10 years film · eebfc1915d - china-dictatorship - Kiwi FOSS\nCiro Santilli 5 years ago\n8 changed files with 34 additions and 14 deletions\n@ -2,14 +2,15 @@\nChinese "Communist" Dictatorship "facts". 中国《共产主义》独裁统治的《事实》\n1. [Movements against the Dictatorship](movements.md)\n1. [Falun Gong](falun-gong.md)\n1. [Stack Overflow](stack-overflow.md)\nevents.md View File\n# Events 事件\n- 2016 Honk Kong (2016) movie https://www.reddit.com/r/China/comments/41y6ig/hong_kong_movie_predicts_dark_future_for_city/\n- 2015 https://news.vice.com/article/the-missing-hong-kong-booksellers-family-isnt-buying-his-televised-confession Gui Minhai, 51, from publisher Mighty Current who was going to publish a book about Xi Jinping's love life.\n- 2015 https://www.washingtonpost.com/world/asia_pacific/christians-in-china-feel-full-force-of-authorities-repression/2015/12/23/7dd0ec5a-a736-11e5-b596-113f59ee069a_story.html\n- 2015 Taiwan #1\n@ -15,12 +18,16 @@\n- 2015-03 China forces real usernames on the net, and provoking ones\n- 2012 Nutech Namiba Hu Haifeng corruption scandal 纳米比亚威视胡海峰腐败丑闻 <https://en.wikipedia.org/wiki/Nuctech>. One of the most blocked on Wikipedia according to: [complete-gfw-rulebook-for-wikipedia-v3.0.pdf]\n- 2011 Chinese pro-democracy protests <https://en.wikipedia.org/wiki/2011_Chinese_pro-democracy_protests> <https://zh.wikipedia.org/wiki/%E4%B8%AD%E5%9C%8B%E8%8C%89%E8%8E%89%E8%8A%B1%E9%9D%A9%E5%91%BD>\n- 2010 10 month Xinjiang internet blackout <http://news.bbc.co.uk/2/hi/asia-pacific/8682145.stm>\n- 2008 Wenzhou train collision 2011年甬台温铁路列车追尾事故: <https://en.wikipedia.org/wiki/Wenzhou_train_collision#Government_response> <https://zh.wikipedia.org/wiki/2011%E5%B9%B4%E7%94%AC%E5%8F%B0%E6%B8%A9%E9%93%81%E8%B7%AF%E5%88%97%E8%BD%A6%E8%BF%BD%E5%B0%BE%E4%BA%8B%E6%95%85>\n- 2008 Tibetan unrest 西藏骚乱 <https://en.wikipedia.org/wiki/2008_Tibetan_unrest> <https://zh.wikipedia.org/wiki/2008%E5%B9%B4%E8%A5%BF%E8%97%8F%E9%AA%9A%E4%B9%B1>\n- 2005 Dongzhou protests <https://en.wikipedia.org/wiki/Dongzhou_protests> <https://zh.wikipedia.org/wiki/%E4%B8%9C%E6%B4%B2%E4%BA%8B%E4%BB%B6>\n- 1999 Apple censors Dalai Lama for China <http://www.lionsroar.com/apple-thinks-different-about-the-dalai-lama-in-china-2/>\n- 1989 Tiananmen Square protests 六四事件: <https://en.wikipedia.org/wiki/Tiananmen_Square_protests_of_1989> <https://zh.wikipedia.org/wiki/%E5%85%AD%E5%9B%9B%E4%BA%8B%E4%BB%B6>\n- <https://en.wikipedia.org/wiki/Goddess_of_Democracy> <https://zh.wikipedia.org/wiki/中国国际广播电台>\n- 1987–1989 Tibetan unrest 年拉萨骚乱 <https://en.wikipedia.org/wiki/1987%E2%80%9389_Tibetan_unrest> <https://zh.wikipedia.org/wiki/1989%E5%B9%B4%E6%8B%89%E8%90%A8%E9%AA%9A%E4%B9%B1>. Videos: <https://www.youtube.com/watch?v=0jylh1qfOG4>, <https://www.youtube.com/watch?v=qMZgoRuPJBg>\nfunny.md View File\n- <https://en.wikipedia.org/wiki/Hong_Kong_97_%28video_game%29> <https://zh.wikipedia.org/wiki/%E9%A6%99%E6%B8%AF97_%28%E9%81%8A%E6%88%B2%29>\n- <https://en.wikipedia.org/wiki/Falun> <https://zh.wikipedia.org/wiki/%E6%B3%95%E5%80%AB> A Swedish city called Falun 名为法倫的瑞典城市\n- <https://en.wikipedia.org/wiki/Falun> <https://zh.wikipedia.org/wiki/%E6%B3%95%E5%80%AB> A Swedish city called Falun 名为法倫的瑞典城市 Oh, BTW, one of the oldest operating companies in the world is from there: <https://en.wikipedia.org/wiki/Falun_Mine>\n- <http://9gag.com/gag/ayn7KmW?ref=fbp> Left Right Slaughterhouse comic, Dictatorships love this one.\nimages/README.md View File\n@ -11,4 +11,5 @@ Original works:\n- Weibo logo vs Sauron's eye: <http://9gag.com/gag/a4LAYRw> original <http://imgur.com/eKPmYyz>\n- Weibo logo vs Sauron's eye: <http://9gag.com/gag/a4LAYRw> original <http://imgur.com/eKPmYyz> I later found out this was not an original idea: <http://robertdouville.tumblr.com/post/26903516703/sina-weibos-eye-of-sauron-logo-social-media>\n- Xi Pooh Parade <https://www.reddit.com/r/China/comments/3z3ztl/this_picture_was_the_most_censored_image_on/>\nmedia.md View File\n# Media 媒体\n## Linked to the Chinese government 联系到中国政府\n- <https://en.wikipedia.org/wiki/Xinhua_News_Agency> <https://zh.wikipedia.org/wiki/%E6%96%B0%E5%8D%8E%E7%A4%BE>\n@ -10,6 +12,7 @@\n## Against the Chinese government 对中国政府\n- <https://en.wikipedia.org/wiki/Radio_Free_Asia> <https://zh.wikipedia.org/wiki/%E8%87%AA%E7%94%B1%E4%BA%9A%E6%B4%B2%E7%94%B5%E5%8F%B0>\n- China News Digest 华夏文摘 <https://zh.wikipedia.org/wiki/%E5%8D%8E%E5%A4%8F%E6%96%87%E6%91%98>\n@ -58,4 +61,3 @@\n- (zh) <https://chinaview.wordpress.com/>\n- (zh) <http://blog.sina.com.cn/slxqnj98>\n- (zh) <http://program-think.blogspot.com/>, <https://my.csdn.net/program_think>\nmovements.md View File\n@ -16,3 +16,4 @@\n- <http://freemuse.org/> <http://freemuse.org/archives/665> <http://freemuse.org/archives/5091>\ntaiwan.md View File\nSomeone who is generally liberal said:\n> Taiwan should not be considered a country by China, because then it would not join back to China when China becomes a democracy, and would be used by the USA to do evil things like they did in the Middle East\nThe power of China's exacerbation of American evil.\nPowered by Gitea Version: 1.13.1 Page: 9058ms Template: 29ms


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Coriolis Sounds: NEWS & EVENTS\nCoriolis Sounds, shelter for sentimental refugees, aller vers son penchant, slacker music for slacker people.