumber": 187, "col_offset": 8, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 187, "col_offset": 25, "nodeName": "clear_button", "type": "Any"}, {"lineNumber": 187, "col_offset": 64, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 196, "col_offset": 9, "nodeName": "capture_exceptions", "type": "Any"}, {"lineNumber": 198, "col_offset": 15, "nodeName": "exp", "type": "Any"}, {"lineNumber": 219, "col_offset": 4, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 220, "col_offset": 26, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 221, "col_offset": 9, "nodeName": "patch", "type": "unittest.mock._patcher"}, {"lineNumber": 222, "col_offset": 8, "nodeName": "addItems", "type": "Any"}, {"lineNumber": 222, "col_offset": 49, "nodeName": "secondary_files", "type": "Any"}, {"lineNumber": 223, "col_offset": 13, "nodeName": "expectation", "type": "Any"}, {"lineNumber": 40, "col_offset": 4, "nodeName": "dataSelectionApplet", "type": "Any"}, {"lineNumber": 61, "col_offset": 26, "nodeName": "mock", "type": "module"}, {"lineNumber": 62, "col_offset": 4, "nodeName": "mimeData", "type": "Any"}, {"lineNumber": 63, "col_offset": 4, "nodeName": "mimeData", "type": "Any"}, {"lineNumber": 68, "col_offset": 11, "nodeName": "file_list_widget", "type": "Any"}, {"lineNumber": 71, "col_offset": 11, "nodeName": "drag_n_drop_event", "type": "Any"}, {"lineNumber": 72, "col_offset": 11, "nodeName": "file_list_widget", "type": "Any"}, {"lineNumber": 75, "col_offset": 11, "nodeName": "file_list_widget", "type": "Any"}, {"lineNumber": 77, "col_offset": 16, "nodeName": "file_list_widget", "type": "Any"}, {"lineNumber": 77, "col_offset": 48, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 78, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 78, "col_offset": 19, "nodeName": "items", "type": "Any"}, {"lineNumber": 82, "col_offset": 29, "nodeName": "select_button", "type": "Any"}, {"lineNumber": 83, "col_offset": 29, "nodeName": "clear_button", "type": "Any"}, {"lineNumber": 85, "col_offset": 11, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 87, "col_offset": 4, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 91, "col_offset": 15, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 99, "col_offset": 8, "nodeName": "instances", "type": "Union[Any, List[unittest.mock.Mock]]"}, {"lineNumber": 99, "col_offset": 25, "nodeName": "Mock", "type": "Type[unittest.mock.Mock]"}, {"lineNumber": 107, "col_offset": 11, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 107, "col_offset": 46, "nodeName": "isVisible", "type": "Any"}, {"lineNumber": 112, "col_offset": 15, "nodeName": "isEnabled", "type": "Any"}, {"lineNumber": 112, "col_offset": 46, "nodeName": "cancel_button", "type": "Any"}, {"lineNumber": 112, "col_offset": 80, "nodeName": "cancel_button", "type": "Any"}, {"lineNumber": 116, "col_offset": 11, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 121, "col_offset": 9, "nodeName": "mock", "type": "module"}, {"lineNumber": 121, "col_offset": 86, "nodeName": "request_mock", "type": "Callable[..., Any]"}, {"lineNumber": 122, "col_offset": 8, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 122, "col_offset": 25, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 122, "col_offset": 58, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 123, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 123, "col_offset": 19, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 126, "col_offset": 8, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 128, "col_offset": 8, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 128, "col_offset": 25, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 128, "col_offset": 58, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 129, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 129, "col_offset": 19, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 133, "col_offset": 14, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 134, "col_offset": 39, "nodeName": "req", "type": "nothing"}, {"lineNumber": 135, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 135, "col_offset": 19, "nodeName": "success_args", "type": "nothing"}, {"lineNumber": 136, "col_offset": 8, "nodeName": "success_args", "type": "nothing"}, {"lineNumber": 140, "col_offset": 8, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 140, "col_offset": 25, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 140, "col_offset": 58, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 141, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 141, "col_offset": 19, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 145, "col_offset": 8, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 145, "col_offset": 25, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 145, "col_offset": 61, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 146, "col_offset": 14, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 147, "col_offset": 8, "nodeName": "cancel", "type": "nothing"}, {"lineNumber": 148, "col_offset": 37, "nodeName": "req", "type": "nothing"}, {"lineNumber": 149, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 149, "col_offset": 19, "nodeName": "cancel_args", "type": "nothing"}, {"lineNumber": 151, "col_offset": 26, "nodeName": "mock", "type": "module"}, {"lineNumber": 152, "col_offset": 13, "nodeName": "patch", "type": "unittest.mock._patcher"}, {"lineNumber": 156, "col_offset": 12, "nodeName": "assert_called_once", "type": "Callable[[], None]"}, {"lineNumber": 161, "col_offset": 8, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 161, "col_offset": 25, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 161, "col_offset": 58, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 162, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 162, "col_offset": 19, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 165, "col_offset": 14, "nodeName": "request_instances", "type": "List[nothing]"}, {"lineNumber": 166, "col_offset": 37, "nodeName": "req", "type": "nothing"}, {"lineNumber": 167, "col_offset": 15, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 167, "col_offset": 19, "nodeName": "failed_args", "type": "nothing"}, {"lineNumber": 169, "col_offset": 30, "nodeName": "mock", "type": "module"}, {"lineNumber": 170, "col_offset": 13, "nodeName": "patch", "type": "unittest.mock._patcher"}, {"lineNumber": 174, "col_offset": 12, "nodeName": "assert_called_once", "type": "Callable[[], None]"}, {"lineNumber": 181, "col_offset": 35, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 181, "col_offset": 75, "nodeName": "role_name", "type": "Any"}, {"lineNumber": 182, "col_offset": 8, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 183, "col_offset": 15, "nodeName": "count", "type": "Any"}, {"lineNumber": 183, "col_offset": 63, "nodeName": "len", "type": "Callable[[Sized], int]"}, {"lineNumber": 183, "col_offset": 67, "nodeName": "filenames", "type": "Any"}, {"lineNumber": 186, "col_offset": 35, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 186, "col_offset": 75, "nodeName": "role_name", "type": "Any"}, {"lineNumber": 187, "col_offset": 8, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 187, "col_offset": 25, "nodeName": "current_data_role_widget", "type": "Any"}, {"lineNumber": 187, "col_offset": 64, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 188, "col_offset": 15, "nodeName": "count", "type": "Any"}, {"lineNumber": 199, "col_offset": 18, "nodeName": "AssertionError", "type": "Type[AssertionError]"}, {"lineNumber": 200, "col_offset": 13, "nodeName": "issubclass", "type": "Callable[[Type[Any], Union[Type[Any], Tuple[Union[Type[Any], Tuple[Type[Any], ...]], ...]]], bool]"}, {"lineNumber": 200, "col_offset": 35, "nodeName": "exception_type", "type": "Any"}, {"lineNumber": 220, "col_offset": 66, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 221, "col_offset": 9, "nodeName": "mock", "type": "module"}, {"lineNumber": 222, "col_offset": 8, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 224, "col_offset": 12, "nodeName": "mouseClick", "type": "Any"}, {"lineNumber": 224, "col_offset": 29, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 224, "col_offset": 62, "nodeName": "LeftButton", "type": "Any"}, {"lineNumber": 40, "col_offset": 4, "nodeName": "parent_applet", "type": "unittest.mock.Mock"}, {"lineNumber": 62, "col_offset": 4, "nodeName": "dndevent", "type": "unittest.mock.Mock"}, {"lineNumber": 63, "col_offset": 4, "nodeName": "dndevent", "type": "unittest.mock.Mock"}, {"lineNumber": 82, "col_offset": 29, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 83, "col_offset": 29, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 85, "col_offset": 11, "nodeName": "batch_role_widget", "type": "Any"}, {"lineNumber": 99, "col_offset": 25, "nodeName": "mock", "type": "module"}, {"lineNumber": 107, "col_offset": 11, "nodeName": "gui", "type": "Any"}, {"lineNumber": 107, "col_offset": 46, "nodeName": "cancel_button", "type": "Any"}, {"lineNumber": 112, "col_offset": 15, "nodeName": "run_button", "type": "Any"}, {"lineNumber": 112, "col_offset": 46, "nodeName": "gui", "type": "Any"}, {"lineNumber": 112, "col_offset": 80, "nodeName": "gui", "type": "Any"}, {"lineNumber": 147, "col_offset": 8, "nodeName": "req", "type": "nothing"}, {"lineNumber": 152, "col_offset": 13, "nodeName": "mock", "type": "module"}, {"lineNumber": 153, "col_offset": 94, "nodeName": "info_messagebox", "type": "unittest.mock.Mock"}, {"lineNumber": 155, "col_offset": 12, "nodeName": "cancel_args", "type": "nothing"}, {"lineNumber": 156, "col_offset": 12, "nodeName": "info_messagebox", "type": "unittest.mock.Mock"}, {"lineNumber": 170, "col_offset": 13, "nodeName": "mock", "type": "module"}, {"lineNumber": 171, "col_offset": 91, "nodeName": "critical_messagebox", "type": "unittest.mock.Mock"}, {"lineNumber": 173, "col_offset": 12, "nodeName": "failed_args", "type": "nothing"}, {"lineNumber": 173, "col_offset": 27, "nodeName": "MagicMock", "type": "Type[unittest.mock.MagicMock]"}, {"lineNumber": 173, "col_offset": 42, "nodeName": "Exception", "type": "Type[Exception]"}, {"lineNumber": 174, "col_offset": 12, "nodeName": "critical_messagebox", "type": "unittest.mock.Mock"}, {"lineNumber": 182, "col_offset": 8, "nodeName": "current_data_role_widget", "type": "Any"}, {"lineNumber": 183, "col_offset": 15, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 188, "col_offset": 15, "nodeName": "list_widget", "type": "Any"}, {"lineNumber": 203, "col_offset": 18, "nodeName": "AssertionError", "type": "Type[AssertionError]"}, {"lineNumber": 219, "col_offset": 4, "nodeName": "_data_role_widgets", "type": "Any"}, {"lineNumber": 221, "col_offset": 86, "nodeName": "RequestMock", "type": "Callable[..., Any]"}, {"lineNumber": 222, "col_offset": 8, "nodeName": "secondary_data_role", "type": "Any"}, {"lineNumber": 224, "col_offset": 12, "nodeName": "qtbot", "type": "Any"}, {"lineNumber": 224, "col_offset": 29, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 224, "col_offset": 62, "nodeName": "Qt", "type": "Any"}, {"lineNumber": 107, "col_offset": 46, "nodeName": "gui", "type": "Any"}, {"lineNumber": 112, "col_offset": 15, "nodeName": "gui", "type": "Any"}, {"lineNumber": 126, "col_offset": 8, "nodeName": "_data_role_widgets", "type": "Any"}, {"lineNumber": 173, "col_offset": 27, "nodeName": "mock", "type": "module"}, {"lineNumber": 183, "col_offset": 15, "nodeName": "current_data_role_widget", "type": "Any"}, {"lineNumber": 188, "col_offset": 15, "nodeName": "current_data_role_widget", "type": "Any"}, {"lineNumber": 200, "col_offset": 24, "nodeName": "exp", "type": "Any"}, {"lineNumber": 219, "col_offset": 4, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 126, "col_offset": 8, "nodeName": "batch_processing_gui", "type": "Any"}, {"lineNumber": 203, "col_offset": 92, "nodeName": "exception_type", "type": "Any"}, {"lineNumber": 219, "col_offset": 44, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 126, "col_offset": 48, "nodeName": "role_names", "type": "Any"}, {"lineNumber": 203, "col_offset": 51, "nodeName": "exp", "type": "Any"}] |
[{"name": "SS", "zip": "65804", "created": 1342064948, "time": "23:49:08", "date": "12-07-11", "id": "4ffe4934709f03d568000000"}, {"name": "tw", "zip": "94618", "created": 1342064736, "time": "23:45:36", "date": "12-07-11", "id": "4ffe4860fb7528c052000001"}, {"name": "TK", "zip": "95014", "created": 1342064191, "time": "23:36:31", "date": "12-07-11", "id": "4ffe463f2ee8d0d76e000001"}, {"name": "AS", "zip": "20194", "created": 1342063836, "time": "23:30:36", "date": "12-07-11", "id": "4ffe44dc11fb9cb342000000"}, {"name": "JK", "zip": "87110", "created": 1342063649, "time": "23:27:29", "date": "12-07-11", "id": "4ffe4421de88f4d263000002"}, {"name": "YR", "zip": "98103", "created": 1342047608, "time": "19:00:08", "date": "12-07-11", "id": "4ffe0578fb75283a02000003"}, {"name": "MP", "zip": "29063", "created": 1342038047, "time": "16:20:47", "date": "12-07-11", "id": "4ffde01f8d8c37ae1c000013"}, {"name": "AG", "zip": "", "created": 1342037670, "time": "16:14:30", "date": "12-07-11", "id": "4ffddea69cd1c1a151000009"}, {"name": "BS", "zip": null, "created": 1342037577, "time": "16:12:57", "date": "12-07-11", "id": "4ffdde49a4ae8ca83900000d"}, {"name": "EG", "zip": null, "created": 1342037500, "time": "16:11:40", "date": "12-07-11", "id": "4ffdddfc0353f38b4a00000a"}, {"name": "AC", "zip": null, "created": 1342035928, "time": "15:45:28", "date": "12-07-11", "id": "4ffdd7d83d2d5b986b00001d"}, {"name": "RS", "zip": "19066", "created": 1342035620, "time": "15:40:20", "date": "12-07-11", "id": "4ffdd6a4204027774100000c"}, {"name": "RS", "zip": "99353", "created": 1342035532, "time": "15:38:52", "date": "12-07-11", "id": "4ffdd64cc19bc09f3f00000c"}, {"name": "CS", "zip": "02155", "created": 1342035280, "time": "15:34:40", "date": "12-07-11", "id": "4ffdd550fb75288366000014"}, {"name": "DW", "zip": "94608", "created": 1342034617, "time": "15:23:37", "date": "12-07-11", "id": "4ffdd2b99cd1c10729000008"}, {"name": "BM", "zip": "98126", "created": 1342034328, "time": "15:18:48", "date": "12-07-11", "id": "4ffdd1984bd5046f76000014"}, {"name": "jv", "zip": "33611", "created": 1342034247, "time": "15:17:27", "date": "12-07-11", "id": "4ffdd1472040272d30000009"}, {"name": "SF", "zip": "91737", "created": 1342034235, "time": "15:17:15", "date": "12-07-11", "id": "4ffdd13bcf436abb32000010"}, {"name": "MC", "zip": "98004", "created": 1342033934, "time": "15:12:14", "date": "12-07-11", "id": "4ffdd00ecf436aec5a000002"}, {"name": "JM", "zip": "60047", "created": 1342033883, "time": "15:11:23", "date": "12-07-11", "id": "4ffdcfdb8d8c37953c00001d"}, {"name": "BG", "zip": "95404", "created": 1342033779, "time": "15:09:39", "date": "12-07-11", "id": "4ffdcf73cf436a8f75000012"}, {"name": "KB", "zip": "02453", "created": 1342033702, "time": "15:08:22", "date": "12-07-11", "id": "4ffdcf26ec309e8d6c000012"}, {"name": "DN", "zip": "30005", "created": 1342033632, "time": "15:07:12", "date": "12-07-11", "id": "4ffdcee00353f3347f000012"}, {"name": "JP", "zip": "20902", "created": 1342033016, "time": "14:56:56", "date": "12-07-11", "id": "4ffdcc78c19bc01744000003"}, {"name": "AW", "zip": "46249", "created": 1342032889, "time": "14:54:49", "date": "12-07-11", "id": "4ffdcbf93d2d5b156f00000f"}, {"name": "EL", "zip": "92126", "created": 1342032535, "time": "14:48:55", "date": "12-07-11", "id": "4ffdca9711fb9ce847000010"}, {"name": "JR", "zip": "66046", "created": 1342031088, "time": "14:24:48", "date": "12-07-11", "id": "4ffdc4f07fe48d1e49000012"}, {"name": "hk", "zip": "", "created": 1342030822, "time": "14:20:22", "date": "12-07-11", "id": "4ffdc3e62ee8d0731c000008"}, {"name": "DS", "zip": "70123", "created": 1342030680, "time": "14:18:00", "date": "12-07-11", "id": "4ffdc3589cd1c13527000008"}, {"name": "PJ", "zip": "21211", "created": 1342030619, "time": "14:16:59", "date": "12-07-11", "id": "4ffdc31bfb7528af2a000002"}, {"name": "JF", "zip": "45202", "created": 