\njeudi 19 mai 2022 @ bpm, montpellier\nJames S. Taylor & dj 16:9\nsamedi 21 mai 2022 @ Delco festival, Nîmes\nWilly Comet "Another Place, Another Time"\n1st tuesday of the month, Alter Sapukai radio\ndj 16:9 "endless"\nOn sunday, Alter Sapukai radio, 20:00 - 23:00 UTC+1\nCoriolis Sounds, is deeply involved in programming music/sound/art related events\n+ La Belle Air (2019)\n+ Cassiopeia (2015-2016)\n+ Le Soleil se Lève et se Couche sans Obstacle (2015, 2018) w/ Tropicool Cie\n+ Le Son du Salagou (2012-2013) w/ Markus Detmer / Staubgold\n2022-FEV-25, radio coriolis #34, James S. Taylor & Ced o' Coriolis @ La Halle Tropisme + youtube live stream\n2022-MAR-10, Nunes B2B dj 16:9 @ radio campus\n2022-FEV-24, dj 16:9 @ BPM, Montpellier\n2021-DEC-03, dj 16:9 @ atelier La Halle Tropisme opening, Montpellier\n2021-NOV-25, dj 16:9 @ BPM, Montpellier\n2021-OCT-23, dj 16:9 @ Total record, Montpellier\n2022-OCT-08, 16:9 (live), cargo collective, mc prine @ Montpeyroux\n2021-JULY-28, dj 16:9, dj giu, bonnie banane @ le roquerols, Sète\n2021-JULY-26, James S. Taylor & Ced o' Coriolis @ Tohu Bohu festival radio France, Domaine d'O, Montpellier\n2020-JUN-12 marmona (aka axelle von dorpp), dj 16:9 (aka ced o' coriolis), @tralala, montpellier\n2020-MAR-11 dj 16:9 @ radio Echo des Garrigues\n2020-MAR-06 James S. Taylor live, w/ students, workshop Lycée Pompidou, 19h @ Agora, Le Crès\n2020-MAR-02 radio coriolis 28. james s. taylor & ced o' coriolis. youtube stream\n2020-FEV-21 BUTOH dance w/ IxIx Hool, music by ced o' coriolis (live), @subsonic, Montpellier\n2020-FEV-08 BUTOH dance w/ IxIx Hool, music by ced o' coriolis (live), @subsonic, Montpellier\n2020-JAN-18 coriolislab rdv 001. james s. taylor, cedrick eymenier. email us for more infos\n2020-JAN-13 radio coriolis 26. james s. taylor & ced o' coriolis. youtube stream\n2020-JAN-06 radio coriolis 25. james s. taylor & ced o' coriolis. youtube stream\n2019-DEC-2 radio coriolis 24. james s. taylor & ced o' coriolis. youtube stream\n2019-DEC-6+7 16:9 live + dj @ Casa Del sol, Montpell\n2019-NOV-23 CORIOLIS SOUNDS & VISIONS w/ i am a vowel, 16:9, sébastien jamain. An audio-visual experience @ La Générale, Paris\n2019-OCT-14 Annonce lauréat Px Marcel Duchamp. 16:9 dj set @ Centre Pompidou, Chez Georges, Paris\n2019-OCT-13 Danny & Bear (three:Four rec/Supreme Dicks) on tour w/ Cats Hats Gowns @ Black Out, Montpellier\n2019-SEPT-26 James Swayzak (live) bday party + Paul allard (live) + Picola Naine @ Black Out, Montpellier\n2019-AUG-10+11 LA BELLE AIR w/ Franck Rabeyrolles, James S. Taylor, JF Oliver & J. Mauri, K. Blum, Leyka, Madame Rêve, Mc Prine, Nice, Paul Allard, 16:9 @ Villa Bel Air, Montpellier\n2019-JUIL-21 Aller vers son penchant : JST (James Swayzak) + 16:9 + Nice @ Jeannine à la Mer, Ste Marie de la Mer\n2019-JUN-26 I Am A Vowel, Sainte Nitouche, Leyka, McPrine @ Black Out, Montpell\n2019-JUN-21 James S. Taylor & 16:9, Xaan (...) @ Independent & Happy, Sète\n2019-MAY-09 Aller vers son penchant II Sahel Novecento (live), Angola Stools (live), dj Caramello Koala @ Black out, Montpellier\n2019-AVR-24 Visions Polaires : Karen Vogt, FKS, Oni Baba, 16:9 @ Black out, Montpellier\n2019-AVR-23 Karen Vogt, Milena Skriabine, Lugano Fell @ wearealoneinthedark, Montpellier\n2019-APR-13 Do u really love techno II w/ James S. Taylor & 16:9, Sainte Nitouche, Arthur Roussel, @ Secret party, Montpellier\n2019-AVR-08 Xaan (Independent & Happy) x 16:9 @ Radio Campus, Montpellier\n2019-MAR-29 Nice x dj 16:9 @ Total Record, Montpellier\n2019-MAR-15 Degrees of Caution II, exhibition opening w/ Jeff Rian (live), Angola Stools (live), dj 16:9 @ Centre Photographique, Marseille\n2019-FEB-27 Aller vers son penchant I, Albanian Stools (James S. Taylor), dj 16:9 @ Black out, Montpellier\n2019-FEB-15 Degrees of Caution I, exhibition opening w/ Mark Borthwick (live) @ Centre Photographique, Marseille\n2018-DEC-16 Do you really love techno? dj 16:9, Crystal Distortion (...) @ secret party, Montpellier,\n2018-OCT-23 - coriolis sounds, label night w/ i am a vowel, y&g, lugano, 16:9 @ black out, montpellier\n2018-OCT-18 - james s. taylor + dj 16:9 @ apothicaire bar, montpellier\n2018-OCT-15 - 16:9 @ remise Px Marcel Duchamp, chez Georges, Centre Pompidou, Paris (private)\n2018-SEP-27 / OCT-15 - LE SOLEIL SE LEVE ET SE COUCHE SANS OBSTACLE - A Tropicool Cie event\nw/ james s. taylor & cedrick eymenier - 10 days multichannel sound installation @ Nuit Blanche, gare de lyon, Paris\n2018-SEP-01 - becoming LIVE (james s. taylor & cedrick eymenier), Du Beurre au Soleil festival\n2018-JUN-28 - 16:9 dj set, vernissage Berdarguer & Péjus, toit terrasse friche belle de mai, Marseille\n2018-JUN-24 - james s. taylor & 16:9 aka becoming dj set - st bau 2018\n2018-JUN-21 - james s. taylor, 16:9 dj set + guest LIVE music by Leyka Aperto, Montpellier\n2018-JUN-09 - vinyl market coriolis stand, nuances festival, victoire II, Montpellier\ncs25 - karen vogt "i just want to feel"\ncs24 - leyka "level up"\ncs23 - 16:9 "eigenstate"\ncs22 - cats hats gowns "perpetual sunset"\ncs21 - i am a vowel "io"\ncs20 - Y&G "3rd"\ncs19 - lugano fell "radelec tevelec"


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Unnao to Kanpur: 55 Trains, Shortest Distance: 17 km - Railway Enquiry\nWed Aug 23, 2017 04:23:25 IST HomeTrains\nWed Aug 23, 2017 04:23:25 IST\nON/Unnao Junction to CNB/Kanpur Central\n24 Mail/Express Trains 7 Passenger Trains 12 MEMU Trains 12 SuperFast Trains\nAvailability Calendar ←hk;Return: CNB⇒ON Filters\nDep: ON Arr: CNB\nShortest Route: 17.55 km\n55 Long-Distance Trains between ON/Unnao Junction and CNB/Kanpur Central\n12225 Kaifiyaat SF Express SF NR ON 2 00:10 +12 CNB 8,9 00:45 +27 0h 35m 0\n19166 Sabarmati Express Exp WR ON 2 00:24 +55 CNB 6 01:02 +1:33 0h 38m 0\n19168 Sabarmati Express Exp WR ON 2 00:24 +23 CNB 6 01:02 +46 0h 38m 0\n13237 Patna - Kota Express... Exp ECR ON 0 00:46 +1:12 CNB 7 01:20 +1:33 0h 34m 0\nRLWL 18 km 31 km/hr\n14853 Marudhar Express (vi... Exp NWR ON 2 01:21 +1:31 CNB 8 01:55 +1:45 0h 34m 0\n14863 Marudhar Express (vi... Exp NWR ON 2 01:21 +1:08 CNB 8 01:55 +1:31 0h 34m 0\n14865 Marudhar Express (vi... Exp NWR ON 2 01:23 +1:49 CNB 8 01:55 +2:12 0h 32m 0\nRLWL 18 km 33 km/hr\n11016 Kushinagar Express (... Exp CR ON 2 01:35 +29 CNB 4 02:15 +47 0h 40m 0\n22446 Amritsar - Kanpur We... SF NCR ON 0 04:15 +5:08 CNB* 3 04:50 +5:03 0h 35m 0\n64201 Lucknow Kanpur MEMU MEMU NR ON 2 05:15 +9 CNB* 3 05:55 +17 0h 40m 2\n55325 Lucknow Junction - K... Pass NER ON 2 05:55 +5 CNB 9 06:35 +15 0h 40m 2\n64251 Lucknow Jn - Kanpur ... MEMU NR ON 2 06:46 +17 CNB* 8 07:20 +24 0h 34m 2\n12419 Gomti Express SF NR ON 2 06:58 +13 CNB 7 07:35 +27 0h 37m 0\nGNWL 18 km 28 km/hr\n54153 Rae Bareli Kanpur Pa... Pass NCR ON 0 07:33 +19 CNB* 3 08:05 +32 0h 32m 2\n64203 Lucknow Kanpur MEMU MEMU NR ON 2 08:25 +27 CNB* 9 09:00 +36 0h 35m 2\n18191 Utsarg Express