1342030583, "time": "14:16:23", "date": "12-07-11", "id": "4ffdc2f74bd504052900000e"}, {"name": "NN", "zip": "54136", "created": 1342028891, "time": "13:48:11", "date": "12-07-11", "id": "4ffdbc5bfb75281c2c000001"}, {"name": "VY", "zip": "94121", "created": 1342028637, "time": "13:43:57", "date": "12-07-11", "id": "4ffdbb5da4ae8ceb38000001"}, {"name": "MG", "zip": "97212", "created": 1342026649, "time": "13:10:49", "date": "12-07-11", "id": "4ffdb39962f759ec27000008"}, {"name": "MB", "zip": "95051", "created": 1342026623, "time": "13:10:23", "date": "12-07-11", "id": "4ffdb37fc19bc07d1d000010"}, {"name": "RH", "zip": "76446", "created": 1342025797, "time": "12:56:37", "date": "12-07-11", "id": "4ffdb045bc45edf24600000a"}, {"name": "AH", "zip": "06238", "created": 1342025793, "time": "12:56:33", "date": "12-07-11", "id": "4ffdb04153b2cfdc79000007"}, {"name": "JH", "zip": "20190", "created": 1342025661, "time": "12:54:21", "date": "12-07-11", "id": "4ffdafbdec309e1f24000009"}, {"name": "CL", "zip": "55106", "created": 1342024652, "time": "12:37:32", "date": "12-07-11", "id": "4ffdabcd2040271a25000006"}, {"name": "JG", "zip": "14620", "created": 1342024330, "time": "12:32:10", "date": "12-07-11", "id": "4ffdaa8a3d2d5b4366000002"}, {"name": "BM", "zip": "64114", "created": 1342024009, "time": "12:26:49", "date": "12-07-11", "id": "4ffda9490353f3681c000003"}, {"name": "DB", "zip": "80206", "created": 1342023987, "time": "12:26:27", "date": "12-07-11", "id": "4ffda9338d8c37fc15000003"}, {"name": "JB", "zip": "50158", "created": 1342023793, "time": "12:23:13", "date": "12-07-11", "id": "4ffda871de88f46a13000004"}, {"name": "CC", "zip": "75001", "created": 1342022645, "time": "12:04:05", "date": "12-07-11", "id": "4ffda3f5c19bc09604000001"}, {"name": "JP", "zip": null, "created": 1342021698, "time": "11:48:18", "date": "12-07-11", "id": "4ffda042a4ae8c501000000d"}, {"name": "DF", "zip": null, "created": 1342019434, "time": "11:10:34", "date": "12-07-11", "id": "4ffd976ac19bc03246000007"}, {"name": "JR", "zip": "11238", "created": 1342018937, "time": "11:02:17", "date": "12-07-11", "id": "4ffd9579bc45edf372000002"}, {"name": "CH", "zip": "94561", "created": 1342016798, "time": "10:26:38", "date": "12-07-11", "id": "4ffd8d1e0353f3681c000001"}, {"name": "AB", "zip": "44203", "created": 1342016286, "time": "10:18:06", "date": "12-07-11", "id": "4ffd8b1e11fb9c485e000008"}, {"name": "EP", "zip": "48420", "created": 1342014294, "time": "09:44:54", "date": "12-07-11", "id": "4ffd8356a4ae8c5010000005"}, {"name": "JZ", "zip": "53132", "created": 1342014028, "time": "09:40:28", "date": "12-07-11", "id": "4ffd824cec309e0074000006"}, {"name": "AB", "zip": "33158", "created": 1342011823, "time": "09:03:43", "date": "12-07-11", "id": "4ffd79af11fb9c3d4c000003"}, {"name": "NV", "zip": "91730", "created": 1342001007, "time": "06:03:27", "date": "12-07-11", "id": "4ffd4f6ffb75281207000000"}, {"name": "AK", "zip": "123456", "created": 1341992131, "time": "03:35:31", "date": "12-07-11", "id": "4ffd2cc33d2d5bdc01000000"}, {"name": "WG", "zip": null, "created": 1341989558, "time": "02:52:38", "date": "12-07-11", "id": "4ffd22b69cd1c11e5e000002"}, {"name": "FS", "zip": "79936", "created": 1341989147, "time": "02:45:47", "date": "12-07-11", "id": "4ffd211b4bd504d83d000009"}, {"name": "RA", "zip": "02052", "created": 1341985720, "time": "01:48:40", "date": "12-07-11", "id": "4ffd13b87fe48d3a65000003"}, {"name": "AB", "zip": "77386", "created": 1341983349, "time": "01:09:09", "date": "12-07-11", "id": "4ffd0a7511fb9c5e0b000000"}, {"name": "JM", "zip": "25425", "created": 1341979343, "time": "00:02:23", "date": "12-07-11", "id": "4ffcfacffb75288a5d00000b"}] |
{"id":"a0308165a339671d5b1614c4c88f5df76860f4a9b570c96b1e72b8b8fd6abbe7","name":"横浜相鉄ビル皮膚泌尿器科医院","prefId":"14","mdepart1":"皮膚泌尿器科","mdepart2":"","mdepart3":"","address1":"神奈川県横浜市西区北幸１−１１−５相鉄ＫＳビル２Ｆ","address2":"","tel":null,"url":null,"firstvisit":true,"rediagnosis":true,"covid19":"-","doctors":"江川ゆり","otherinfo":"-"}
|
{
"id": 907231199,
"type": "Feature",
"properties": {
"addr:full":"5/6 Highfield Parade Waterlooville Hampshire PO7 7QH",
"addr:housenumber":"5 / 6",
"addr:postcode":"po7 7qh",
"addr:street":"Highfield Parade",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"-1.0252599716,50.8869094849,-1.0252599716,50.8869094849",
"geom:latitude":50.886909,
"geom:longitude":-1.02526,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"5/6 Highfield Parade",
"sg:city":"Waterlooville",
"sg:classifiers":[
{
"category":"Home & Garden",
"subcategory":"Household Appliances",
"type":"Retail Goods"
},
{
"category":"Retail",
"subcategory":"Repair Shop",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 23 9223 3100",
"sg:postcode":"PO7 7QH",
"sg:province":"Hampshire",
"sg:tags":[
"machine",
"electrical",
"washing"
],
"src:geom":"simplegeo",
"wof:belongsto":[
85633159,
85785573,
85684375
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_1C4A7V9I3VkvxcgqWrFJxy_50.886909_-1.025260@1300740332"
},
"wof:country":"GB",
"wof:created":1471891541,
"wof:geomhash":"b2913b960e9a70068ec123f3127acb7d",
"wof:hierarchy":[
{
"country_id":85633159,
"neighbourhood_id":85785573,
"region_id":85684375,
"venue_id":907231199
}
],
"wof:id":907231199,
"wof:lastmodified":1496973112,
"wof:name":"Peter Jay Services",
"wof:parent_id":85785573,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"machine",
"electrical",
"washing"
]
},
"bbox": [
-1.0252599716,
50.8869094849,
-1.0252599716,
50.8869094849
],
"geometry": {"coordinates":[-1.0252599716,50.8869094849],"type":"Point"}
} |
{"Name":"Chaos Ominous Boots♀·Sealed","Id":23871,"data":["23871","145","23060","Chaos Ominous Boots♀·Sealed","1","8","0","7416","0","7416","584","7539","5","0","0","Destiny","8","0","1","100","0","0","7","2154","1998","0","0","0","0","109","106","0","0","0","2945","29454","0","0","0","0","0","0","0","0","0","0","35182","35182","1","35182","1","24020","10","1","Ƙ\u0000ʏ\u0000Π\u0000ӕ\u0000ۄ\u0000ফ\u0000ฅ\u0000ፗ\u0000ᤱ\u0000ᾟ\u0000⚱\u0000\u0000㜅\u0000䁭\u0000䫅\u0000嘦\u0000抩\u0000灭\u0000羑\u0000逸\u0000","6","0","0","1","11370","0","0","\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000","\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000","0","0","0","1","4","14978","0","1","64","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: ","ERROR UNKOWN TYPE: "]} |
{"poster":"darth mother","date":"2018-10-11T20:58:25.101+0000","title":"ASPECTO VICTORIOSO 2018","subforum":"Charlas Generales","up_votes":2,"down_votes":0,"body":"Hola estaba pensado en el tema del nuevo aspecto para las personas que lleguen a oro o mas y recientemente en la actualizacion 8.20 pusieron esto en el las notas de parche "Agregamos un ícono a la sección de perfil para que puedan verificar si cumplen los requisitos para recibir el aspecto victorioso de este año. ¡Coloquen su cursor sobre él para descubrir las recompensas!" y yo recientemente llegue a oro y queria verificar ese mensaje pero no se que es lo que puso riot alguien que lo haya visto me podria decir?","replies":[{"poster":"Rakan Wawita","date":"2018-10-11T23:24:49.852+0000","up_votes":4,"down_votes":0,"body":"Todos sabemos que es el:https://i.imgur.com/kfA2xAz.jpg[/img]","replies":[]},{"poster":"ZanahoriaAsesina","date":"2018-10-11T21:01:57.571+0000","up_votes":1,"down_votes":0,"body":"Aun no esta disponible pero lo van a habilitar durante este parche. :)\n\nCon respecto a las recompensas, por ahora, el único requisito es ser Honor 2.","replies":[{"poster":"Roxguel","date":"2018-10-11T22:58:20.195+0000","up_votes":1,"down_votes":0,"body":"También estoy esperando que aparezca este icono, pero me suena que antes van a lanzar un teaser del aspecto victorioso. Y una vez todos sepamos cuál es el campeón afortunado, aparecerá este icono indicándonos si lo podremos recibir como recompensa.","replies":[{"poster":"ZanahoriaAsesina","date":"2018-10-12T01:56:50.206+0000","up_votes":2,"down_votes":0,"body":"Puede ser, si.","replies":[]}]},{"poster":"darth mother","date":"2018-10-11T21:06:41.039+0000","up_votes":1,"down_votes":0,"body":"Hola gracias por la ayuda compañero","replies":[]}]},{"poster":"Seiya322","date":"2018-10-14T20:27:19.120+0000","up_votes":1,"down_votes":0,"body":"La skin victoriosa de este año será Syndra{{champion:134}} estoy seguro.","replies":[]},{"poster":"Silcardo Jenazad","date":"2018-10-11T22:36:45.586+0000","up_votes":1,"down_votes":0,"body":"Lo q yo quiero saber es para q champ va a ser el aspecto lpm... el año pasado no me interesaba por q no rankeaba.. este año me puse serio para llegar a oro y todvia sigue el misterio co el aspecto... el año pasado a esta fecha ya casi se sabia..no puedooo mas carajooo aaaahhh","replies":[{"poster":"AleeHjandrO","date":"2018-10-12T01:07:45.541+0000","up_votes":1,"down_votes":0,"body":"> [{quoted}](name=Silcardo Jenazad,realm=LAS,application-id=v7qsfXsE,discussion-id=3wyEO3LT,comment-id=0001,timestamp=2018-10-11T22:36:45.586+0000)\n>\n> Lo q yo quiero saber es para q champ va a ser el aspecto lpm... el año pasado no me interesaba por q no rankeaba.. este año me puse serio para llegar a oro y todvia sigue el misterio co el aspecto... el año pasado a esta fecha ya casi se sabia..no puedooo mas carajooo aaaahhh\n\nL skin si o si va a ser para un adc o mid y creo q puede estar entre {{champion:202}} ,{{champion:142}} o si no puede ser {{champion:145}} \nSon los más probables para q le den la skin victoriosa","replies":[{"poster":"Silcardo Jenazad","date":"2018-10-12T02:08:22.020+0000","up_votes":1,"down_votes":0,"body":"kai sa y zoe no van a ser...tuvieron skins recientes...y ninguna tiene muchas skin...dijeron bien claro que no le van a dar skin victoriosa a campeones que tengan pocas skin por que no quieren sacar una skin que no todos puedan tener teniendo ese champ poca variedad...","replies":[]}]}]},{"poster":"SparkkNimoTV","date":"2018-10-12T01:25:32.698+0000","up_votes":1,"down_votes":0,"body":"Hola wapo,aguante isurus adeus","replies":[]},{"poster":"Blaknight","date":"2018-10-12T01:10:17.357+0000","up_votes":1,"down_votes":0,"body":"Espero que sea Jhin :v","replies":[]}]} |
{"url": "https://bland.is/umraeda/ad-poppa-med-kokosoliu-spurning-/29520681/?page=2266", "date": "2012-11-17 23:42:10", "id": "29520681", "items": [{"text": "Ég er að hugsa um að prófa popp með kókosolíu, setur maður jafn mikið af henni og þegar poppað er með smjörlíki?", "title": "Að poppa með kókosolíu spurning...", "username": "the fruit cake lady", "message_id": "29520681", "user_id": "69930", "response_to": null, "datetime": "2012-11-17 23:42:10", "datestring": "17. nóv. '12, kl: 23:42:10"}, {"response_to": "29520681", "username": "nóvemberpons", "message_id": "29520698", "user_id": "69661", "datetime": "2012-11-17 23:46:00", "datestring": "17. nóv. '12, kl: 23:46:00", "text": "ég las \" að poppa með kókosolíu í augunum\" Ég er farin að sofa !"}, {"response_to": "29520698", "username": "the fruit cake lady", "message_id": "29520707", "user_id": "69930", "datetime": "2012-11-17 23:50:01", "datestring": "17. nóv. '12, kl: 23:50:01", "text": "hhaha góða nótt"}, {"response_to": "29520681", "username": "Tinna1991", "message_id": "29520733", "user_id": "162707", "datetime": "2012-11-17 23:56:49", "datestring": "17. nóv. '12, kl: 23:56:49", "text": "það poppar enginn uppur smjorliki!!"}, {"response_to": "29520733", "username": "the fruit cake lady", "message_id": "29520963", "user_id": "69930", "datetime": "2012-11-18 01:03:40", "datestring": "18. nóv. '12, kl: 01:03:40", "text": "jú!!!! það er langbest!"}, {"response_to": "29520681", "username": "Miss Skreamer", "message_id": "29520973", "user_id": "179403", "datetime": "2012-11-18 01:06:32", "datestring": "18. nóv. '12, kl: 01:06:32", "text": "Hún á bara að húða maísbaunirnar"}, {"response_to": "29520973", "username": "the fruit cake lady", "message_id": "29520978", "user_id": "69930", "datetime": "2012-11-18 01:08:40", "datestring": "18. nóv. '12, kl: 01:08:40", "text": "ok takk :)"}, {"response_to": "29520681", "username": "smusmu", "message_id": "29521310", "user_id": "9059", "datetime": "2012-11-18 09:06:48", "datestring": "18. nóv. '12, kl: 09:06:48", "text": "Ég set bara sama magn já"}, {"response_to": "29520681", "username": "raudmagi", "message_id": "29521336", "user_id": "172985", "datetime": "2012-11-18 09:30:54", "datestring": "18. nóv. '12, kl: 09:30:54", "text": "Ég hef sett minna af henni af því að hún er þynnri."}]} |
{"poster":"Analyser","date":"2018-02-13T18:08:48.367+0000","title":"Busco duo que juegue botline con discord (Elo Platino-diamante)","subforum":"Reclutamiento","up_votes":4,"down_votes":1,"body":"Busco main adc o supp que tenga discord para mayor comunicación yo estoy bastante fuerte en top con urgot, pero me pasa que la botline casí siempre se va stompeada en early cuando yo aún no puedo ayudarlos. (esto se adjudica a que les toca comodín casi siempre al supp o al adc)\r\nNickname: Analyser por cualqueir cosa","replies":[{"poster":"Døkkálfr","date":"2018-02-26T17:13:22.875+0000","up_votes":1,"down_votes":0,"body":"Main Supp\n\nhttp://las.op.gg/summoner/userName=KNT%20LastShadow7\n\n{{sticker:sg-kiko}}","replies":[]}]} |
{"title":"朋「夢と違うじゃない」","url":"http://elephant.2chblog.jp/archives/52193207.html","time":"2017-03-12 06:40:59 UTC"} |
{"poster":"macromite","date":"2016-12-18T03:10:51.976+0000","title":"Random Thought: Sustain.","subforum":"Gameplay","up_votes":2,"down_votes":1,"body":"It seems that if sustain is inherent in a champion's kit it takes a lot of power budget away from the rest of the champion,\r\n\r\nhowever, it also seems that sustain hardly affect the budget of items.\r\n\r\nShouldn't it be the other way around?","replies":[{"poster":"TheKemperor","date":"2016-12-18T03:17:07.457+0000","up_votes":1,"down_votes":0,"body":"How so?","replies":[{"poster":"macromite","date":"2016-12-18T03:54:35.015+0000","up_votes":1,"down_votes":0,"body":"Many sustain based items have a lot of power in non-sustain facets, but most of the sustain based champions are consistantly trash through the seasons.","replies":[{"poster":"Shadowfang TC ","date":"2016-12-18T03:57:13.932+0000","up_votes":1,"down_votes":0,"body":"> [{quoted}](name=macromite,realm=NA,application-id=3ErqAdtq,discussion-id=yAFIssV9,comment-id=00000000,timestamp=2016-12-18T03:54:35.015+0000)\n>\n> Many sustain based items have a lot of power in non-sustain facets, but most of the sustain based champions are consistantly trash through the seasons.\n\nVladimir does extremely well. I really can't think of any other examples right now.","replies":[{"poster":"macromite","date":"2016-12-18T04:04:58.406+0000","up_votes":1,"down_votes":0,"body":"Vlad is meh, he is a low range mage with sustained based damage and a lot of tools that are not as effective as his enemy has. his power budget is very clearly affected by his sustain.\nAnother good example to look at would be trox, who has all of his power budget locked up in his sustain even though his sustain is only marginally stronger than a normal carries is with the same build.","replies":[{"poster":"Shadowfang TC ","date":"2016-12-18T04:09:43.187+0000","up_votes":1,"down_votes":0,"body":"> [{quoted}](name=macromite,realm=NA,application-id=3ErqAdtq,discussion-id=yAFIssV9,comment-id=0000000000000000,timestamp=2016-12-18T04:04:58.406+0000)\n>\n> Vlad is meh, he is a low range mage with sustained based damage and a lot of tools that are not as effective as his enemy has. his power budget is very clearly affected by his sustain.\n> Another good example to look at would be trox, who has all of his power budget locked up in his sustain even though his sustain is only marginally stronger than a normal carries is with the same build.\n\nVlad is meh? Every Vlad I've ever seen hits like a truck and can outheal nearly all damage done to him... to include the jungle ganking said lane and assisting. Old Vladimir wasn't a big deal, but the new one has some extreme healing.","replies":[]}]}]},{"poster":"Tukan Tonko","date":"2016-12-18T04:02:29.054+0000","up_votes":1,"down_votes":0,"body":"> [{quoted}](name=macromite,realm=NA,application-id=3ErqAdtq,discussion-id=yAFIssV9,comment-id=00000000,timestamp=2016-12-18T03:54:35.015+0000)\n>\n> Many sustain based items have a lot of power in non-sustain facets, but most of the sustain based champions are consistantly trash through the seasons.\n\nWhat about Swain? He is not popular but he can heal a ton. And he is also strong. \nMundo, maybe? He was good jungler and I think he still is. \n\nCould you explain it a bit better? I think I don't get the point. Or I am just stupid? xD","replies":[]}]}]}]} |
{
"name": "utm-params",
"repo": "segmentio/utm-params",
"version": "1.1.0",
"description": "Parse a URL, returning all UTM parameters.",
"main": "index.js",
"scripts": [
"index.js"
],
"dependencies": {
"component/querystring": "2.0.0",
"ndhoule/includes": "^1.0.0",
"ndhoule/foldl": "^1.0.3"
},
"development": {
"component/assert": "*",
"visionmedia/mocha": "*"
}
}
|
{
"name": "magento2/hiberus-test",
"type": "magento2-module",
"require": {
"php": "~5.5.0|~5.6.0|~7.0.0"
},
"description": "Students Management",
"keywords": ["relevant","tags","go","here"],
"license": "MIT",
"authors": [
{
"name": "Javier López Girela",
"email": "javi_lg2014@hotmail.com",
"role": "Developer"
}
],
"repositories": [
{
"type": "composer",
"url": "http://packages.magento.com/"
},
{
"type": "vcs",
"url": "https://github.com/str-javierlopez/magento-2"
}
],
"autoload": {
"files": [
"registration.php"
],
"psr-4": {
"Hiberus\\Lopez\\": ""
}
}
}
|
["1a436c683ca905c3ceb37d18bb383d007fd175d4","d721c8b267e6c58a1bd34f3d120963a0c49f5e8c","40ae6abbcb5b22beea92515194bacbaef3dddb37","48b153b925d93254725b84f85887f759403e2fb0","bb5e320b07e5eef9bef8836d82f5806e23e217ea","304dbd6bffb709f455fbf9f579643d2aef944e81","86bf64e940648811af3057b2b67137b201581bc2","f2cffa432bd24ade29aafafbfdfcf1fe9ededa65","ec83b36769c0ec321995afeb7862d9649b0a793f","2329b177f57171f7251f7ce291899395ed3afd70","8227f0d460251a01fcf546e27f99efe684caa397","a8ed7be58e3008f4e2ebf5afc76775adeb55dbb8","37e075884d62cfd73b4f3a918170f46391179f13","65f9ca91c8406b780f69eef9d3d50f794eb2fa71","4613076ab48d93f5d1501122f63a42b7b19ac731","69d7c8e7772ec359e3b50e3e3ec729bb7ad7e255","13a3c17f4c31387119e140d07fa62e89f53ccae5","f82bfe72c0a3ce0ba25dc69bffdbc4500e52713d","fe7518bf7b588408d464bc560e0d051c551457d5","6aa9ceaa69daac71ff232026d06c5c885bbf8133","0ac24b189a3665c2da42f2e9273a077c0cff709b","67afc33649490391f1b36c77788eb8ccd1c007d0","9a4bd6ad3c6c563215169cfe634d34a2c26bc60d","e059084094d02b8f33f3957be4d36daaf138b4eb","974aaaf64f40b244d5e81939ee19cd8066b73463","53ddbbc2dcb94da6513a1bd3d8d58b17598f63a4","51b09dbfc5616992794101c4a9ee27e5ada9ff61","0f99a4e990b1c77e58fa9a99cddfe7fb25c36f80","e0fba7a69bfc85229b10d980953a627db3b8759d","1f601a82e6c846324001778c4ce7935252470091","2b9b41bbde2cfe6c64ef54ea58c782c9e77a27a9","9550dc3669af5c83fad1f3e77ef1ef4cfba4465e","61e8eea135b21e6a54d74c6dece227ce71b48c3c","9121d60a526d4de9cea1c187c81464d8dd444870","43cad167e3947bac79745dd1ab32f936a17e0657","834ea1a91bd1edf9cb541a96eb979bc8fad8fe16","58041f5501a116985e66ce6ffba7fdbac601abca","64d9a0d125dfb1e9ed442174b48bcd0255037138","decb6a32a1a26786fb803339607d7b441e9a35fd","4d4e79ecd5e5be3520ec0647e54a4212d084fdea","f4475c5d1a107ec29e2c968ec9fedeb0d59743d9","e41b9d4e937c6bdbde80cad89188fb0480e84376","82355b20845b3768df3f8c4ec92fa738157201d0","0578f5e29f5fe58402eaea81862f03a016a33607","159d63c384b2c5f965426d47e9bb230f14de01f4","d30bc64ecccbac14108d5b4ad5560164a122e3b3","65b7d79b8749a6db9ec9b9d2678b82fd62136941","550fbb7e57cd5d6e3ff102f9e6a0f06a28ac366a","fb76066b5dc6f79701a23b39660e789619c8fb95","e6182d40c5833039f67cd07a31acd7fd63994053","bb012795ea82d0fcc48d4545370482fa4c31f08a","ec843c7ddcf967c27010a332bab6ffebc5bd82b3","1614491c86f1d0a95428d105b265576c2067a236","cb0f48b4da6ffa0e936eba169c21a1b84dd61674","7eebb754fa4f1a1ef066a745810647e941b32271","0c751d5d5ecf55b61c4960246906118cf017e0f5","2771ac4e78c8c40a834c6f7da0016495ab8f021b","2f95ce79c9a6e1d8ad776baa89e24be5fb3dd4e8","85a9bf79c145190a9dbba0753254c02f0ea99e01","02ff424c0c4f683f576a0d5b80e23bbaa345c95c","3e6712f434ca6b881ea650840962b91c2f015c68","21ef3aa2577cd4cfd9f10ff731fbdce1a1784b98","888b665a35635e621d7fca2dcfa5f1aba6da4e40","b0ade47185962df38c89cf57e6c0af3e0d3c2415","36339be9a05e83b8139f996f6b8f033e47a7d69b","134a45100b645d4243d263760fad80395ec5f701","6705d41ab3ce4f8e6160e45176700a2db3dce130","a8679e4050ed50f897dabbe16088d69184e119bb"] |
{
"id": 12239,
"source": "mhm",
"verse_id": 18462,
"verse_count": 11,
"reference": "41:10-20",
"title": "",
"html": "<p> 10 Fear thou not for I <i>am<\/i> with thee: be not dismayed for I <i>am<\/i> thy God: I will strengthen thee yea, I will help thee yea, I will uphold thee with the right hand of my righteousness. 11 Behold, all they that were incensed against thee shall be ashamed and confounded: they shall be as nothing and they that strive with thee shall perish. 12 Thou shalt seek them, and shalt not find them, <i>even<\/i> them that contended with thee: they that war against thee shall be as nothing, and as a thing of nought. 13 For I the L<b>ORD<\/b> thy God will hold thy right hand, saying unto thee, Fear not I will help thee. 14 Fear not, thou worm Jacob, <i>and<\/i> ye men of Israel I will help thee, saith the L<b>ORD<\/b>, and thy redeemer, the Holy One of Israel. 15 Behold, I will make thee a new sharp threshing instrument having teeth: thou shalt thresh the mountains, and beat <i>them<\/i> small, and shalt make the hills as chaff. 16 Thou shalt fan them, and the wind shall carry them away, and the whirlwind shall scatter them: and thou shalt rejoice in the L<b>ORD<\/b>, <i>and<\/i> shalt glory in the Holy One of Israel. 17 <i>When<\/i> the poor and needy seek water, and <i>there is<\/i> none, <i>and<\/i> their tongue faileth for thirst, I the L<b>ORD<\/b> will hear them, I the God of Israel will not forsake them. 18 I will open rivers in high places, and fountains in the midst of the valleys: I will make the wilderness a pool of water, and the dry land springs of water. 19 I will plant in the wilderness the cedar, the shittah tree, and the myrtle, and the oil tree I will set in the desert the fir tree, <i>and<\/i> the pine, and the box tree together: 20 That they may see, and know, and consider, and understand together, that the hand of the L<b>ORD<\/b> hath done this, and the Holy One of Israel hath created it. <\/p> <p> The scope of these verses is to silence the fears, and encourage the faith, of the servants of God in their distresses. Perhaps it is intended, in the first place, for the support of God's Israel, in captivity but all that faithfully serve God <i>through patience and comfort of this scripture may have hope.<\/i> And it is addressed to Israel as a single person, that it might the more easily and readily be accommodated and applied by every Israelite indeed to himself. That is a word of caution, counsel, and comfort, which is so often repeated, <i>Fear thou not <\/i> and again (<a class=\"isa\" verses=\"MTg0NjU=\">Isaiah 41:13<\/a>), <i>Fear not <\/i> and (<a class=\"isa\" verses=\"MTg0NjY=\">Isaiah 41:14<\/a>), \"<i>Fear not, thou worm Jacob <\/i> fear not the threatenings of the enemy, doubt not the promise of thy God fear not that thou shalt perish in thy affliction or that the promise of thy deliverance shall fail.\" It is against the mind of God that his people should be a timorous people. For the suppressing of fear he assures them,<\/p> <p> I. That they may depend upon his presence with them as their God, and a God all-sufficient for them in the worst of times. Observe with what tenderness God speaks, and how willing he is to let the heirs of promise know the immutability of his counsel, and how desirous to make them easy: \"<i>Fear thou not, for I am with thee,<\/i> not only within call, but present with thee <i>be not dismayed<\/i> at the power of those that are against thee, for <i>I am thy God,<\/i> and engaged for thee. Art thou weak? <i>I will strengthen thee.<\/i> Art thou destitute of friends? <i>I will help thee<\/i> in the time of need. Art thou ready to sink, ready to fall? <i>I will uphold thee with the right hand of my righteousness,<\/i> that right hand which is full of righteousness, in dispensing rewards and punishments,\" <a class=\"ref\">Psalm 48:10<\/a>. And again (<a class=\"isa\" verses=\"MTg0NjU=\">Isaiah 41:13<\/a>) it is promised, 1. That God will strengthen their hands, that is, will help them: \"<i>I will hold thy right hand,<\/i> go hand in hand with thee\" (so some): he will take us by the hand as our guide, to lead us in our way, will help us up when we are fallen or prevent our falls when we are weak he will hold us up-wavering, he will fix us-trembling, he will encourage us, and so <i>hold us by the right hand,<\/i> <a class=\"ref\">Psalm 73:23<\/a>. 2. That he will silence their fears: <i>Saying unto thee, Fear not.<\/i> He has said it again and again in his word, and has there provided sovereign antidotes against fear: but he will go further he will by his Spirit say it to their hearts, and make them to hear it, and so will help them.<\/p> <p> II. That though their enemies be now very formidable, insolent, and severe, yet the day is coming when God will reckon with them and they shall triumph over them. There are those that are incensed against God's people, that <i>strive with them<\/i> (<a class=\"isa\" verses=\"MTg0NjM=\">Isaiah 41:11<\/a>), that war against them (<a class=\"isa\" verses=\"MTg0NjQ=\">Isaiah 41:12<\/a>), that hate them, that seek their ruin, and are continually picking quarrels with them. But let not God's people be incensed at them, nor strive with them, nor render evil for evil but wait God's time, and believe, 1. That they shall be convinced of the folly, at least, if not of the sin of striving with God's people and, finding it to no purpose, <i>they shall be ashamed and confounded,<\/i> which might bring them to repentance, but will rather fill them with rage. 2. That they shall be quite ruined and undone (<a class=\"isa\" verses=\"MTg0NjM=\">Isaiah 41:11<\/a>): <i>They shall be as nothing<\/i> before the justice and power of God. When God comes to deal with his proud enemies he makes nothing of them. Or they shall be brought to nothing, shall be as if they had never been. This is repeated (<a class=\"isa\" verses=\"MTg0NjQ=\">Isaiah 41:12<\/a>): They <i>shall be as nothing and as a thing of nought,<\/i> or as that which is gone and has failed. Those that were formidable shall become despicable those that fancied they could do any thing shall be able to bring nothing to pass those that made a figure in the world, and a mighty noise, shall become mere ciphers and be buried in silence. They shall perish, not only be nothing, but be miserable: <i>Thou shalt seek them,<\/i> shalt enquire what has become of them, that they do not appear as usual, but thou <i>shalt not find them<\/i> as David, <a class=\"ref\">Psalm 37:36<\/a>. <i>I sought him, but he could not be found.<\/i><\/p> <p> III. That they themselves should become a terror to those who were now a terror to them, and victory should turn on their side, <a class=\"isa\" verses=\"bnVsbA==\">Isaiah 41:14-16<\/a>. See here, 1. How Jacob and Israel are reduced and brought very low. It is the <i>worm Jacob,<\/i> so little, so weak, and so defenceless, despised and trampled on by every body, forced to creep even into the earth for safety and we must not wonder that Jacob has become a worm, when even Jacob's King calls himself <i>a worm and no man,<\/i> <a class=\"ref\">Psalm 22:6<\/a>. God's people are sometimes as worms, in their humble thoughts of themselves and their enemies' haughty thoughts of them--worms, but not vipers, as their enemies are, not of the serpent's seed. God regards Jacob's low estate, and says, \"<i>Fear not, thou worm Jacob <\/i> fear not that thou shalt be crushed and <i>you men of Israel<\/i>\" (<i>you few men,<\/i> so some read it, <i>you dead men,<\/i> so others) \"do not give up yourselves for gone notwithstanding.\" Note, The grace of God will silence fears even when there seems to be the greatest cause for them. <i>Perplexed but not in despair.<\/i> 2. How Jacob and Israel are advanced from this low estate, and made as formidable as ever they have been despicable. But <i>by whom shall Jacob arise, for he is small?<\/i> We are here told: <i>I will help thee, saith the Lord <\/i> and it is the honour of God to help the weak. He will help them, for he is their Redeemer, who is wont to redeem them, who has undertaken to do it. Christ is the Redeemer, from him is our help found. He will help them, for he is the <i>Holy One of Israel,<\/i> worshipped among them in the beauty of holiness and engaged by promise to them. The Lord will help them by enabling them to help themselves and making Jacob to become <i>a threshing instrument.<\/i> Observe, He is but an instrument, a tool in God's hand, that he is pleased to make use of and he is an instrument of God's making and is no more than God makes him. But, if God make him a threshing instrument, he will make use of him, and therefore will make him fit for use, <i>new<\/i> and <i>sharp,<\/i> and <i>having teeth,<\/i> or sharp spikes and then, by divine direction and strength, <i>thou shalt thresh the mountains,<\/i> the highest, and strongest, and most stubborn of thy enemies: thou shalt not only be at them, but <i>beat them small <\/i> they shall not be a corn threshed out, which is valuable, and is carefully preserved (such God's people are when they are under the flail, <a class=\"isa\" verses=\"MTgwNDY=\">Isaiah 21:10<\/a>: <i>O my threshing!<\/i> yet <i>the corn of my floor,<\/i> that shall not be lost) but these are made <i>as chaff,<\/i> which is good for nothing, and which the husbandman is glad to get rid of. He pursues the metaphor, <a class=\"isa\" verses=\"MTg0Njg=\">Isaiah 41:16<\/a>. Having threshed them, <i>thou shalt winnow them, and the wind shall scatter them.<\/i> This perhaps had its accomplishment, in part, in the victories of the Jews over their enemies in the times of the Maccabees but it seems in general designed to read the final doom of all the implacable enemies of the church of God, and to have its accomplishment like wise in the triumphs of the cross of Christ, the gospel of Christ, and all the faithful followers of Christ, over the powers of darkness, which, first or last, shall all be dissipated, and in Christ all believers shall be more than conquerors, and <i>he that overcomes shall have power over the nations,<\/i> <a class=\"ref\">Revelation 2:26<\/a>.<\/p> <p> IV. That, hereupon, they shall have abundance of comfort in God, and God shall have abundance of honour from them: <i>Thou shalt rejoice in the Lord,<\/i> <a class=\"isa\" verses=\"MTg0Njg=\">Isaiah 41:16<\/a>. When we are freed from that which hindered our joy, and are blessed with that which is the matter of it, we ought to remember that God is our exceeding joy and in him all our joys must terminate. When we rejoice over our enemies we must rejoice in the Lord, for to him alone we owe our liberties and victories. \"Thou shalt also <i>glory in the Holy One of Israel,<\/i> in thy interest in him and relation to him, and what he has done for thee.\" And, if thus we make God our praise and glory, we become to him for a praise and a glory.