Exp SER ON 2 08:45 +23 CNB 5 09:20 +21 0h 35m 0\nGNWL 18 km 30 km/hr\n64235 Barabanki - Kanpur C... MEMU NR ON 2 08:53 +56 CNB* 3 10:15 +36 1h 22m 2\n14123 Pratapgarh - Kanpur ... Exp NCR ON 2 09:02 +15 CNB* 2 09:40 +31 0h 38m 0\n14221 Faizabad - Kanpur An... Exp NR ON 2 09:25 +14 CNB 8 10:00 +7 0h 35m 0\n54211 Rae Bareli - Kanpur ... Pass NR ON 1 09:59 +0 CNB* 3 10:35 +13 0h 36m 2\n54335 Balamau Kanpur Passe... Pass NR ON 0 10:17 +8 CNB* 0 10:50 +27 0h 33m 2\n64205 Lucknow Jn - Kanpur ... MEMU NR ON 2 10:39 - CNB* 8 11:15 +0 0h 36m 2\n24227 Varuna Express Exp NR ON 2 10:55 +59 CNB* 9 11:35 +1:14 0h 40m 0\nGNWL 18 km 26 km/hr\n64207 Lucknow Kanpur MEMU MEMU NR ON 2 12:24 - CNB* 8 13:00 +0 0h 36m 2\n12591 Gorakhpur - Yesvantp... SF NER ON 2 12:42 +1:16 CNB 5 13:17 +1:49 0h 35m 0\n12511 Rapti Sagar SF Expre... SF NER ON 2 12:42 +1:14 CNB 4 13:17 +1:30 0h 35m 0\n12521 Barauni - Ernakulam ... SF ECR ON 2 12:42 +2:15 CNB 5 13:17 +2:27 0h 35m 0\n12589 Gorakhpur - Secunder... SF NER ON 2 12:42 +1:33 CNB 4 13:17 +1:47 0h 35m 0\n15015 Gorakhpur - Yesvantp... Exp NER ON 2 12:42 - CNB 5 13:17 - 0h 35m 0\n11123 Barauni - Gwalior Ma... Exp NCR ON 2 12:58 +38 CNB 1 13:35 +1:00 0h 37m 0\n12470 JammuTawi - Kanpur (... SF NR ON 2 14:15 +14 CNB* 6 14:50 +25 0h 35m 0\n12875 Neelachal SF Express... SF ECoR ON 2 14:47 +2:11 CNB 4 15:20 +2:24 0h 33m 0\nRLWL 18 km 32 km/hr\n64209 Lucknow - Kanpur MEM... MEMU NR ON 2 15:18 +52 CNB* 8 15:50 +1:16 0h 32m 2\n64253 Lucknow Jn. - Kanpur... MEMU NR ON 2 16:40 +1:29 CNB 8 17:08 +1:39 0h 28m 2\n12179 Lucknow Jn. - Agra C... SF NCR ON 2 16:50 +31 CNB 7 17:25 +47 0h 35m 0\n64211 Lucknow Kanpur MEMU MEMU NR ON 2 17:06 +29 CNB* 8 17:40 +52 0h 34m 2\n11110 Lucknow Jn. - Jhansi... Exp NCR ON 2 17:37 +12 CNB 1 18:15 +24 0h 38m 0\n51814 Lucknow-Jhansi Passe... Pass NCR ON 2 18:10 +1:09 CNB 4 18:50 +1:32 0h 40m 2\n15205 Chitrakoot Express Exp ECR ON 2 18:25 +6 CNB 2 19:05 +15 0h 40m 0\n14217 Unchahar Express (PT... Exp NR ON 1 19:03 +1:23 CNB 2 19:35 +1:47 0h 32m 0\n15707 Amrapali Express (PT... Exp NFR ON 2 19:15 +30 CNB 7 19:50 +45 0h 35m 0\n54325 Sitapur Kanpur Passe... Pass NR ON 3 19:33 +14 CNB* 1 20:05 +33 0h 32m 2\n64255 Lucknow - Kanpur - K... MEMU NR ON 2 19:45 +19 CNB 8 20:20 +35 0h 35m 2\n14101 Prayag - Kanpur Inte... Exp NCR ON 0 20:14 +30 CNB* 0 20:50 +49 0h 36m 0\n13413 Farakka Express (via... Exp ER ON 0 20:27 +1:37 CNB 6,7 21:05 +1:55 0h 38m 0\n13483 Farakka Express (via... Exp ER ON 2 20:29 +1:35 CNB 7 21:05 +1:51 0h 36m 0\n12533 Pushpak Express (PT) SF NER ON 1 20:44 +6 CNB 4 21:20 +29 0h 36m 0\n64213 Lucknow Kanpur MEMU MEMU NR ON 2 21:28 +3:28 CNB* 8 22:05 +2:52 0h 37m 2\n19038 Avadh Express (PT) Exp WR ON 3 21:50 +31 CNB 9 22:30 +46 0h 40m 0\n19040 Avadh Express (PT) Exp WR ON 0 21:50 +4:20 CNB 9 22:30 +4:34 0h 40m 0\n54101 Prayag-Kanpur Centra... Pass NCR ON 1 22:26 +2:46 CNB* 9 23:05 +2:54 0h 39m 2\n12555 Gorakhdham SF Expres... SF NER ON 2 22:45 +20 CNB 7 23:23 +39 0h 38m 0\nPQWL 18 km 28 km/hr\n64257 Lucknow Jn. - Kanpur... MEMU NR ON 2 23:18 +3:15 CNB* 9 23:55 +1:48 0h 37m 2\n15029 Gorakhpur - Pune Wee... Exp NER ON 2 23:55 +1:18 CNB 4 00:30 +1:45 0h 35m 0\n19402 Lucknow - Ahmedabad ... Exp WR ON 2 23:55 +2 CNB 8 00:30 +13 0h 35m 0


10M cpte rendu du 23/4 par Tony G4CJC - Radioamateurs France\nAccueilContest10M cpte rendu du 23/4 par Tony G4CJC\nLundi DX: C92RU, CA4PSH, CE7VPQ, DT8A, XE1XR, LW2DOD, CT7AIU, NP4LA, LZ2WP, JR7CJS.\nMardi DX: C92RU, VP8LP, EH4WRD, XE1XR, HH2AA.\nMercredi DX: FY5HB, ZW61DF, S01WS, FY5KE, CE4WJK, OA4DVC, L21RCA, IZ8XXE, IK7FPV, CD7CKU, XE1XR, PU8YAB, PP2CS, LW8EMD, LU1COP, LU1AAX, K4TR E8\nJeudi DX: PY5QW, FY5KE, S01WS, L21RCA, XQ6CF, VP8LP, CA4PSH, CT2GQA, PJ4DX, LU5FC, II5LDV, ZL4YL, CE2SV, XQ3WD, PZ2YT, EH4WR44, ZD7FT, EH4WRD, ZD7WFD1, CX4WD1, CX2WD1, CX4WD1, CX5WXD, CX3WXF, DXWD1XWD , CE4WJK, 9K2NO, PY2FZ, PU1JSV, LU9MBY, LU8MIL, EG5WRD, EA1GAR, CE4BRO, CA3SOC, A71AE, 4Z73DZ, 4X5MZ, YV5JAU, YD2DBY, LU8MIL, EG5WRD, EA1GAR, CE4BRO, CA3SOC, A71AE, 4Z73DZ, 4X5MZ, YV5JAU, YD2DBW, YB6VT2 , EA4DEM, EA1IF, CX8RP, CE7VPQ, 9M2TO.\nVendredi DX: CA4PSH, L21RCA, S01WS, FY5KE, FY5HB, CE7VPQ, 5Z4VJ, 3D2AG, EA8DHV, EA8AM, ZD7FT, EH4WRD, EA8JK, EA8CTF, CE4WJK, EG5WRD, DT8 CTA, CX4T4RT , CX2SA, ZS1OPB, ZL4TT, VP8NO, PZ2YT, PY3JFS, PY2YW, PY1CG, LW2DAF, KP4WQ, IK0ETA, HI3T, FM4SA, EA8OM, CX6DZ, CX4ACH, CE5CO YCZQ, CA6GLF, ZBNCD4, CE5CO YCYBY9, ZBNCD4WH, , PU4MRA, PU4AFG, OA4DVC, LU8EKC, LU1XAW, J35X, IU0OVB, HI3AA, EA3KE, CU3AN, CP1XRM, AH6U, A71AM, 8C2YOTA, 7Z1SJ.\n(5 contacts signalés nécessaires le week-end)\nSamedi DX: EH4WRD, PP5BT, ZX6T, CX1AA, YT3GTI, PY3KN, 5Z4VJ, PY2WB, FY5HB, CE7VPQ, LZ4TX, FY5KE, 3D2AG, PY2EL, N4BP, FM4SA, CE2LR, N4BP, FM4SA, CE2LR, ZLOM KH07, WR5 , CA6GPH, CA4PSH, AA3B, ZS4JAN, ZS1TAF, YT3X, XV1X, TG9AOR, RK6C, PZ5JW, PY5ZW, PY5ZHP, LU6DOT, LU4HA, LU1DZ, HP1RIS, EA7D, A71AM, ZTT4S1RIS, EA7D, A71AM, ZTT4S1RIS, EA7D, A71AM, Z5T4S1D, ZG3G3 , ZL3IO, ZB2JK, YV5LAY, YB3EDD, UA6AK, TM96WARD, SX200MNE, R60MCC, R2EA / P, R1BED / M, PZ5RA, PY3DX, PY2EX, PY1MK, PT9AL, OA4DVC, N8OOJLU9AL, OA4DVC, N8OO, LUZ8 FVO8, HBK9E9 , CP1XRM, CE8EIO, CE4JZO, CE1PTT, 9V1YC, 9H1GP, 9G5FI.\nDimanche DX: XR96IARU, L21RCA, VP8NO, 5Z4VJ, ZD7FT, ZW61DF, PY2TMV, LZ4TX, HZ1WARD, EH4WRD, ZV5M, ZS1OPB, CE7VPQ, 9Y4DG, WP4SD, WPR ZWR5, PYFHL5, PZWR5, PZFHL5, PZW5L, PZW5, PZFHL5 , ZD7BG, PY2WB, PA21AGR, J73ESL, IU8HPD, EA8CNR, AA3B, 9G5FI, ZL4YL, ZD7JC, VE7RAC, V51WW, TA1CQ, PY3PA, PY2BN, PU5ABBB, PP5BT, ZD5 / FXB3D CE2, ZAG3D3 , Z34K, V51LZ, SZ1SV, PU4TNT, PS2T, LU3WC, IW9EZO, HS5NMF, HP1RIS, HI8AT, EA8RH, EA8DET, EA3KE, CX5CDV, CX1AA, CT3MD, CE4WJK, 9J2BS, 9ABECD, CE4WJK, 9J2BS, 9ABE5 VCR1 , PZ2YT, PY4DY, PY2FZ, PY2EX, PV8ABC, PU1JSV, N8OO, LU1HW, KP2M, K3WW, J69BB, IU3BTY, IK8OXQ, HA230R, G0FWX, CX8DSK, CE6VWL, 5B4\n24-26/4/2021 Evénement spécial OE21M