<\/p> <p> V. That they shall have seasonable and suitable supplies of every thing that is proper for them in the time of need and, if there be occasion, God will again do for them as he did for Israel in their march from Egypt to Canaan, <a class=\"isa\" verses=\"bnVsbA==\">Isaiah 41:17-19<\/a>. When the captives, either in Babylon or in their return thence, are in distress for want of water or shelter, God will take care of them, and, one way or other, make their journey, even through a wilderness, comfortable to them. But doubtless this promise has more than such a private interpretation. Their return out of Babylon was typical of our redemption by Christ and so the contents of these promises, 1. Were provided by the gospel of Christ. That glorious discovery of his love has given full assurance to all those who hear this joyful sound that God has provided inestimable comforts for them, sufficient for the supply of all their wants, the balancing of all their griefs, and the answering of all their prayers. 2. They are applied by the grace and Spirit of Christ to all believers, that they may have strong consolation in their way and a complete happiness in their end. Our way to heaven lies through the wilderness of this world. Now, (1.) It is here supposed that the people of God, in their passage through this world, are often in straits: <i>The poor and needy seek water, and there is none the poor in spirit hunger and thirst after righteousness.<\/i> The soul of man, finding itself empty and necessitous, seeks for satisfaction somewhere, but soon despairs of finding it in the world, that has nothing in it to make it easy: creatures are <i>broken cisterns, that can hold no water <\/i> so that <i>their tongue fails for thirst,<\/i> they are weary of seeking that satisfaction in the world which is not to be had in it. Their sorrow makes them thirsty so does their toil. (2.) It is here promised that, one way or other, all their grievances shall be redressed and they shall be made easy. [1.] God himself will be nigh unto them in all that which they call upon him for. Let all the praying people of God take notice of this, and take comfort of it he has said, \"<i>I the Lord will hear them,<\/i> will answer them <i>I, the God of Israel, will not forsake them <\/i> I will be with them, as I have always been, in their distresses.\" While we are in the wilderness of this world this promise is to us what the pillar of cloud and fire was to Israel, an assurance of God's gracious presence. [2.] They shall have a constant supply of fresh water, as Israel had in the wilderness, even where one would least expect it (<a class=\"isa\" verses=\"MTg0NzA=\">Isaiah 41:18<\/a>): <i>I will open rivers in high places,<\/i> rivers of grace, rivers of pleasure, <i>rivers of living water,<\/i> which he spoke of the Spirit (<a class=\"ref\">John 7:38<\/a>), that Spirit which should be poured out upon the Gentiles, who had been as high places, dry and barren, and lifted up on their own conceit above the necessity of that gift. And there shall be <i>fountains in the midst of the valleys,<\/i> the valleys of Baca (<a class=\"ref\">Psalm 84:6<\/a>), that are sandy and wearisome or among the Jews, who had been as fruitful valleys in comparison with the Gentile mountains. The preaching of the gospel to the world turned that wilderness into a pool of water, yielding fruit to the owner of it and relief to the travellers through it. [3.] They shall have a pleasant shade to screen them from the scorching heat of the sun, as Israel when they pitched at Elim, where they had not only wells of water, but palm-trees (<a class=\"ref\">Exodus 15:27<\/a>): \"<i>I will plant in the wilderness the cedar,<\/i> <a class=\"isa\" verses=\"MTg0NzE=\">Isaiah 41:19<\/a>. I will turn the wilderness into an orchard or garden, such as used to be planted with these pleasant trees, so that they shall pass through the wilderness with as much ease and delight as a man walks in his grove. These trees shall be to them what the pillar of cloud was to Israel in the wilderness, a shelter from the heat.\" Christ and his grace are so to believers, <i>as the shadow of a great rock,<\/i> <a class=\"isa\" verses=\"MTgyNjI=\">Isaiah 32:2<\/a>. When God sets up his church in the Gentile wilderness there shall be as great a change made by it in men's characters as if thorns and briers were turned into cedars, and fir-trees, and myrtles and by this a blessed change is described, <a class=\"isa\" verses=\"MTg3NTQ=\">Isaiah 55:13<\/a>. [4.] They shall see and acknowledge the hand of God, his power and his favour, in this, <a class=\"isa\" verses=\"MTg0NzI=\">Isaiah 41:20<\/a>. God will do these strange and surprising things on purpose to awaken them to a conviction and consideration of his hand in all: <i>That they may see<\/i> this wonderful change, <i>and knowing<\/i> that it is above the ordinary course and power of nature may consider that therefore it comes from a superior power, and, comparing notes upon it, <i>may understand together,<\/i> and concur in the acknowledgment of it, <i>that the hand of the Lord,<\/i> that mighty hand of his which is stretched out for his people and stretched out to them, <i>has done this,<\/i> and <i>the Holy One of Israel has created it,<\/i> made it anew, made it out of nothing, made it for the comfort of his people. Note, God does great things for his people, that he may be taken notice of.<\/p>",
"audit": null
} |
{"poster":"ilmiticoBosa","date":"2015-09-01T20:56:06.749+0000","title":"Consiglio su una buona bulid di ekko","subforum":"Aiuto e supporto","up_votes":1,"down_votes":0,"body":"ciao ragazzi, volevo chiedere se sapreste indicarmi una build per ekko mid è una per ekko support( lo so ekko support sembra strano ma è veramente forte). Grazie","replies":[{"poster":"Polarize","date":"2015-09-01T21:13:41.944+0000","up_votes":2,"down_votes":0,"body":"1)non usare ekko support\n\n2) a me piace fare: morello sorcerer luden lich void raba.\nSe arrivi a lategame inoltrato vendi morello e sorcerer, compri ionia e zhonya.\n\nhttp://matchhistory.euw.leagueoflegends.com/it/#match-details/EUW1/2260462700/40316191?tab=builds Circa questa","replies":[{"poster":"Ste the ripper","date":"2015-09-02T06:35:42.212+0000","up_votes":1,"down_votes":0,"body":"Ti avevo carriato io in quella partita 8)","replies":[{"poster":"Polarize","date":"2015-09-02T06:59:07.442+0000","up_votes":1,"down_votes":0,"body":"Sure 8}","replies":[]}]}]},{"poster":"Sorcio84","date":"2015-09-02T02:46:32.500+0000","up_votes":1,"down_votes":0,"body":"sezione aiuto e supporto.\n\ntorna indietro nel corridoio e poi svolta verso\n\ncampioni e gameplay\n\nciauz","replies":[]},{"poster":"Heavenly Yasha","date":"2015-09-01T21:26:22.177+0000","up_votes":1,"down_votes":0,"body":"Questa è secondo me una buona build, affiancata magari a rune con CDR per livello.\n{{item:3165}} {{item:3020}} {{item:3100}} {{item:3089}} {{item:3157}} {{item:3135}} \n\nNon giocarlo support se non for fun in normal o al massimo in bronzo.","replies":[]},{"poster":"Arkanim94","date":"2015-09-01T21:25:21.726+0000","up_votes":1,"down_votes":0,"body":"build da jungler:\n\n{{item:3724}} {{item:3020}} {{item:3089}} {{item:3285}} {{item:3157}} e a scelta tra {{item:3116}} {{item:3026}} {{item:3040}}{{item:3027}} ","replies":[]}]} |
{"type": "Feature", "id": 2123805, "geometry": {"type": "MultiPolygon", "coordinates": [[[[38.916779, 47.204609], [38.916779, 47.22279], [38.890018, 47.22279], [38.890018, 47.204609], [38.916779, 47.204609]]]]}, "properties": {"woe:id": 2123805, "woe:parent_id": 12599124, "woe:name": "Taganrog", "woe:placetype": 7, "woe:placetype_name": "Town", "woe:lang": "ENG", "iso:country": "RU", "meta:provider": ["geoplanet:7.3.1", "geoplanet:7.3.2", "geoplanet:7.4.0", "geoplanet:7.4.1", "geoplanet:7.5.1", "geoplanet:7.5.2", "geoplanet:7.6.0", "geoplanet:7.8.1", "geoplanet:7.9.0", "geoplanet:7.10.0", "geoplanet:8.0.0", "woeplanet:8.0.0"], "meta:indexed": "2020-08-20T20:56:28.254328", "woe:alias_UNK_V": ["Naberezhnye Chelny", "\u0422\u0430\u0433\u0430\u043d\u0440\u043e\u0433"], "meta:updated": "2020-10-14T10:58:24.359365", "woe:adjacent": [2055385, 2051671, 2097348, 1989294, 2006589, 2086230, 2120375, 2056386, 2124297, 2079894, 2123177, 2015966], "woe:alias_UNK_Q": ["Dzerzhinsk"], "woe:hierarchy": {"continent": 24865671, "country": 23424936, "town": 0, "planet": 1, "county": 12599124, "suburb": 0, "state": 2346921, "region": 0, "localadmin": 0}, "woe:timezone_id": 56043614, "woe:concordances": {"gn:id": 484907, "qs:id": 240014}, "woe:hash": "ub7st6bxdzms", "geom:min_latitude": 47.204609, "woe:centroid": [38.9034, 47.213699], "geom:latitude": 47.213699, "woe:max_longitude": 38.916779, "geom:max_longitude": 38.916779, "geom:centroid": [38.9034, 47.213699], "geom:max_latitude": 47.22279, "woe:bbox": [38.890018, 47.204609, 38.916779, 47.22279], "geom:bbox": [38.890018, 47.204609, 38.916779, 47.22279], "woe:min_latitude": 47.204609, "woe:min_longitude": 38.890018, "geom:longitude": 38.9034, "geom:hash": "ub7st6bxdzms", "woe:latitude": 47.213699, "geom:min_longitude": 38.890018, "woe:longitude": 38.9034, "woe:max_latitude": 47.22279, "woe:repo": "woeplanet-town-ru", "geom:area": 4097534.501596451, "woe:scale": 16}} |
{
"id": 319939507,
"type": "Feature",
"properties": {
"addr:full":"5 Bleak House Cottages, Catfield Great Yarmouth Norfolk NR29 5BE",
"addr:housenumber":"5",
"addr:postcode":"nr29 5be",
"addr:street":"Bleak House Cottages;Yarmouth",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"1.53337,52.743351,1.53337,52.743351",
"geom:latitude":52.743351,
"geom:longitude":1.53337,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"5 Bleak House Cottages, Catfield",
"sg:city":"Great Yarmouth",
"sg:classifiers":[
{
"category":"Building & Trades",
"subcategory":"Construction",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 1692 582980",
"sg:postcode":"NR29 5BE",
"sg:province":"Norfolk",
"sg:tags":[
"decorator",
"painter"
],
"src:geom":"simplegeo",
"wof:belongsto":[
85633159,
85683947
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_3VuXyilhd1RmGbmTSt4bvy_52.743351_1.533370@1300740633"
},
"wof:country":"GB",
"wof:geomhash":"8a90dbe5b9d09eb391ab2d24511ae359",
"wof:hierarchy":[
{
"continent_id":-1,
"country_id":85633159,
"locality_id":-1,
"neighbourhood_id":-1,
"region_id":85683947,
"venue_id":319939507
}
],
"wof:id":319939507,
"wof:lastmodified":1496960066,
"wof:name":"Andy Man",
"wof:parent_id":-1,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"decorator",
"painter"
]
},
"bbox": [
1.53337,
52.743351,
1.53337,
52.743351
],
"geometry": {"coordinates":[1.53337,52.743351],"type":"Point"}
} |
{"title":"杏「ボードゲーム部活動記録」奈緒「その2」","url":"http://horahorazoon.blog134.fc2.com/blog-entry-11187.html","time":"2017-04-27 09:01:07 UTC"} |
{"fileName":"Barclays_Church__Dwight_Co_Inc_Vitamin_Deficient.pdf","numberSentences":255,"text":[{"sentence":"Consumer | U.S. Cosmetics; Household and Personal Care twenty nine October two thousand fifteen Stock Rating","wordCount":17},{"sentence":"We acknowledge that our Underweight rating on CHD has proven to be off base, though we have seen top line repeatedly disappoint over the past two years while CHD shares have been more than resilient.","wordCount":35},{"sentence":"Still, the math behind the recent guidance change suggests CHD does not anticipate its core Consumer Domestic business reaccelerating to Recession time highs and that just two two.five","wordCount":29},{"sentence":"For twoHfifteen specifically, while tougher comparisons have already constrained Street estimates, we believe fourQ sales expectations may still prove too high.","wordCount":21},{"sentence":"Although twoQ marked the strongest print for Consumer Domestic in the last six quarters, we expect four.five","wordCount":18},{"sentence":"A combination of toughening comparisons and ongoing weakness in Vitamins have already been incorporated in the twoH outlook but recent Nielsen trends suggest top line results could still disappoint.","wordCount":29},{"sentence":"Specifically, data corresponding to threeQ suggests a more significant than anticipated deceleration in shipments is coming and we now expect twoHfifteen sales growth to be ~two.two","wordCount":27},{"sentence":"percent in oneHfifteen, which we believe is ~fifty one hundred bps below the Street and could be indicative of what is to come in two thousand sixteen and beyond.","wordCount":29},{"sentence":"Looking further out, we see Vitamins and Cat Litter as the main sources of top line risk over the next twelve twenty four months.","wordCount":24},{"sentence":"Though this summer was the first time management highlighted operating issues within the business, the reality is that Vitamins' contribution to total company growth has been light versus our expectations since the beginning of two thousand fourteen .","wordCount":37},{"sentence":"As such, even though we appreciated the company's candor in discussing recent missteps on the twoQfifteen call, we note that in market data, the summer slowdown just looks like the continuation of an already established trend.","wordCount":36},{"sentence":"In Litter, we've been wary of a competitive response in a category that has proven so receptive to innovation specifically remembering that Church's business declined two percent in two thousand thirteen as on the heels of Nestle's lightweight launch.","wordCount":40},{"sentence":"In the next several months, Clorox will launch a co branded Fresh Step with Febreze litter which strikes us as interesting given the success of Glad Odor Shield with Febreze trash bags and the natural fit for Febreze in the litter category.","wordCount":42},{"sentence":"Price Potential Upside/Downside Tickers Market Cap Shares Outstanding Free Float fifty two Wk Avg Daily Volume fifty two Wk Avg Daily Value Dividend Yield Return on Equity TTM Current BVPS","wordCount":30},{"sentence":"nine seven percent CHD eleven thousand four hundred fifty seven one hundred thirty.ninety","wordCount":14},{"sentence":"U.S. Cosmetics; Household and Personal Care Lauren R. Lieberman one.two","wordCount":12},{"sentence":"at barclays dot com BCI, US Shirley C. Serrao, CFA +one two hundred twelve five hundred twenty six seven thousand five hundred eighteen shirley.serrao at barclays dot com BCI, US Katie Grafstein one.two","wordCount":35},{"sentence":"hundred twenty six.five thousand seven hundred fifty five katie.grafstein","wordCount":11},{"sentence":"Barclays Capital Inc. and/or one of its affiliates does and seeks to do business with companies covered in its research reports.","wordCount":21},{"sentence":"As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report.","wordCount":24},{"sentence":"Investors should consider this report as only a single factor in making their investment decision.","wordCount":15},{"sentence":"PLEASE SEE ANALYST CERTIFICATION AND IMPORTANT DISCLOSURES BEGINNING ON PAGE eight.","wordCount":11},{"sentence":"U.S. Cosmetics; Household and Personal Care Industry View: POSITIVE Stock Rating: UNDERWEIGHT two thousand fifteenE three thousand three hundred seventy two seven hundred seventy two six hundred seventy eight six hundred fifty seven four hundred thirty two three.twenty","wordCount":40},{"sentence":"four two thousand sixteenE three thousand four hundred forty five eight hundred two seven hundred six six hundred eighty five four hundred fifty two three.fifty","wordCount":26},{"sentence":"three two thousand seventeenE three thousand five hundred forty N/A N/A N/A N/A three.seventy","wordCount":15},{"sentence":"three CAGR two.four percent N/A N/A N/A N/A seven.eight","wordCount":11},{"sentence":"While we commend CHD for its ability to consistently deliver strong EPS growth, we find its premium valuation in light of slower organic sales growth more grounded in potential for M and A. With recent deals within Staples showing buyer multiples contracting, we believe current valuation will prove stretched even should an attractive deal be announced.","wordCount":56},{"sentence":"Church and Dwight gets better than expected gross margin expansion, consumers continue to trade down and commodity inflation dissipates.","wordCount":19},{"sentence":"Based on +fifteen percent premium multiple to Large Cap with +ten percent EPS growth in Csixteen.","wordCount":16},{"sentence":"Innovation across portfolio has less of an impact than initially