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Mont-Saint-Aignan Tourist attractions - ViaMichelin\n- F - Seine-Maritime: Mont-Saint-Aignan (76130)\n1 star (13) 2 stars (11) 3 stars (4)\n1.71 Km - Allée du Fond-du-Val, 76000 Rouen\n2.38 Km - Rue de la Rochefoucauld, 76000 Rouen\n2.56 Km - 198 rue Beauvoisine, 76000 Rouen\n2.56 Km - Rue Bouvreuil, 76000 Rouen\n2.6 Km - 76000 Rouen\n2.69 Km - 1 rue Faucon, 76000 Rouen\n2.69 Km - 51 rue Lecat, 76000 Rouen\n2.71 Km - 1 esplanade Marcel-Duchamp, 76000 Rouen\n2.75 Km - Place St-Godard, 76000 Rouen\n2.77 Km - Rue Joyeuse, 76000 Rouen\n2.81 Km - Rue Jacques-Villon, 76000 Rouen\n2.88 Km - Place du Vieux-Marché, 76000 Rouen\n2.92 Km - Place du Vieux-Marché, 76000 Rouen\n2.93 Km - Rue Ganterie, 76000 Rouen\n2.93 Km - 502 rue Pierre-Corneille, 76000 Rouen\n3.02 Km - 36 rue aux Juifs, 76000 Rouen\n3.04 Km - 3 place du Gén.-de-Gaulle, 76000 Rouen\n3.09 Km - Rue du Gros-Horloge, 76000 Rouen\n3.13 Km - Rue du Gros-Horloge, 76000 Rouen\n3.15 Km - Près du hangar 2 (face au hangar 106 de la Rive gauche), 76000 Rouen\n3.17 Km - Rue d’Amiens, 76000 Rouen\n3.21 Km - 76000 Rouen\n3.22 Km - Place de la Cathédrale, 76000 Rouen\n3.22 Km - Rue St-Romain, 76000 Rouen\n3.23 Km - Place de la Cathédrale, 76000 Rouen\n3.23 Km - 185 rue Eau-de-Robec, 76000 Rouen\n3.25 Km - Rue Eau-de-Robec, 76000 Rouen\n3.25 Km - Rue Damiette, 76000 Rouen\n3.28 Km - Rue Eau-de-Robec, 76000 Rouen\n3.32 Km - 7 rue St-Romain, 76000 Rouen\n3.35 Km - Place St-Barthélémy, 76000 Rouen\n3.35 Km - 184-186 rue Martainville, 76000 Rouen\n3.47 Km - Rue Martainville, 76000 Rouen\n3.48 Km - Place de la Haute-Vieille-Tour, 76000 Rouen\n4.28 Km - D 95, 76000 Rouen\n5.35 Km - 114 avenue des Martyrs-de-la-Résistance, 76000 Rouen\n5.83 Km - Rue Notre-Dame, 22200 Guingamp\n9.45 Km - Place de l’Abbaye, 76840 St-Martin-de-Boscherville\n14.3 Km - Avenue du Parc, 76690 Clères\n14.8 Km - Route du Château, 76116 Martainville-Épreville\nMont-Saint-Aignan Tourist Attractions - the Michelin Green Guide review\nFind the best tourist attractions in Mont-Saint-Aignan and prepare your Mont-Saint-Aignan holidays thanks to the Michelin Green Guide. Mont-Saint-Aignan travel: Michelin rating (including Michelin stars) and reviews for the main tourist attractions.\nAll ViaMichelin for Mont-Saint-Aignan\nMont-Saint-Aignan - Rouen directions\nMont-Saint-Aignan - Bois-Guillaume directions\nMont-Saint-Aignan - Notre-Dame-de-Bondeville directions\nMont-Saint-Aignan - Maromme directions\nMont-Saint-Aignan - Le Petit-Quevilly directions\nMont-Saint-Aignan - Darnétal directions\nMont-Saint-Aignan - Sotteville-lès-Rouen directions\nMont-Saint-Aignan - Le Grand-Quevilly directions\nMont-Saint-Aignan - Saint-Étienne-du-Rouvray directions\nMont-Saint-Aignan - Boos directions\nMont-Saint-Aignan - Barentin directions\nMont-Saint-Aignan - Grand-Couronne directions\nMont-Saint-Aignan - Duclair directions\nMont-Saint-Aignan - Pont-de-l'Arche directions\nMont-Saint-Aignan - Bihorel directions\nMont-Saint-Aignan - Déville-lès-Rouen directions\nMont-Saint-Aignan - Canteleu directions\nMont-Saint-Aignan - Isneauville directions\nMont-Saint-Aignan - Bonsecours directions\nMont-Saint-Aignan - Le Houlme directions\nService stations of Isneauville\nService stations of Le Houlme\nCar parks Isneauville\nCar parks Le Houlme


from tensorflow.keras.layers import Conv2D, MaxPool2D,  \_\nimport cv2 #python -m pip install opencv-python\n# Load cifar10 training and test sets\nX_train = np.array([cv2.resize(img, (img_rows, img_cols)) for img in X_train[:, :, :, :]])\nX_test = np.array([cv2.resize(img, (img_rows, img_cols)) for img in X_test[:, :, :, :]])\nX_train = X_train.astype('float16') / 255.0\nX_test = X_test.astype('float16') / 255.0\nX_train: (50000, 224, 224, 3)\nY_train: (50000, 10)\nX_test: (10000, 224, 224, 3)\nkernel_init = tf.keras.initializers.glorot_uniform()\nbias_init = tf.keras.initializers.Constant(value=0.2)\nconv_1x1 = Conv2D(\nconv_3x3 = Conv2D(\nconv_5x5 = Conv2D(\npool = MaxPool2D(\npool = Conv2D(\noutput = concatenate([conv_1x1, conv_3x3, conv_5x5, pool], axis=3, name=name)\nx = MaxPool2D(\nx = inception_module(\nx1 = AveragePooling2D(pool_size=(5, 5), strides=3)(x)\nx1 = Conv2D(filters=128, kernel_size=(1, 1), padding='same', activation='relu')(x1)\nx1 = Dense(units=1024, activation='relu')(x1)\nx1 = Dropout(rate=0.7)(x1)\nx1 = Dense(units=10, activation='softmax', name='auxilliary_output_1')(x1)\nx2 = AveragePooling2D(pool_size=(5, 5), strides=3)(x)\nx2 = Conv2D(filters=128, kernel_size=(1, 1), padding='same', activation='relu')(x2)\nx2 = Dense(units=1024, activation='relu')(x2)\nx2 = Dropout(rate=0.7)(x2)\nx2 = Dense(units=10, activation='softmax', name='auxilliary_output_2')(x2)\nx = Dense(units=10, activation='softmax', name='output')(x)\nbatch_normalization_v2 (BatchNo (None, 56, 56, 64)   256         max_pool_1_3x3/2\nconv_2a_3x3/1 (Conv2D)          (None, 56, 56, 64)   4160        batch_normalization_v2\nconv_2b_3x3/1 (Conv2D)          (None, 56, 56, 192)  110784      conv_2a_3x3/1\nbatch_normalization_v2_1 (Batch (None, 56, 56, 192)  768         conv_2b_3x3/1\nmax_pool_2_3x3/2 (MaxPooling2D) (None, 28, 28, 192)  0           batch_normalization_v2_1\nconv2d_1 (Conv2D)               (None, 28, 28, 96)   18528       max_pool_2_3x3/2\nconv2d_3 (Conv2D)               (None, 28, 28, 16)   3088        max_pool_2_3x3/2\nmax_pooling2d (MaxPooling2D)    (None, 28, 28, 192)  0           max_pool_2_3x3/2\nconv2d (Conv2D)                 (None, 28, 28, 64)   12352       max_pool_2_3x3/2\nconv2d_2 (Conv2D)               (None, 28, 28, 128)  110720      conv2d_1\nconv2d_4 (Conv2D)               (None, 28, 28, 32)   12832       conv2d_3\nconv2d_5 (Conv2D)               (None, 28, 28, 32)   6176        max_pooling2d\ninception_3a (Concatenate)      (None, 28, 28, 256)  0           conv2d\nconv2d_7 (Conv2D)               (None, 28, 28, 128)  32896       inception_3a\nconv2d_9 (Conv2D)               (None, 28, 28, 32)   8224        inception_3a\nmax_pooling2d_1 (MaxPooling2D)  (None, 28, 28, 256)  0           inception_3a\nconv2d_6 (Conv2D)               (None, 28, 28, 128)  32896       inception_3a\nconv2d_8 (Conv2D)               (None, 28, 28, 192)  221376      conv2d_7\nconv2d_10 (Conv2D)              (None, 28, 28, 96)   76896       conv2d_9\nconv2d_11 (Conv2D)              (None, 28, 28, 64)   16448       max_pooling2d_1\ninception_3b (Concatenate)      (None, 28, 28, 480)  0           conv2d_6\nmax_pool_3_3x3/2 (MaxPooling2D) (None, 14, 14, 480)  0           inception_3b\nconv2d_13 (Conv2D)              (None, 14, 14, 96)   46176       max_pool_3_3x3/2\nconv2d_15 (Conv2D)              (None, 14, 14, 16)   7696        max_pool_3_3x3/2\nmax_pooling2d_2 (MaxPooling2D)  (None, 14, 14, 480)  0           max_pool_3_3x3/2\nconv2d_12 (Conv2D)              (None, 14, 14, 192)  92352       max_pool_3_3x3/2\nconv2d_14 (Conv2D)              (None, 14, 14, 208)  179920      conv2d_13\nconv2d_16 (Conv2D)              (None, 14, 14, 48)   19248       conv2d_15\nconv2d_17 (Conv2D)              (None, 14, 14, 64)   30784       max_pooling2d_2\ninception_4a (Concatenate)      (None, 14, 14, 512)  0           conv2d_12\nconv2d_20 (Conv2D)              (None, 14, 14, 112)  57456       inception_4a\nconv2d_22 (Conv2D)              (None, 14, 14, 24)   12312       inception_4a\nmax_pooling2d_3 (MaxPooling2D)  (None, 14, 14, 512)  0           inception_4a\nconv2d_19 (Conv2D)              (None, 14, 14, 160)  82080       inception_4a\nconv2d_21 (Conv2D)              (None, 14, 14, 224)  226016      conv2d_20\nconv2d_23 (Conv2D)              (None, 14, 14, 64)   38464       conv2d_22\nconv2d_24 (Conv2D)              (None, 14, 14, 64)   32832       max_pooling2d_3\ninception_4b (Concatenate)      (None, 14, 14, 512)  0           conv2d_19\nconv2d_26 (Conv2D)              (None, 14, 14, 128)  65664       inception_4b\nconv2d_28 (Conv2D)              (None, 14, 14, 24)   12312       inception_4b\nmax_pooling2d_4 (MaxPooling2D)  (None, 14, 14, 512)  0           inception_4b\nconv2d_25 (Conv2D)              (None, 14, 14, 128)  65664       inception_4b\nconv2d_27 (Conv2D)              (None, 14, 14, 256)  295168      conv2d_26\nconv2d_29 (Conv2D)              (None, 14, 14, 64)   38464       conv2d_28\nconv2d_30 (Conv2D)              (None, 14, 14, 64)   32832       max_pooling2d_4\ninception_4c (Concatenate)      (None, 14, 14, 512)  0           conv2d_25\nconv2d_32 (Conv2D)              (None, 14, 14, 144)  73872       inception_4c\nconv2d_34 (Conv2D)              (None, 14, 14, 32)   16416       inception_4c\nmax_pooling2d_5 (MaxPooling2D)  (None, 14, 14, 512)  0           inception_4c\nconv2d_31 (Conv2D)              (None, 14, 14, 112)  57456       inception_4c\nconv2d_33 (Conv2D)              (None, 14, 14, 288)  373536      conv2d_32\nconv2d_35 (Conv2D)              (None, 14, 14, 64)   51264       conv2d_34\nconv2d_36 (Conv2D)              (None, 14, 14, 64)   32832       max_pooling2d_5\ninception_4d (Concatenate)      (None, 14, 14, 528)  0           conv2d_31\nconv2d_39 (Conv2D)              (None, 14, 14, 160)  84640       inception_4d\nconv2d_41 (Conv2D)              (None, 14, 14, 32)   16928       inception_4d\nmax_pooling2d_6 (MaxPooling2D)  (None, 14, 14, 528)  0           inception_4d\nconv2d_38 (Conv2D)              (None, 14, 14, 256)  135424      inception_4d\nconv2d_40 (Conv2D)              (None, 14, 14, 320)  461120      conv2d_39\nconv2d_42 (Conv2D)              (None, 14, 14, 128)  102528      conv2d_41\nconv2d_43 (Conv2D)              (None, 14, 14, 128)  67712       max_pooling2d_6\ninception_4e (Concatenate)      (None, 14, 14, 832)  0           conv2d_38\nmax_pool_4_3x3/2 (MaxPooling2D) (None, 7, 7, 832)    0           inception_4e\nconv2d_45 (Conv2D)              (None, 7, 7, 160)    133280      max_pool_4_3x3/2\nconv2d_47 (Conv2D)              (None, 7, 7, 32)     26656       max_pool_4_3x3/2\nmax_pooling2d_7 (MaxPooling2D)  (None, 7, 7, 832)    0           max_pool_4_3x3/2\nconv2d_44 (Conv2D)              (None, 7, 7, 256)    213248      max_pool_4_3x3/2\nconv2d_46 (Conv2D)              (None, 7, 7, 320)    461120      conv2d_45\nconv2d_48 (Conv2D)              (None, 7, 7, 128)    102528      conv2d_47\nconv2d_49 (Conv2D)              (None, 7, 7, 128)    106624      max_pooling2d_7\ninception_5a (Concatenate)      (None, 7, 7, 832)    0           conv2d_44\nconv2d_51 (Conv2D)              (None, 7, 7, 192)    159936      inception_5a\nconv2d_53 (Conv2D)              (None, 7, 7, 48)     39984       inception_5a\nmax_pooling2d_8 (MaxPooling2D)  (None, 7, 7, 832)    0           inception_5a\naverage_pooling2d (AveragePooli (None, 4, 4, 512)    0           inception_4a\naverage_pooling2d_1 (AveragePoo (None, 4, 4, 528)    0           inception_4d\nconv2d_50 (Conv2D)              (None, 7, 7, 384)    319872      inception_5a\nconv2d_52 (Conv2D)              (None, 7, 7, 384)    663936      conv2d_51\nconv2d_54 (Conv2D)              (None, 7, 7, 128)    153728      conv2d_53\nconv2d_55 (Conv2D)              (None, 7, 7, 128)    106624      max_pooling2d_8\nconv2d_18 (Conv2D)              (None, 4, 4, 128)    65664       average_pooling2d\nconv2d_37 (Conv2D)              (None, 4, 4, 128)    67712       average_pooling2d_1\ninception_5b (Concatenate)      (None, 7, 7, 1024)   0           conv2d_50\nflatten (Flatten)               (None, 2048)         0           conv2d_18\nflatten_1 (Flatten)             (None, 2048)         0           conv2d_37\navg_pool_5_3x3/1 (GlobalAverage (None, 1024)         0           inception_5b\ndense (Dense)                   (None, 1024)         2098176     flatten\ndense_1 (Dense)                 (None, 1024)         2098176     flatten_1\ndropout_2 (Dropout)             (None, 1024)         0           avg_pool_5_3x3/1\ndropout (Dropout)               (None, 1024)         0           dense\noutput (Dense)                  (None, 10)           10250       dropout_2\nauxilliary_output_1 (Dense)     (None, 10)           10250       dropout\nauxilliary_output_2 (Dense)     (None, 10)           10250       dropout_1\nTotal params: 10,335,054\nTrainable params: 10,334,542\nlrate = initial_lrate * math.pow(drop, math.floor((1 + epoch) / epochs_drop))\nsgd = SGD(lr=initial_lrate, momentum=0.9, nesterov=True)\nloss=['categorical_crossentropy', 'categorical_crossentropy', 'categorical_crossentropy'],\nloss_weights=[1, 0.3, 