expected.","wordCount":11},{"sentence":"Based on a ~nineteenx multiple on our base case Csixteen EPS estimate of dollar three.fifty","wordCount":16},{"sentence":"Income statement Revenue EBITDA EBIT Pre tax income Net income EPS Diluted shares DPS Margin and return data Gross margin EBITDA margin EBIT margin Pre tax margin Net margin ROIC ROA ROE Balance sheet and cash flow Cash and equivalents Total assets Short and long term debt Total liabilities Net debt/ Shareholders' equity Change in working capital Cash flow from operations Capital expenditure Free cash flow Valuation and leverage metrics P/E EV/sales EV/EBITDA EV/EBIT Equity FCF yield P/BV Dividend yield Total debt/capital Net debt/EBITDA Selected operating metrics Organic sales growth Volume growth Price growth","wordCount":94},{"sentence":"If a tree falls in the forest, and there's nobody around to hear it, does it make a sound?","wordCount":19},{"sentence":"An age old quantum theory conundrum, this question is often posed when debating the relationship between action and perception.","wordCount":19},{"sentence":"In the case of Church and Dwight lowering its long term revenue growth targets to +three percent from +three four percent previously at our Consumer conference in September, there were plenty of people around to hear it yet no one seemed to care.","wordCount":43},{"sentence":"Of course, sometimes the market is simply a step ahead of any such announcement but we don't necessarily think this was the case here.","wordCount":24},{"sentence":"Rather, we see this as another data point to support our view that CHD valuation has been driven by M and A expectations, macro inflows to US centric, oil exposed names and a sticky investor base that often opts to hold existing positions given a perceived lack of viable alternatives.","wordCount":50},{"sentence":"We acknowledge that our Underweight rating on the stock has proven to be off base, though we have seen the top line repeatedly disappoint over the past two years while CHD shares have been more than resilient.","wordCount":37},{"sentence":"Still, the math behind the recent guidance change suggests Church and Dwight does not anticipate its core Consumer Domestic business reaccelerating to the levels seen during the years surrounding the Recession and that just two.","wordCount":35},{"sentence":"We think the acquisition of Avid in two thousand twelve was intended to bridge the gap between a coming deceleration in value laundry to total company growth targets but instead, that business has been a drag versus our expectations since the beginning of two thousand fourteen.","wordCount":46},{"sentence":"Clearly the prevailing view is that CHD is a safe haven in times of currency pressure, emerging market deceleration and commodity downdrafts and this is outweighing any concerns on top line performance.","wordCount":32},{"sentence":"While it will likely be tough to keep fighting against a defensive market for the balance of the year, we can't help but highlight what we see as mounting revenue risks for the company.","wordCount":34},{"sentence":"We believe CHD's current premium to peers to be more reflective of crowding in US centric names coupled with persistent expectations for M and A. With the former being transitory rather than structural, the longstanding hope for a deal being unrequited and top and bottom line growth unlikely to reaccelerate, even as per the company's own expectations, we believe there is risk to valuation.","wordCount":64},{"sentence":"Our dollar eighty one price target reflects a twenty threex P/E on our new dollar three.fifty","wordCount":17},{"sentence":"one Csixteen EPS estimate, or a ~five percent premium to Large Cap Staples peers.","wordCount":14},{"sentence":"The math behind the recent guidance change suggests CHD is not anticipating growth in its core Consumer Domestic business to accelerate beyond the two.","wordCount":24},{"sentence":"Recall that with the Consumer Domestic business representing ~seventy five percent of sales, trends in the U.S. which are well represented in Nielsen data are the key to total company sales growth.","wordCount":33},{"sentence":"While this is mathematically true, we also think it is a fair statement strategically as the Consumer International business has never struck us as terribly cohesive and Specialty is subject to dairy industry cycles.","wordCount":34},{"sentence":"While outsized growth in these two divisions has on occasion made up for shortfalls in the core US portfolio, we expect little in the way of positive surprises on this front for the next twelve months.","wordCount":36},{"sentence":"In the analysis we've done for this note, we assume International and Specialty grow four five percent on average, which is in line with average growth rates over the past five years.","wordCount":32},{"sentence":"percent, which we believe is ~fifty one hundred bps below the Street and could be indicative of future trends For twoHfifteen specifically, while tougher comparisons have already constrained Street estimates, we believe fourQ sales expectations may still prove too high...","wordCount":40},{"sentence":"Although twoQ marked the strongest print for Consumer Domestic in the last six quarters, we expect four.five","wordCount":18},{"sentence":"A combination of easy comparisons flipping to be more challenging and ongoing weakness in Vitamins have already been incorporated to the twoH outlook but recent Nielsen trends have us concerned that top line results could still disappoint.","wordCount":37},{"sentence":"While we know scanner data isn't perfect and that there are quarter to quarter variations in promotional activity, shipments and take away, Church and Dwight's Consumer","wordCount":26},{"sentence":"Domestic organic sales have tended to oscillate around and average out to match Nielsen .","wordCount":14},{"sentence":"Data that roughly corresponds to threeQ suggests a more significant than anticipated deceleration in shipments is coming.","wordCount":17},{"sentence":"In that vein, we now expect twoHfifteen sales growth to be ~two.two","wordCount":13},{"sentence":"percent in oneHfifteen, which we believe is ~fifty one hundred bps below the Street and could be indicative of what is to come in two thousand sixteen and beyond.","wordCount":29},{"sentence":"Likewise, our fourQ and full year two thousand fifteen estimates are now lower by dollar .one/sh to dollar .eighty","wordCount":19},{"sentence":"FIGURE one We believe the deceleration in fourQ sales could be exacerbated by a lag in reorders as retailers work down inventory stock from higher shipments in twoQ.","wordCount":28},{"sentence":"We think the Street hasn't digested the implied revenue downgrade for the core Consumer Domestic business shared at our conference in September.","wordCount":22},{"sentence":"We believe the step change share gains for Laundry have run their course, and do not expect anything stronger than two three percent growth over time.","wordCount":26},{"sentence":"Barclays | Church and Dwight Co., Inc. company also expects market growth to be more balanced across price tiers.","wordCount":19},{"sentence":"We should also mention that our assumption is that if Arm and Hammer launches a liquid unit dose product, benefits would largely come via profitability rather than in top line as shelf space would be sourced from existing SKUs.","wordCount":39},{"sentence":"In Figure two, we diagram both historical and estimated growth drivers for the Consumer Domestic business showing a material deceleration for Litter and sustained sluggishness in Vitamins.","wordCount":27},{"sentence":"We detail the reasons for our assumptions below but do note that we think management's downward guidance revision at our conference in September does incorporate a similar view.","wordCount":28},{"sentence":"FIGURE two With expectations for continued sluggishness in Vitamins and a deceleration in Litter, our outlook for total company sales growth is consistent with the recent downgrade to long term targets.","wordCount":31},{"sentence":"The Vitamin business' contribution to total company sales growth has been fully two points below our expectations since the start of two thousand fourteen","wordCount":24},{"sentence":"Trends in the Vitamins business highlight fundamental issues even prior to recent execution missteps, suggesting slower growth is here to stay.","wordCount":21},{"sentence":"Looking back on our impressions of the Vitamins business when Church and Dwight first acquired Avid in two thousand twelve, while we recognized the attractive growth profile of the company we did worry that the company, having violated its traditional acquisition criteria for asset light businesses that are gross margin accretive, was in relatively unchartered territory.","wordCount":56},{"sentence":"Though this summer was the first time management highlighted operating issues within the business, the reality is that vitamins' contribution to total company growth has been light versus our expectations since the beginning of two thousand fourteen.","wordCount":37},{"sentence":"We had modeled a gradual deceleration from the more than thirty percent growth Avid was enjoying at the time of acquisition, but market dynamics changed far more swiftly than we expected.","wordCount":31},{"sentence":"Even with the benefit of expanding distribution in tracked channels, Avid's results versus our estimates have been a two percent drag to total company revenue growth .","wordCount":26},{"sentence":"As such, even though we appreciated the company's candor in discussing execution, formulation and manufacturing missteps on the twoQfifteen call, we do note that in market data at least, the slowdown over the summer just looks like the continuation of an already established trend.","wordCount":44},{"sentence":"FIGURE three While Avid grew in line with expectations in two thousand thirteen, changing market dynamics have led to a significant shortfall since...","wordCount":23},{"sentence":"Market Growth two thousand twelve two thousand thirteen two thousand fourteen YTD two thousand sixteenE thirty eight percent twenty nine percent twenty two percent nineteen percent fifteen percent Avid Estimate twenty five thirty percent twenty five thirty percent fifteen twenty percent ten percent Avid Actual fifty four percent thirty percent ten percent three percent five percent","wordCount":56},{"sentence":"FIGURE four ...that has created a drag to total company revenue growth versus our estimates.","wordCount":15},{"sentence":"Note: Avid Estimates reflect entries to our earnings model at the time of acquisition.","wordCount":14},{"sentence":"While the adult gummy market has enjoyed a +forty percent CAGR since the acquisition, the children's side in contrast, has declined two percent and Avid is losing significant share in both segments.","wordCount":32},{"sentence":"Gummy vitamin market development has seemingly matched expectations; the disappointment is in Avid's participation in that growth.","wordCount":17},{"sentence":"Part of Church's strategic rationale for acquiring Avid was the opportunity to grow the adult gummy vitamin category and to leverage the company's number one position in this burgeoning market.","wordCount":30},{"sentence":"There was always an assumption of some share slippage but with growth rates remaining very strong thanks to category growth .","wordCount":20},{"sentence":"This looks to have largely played out but with a terribly significant downdraft from both market declines and share losses in the children's side of the market.","wordCount":27},{"sentence":"To frame this numerically, adult market growth has been at a +forty percent CAGR since the acquisition while children's is down two percent and worsening to six percent in two thousand fourteen and nine percent YTD.","wordCount":36},{"sentence":"We think Church has been proactive in trying to protect its moat and expand its offering in adult gummies.","wordCount":19},{"sentence":"Specifically, given the expected pressure on Vitafusion from mainstream vitamin brands like Centrum, Nature Made and Schiff investing behind gummies, the company has attempted to extend audience/usage beyond traditional formulas like Vitamin C and Multivitamins and into more unique variations for Heart Health and Immunity.","wordCount":45},{"sentence":"Unfortunately, new product news has been little more than a hamster wheel to maintain facings and Vitafusion now holds just a ~thirty two percent share of the adult category, down from forty six percent at the peak in two thousand twelve.","wordCount":41},{"sentence":"Total Avid adult gummies grew at half the rate of category growth in two thousand fourteen and is trending at less than one third the rate of category YTD.","wordCount":29},{"sentence":"For children's, performance is even worse but reflective of category trends as quoted above more so than shares.","wordCount":18},{"sentence":"Lil' Critters shares are down three hundred bps to twenty nine percent versus the peak in two thousand thirteen.","wordCount":19},{"sentence":"Coupled with category declines, this has translated into a remarkable seventeen percent decline in the business YTD.","wordCount":17},{"sentence":"Going forward, we have assumed that the execution issues of two thousand fifteen ease and the business performs a bit better in two thousand sixteen.","wordCount":25},{"sentence":"That said, we expect Avid to continue to be a share donor and have assumed four six percent growth for the business longer term.","wordCount":24},{"sentence":"While Cat Litter has picked up the slack from Vitamins thus far, we see near term risk to top line from a competitive resurgence.","wordCount":24},{"sentence":"Church and Dwight has had tremendous momentum in its Cat Litter business since Arm and Hammer Clump and Seal first hit shelves in fourQthirteen.","wordCount":24},{"sentence":"Using Nielsen trends as a gauge, the company's litter business grew ~fourx the category in two thousand fourteen, gaining +two hundred fifty bps of share and contributing ~two.three","wordCount":29},{"sentence":"Of course, we've been wary of a competitive response in a category that has proven so receptive to innovation specifically remembering that Church's business declined two percent in two thousand thirteen on the heels of Nestle Tidy Cats lightweight launch.","wordCount":41},{"sentence":"Barclays | Church and Dwight Co., Inc. performance has been resilient even following Clorox's Fresh Step Lightweight Extreme launch in FoneQfifteen, we do worry that this is about to change.","wordCount":30},{"sentence":"Remarkably, Cat Litter accounted for over forty percent of total company sales growth and ~seventy percent of Consumer Domestic in two thousand fourteen and only a slightly smaller contribution YTD so it makes sense that we'd be watching this business closely.","wordCount":41},{"sentence":"There has already been some moderate slowing in A and H's pace of share gain YTD, surely just as we are nearing the two year mark for the Clump and Seal innovation being in market.","wordCount":35},{"sentence":"We do note that historically the brand has launched a material innovation every two years and that the Clump and Seal launch at the end of two thousand thirteen came a year late versus that historical pattern.","wordCount":37},{"sentence":"More notably, we are keen to see the impact that Clorox's upcoming Fresh Step with Febreze has on the market in light of the co branding success for Febreze with Glad Odor Shield trash bags.","wordCount":35},{"sentence":"Glad trash sales had been declining at an eight percent clip for the year prior to the threeQten launch and staged a major trend reversal, growing +one.five","wordCount":28},{"sentence":"As we see it, Febreze co branding is a stellar fit for the Cat Litter category.","wordCount":16},{"sentence":"We are taking down our estimates for twoHfifteen Consumer Domestic organic sales growth to +two.two percent to reflect a deceleration in shipments as retailers even out inventories, leaving our total company organic estimate at +one.six percent for the balance of the year.","wordCount":44},{"sentence":"We expect softer top line growth to shave ~onec off our current Cfifteen EPS outlook to dollar three.twenty","wordCount":19},{"sentence":"Looking ahead into two thousand sixteen, with Vitamins and Litter expected to be less robust contributors, we now expect Consumer Domestic to grow +two.five","wordCount":25},{"sentence":"percent in two thousand sixteen and are modelling total company organic growth at two.eight percent .","wordCount":16},{"sentence":"Lower sales growth has tweaked our two thousand sixteen estimate down by dollar .four/sh to dollar three.fifty","wordCount":18},{"sentence":"Cat Litter accounted for ~seventy percent of the growth in Consumer Domestic in two thousand fourteen, so it makes sense that we'd be watching this business closely.","wordCount":27},{"sentence":"I, Lauren R. Lieberman, hereby certify that the views expressed in this research report accurately reflect my personal views about any or all of the subject securities or issuers referred to in this research report and no part of my compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this research report.","wordCount":61},{"sentence":"Barclays Research is a part of the Investment Bank of Barclays Bank PLC and its affiliates .","wordCount":16},{"sentence":"For current important disclosures regarding companies that are the subject of this research report, please send a written request to: Barclays Compliance Department, seven hundred forty five Seventh Avenue, thirteenth Floor, New York, NY ten thousand nineteen or refer to http://publicresearch.barclays","wordCount":42},{"sentence":"dot com or call two hundred twelve five million two hundred sixty one thousand seventy two.","wordCount":16},{"sentence":"The analysts responsible for preparing this research report have received compensation based upon various factors including the firm's total revenues, a portion of which is generated by investment banking activities.","wordCount":30},{"sentence":"Analysts regularly conduct site visits to view the material operations of covered companies, but Barclays policy prohibits them from accepting payment or reimbursement by any covered company of their travel expenses for such visits.","wordCount":34},{"sentence":"In order to access Barclays Statement regarding Research Dissemination Policies and Procedures, please refer to http://publicresearch.barcap","wordCount":17},{"sentence":"In order to access Barclays Research Conflict Management Policy Statement, please refer to: http://publicresearch.barcap","wordCount":15},{"sentence":"The Investment Bank's Research Department produces various types of research including, but not limited to, fundamental analysis, equity linked analysis, quantitative analysis, and trade ideas.","wordCount":25},{"sentence":"Recommendations contained in one type of research product may differ from recommendations contained in other types of research, whether as a result of differing time horizons, methodologies, or otherwise.","wordCount":29},{"sentence":"Primary Stocks Church and Dwight Co., Inc., Underweight/Positive, C/J/K/N/O Disclosure Legend: A: Barclays Bank PLC and/or an affiliate has been lead manager or co lead manager of a publicly disclosed offer of securities of the issuer in the previous twelve months.","wordCount":41},{"sentence":"B: An employee of Barclays Bank PLC and/or an affiliate is a director of this issuer.","wordCount":16},{"sentence":"C: Barclays Bank PLC and/or an affiliate is a market maker in equity securities issued by this issuer.","wordCount":18},{"sentence":"D: Barclays Bank PLC and/or an affiliate has received compensation for investment banking services from this issuer in the past twelve months.","wordCount":22},{"sentence":"E: Barclays Bank PLC and/or an affiliate expects to receive or intends to seek compensation for investment banking services from this issuer within the next three months.","wordCount":27},{"sentence":"F: Barclays Bank PLC and/or an affiliate beneficially owned one percent or more of a class of equity securities of the issuer as of the end of the month prior to the research report's issuance.","wordCount":35},{"sentence":"G: One of the analysts on the coverage team owns shares of the common stock of this issuer.","wordCount":18},{"sentence":"H: This issuer beneficially owns five percent or more of any class of common equity securities of Barclays PLC.","wordCount":19},{"sentence":"I: Barclays Bank PLC and/or an affiliate has a significant financial interest in the securities of this issuer.","wordCount":18},{"sentence":"J: Barclays Bank PLC and/or an affiliate is a liquidity provider and/or trades regularly in the securities of this issuer and/or in any related derivatives.","wordCount":25},{"sentence":"K: Barclays Bank PLC and/or an affiliate has received non investment banking related compensation from this issuer within the past twelve months.","wordCount":22},{"sentence":"L: This issuer is, or during the past twelve months has been, an investment banking client of Barclays Bank PLC and/or an affiliate.","wordCount":23},{"sentence":"M: This issuer is, or during the past twelve months has been, a non investment banking client of Barclays Bank PLC and/or an affiliate.","wordCount":24},{"sentence":"N: This issuer is, or during the past twelve months has been, a non investment banking client of Barclays Bank PLC and/or an affiliate.","wordCount":24},{"sentence":"O: Barclays Capital Inc., through Barclays Market Makers, is a Designated Market Maker in this issuer's stock, which is listed on the New York Stock Exchange.","wordCount":26},{"sentence":"At any given time, its associated Designated Market Maker may have long or short inventory position in the stock; and its associated Designated Market Maker may be on the opposite side of orders executed on the floor of the New York Stock Exchange in the stock.","wordCount":46},{"sentence":"P: A partner, director or officer of Barclays Capital Canada Inc. has, during the preceding twelve months, provided services to the subject company for remuneration, other than normal course investment advisory or trade execution services.","wordCount":35},{"sentence":"Q: Barclays Bank PLC and/or an affiliate is a Corporate Broker to this issuer.","wordCount":14},{"sentence":"R: Barclays Capital Canada Inc. and/or an affiliate has received compensation for investment banking services from this issuer in the past twelve months.","wordCount":23},{"sentence":"S: Barclays Capital Canada Inc. is a market maker in an equity or equity related security issued by this issuer.","wordCount":20},{"sentence":"T: Barclays Bank PLC and/or an affiliate is providing equity advisory services to this issuer.","wordCount":15},{"sentence":"U: The equity securities of this Canadian issuer include subordinate voting restricted shares.","wordCount":13},{"sentence":"V: The equity securities of this Canadian issuer include non voting restricted shares.","wordCount":13},{"sentence":"W: Barclays Bank PLC and/or an affiliate should be assumed to be an actual beneficial owner of one percent or more of all the securities of this issuer as of the end of the month prior to the research report's issuance.","wordCount":41},{"sentence":"Risk Disclosure Master limited partnerships are pass through entities structured as publicly listed partnerships.","wordCount":14},{"sentence":"For tax purposes, distributions to MLP unit holders may be treated as a return of principal.","wordCount":16},{"sentence":"Investors should consult their own tax advisors before investing in MLP units.","wordCount":12},{"sentence":"Guide to the Barclays Fundamental Equity Research Rating System: Our coverage analysts use a relative rating system in which they rate stocks as Overweight, Equal Weight or Underweight relative to other companies covered by the analyst or a team of analysts that are deemed to be in the same industry .","wordCount":50},{"sentence":"In addition to the stock rating, we provide industry views which rate the outlook for the industry coverage universe as Positive, Neutral or Negative .","wordCount":24},{"sentence":"A rating system using terms such as buy, hold and sell is not the equivalent of our rating system.","wordCount":19},{"sentence":"Investors should carefully read the entire research report including the definitions of all ratings and not infer its contents from ratings alone.","wordCount":22},{"sentence":"Stock Rating Overweight The stock is expected to outperform the unweighted expected total return of the industry coverage universe over a twelve month investment horizon.","wordCount":25},{"sentence":"Equal Weight The stock is expected to perform in line with the unweighted expected total return of the industry coverage universe over a twelvemonth investment horizon.","wordCount":26},{"sentence":"Underweight The stock is expected to underperform the unweighted expected total return of the industry coverage universe over a twelve month investment horizon.","wordCount":23},{"sentence":"Rating Suspended The rating and target price have been suspended temporarily due to market events that made coverage impracticable or to comply with applicable regulations and/or firm policies in certain circumstances including where the Investment Bank of Barclays Bank PLC is acting in an advisory capacity in a merger or strategic transaction involving the company.","wordCount":55},{"sentence":"Neutral industry coverage universe fundamentals/valuations are steady, neither improving nor deteriorating.","wordCount":11},{"sentence":"Below is the list of companies that constitute the industry coverage universe: U.S. Cosmetics; Household and Personal Care Avon Products Colgate Palmolive Jarden Corporation Procter and Gamble Distribution of Ratings: Barclays Equity Research has two thousand seven hundred sixty three companies under coverage.","wordCount":44},{"sentence":"forty three percent have been assigned an Overweight rating which, for purposes of mandatory regulatory disclosures, is classified as a Buy rating; fifty one percent of companies with this rating are investment banking clients of the Firm.","wordCount":37},{"sentence":"forty percent have been assigned an Equal Weight rating which, for purposes of mandatory regulatory disclosures, is classified as a Hold rating; forty three percent of companies with this rating are investment banking clients of the Firm.","wordCount":37},{"sentence":"fourteen percent have been assigned an Underweight rating which, for purposes of mandatory regulatory disclosures, is classified as a Sell rating; forty percent of companies with this rating are investment banking clients of the Firm.","wordCount":35},{"sentence":"Guide to the Barclays Research Price Target: Each analyst has a single price target on the stocks that they cover.","wordCount":20},{"sentence":"The price target represents that analyst's expectation of where the stock will trade in the next twelve months.","wordCount":18},{"sentence":"Upside/downside scenarios, where provided, represent potential upside/potential downside to each analyst's price target over the same twelve month period.","wordCount":19},{"sentence":"Top Picks: Barclays Equity Research's Top Picks represent the single best alpha generating investment idea within each industry, taken from among the Overweight rated stocks within that industry.","wordCount":28},{"sentence":"Barclays Equity Research publishes global and regional Top Picks reports every quarter and analysts may also publish intra quarter changes to their Top Picks, as necessary.","wordCount":26},{"sentence":"While analysts may highlight other Overweight rated stocks in their published research in addition to their Top Pick, there can only be one Top Pick for each industry.","wordCount":28},{"sentence":"To view the current list of Top Picks, go to the Top Picks page on Barclays Live .","wordCount":17},{"sentence":"To see a list of companies that comprise a particular industry coverage universe, please go to http://publicresearch.barclays","wordCount":18},{"sentence":"twenty nine October two thousand fifteen nine Church and Dwight Co., Inc.","wordCount":12},{"sentence":"The Estée Lauder Companies Clorox Company International Flavors and Fragrances Newell Rubbermaid Inc.","wordCount":13},{"sentence":"Barclays legal entities involved in publishing research: Barclays Bank PLC Barclays Capital Inc. Barclays Securities Japan Limited Barclays Bank PLC, Tokyo branch Barclays Bank PLC, Hong Kong branch Barclays Capital Canada Inc. Absa Bank Limited Barclays Bank Mexico, S.A. Barclays Capital Securities Taiwan Limited Barclays Capital Securities Limited Barclays Securities Private Limited Barclays Bank PLC, India branch Barclays Bank PLC, Singapore branch Barclays Bank PLC, Australia branch","wordCount":68},{"sentence":"Stock Rating UNDERWEIGHT Currency=USD Date nineteen Aug two thousand fifteen five Aug two thousand fifteen seventeen Feb two thousand fifteen four Nov two thousand fourteen six Oct two thousand fourteen four Aug two thousand fourteen Closing Price eighty nine.seventy","wordCount":40},{"sentence":"Historical stock prices and price targets may have been adjusted for stock splits and dividends.","wordCount":15},{"sentence":"C: Barclays Bank PLC and/or an affiliate is a market maker in equity securities issued by Church and Dwight Co., Inc.. J: Barclays Bank PLC and/or an affiliate is a liquidity provider and/or trades regularly in the securities by Church and Dwight Co., Inc. and/or in any related derivatives.","wordCount":49},{"sentence":"K: Barclays Bank PLC and/or an affiliate has received non investment banking related compensation from Church and Dwight Co., Inc. within the past twelve months.","wordCount":25},{"sentence":"N: Church and Dwight Co., Inc. is, or during the past twelve months has been, a non investment banking client of Barclays Bank PLC and/or an affiliate.","wordCount":27},{"sentence":"O: Barclays Capital Inc., through Barclays Market Makers, is a Designated Market Maker in Church and Dwight Co., Inc. stock, which is listed on the New York Stock Exchange.","wordCount":29},{"sentence":"At any given time, its associated Designated Market Maker may have long or short inventory position in the stock; and its associated Designated Market Maker may be on the opposite side of orders executed on the floor of the New York Stock Exchange in the stock.","wordCount":46},{"sentence":"Valuation Methodology: Our dollar eighty one price target reflects a ~twenty threex multiple on our two thousand sixteen EPS estimate of dollar three.fifty","wordCount":24},{"sentence":"Risks which May Impede the Achievement of the Barclays Research Price Target: Various risk factors which could affect our rating and price target include macroeconomic uncertainty, the competitive pricing environment, integration execution and related business or supply chain disruption, marketing/advertising effectiveness, consumer acceptance of new products, raw material cost inflation and foreign currency translation.","wordCount":54},{"sentence":"DISCLAIMER: This publication has been produced by the Investment Bank of Barclays Bank PLC and/or one or more of its affiliates .","wordCount":21},{"sentence":"It has been distributed by one or more Barclays legal entities that are a part of the Investment Bank as provided below.","wordCount":22},{"sentence":"It is provided to our clients for information purposes only, and Barclays makes no express or implied warranties, and expressly disclaims all warranties of merchantability or fitness for a particular purpose or use with respect to any data included in this publication.","wordCount":42},{"sentence":"Barclays will not treat unauthorized