0.3],\ny=[y_train, y_train, y_train],\nvalidation_data=(X_test, [y_test, y_test, y_test]),\nepochs=epochs, batch_size=256, callbacks=[lr_sc]\n50000/50000 [==============================] - 164s 3ms/sample - loss: 2.9533 - output_loss: 1.8562 - auxilliary_output_1_loss: 1.8227 - auxilliary_output_2_loss: 1.8345 - output_accuracy: 0.3027 - auxilliary_output_1_accuracy: 0.3276 - auxilliary_output_2_accuracy: 0.3150 - val_loss: 4.5563 - val_output_loss: 3.0333 - val_auxilliary_output_1_loss: 2.4836 - val_auxilliary_output_2_loss: 2.5931 - val_output_accuracy: 0.1187 - val_auxilliary_output_1_accuracy: 0.1212 - val_auxilliary_output_2_accuracy: 0.1250\n50000/50000 [==============================] - 153s 3ms/sample - loss: 2.2156 - output_loss: 1.3932 - auxilliary_output_1_loss: 1.3651 - auxilliary_output_2_loss: 1.3764 - output_accuracy: 0.4935 - auxilliary_output_1_accuracy: 0.5027 - auxilliary_output_2_accuracy: 0.5000 - val_loss: 2.6316 - val_output_loss: 1.6831 - val_auxilliary_output_1_loss: 1.6117 - val_auxilliary_output_2_loss: 1.5500 - val_output_accuracy: 0.3811 - val_auxilliary_output_1_accuracy: 0.4099 - val_auxilliary_output_2_accuracy: 0.4276\n50000/50000 [==============================] - 153s 3ms/sample - loss: 1.8995 - output_loss: 1.1896 - auxilliary_output_1_loss: 1.1817 - auxilliary_output_2_loss: 1.1845 - output_accuracy: 0.5721 - auxilliary_output_1_accuracy: 0.5759 - auxilliary_output_2_accuracy: 0.5756 - val_loss: 2.2723 - val_output_loss: 1.4853 - val_auxilliary_output_1_loss: 1.2706 - val_auxilliary_output_2_loss: 1.3526 - val_output_accuracy: 0.4863 - val_auxilliary_output_1_accuracy: 0.5444 - val_auxilliary_output_2_accuracy: 0.5189\n50000/50000 [==============================] - 153s 3ms/sample - loss: 1.6338 - output_loss: 1.0108 - auxilliary_output_1_loss: 1.0438 - auxilliary_output_2_loss: 1.0328 - output_accuracy: 0.6377 - auxilliary_output_1_accuracy: 0.6264 - auxilliary_output_2_accuracy: 0.6311 - val_loss: 2.1753 - val_output_loss: 1.3962 - val_auxilliary_output_1_loss: 1.3135 - val_auxilliary_output_2_loss: 1.2836 - val_output_accuracy: 0.5251 - val_auxilliary_output_1_accuracy: 0.5385 - val_auxilliary_output_2_accuracy: 0.5524\n50000/50000 [==============================] - 153s 3ms/sample - loss: 1.4652 - output_loss: 0.8989 - auxilliary_output_1_loss: 0.9535 - auxilliary_output_2_loss: 0.9339 - output_accuracy: 0.6800 - auxilliary_output_1_accuracy: 0.6602 - auxilliary_output_2_accuracy: 0.6698 - val_loss: 1.8850 - val_output_loss: 1.1790 - val_auxilliary_output_1_loss: 1.2361 - val_auxilliary_output_2_loss: 1.1172 - val_output_accuracy: 0.5860 - val_auxilliary_output_1_accuracy: 0.5590 - val_auxilliary_output_2_accuracy: 0.5984\nEpoch 00006: LearningRateScheduler reducing learning rate to 0.01.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 1.3292 - output_loss: 0.8077 - auxilliary_output_1_loss: 0.8834 - auxilliary_output_2_loss: 0.8547 - output_accuracy: 0.7121 - auxilliary_output_1_accuracy: 0.6860 - auxilliary_output_2_accuracy: 0.6978 - val_loss: 1.5946 - val_output_loss: 1.0136 - val_auxilliary_output_1_loss: 0.9595 - val_auxilliary_output_2_loss: 0.9773 - val_output_accuracy: 0.6573 - val_auxilliary_output_1_accuracy: 0.6597 - val_auxilliary_output_2_accuracy: 0.6644\nEpoch 00007: LearningRateScheduler reducing learning rate to 0.01.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 1.2210 - output_loss: 0.7373 - auxilliary_output_1_loss: 0.8240 - auxilliary_output_2_loss: 0.7883 - output_accuracy: 0.7387 - auxilliary_output_1_accuracy: 0.7077 - auxilliary_output_2_accuracy: 0.7228 - val_loss: 1.5383 - val_output_loss: 0.9826 - val_auxilliary_output_1_loss: 0.9048 - val_auxilliary_output_2_loss: 0.9476 - val_output_accuracy: 0.6625 - val_auxilliary_output_1_accuracy: 0.6810 - val_auxilliary_output_2_accuracy: 0.6666\nEpoch 00008: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 1.1247 - output_loss: 0.6722 - auxilliary_output_1_loss: 0.7776 - auxilliary_output_2_loss: 0.7306 - output_accuracy: 0.7603 - auxilliary_output_1_accuracy: 0.7241 - auxilliary_output_2_accuracy: 0.7433 - val_loss: 1.5842 - val_output_loss: 0.9958 - val_auxilliary_output_1_loss: 0.9800 - val_auxilliary_output_2_loss: 0.9811 - val_output_accuracy: 0.6529 - val_auxilliary_output_1_accuracy: 0.6490 - val_auxilliary_output_2_accuracy: 0.6498\nEpoch 00009: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 1.0491 - output_loss: 0.6233 - auxilliary_output_1_loss: 0.7345 - auxilliary_output_2_loss: 0.6850 - output_accuracy: 0.7813 - auxilliary_output_1_accuracy: 0.7409 - auxilliary_output_2_accuracy: 0.7617 - val_loss: 1.3636 - val_output_loss: 0.8544 - val_auxilliary_output_1_loss: 0.8970 - val_auxilliary_output_2_loss: 0.8004 - val_output_accuracy: 0.7013 - val_auxilliary_output_1_accuracy: 0.6760 - val_auxilliary_output_2_accuracy: 0.7185\nEpoch 00010: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 150s 3ms/sample - loss: 0.9725 - output_loss: 0.5719 - auxilliary_output_1_loss: 0.6970 - auxilliary_output_2_loss: 0.6385 - output_accuracy: 0.7988 - auxilliary_output_1_accuracy: 0.7548 - auxilliary_output_2_accuracy: 0.7756 - val_loss: 1.2748 - val_output_loss: 0.8038 - val_auxilliary_output_1_loss: 0.7835 - val_auxilliary_output_2_loss: 0.7867 - val_output_accuracy: 0.7149 - val_auxilliary_output_1_accuracy: 0.7213 - val_auxilliary_output_2_accuracy: 0.7206\nEpoch 00011: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.8996 - output_loss: 0.5218 - auxilliary_output_1_loss: 0.6622 - auxilliary_output_2_loss: 0.5970 - output_accuracy: 0.8181 - auxilliary_output_1_accuracy: 0.7681 - auxilliary_output_2_accuracy: 0.7938 - val_loss: 1.2819 - val_output_loss: 0.8424 - val_auxilliary_output_1_loss: 0.7392 - val_auxilliary_output_2_loss: 0.7258 - val_output_accuracy: 0.7185 - val_auxilliary_output_1_accuracy: 0.7391 - val_auxilliary_output_2_accuracy: 0.7431\nEpoch 00012: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 0.8250 - output_loss: 0.4713 - auxilliary_output_1_loss: 0.6221 - auxilliary_output_2_loss: 0.5569 - output_accuracy: 0.8352 - auxilliary_output_1_accuracy: 0.7816 - auxilliary_output_2_accuracy: 