recipients of this report as its clients.","wordCount":12},{"sentence":"Prices shown are indicative and Barclays is not offering to buy or sell or soliciting offers to buy or sell any financial instrument.","wordCount":23},{"sentence":"Without limiting any of the foregoing and to the extent permitted by law, in no event shall Barclays, nor any affiliate, nor any of their respective officers, directors, partners, or employees have any liability for any special, punitive, indirect, or consequential damages; or any lost profits, lost revenue, loss of anticipated savings or loss of opportunity or other financial loss, even if notified of the possibility of such damages, arising from any use of this publication or its contents.","wordCount":79},{"sentence":"Other than disclosures relating to Barclays, the information contained in this publication has been obtained from sources that Barclays Research believes to be reliable, but Barclays does not represent or warrant that it is accurate or complete.","wordCount":37},{"sentence":"Barclays is not responsible for, and makes no warranties whatsoever as to, the content of any third party web site accessed via a hyperlink in this publication and such information is not incorporated by reference.","wordCount":35},{"sentence":"The views in this publication are those of the author and are subject to change, and Barclays has no obligation to update its opinions or the information in this publication.","wordCount":30},{"sentence":"The analyst recommendations in this publication reflect solely and exclusively those of the author, and such opinions were prepared independently of any other interests, including those of Barclays and/or its affiliates.","wordCount":31},{"sentence":"This publication does not constitute personal investment advice or take into account the individual financial circumstances or objectives of the clients who receive it.","wordCount":24},{"sentence":"The securities discussed herein may not be suitable for all investors.","wordCount":11},{"sentence":"Barclays recommends that investors independently evaluate each issuer, security or instrument discussed herein and consult any independent advisors they believe necessary.","wordCount":21},{"sentence":"The value of and income from any investment may fluctuate from day to day as a result of changes in relevant economic markets .","wordCount":23},{"sentence":"The information herein is not intended to predict actual results, which may differ substantially from those reflected.","wordCount":17},{"sentence":"This material has been issued and approved for distribution in the UK and European Economic Area by Barclays Bank PLC.","wordCount":20},{"sentence":"It is being made available primarily to persons who are investment professionals as that term is defined in Article nineteen of the Financial Services and Markets Act two thousand Order two thousand five.","wordCount":33},{"sentence":"It is directed at, and therefore should only be relied upon by, persons who have professional experience in matters relating to investments.","wordCount":22},{"sentence":"The investments to which it relates are available only to such persons and will be entered into only with such persons.","wordCount":21},{"sentence":"Barclays Bank PLC is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority and is a member of the London Stock Exchange.","wordCount":31},{"sentence":"The Investment Bank of Barclays Bank PLC undertakes U.S. securities business in the name of its wholly owned subsidiary Barclays Capital Inc., a FINRA and SIPC member.","wordCount":28},{"sentence":"Barclays Capital Inc., a U.S. registered broker/dealer, is distributing this material in the United States and, in connection therewith accepts responsibility for its contents.","wordCount":25},{"sentence":"Any U.S. person wishing to effect a transaction in any security discussed herein should do so only by contacting a representative of Barclays Capital Inc. in the U.S. at seven hundred forty five Seventh Avenue, New York, New York ten thousand nineteen.","wordCount":44},{"sentence":"Non U.S. persons should contact and execute transactions through a Barclays Bank PLC branch or affiliate in their home jurisdiction unless local regulations permit otherwise.","wordCount":26},{"sentence":"Barclays Bank PLC, Paris Branch is regulated by the Autorité des marchés financiers and the Autorité de contrôle prudentiel.","wordCount":19},{"sentence":"Registered office thirty four/thirty six Avenue de Friedland seventy five thousand eight Paris.","wordCount":13},{"sentence":"This material is distributed in Canada by Barclays Capital Canada Inc., a registered investment dealer, a Dealer Member of IIROC, and a Member of the Canadian Investor Protection Fund .","wordCount":29},{"sentence":"Subject to the conditions of this publication as set out above, the Corporate and Investment Banking Division of Absa Bank Limited, an authorised financial services provider, is distributing this material in South Africa.","wordCount":33},{"sentence":"Absa Bank Limited is regulated by the South African Reserve Bank.","wordCount":11},{"sentence":"This publication is not, nor is it intended to be, advice as defined and/or contemplated in the Financial Advisory and Intermediary Services Act, thirty seven of two thousand two, or any other financial, investment, trading, tax, legal, accounting, retirement, actuarial or other professional advice or service whatsoever.","wordCount":47},{"sentence":"Any South African person or entity wishing to effect a transaction in any security discussed herein should do so only by contacting a representative of the Corporate and Investment Banking Division of Absa Bank Limited in South Africa, fifteen Alice Lane, Sandton, Johannesburg, Gauteng two thousand one hundred ninety six.","wordCount":50},{"sentence":"In Japan, foreign exchange research reports are prepared and distributed by Barclays Bank PLC Tokyo Branch.","wordCount":16},{"sentence":"Other research reports are distributed to institutional investors in Japan by Barclays Securities Japan Limited.","wordCount":15},{"sentence":"Barclays Securities Japan Limited is a joint stock company incorporated in Japan with registered office of six ten one Roppongi, Minato ku, Tokyo one hundred six six thousand one hundred thirty one, Japan.","wordCount":33},{"sentence":"It is a subsidiary of Barclays Bank PLC and a registered financial instruments firm regulated by the Financial Services Agency of Japan.","wordCount":22},{"sentence":"Barclays Bank PLC, Hong Kong Branch is distributing this material in Hong Kong as an authorised institution regulated by the Hong Kong Monetary Authority.","wordCount":24},{"sentence":"Registered Office: forty one/F, Cheung Kong Center, two Queen's Road Central, Hong Kong.","wordCount":13},{"sentence":"Information on securities/instruments that trade in Taiwan or written by a Taiwan based research analyst is distributed by Barclays Capital Securities Taiwan Limited to its clients.","wordCount":26},{"sentence":"The material on securities/instruments not traded in Taiwan is not to be construed as 'recommendation' in Taiwan.","wordCount":17},{"sentence":"Barclays Capital Securities Taiwan Limited does not accept orders from clients to trade in such securities.","wordCount":16},{"sentence":"This material may not be distributed to the public media or used by the public media without prior written consent of Barclays.","wordCount":22},{"sentence":"This material is distributed in South Korea by Barclays Capital Securities Limited, Seoul Branch.","wordCount":14},{"sentence":"All Indian securities related research and other equity research produced by the Investment Bank are distributed in India by Barclays Securities Private Limited .","wordCount":23},{"sentence":"BSIPL is a company incorporated under the Companies Act, one thousand nine hundred fifty six having CIN Usixty seven thousand one hundred twentyMHtwo thousand sixPTCone hundred sixty one thousand sixty three.","wordCount":31},{"sentence":"BSIPL is registered and regulated by the Securities and Exchange Board of India as a Research Analyst: INHone thousand five hundred nineteen; Portfolio Manager INPtwo thousand five hundred eighty five; Stock Broker/Trading and Clearing Member: National Stock Exchange of India Limited Capital Market INBtwo hundred thirty one million two hundred ninety two thousand seven hundred thirty two, NSE Futures and Options INFtwo hundred thirty one million two hundred ninety two thousand seven hundred thirty two, NSE Currency derivatives INEtwo hundred thirty one million four hundred fifty thousand three hundred thirty four, Bombay Stock Exchange Limited Capital Market INBeleven million two hundred ninety two thousand seven hundred thirty eight, BSE Futures and Options INFeleven million two hundred ninety two thousand seven hundred thirty eight; Merchant Banker: INMeleven thousand one hundred ninety five; Depository Participant with the National Securities and Depositories Limited : DP ID: IN DPNSDL two hundred ninety nine two thousand eight; Investment Adviser: INAthree hundred ninety one.","wordCount":158},{"sentence":"The registered office of BSIPL is at two hundred eight, Ceejay House, Shivsagar Estate, Doctor A. Besant Road, Worli, Mumbai four hundred eighteen, India.","wordCount":25},{"sentence":"Telephone No: +ninety one two billion two hundred sixty seven million one hundred ninety six thousand.","wordCount":16},{"sentence":"Fax number: +ninety one twenty two sixty seven million one hundred ninety six thousand one hundred.","wordCount":16},{"sentence":"Any other reports produced by the Investment Bank are distributed in India by Barclays Bank PLC, India Branch, an associate of BSIPL in India that is registered with Reserve Bank of India as a Banking Company under the provisions of The Banking Regulation Act, one thousand nine hundred forty nine and registered with SEBI as Merchant Banker and also as Banker to the Issue .","wordCount":64},{"sentence":"Banking Financial Company, and Barclays Wealth Trustees Private Limited, registered with Registrar of Companies, are associates of BSIPL in India that are not authorised to distribute any reports produced by the Investment Bank.","wordCount":33},{"sentence":"Barclays Bank PLC Frankfurt Branch distributes this material in Germany under the supervision of Bundesanstalt für Finanzdienstleistungsaufsicht .","wordCount":17},{"sentence":"This material is distributed in Malaysia by Barclays Capital Markets Malaysia Sdn Bhd.","wordCount":13},{"sentence":"This material is distributed in Brazil by Banco Barclays S.A.","wordCount":11},{"sentence":"This material is distributed in Mexico by Barclays Bank Mexico, S.A. Barclays Bank PLC in the Dubai International Financial Centre is regulated by the Dubai Financial Services Authority .","wordCount":29},{"sentence":"Principal place of business in the Dubai International Financial Centre: The Gate Village, Building four, Level four, PO Box five hundred six thousand five hundred four, Dubai, United Arab Emirates.","wordCount":30},{"sentence":"Barclays Bank PLC DIFC Branch, may only undertake the financial services activities that fall within the scope of its existing DFSA licence.","wordCount":22},{"sentence":"Related financial products or services are only available to Professional Clients, as defined by the Dubai Financial Services Authority.","wordCount":19},{"sentence":"Barclays Bank PLC in the UAE is regulated by the Central Bank of the UAE and is licensed to conduct business activities as a branch of a commercial bank incorporated outside the UAE in Dubai and Abu Dhabi .","wordCount":38},{"sentence":"Barclays Bank PLC in the Qatar Financial Centre is authorised by the Qatar Financial Centre Regulatory Authority .","wordCount":17},{"sentence":"Barclays Bank PLC QFC Branch may only undertake the regulated activities that fall within the scope of its existing QFCRA licence.","wordCount":21},{"sentence":"Principal place of business in Qatar: Qatar Financial Centre, Office one thousand two, tenth Floor, QFC Tower, Diplomatic Area, West Bay, PO Box fifteen thousand eight hundred ninety one, Doha, Qatar.","wordCount":31},{"sentence":"Related financial products or services are only available to Business Customers as defined by the Qatar Financial Centre Regulatory Authority.","wordCount":20},{"sentence":"This material is distributed in the UAE and Qatar by Barclays Bank PLC.","wordCount":13},{"sentence":"This material is distributed in Russia by OOO Barclays Capital, affiliated company of Barclays Bank PLC, registered and regulated in Russia by the FSFM.","wordCount":24},{"sentence":"Broker License number one hundred seventy seven eleven thousand eight hundred fifty one hundred thousand; Dealer License number one hundred seventy seven eleven thousand eight hundred fifty five ten thousand.","wordCount":30},{"sentence":"Registered address in Russia: one hundred twenty five thousand forty seven Moscow, onest Tverskaya Yamskaya str.","wordCount":16},{"sentence":"This material is distributed in Singapore by the Singapore branch of Barclays Bank PLC, a bank licensed in Singapore by the Monetary Authority of Singapore.","wordCount":25},{"sentence":"For matters in connection with this report, recipients in Singapore may contact the Singapore branch of Barclays Bank PLC, whose registered address is ten Marina Boulevard, number twenty three one Marina Bay Financial Centre Tower two, Singapore eighteen thousand nine hundred eighty three.","wordCount":43},{"sentence":"Barclays Bank PLC, Australia Branch is distributing this material in Australia.","wordCount":11},{"sentence":"It is directed at 'wholesale clients' as defined by Australian Corporations Act two thousand one.","wordCount":15},{"sentence":"IRS Circular two hundred thirty Prepared Materials Disclaimer: Barclays does not provide tax advice and nothing contained herein should be construed to be tax advice.","wordCount":25},{"sentence":"Please be advised that any discussion of U.S. tax matters contained herein is not intended or written to be used, and cannot be used, by you for the purpose of avoiding U.S. tax related penalties; and was written to support the promotion or marketing of the transactions or other matters addressed herein.","wordCount":54},{"sentence":"Accordingly, you should seek advice based on your particular circumstances from an independent tax advisor.","wordCount":15},{"sentence":"No part of this publication may be reproduced or redistributed in any manner without the prior written permission of Barclays.","wordCount":20},{"sentence":"Barclays Bank PLC is registered in England No. one million twenty six thousand one hundred sixty seven.","wordCount":17}]}
|
{
"detail.css": "detail-1326a3957b.css",
"footer.css": "footer-c813bbb07b.css",
"header01.css": "header01-4fc3a336cb.css",
"header2.css": "header2-ad02de3214.css",
"index.css": "index-b8c1d2c1fa.css",
"list.css": "list-aa91d52ca5.css",
"login.css": "login-56c65de1e8.css",
"register.css": "register-464581ee2f.css",
"rightnav.css": "rightnav-4c4d172e6f.css",
"swiper.css": "swiper-1ea689d228.css"
} |
{
"doi": "10.1016/j.amepre.2008.08.017",
"href": "http://data.globalchange.gov/article/10.1016/j.amepre.2008.08.017.json",
"identifier": "10.1016/j.amepre.2008.08.017",
"journal_identifier": "american-journal-preventive-medicine",
"journal_pages": "517-526",
"journal_vol": "35",
"notes": null,
"title": "The Built Environment, Climate Change, and Health",
"uri": "/article/10.1016/j.amepre.2008.08.017",
"url": null,
"year": 2008
} |
{
"directions": [
"Preheat oven to 350\u00b0F with rack in middle.",
"Overlap ham slices on a baking sheet and cover with foil. Bake until just heated through, 8 to 10 minutes.",
"Bring cream to a simmer, then whisk in cheese until smooth. (If necessary, pur\u00e9e in a blender, then reheat.) Whisk in yolk and cook, whisking, 1 minute.",
"Serve ham with sauce."