0.8041 - val_loss: 1.2184 - val_output_loss: 0.7657 - val_auxilliary_output_1_loss: 0.7640 - val_auxilliary_output_2_loss: 0.7453 - val_output_accuracy: 0.7486 - val_auxilliary_output_1_accuracy: 0.7362 - val_auxilliary_output_2_accuracy: 0.7453\nEpoch 00013: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.7628 - output_loss: 0.4293 - auxilliary_output_1_loss: 0.5953 - auxilliary_output_2_loss: 0.5165 - output_accuracy: 0.8507 - auxilliary_output_1_accuracy: 0.7903 - auxilliary_output_2_accuracy: 0.8203 - val_loss: 1.0461 - val_output_loss: 0.6546 - val_auxilliary_output_1_loss: 0.6596 - val_auxilliary_output_2_loss: 0.6451 - val_output_accuracy: 0.7781 - val_auxilliary_output_1_accuracy: 0.7688 - val_auxilliary_output_2_accuracy: 0.7761\nEpoch 00014: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 150s 3ms/sample - loss: 0.7022 - output_loss: 0.3888 - auxilliary_output_1_loss: 0.5676 - auxilliary_output_2_loss: 0.4773 - output_accuracy: 0.8653 - auxilliary_output_1_accuracy: 0.8003 - auxilliary_output_2_accuracy: 0.8332 - val_loss: 1.0753 - val_output_loss: 0.6545 - val_auxilliary_output_1_loss: 0.7364 - val_auxilliary_output_2_loss: 0.6663 - val_output_accuracy: 0.7858 - val_auxilliary_output_1_accuracy: 0.7425 - val_auxilliary_output_2_accuracy: 0.7744\nEpoch 00015: LearningRateScheduler reducing learning rate to 0.0096.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 0.6623 - output_loss: 0.3618 - auxilliary_output_1_loss: 0.5464 - auxilliary_output_2_loss: 0.4550 - output_accuracy: 0.8748 - auxilliary_output_1_accuracy: 0.8087 - auxilliary_output_2_accuracy: 0.8435 - val_loss: 1.2700 - val_output_loss: 0.7735 - val_auxilliary_output_1_loss: 0.8887 - val_auxilliary_output_2_loss: 0.7663 - val_output_accuracy: 0.7482 - val_auxilliary_output_1_accuracy: 0.6993 - val_auxilliary_output_2_accuracy: 0.7402\nEpoch 00016: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.6434 - output_loss: 0.3534 - auxilliary_output_1_loss: 0.5298 - auxilliary_output_2_loss: 0.4369 - output_accuracy: 0.8816 - auxilliary_output_1_accuracy: 0.8150 - auxilliary_output_2_accuracy: 0.8500 - val_loss: 0.9491 - val_output_loss: 0.5861 - val_auxilliary_output_1_loss: 0.6254 - val_auxilliary_output_2_loss: 0.5848 - val_output_accuracy: 0.8084 - val_auxilliary_output_1_accuracy: 0.7832 - val_auxilliary_output_2_accuracy: 0.8001\nEpoch 00017: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 149s 3ms/sample - loss: 0.5504 - output_loss: 0.2868 - auxilliary_output_1_loss: 0.4949 - auxilliary_output_2_loss: 0.3839 - output_accuracy: 0.8988 - auxilliary_output_1_accuracy: 0.8279 - auxilliary_output_2_accuracy: 0.8674 - val_loss: 0.9856 - val_output_loss: 0.6218 - val_auxilliary_output_1_loss: 0.6089 - val_auxilliary_output_2_loss: 0.6039 - val_output_accuracy: 0.8050 - val_auxilliary_output_1_accuracy: 0.7880 - val_auxilliary_output_2_accuracy: 0.7967\nEpoch 00018: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 0.5103 - output_loss: 0.2599 - auxilliary_output_1_loss: 0.4763 - auxilliary_output_2_loss: 0.3584 - output_accuracy: 0.9110 - auxilliary_output_1_accuracy: 0.8339 - auxilliary_output_2_accuracy: 0.8742 - val_loss: 0.9422 - val_output_loss: 0.5915 - val_auxilliary_output_1_loss: 0.5975 - val_auxilliary_output_2_loss: 0.5714 - val_output_accuracy: 0.8130 - val_auxilliary_output_1_accuracy: 0.7917 - val_auxilliary_output_2_accuracy: 0.8108\nEpoch 00019: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 150s 3ms/sample - loss: 0.4671 - output_loss: 0.2319 - auxilliary_output_1_loss: 0.4546 - auxilliary_output_2_loss: 0.3292 - output_accuracy: 0.9197 - auxilliary_output_1_accuracy: 0.8425 - auxilliary_output_2_accuracy: 0.8863 - val_loss: 0.8964 - val_output_loss: 0.5679 - val_auxilliary_output_1_loss: 0.5555 - val_auxilliary_output_2_loss: 0.5394 - val_output_accuracy: 0.8252 - val_auxilliary_output_1_accuracy: 0.8070 - val_auxilliary_output_2_accuracy: 0.8234\nEpoch 00020: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 150s 3ms/sample - loss: 0.4286 - output_loss: 0.2064 - auxilliary_output_1_loss: 0.4349 - auxilliary_output_2_loss: 0.3058 - output_accuracy: 0.9276 - auxilliary_output_1_accuracy: 0.8483 - auxilliary_output_2_accuracy: 0.8933 - val_loss: 1.0719 - val_output_loss: 0.6870 - val_auxilliary_output_1_loss: 0.6381 - val_auxilliary_output_2_loss: 0.6451 - val_output_accuracy: 0.7965 - val_auxilliary_output_1_accuracy: 0.7826 - val_auxilliary_output_2_accuracy: 0.7904\nEpoch 00021: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 152s 3ms/sample - loss: 0.3874 - output_loss: 0.1780 - auxilliary_output_1_loss: 0.4180 - auxilliary_output_2_loss: 0.2800 - output_accuracy: 0.9386 - auxilliary_output_1_accuracy: 0.8527 - auxilliary_output_2_accuracy: 0.9032 - val_loss: 0.9553 - val_output_loss: 0.6273 - val_auxilliary_output_1_loss: 0.5463 - val_auxilliary_output_2_loss: 0.5472 - val_output_accuracy: 0.8168 - val_auxilliary_output_1_accuracy: 0.8066 - val_auxilliary_output_2_accuracy: 0.8207\nEpoch 00022: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 149s 3ms/sample - loss: 0.3612 - output_loss: 0.1615 - auxilliary_output_1_loss: 0.4025 - auxilliary_output_2_loss: 0.2630 - output_accuracy: 0.9444 - auxilliary_output_1_accuracy: 0.8603 - auxilliary_output_2_accuracy: 0.9093 - val_loss: 0.9709 - val_output_loss: 0.6197 - val_auxilliary_output_1_loss: 0.5941 - val_auxilliary_output_2_loss: 0.5765 - val_output_accuracy: 0.8295 - val_auxilliary_output_1_accuracy: 0.7996 - val_auxilliary_output_2_accuracy: 0.8199\nEpoch 00023: LearningRateScheduler reducing learning rate to 0.009216.