],
"ingredients": [
"1 1/2 pounds thick-sliced good-quality baked ham such as Virginia",
"1/2 cup heavy cream",
"1 firm-ripe Camembert, chilled, rind discarded and cheese coarsely chopped",
"1 large egg yolk",
"Broccolini or green beans"
],
"language": "en-US",
"source": "www.epicurious.com",
"tags": [
"Milk/Cream",
"Egg",
"Quick & Easy",
"Buffet",
"Brie",
"Ham",
"Winter",
"Gourmet"
],
"title": "Sliced Baked Ham with Camembert Sauce",
"url": "http://www.epicurious.com/recipes/food/views/sliced-baked-ham-with-camembert-sauce-243536"
}
|
{
"id": 924698701,
"type": "Feature",
"properties": {
"addr:full":"56 Bixley Road Ipswich Suffolk IP3 8PG",
"addr:housenumber":"56",
"addr:postcode":"ip3 8pg",
"addr:street":"Bixley Road",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"1.2002249956,52.0473594666,1.2002249956,52.0473594666",
"geom:latitude":52.047359,
"geom:longitude":1.200225,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"56 Bixley Road",
"sg:city":"Ipswich",
"sg:classifiers":[
{
"category":"Professional",
"subcategory":"Engineering",
"type":"Services"
},
{
"category":"Building & Trades",
"subcategory":"Electrical",
"type":"Services"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 1473 729881",
"sg:postcode":"IP3 8PG",
"sg:province":"Suffolk",
"sg:tags":[
"engineer",
"electrician",
"contractor"
],
"src:geom":"simplegeo",
"wof:belongsto":[
85793225,
"102191581",
404437431,
"85633159",
404227469,
101750447,
85684515
],
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_0xtd4j9nIVReEN7QbPiC6N_52.047359_1.200225@1300740856"
},
"wof:country":"GB",
"wof:created":1471975029,
"wof:geomhash":"dd2293ca8e258f968c6e7eb433b6ffab",
"wof:hierarchy":[
{
"continent_id":"102191581",
"country_id":"85633159",
"localadmin_id":404437431,
"locality_id":101750447,
"macroregion_id":404227469,
"neighbourhood_id":85793225,
"region_id":85684515,
"venue_id":924698701
}
],
"wof:id":924698701,
"wof:lastmodified":1496943291,
"wof:name":"B D Goldsmith",
"wof:parent_id":85793225,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"engineer",
"electrician",
"contractor"
]
},
"bbox": [
1.2002249956,
52.0473594666,
1.2002249956,
52.0473594666
],
"geometry": {"coordinates":[1.2002249956,52.0473594666],"type":"Point"}
} |
{"id":12637,"name":"Hard Leather Boots♂","description":"Imbue to increase Defense and Resistance."} |
{
"id": 907622825,
"type": "Feature",
"properties": {
"addr:full":"61 Otley Road Leeds West Yorkshire LS6 3AB",
"addr:housenumber":"61",
"addr:postcode":"ls6 3ab",
"addr:street":"Otley Road",
"edtf:cessation":"uuuu",
"edtf:inception":"uuuu",
"geom:area":0.0,
"geom:area_square_m":0.0,
"geom:bbox":"-1.5777590275,53.8212432861,-1.5777590275,53.8212432861",
"geom:latitude":53.821243,
"geom:longitude":-1.577759,
"iso:country":"GB",
"mz:hierarchy_label":1,
"mz:is_current":-1,
"sg:address":"61 Otley Road",
"sg:city":"Leeds",
"sg:classifiers":[
{
"category":"Banks & Credit Unions",
"subcategory":"Bureau De Change",
"type":"Services"
},
{
"category":"Food & Beverages",
"subcategory":"Liquor & Beverages",
"type":"Retail Goods"
}
],
"sg:owner":"simplegeo",
"sg:phone":"+44 113 274 7483",
"sg:postcode":"LS6 3AB",
"sg:province":"West Yorkshire",
"sg:tags":[
"beer",
"wine",
"spirit",
"wines"
],
"sg:website":"http://www.oddbins.co.uk",
"src:geom":"simplegeo",
"wof:belongsto":[
85788039,
"102191581",
404432293,
"85633159",
404227469,
101750579,
85684321
],
"wof:brand_id":1125153113,
"wof:breaches":[],
"wof:concordances":{
"sg:id":"SG_6Ect7z5uftChchI06bDfbj_53.821243_-1.577759@1300740417"
},
"wof:country":"GB",
"wof:created":1471909994,
"wof:geomhash":"de49ed49046a5d3a989309eac2936716",
"wof:hierarchy":[
{
"continent_id":"102191581",
"country_id":"85633159",
"localadmin_id":404432293,
"locality_id":101750579,
"macroregion_id":404227469,
"neighbourhood_id":85788039,
"region_id":85684321,
"venue_id":907622825
}
],
"wof:id":907622825,
"wof:lastmodified":1511317745,
"wof:name":"Oddbins Ltd.",
"wof:parent_id":85788039,
"wof:placetype":"venue",
"wof:repo":"whosonfirst-data-venue-gb",
"wof:superseded_by":[],
"wof:supersedes":[],
"wof:tags":[
"beer",
"wine",
"spirit",
"wines"
]
},
"bbox": [
-1.5777590275,
53.8212432861,
-1.5777590275,
53.8212432861
],
"geometry": {"coordinates":[-1.5777590275,53.8212432861],"type":"Point"}
} |
{
"dependencies": {
"express": "^4.16.2",
"jss": "8.1.0",
"jss-preset-default": "^3.0.0",
"lodash": "^4.17.0",
"material-ui": "^1.0.0-beta.19",
"material-ui-icons": "^1.0.0-beta.15",
"mobx": "^3.3.1",
"mobx-react": "^4.3.3",
"react": "^16.0.0",
"react-color": "^2.13.8",
"react-dom": "^16.0.0",
"react-jss": "^7.0.0",
"source-map-support": "^0.5.0",
"tslib": "^1.8.0",
"typeface-roboto": "^0.0.43"
},
"devDependencies": {
"@commitlint/cli": "^4.2.0",
"@commitlint/config-angular": "^4.2.0",
"@types/express": "^4.0.37",
"@types/extract-text-webpack-plugin": "^3.0.0",
"@types/html-webpack-plugin": "^2.28.0",
"@types/jss": "^0.6.28",
"@types/react": "^16.0.10",
"@types/react-color": "^2.13.2",
"@types/react-dom": "^16.0.1",
"@types/webpack": "^3.0.14",
"autoprefixer": "^7.1.6",
"clean-webpack-plugin": "^0.1.17",
"css-loader": "^0.28.7",
"extract-text-webpack-plugin": "^3.0.1",
"file-loader": "^1.1.5",
"html-webpack-plugin": "^2.30.1",
"husky": "^0.14.3",
"node-sass": "^4.5.3",
"npm-run-all": "^4.1.1",
"npmtoken": "^1.0.0",
"postcss": "^6.0.13",
"postcss-loader": "^2.0.8",
"react-hot-loader": "^3.1.1",
"sass-loader": "^6.0.6",
"style-loader": "^0.19.0",
"ts-loader": "^3.1.0",
"ts-node": "^3.3.0",
"tslint": "^5.7.0",
"tslint-eslint-rules": "^4.1.1",
"tslint-loader": "^3.5.3",
"typescript": "^2.5.3",
"uglifyjs-webpack-plugin": "^1.0.0-rc.0",
"webpack": "^3.7.1",
"webpack-dev-server": "^2.9.2",
"webpack-merge": "^4.1.0",
"webpack-node-externals": "^1.6.0"
}
}
|
{"title":"[Narutoverse]_NARUTO_Shippuden_280_[720p].mkv","uid":7655679,"size":330145294,"categoryP":"video","categoryS":"hd___tv_shows","magnet":"?xt=urn:btih:268ab15d22f87fd8aa3a8db48332ded33edfb0b4&dn=%5BNarutoverse%5D_NARUTO_Shippuden_280_%5B720p%5D.mkv&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Fopen.demonii.com%3A1337&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fexodus.desync.com%3A6969","seeders":0,"leechers":1,"uploader":"k1ng_ruLz","files":-1,"time":1348147197,"description":"Naruto: Shippuden - Episode 280 "Aesthetics of an Artist"\t","torrent":{"xt":"urn:btih:268ab15d22f87fd8aa3a8db48332ded33edfb0b4","amp;dn":"%5BNarutoverse%5D_NARUTO_Shippuden_280_%5B720p%5D.mkv","amp;tr":["udp%3A%2F%2Ftracker.openbittorrent.com%3A80","udp%3A%2F%2Fopen.demonii.com%3A1337","udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969","udp%3A%2F%2Fexodus.desync.com%3A6969"],"infoHash":"268ab15d22f87fd8aa3a8db48332ded33edfb0b4","infoHashBuffer":{"type":"Buffer","data":[38,138,177,93,34,248,127,216,170,58,141,180,131,50,222,211,62,223,176,180]},"announce":[],"urlList":[]}} |
{"latitude":41.2408,"longitude":-80.5744,"zipcode":"44403","msa":"9320","dma":"536","state":"OH"} |
{
"access_dt": null,
"attributes": null,
"cite_metadata": null,
"data_qualifier": null,
"description": "ABSTRACT: The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) collected surface meteorological data from 21-May to 20-Sep-1994 near the SSA-OJP tower site.",
"description_attribution": "http://daac.ornl.gov//BOREAS/guides/AFM06_SurfMet.html",
"doi": "10.3334/ORNLDAAC/243",
"end_time": "1994-09-20T00:00:00",
"href": "http://data.globalchange.gov/dataset/nasa-ornldaac-243.json",
"identifier": "nasa-ornldaac-243",
"lat_max": "53.91",
"lat_min": "53.91",
"lon_max": "-104.4",
"lon_min": "-104.4",
"name": "BOREAS AFM-06 Surface Meteorological Data",
"native_id": "BOREAS_AFM06SMD",
"processing_level": null,
"publication_year": null,
"release_dt": "1999-01-01T00:00:00",
"scale": null,
"scope": null,
"spatial_extent": null,
"spatial_ref_sys": null,
"spatial_res": null,
"start_time": "1994-05-21T00:00:00",
"temporal_extent": null,
"temporal_resolution": null,
"type": null,
"uri": "/dataset/nasa-ornldaac-243",
"url": "http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=243",
"variables": null,
"version": null,
"vertical_extent": null
} |
{"movie":"The Impossible","targetUser":"Alex","totalTweets":"9","overallSentiment":"2.89","nodes":[{"name":"BoobsRadley","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":0},{"name":"FATJEW","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":1},{"name":"Dodgers","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":2},{"name":"ZooeyDeschanel","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":3},{"name":"sagemintblue","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":4},{"name":"RobSchrab","sentiment":2, "level":2, "noTweets":1, "tweets":[{"text":"The Impossible --great movie."}],"id":5},{"name":"thelonelyisland","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":6},{"name":"bacons","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":7},{"name":"Alyssa_Milano","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":8},{"name":"iamBenLyons","sentiment":2, "level":2, "noTweets":1, "tweets":[{"text":"I watched 'The Impossible' & 'Promised Land' tonight. Solid Saturday evening. #DoubleFeature @CineFixNetwork @extratv @extratv @ESPNLA710"}],"id":9},{"name":"mcgregor_ewan","sentiment":3.14, "level":2, "noTweets":7, "tweets":[{"text":"Morning everyone. I've been very busy supporting #TheImpossible which is out soon here in the States. Q&As, talks stuff like that. Been good"},{"text":"@aycamutlucan Excellent time in London with all the cast of #TheImpossible great start for the film here. How are you?"},{"text":"@Elessar yeh the press is possible and it's for a film called #TheImpossible"},{"text":"Morning London Town. Press day today. #TheImpossible"},{"text":"Great Night at the two screenings of #TheImpossible tonight in London Town! Thanks to all the supporters of this film. X"},{"text":"Looking forward to the Brit premier of #TheImpossible Tonight. See some of you there. IMAX Waterloo! 6pm ish."},{"text":"@MelissaPinner Let me know where your are at the event and I'll make sure to say hello. #TheImpossible"}],"id":10},{"name":"E_I_T","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":11},{"name":"JDRyznar","sentiment":1, "level":2, "noTweets":0, "tweets":[],"id":12}],"links":[{"source":0,"target":7},{"source":0,"target":10},{"source":0,"target":1},{"source":11,"target":5},{"source":10,"target":4},{"source":10,"target":8},{"source":9,"target":3},{"source":9,"target":2},{"source":9,"target":1},{"source":6,"target":3},{"source":6,"target":5},{"source":6,"target":12},{"source":12,"target":5},{"source":8,"target":2},{"source":5,"target":7},{"source":3,"target":7},{"source":7,"target":1}]} |
{
"directions": [
"Wash leeks in a bowl of cold water, then lift out and drain well.",
"Dissolve 1/2 teaspoon salt in 2 cups hot water in a medium bowl, and add tofu. Soak 5 minutes, then drain in a sieve.",
"Heat a wok or 12-inch heavy skillet over high heat until a bead of water dropped onto cooking surface evaporates immediately. Add oil, swirling wok to coat, and heat until it begins to smoke. Add beef and stir-fry, breaking up lumps, until no longer pink, about 2 minutes. Stir in wine and 1/2 teaspoon soy sauce and stir-fry 30 seconds.",
"Add chile bean sauce and stir-fry until fragrant, about 15 seconds. Add ginger, chile powder, and black beans and stir-fry until fragrant, about 30 seconds. Add broth, remaining 1/2 teaspoon soy sauce, and tofu, then reduce heat and simmer, stirring gently, about 2 minutes.",
"Add leeks and simmer, stirring occasionally, until just tender, 2 to 3 minutes. Stir potato starch mixture and add to tofu, then cook, stirring, until sauce is thickened, about 1 minute."
],
"ingredients": [
"2 baby leeks (1/2 lb total; white and pale green parts only), cut diagonally into 1/8-inch-thick slices",
"10 oz soft or firm tofu (not silken or extra-firm), drained and cut into 1/2-inch cubes",
"1 tablespoon peanut oil",
"1/4 lb ground beef",
"1 teaspoon Chinese rice wine (preferably Shaoxing) or medium-dry Sherry",
"1 teaspoon dark soy sauce",
"1/2 tablespoon Chinese chile bean sauce or paste",
"1 teaspoon minced peeled fresh ginger",
"1 teaspoon mild pure chile powder",
"1/2 teaspoon Chinese fermented black beans, soaked in water 10 minutes and drained",
"1/4 cup chicken broth",
"1/2 teaspoon potato starch or cornstarch mixed with 2 teaspoons cold water",
"Accompaniment: white rice",
"Garnish: 1/2 teaspoon Sichuan peppercorns, toasted and ground with a mortar and pestle"
],
"language": "en-US",
"source": "www.epicurious.com",
"tags": [
"Wok",
"Stir-Fry",
"Quick & Easy",
"Dinner",
"Ground Beef",
"Tofu",
"Leek",
"Gourmet",
"Dairy Free",
"Tree Nut Free",
"Kosher"
],
"title": "Tofu and Leek Stir-Fry with Ground Beef",
"url": "http://www.epicurious.com/recipes/food/views/tofu-and-leek-stir-fry-with-ground-beef-107984"
}
|
{"latitude":36.8203,"longitude":-84.8151,"zipcode":"42633","msa":"541","dma":"NULL","state":"KY"} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.