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.3243 - output_loss: 0.1368 - auxilliary_output_1_loss: 0.3857 - auxilliary_output_2_loss: 0.2392 - output_accuracy: 0.9514 - auxilliary_output_1_accuracy: 0.8644 - auxilliary_output_2_accuracy: 0.9160 - val_loss: 1.2389 - val_output_loss: 0.8357 - val_auxilliary_output_1_loss: 0.6289 - val_auxilliary_output_2_loss: 0.7153 - val_output_accuracy: 0.7963 - val_auxilliary_output_1_accuracy: 0.7918 - val_auxilliary_output_2_accuracy: 0.7966\n50000/50000 [==============================] - 148s 3ms/sample - loss: 0.2799 - output_loss: 0.1105 - auxilliary_output_1_loss: 0.3578 - auxilliary_output_2_loss: 0.2067 - output_accuracy: 0.9618 - auxilliary_output_1_accuracy: 0.8731 - auxilliary_output_2_accuracy: 0.9287 - val_loss: 0.9797 - val_output_loss: 0.6449 - val_auxilliary_output_1_loss: 0.5490 - val_auxilliary_output_2_loss: 0.5670 - val_output_accuracy: 0.8382 - val_auxilliary_output_1_accuracy: 0.8146 - val_auxilliary_output_2_accuracy: 0.8305\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.2462 - output_loss: 0.0886 - auxilliary_output_1_loss: 0.3424 - auxilliary_output_2_loss: 0.1828 - output_accuracy: 0.9689 - auxilliary_output_1_accuracy: 0.8792 - auxilliary_output_2_accuracy: 0.9367 - val_loss: 1.4342 - val_output_loss: 0.9446 - val_auxilliary_output_1_loss: 0.7465 - val_auxilliary_output_2_loss: 0.8855 - val_output_accuracy: 0.7930 - val_auxilliary_output_1_accuracy: 0.7680 - val_auxilliary_output_2_accuracy: 0.7735\nEpoch 00026: LearningRateScheduler reducing learning rate to 0.008847359999999999.\n50000/50000 [==============================] - 149s 3ms/sample - loss: 2.1851 - output_loss: 1.5497 - auxilliary_output_1_loss: 0.8251 - auxilliary_output_2_loss: 1.2929 - output_accuracy: 0.4705 - auxilliary_output_1_accuracy: 0.7110 - auxilliary_output_2_accuracy: 0.5651 - val_loss: 1.3100 - val_output_loss: 0.8958 - val_auxilliary_output_1_loss: 0.6269 - val_auxilliary_output_2_loss: 0.7537 - val_output_accuracy: 0.6829 - val_auxilliary_output_1_accuracy: 0.7865 - val_auxilliary_output_2_accuracy: 0.7363\nEpoch 00027: LearningRateScheduler reducing learning rate to 0.008847359999999999.\n50000/50000 [==============================] - 150s 3ms/sample - loss: 0.8369 - output_loss: 0.5528 - auxilliary_output_1_loss: 0.4395 - auxilliary_output_2_loss: 0.5074 - output_accuracy: 0.8092 - auxilliary_output_1_accuracy: 0.8467 - auxilliary_output_2_accuracy: 0.8243 - val_loss: 0.9434 - val_output_loss: 0.6110 - val_auxilliary_output_1_loss: 0.5392 - val_auxilliary_output_2_loss: 0.5690 - val_output_accuracy: 0.7945 - val_auxilliary_output_1_accuracy: 0.8126 - val_auxilliary_output_2_accuracy: 0.8054\nEpoch 00028: LearningRateScheduler reducing learning rate to 0.008847359999999999.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.5419 - output_loss: 0.3264 - auxilliary_output_1_loss: 0.3704 - auxilliary_output_2_loss: 0.3478 - output_accuracy: 0.8866 - auxilliary_output_1_accuracy: 0.8696 - auxilliary_output_2_accuracy: 0.8797 - val_loss: 1.0697 - val_output_loss: 0.7135 - val_auxilliary_output_1_loss: 0.5587 - val_auxilliary_output_2_loss: 0.6288 - val_output_accuracy: 0.7912 - val_auxilliary_output_1_accuracy: 0.8168 - val_auxilliary_output_2_accuracy: 0.8047\nEpoch 00029: LearningRateScheduler reducing learning rate to 0.008847359999999999.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.4062 - output_loss: 0.2246 - auxilliary_output_1_loss: 0.3364 - auxilliary_output_2_loss: 0.2690 - output_accuracy: 0.9229 - auxilliary_output_1_accuracy: 0.8818 - auxilliary_output_2_accuracy: 0.9079 - val_loss: 1.2388 - val_output_loss: 0.7876 - val_auxilliary_output_1_loss: 0.6928 - val_auxilliary_output_2_loss: 0.8112 - val_output_accuracy: 0.7708 - val_auxilliary_output_1_accuracy: 0.7752 - val_auxilliary_output_2_accuracy: 0.7640\nEpoch 00030: LearningRateScheduler reducing learning rate to 0.008847359999999999.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.3310 - output_loss: 0.1704 - auxilliary_output_1_loss: 0.3141 - auxilliary_output_2_loss: 0.2212 - output_accuracy: 0.9411 - auxilliary_output_1_accuracy: 0.8905 - auxilliary_output_2_accuracy: 0.9228 - val_loss: 1.0383 - val_output_loss: 0.6780 - val_auxilliary_output_1_loss: 0.5576 - val_auxilliary_output_2_loss: 0.6432 - val_output_accuracy: 0.8133 - val_auxilliary_output_1_accuracy: 0.8152 - val_auxilliary_output_2_accuracy: 0.8093\nEpoch 00031: LearningRateScheduler reducing learning rate to 0.008847359999999999.\n50000/50000 [==============================] - 150s 3ms/sample - loss: 0.2615 - output_loss: 0.1211 - auxilliary_output_1_loss: 0.2896 - auxilliary_output_2_loss: 0.1784 - output_accuracy: 0.9584 - auxilliary_output_1_accuracy: 0.8969 - auxilliary_output_2_accuracy: 0.9386 - val_loss: 1.1760 - val_output_loss: 0.7806 - val_auxilliary_output_1_loss: 0.6153 - val_auxilliary_output_2_loss: 0.7028 - val_output_accuracy: 0.8010 - val_auxilliary_output_1_accuracy: 0.8003 - val_auxilliary_output_2_accuracy: 0.8034\nEpoch 00032: LearningRateScheduler reducing learning rate to 0.008493465599999998.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.2185 - output_loss: 0.0944 - auxilliary_output_1_loss: 0.2668 - auxilliary_output_2_loss: 0.1468 - output_accuracy: 0.9672 - auxilliary_output_1_accuracy: 0.9058 - auxilliary_output_2_accuracy: 0.9501 - val_loss: 1.7912 - val_output_loss: 1.2432 - val_auxilliary_output_1_loss: 0.7684 - val_auxilliary_output_2_loss: 1.0583 - val_output_accuracy: 0.7643 - val_auxilliary_output_1_accuracy: 0.7729 - val_auxilliary_output_2_accuracy: 0.7648\nEpoch 00033: LearningRateScheduler reducing learning rate to 0.008493465599999998.\n50000/50000 [==============================] - 149s 3ms/sample - loss: 0.1932 - output_loss: 0.0769 - auxilliary_output_1_loss: 0.2563 - auxilliary_output_2_loss: 0.1313 - output_accuracy: 0.9742 - auxilliary_output_1_accuracy: 0.9102 - auxilliary_output_2_accuracy: 0.9544 - val_loss: 1.1805 - val_output_loss: 0.8059 - val_auxilliary_output_1_loss: 0.5830 - val_auxilliary_output_2_loss: 0.6655 - val_output_accuracy: 0.8262 - val_auxilliary_output_1_accuracy: 0.8147 - val_auxilliary_output_2_accuracy: 0.8257\nEpoch 00034: LearningRateScheduler reducing learning rate to 0.008493465599999998.\n50000/50000 [==============================] - 151s 3ms/sample - loss: 0.1730 - output_loss: 0.0660 - auxilliary_output_1_loss: 0.2420 - auxilliary_output_2_loss: 0.1149 - output_accuracy: 0.9772 - auxilliary_output_1_accuracy: 0.9165 - auxilliary_output_2_accuracy: 0.9600 - val_loss: 1.0301 - val_output_loss: 0.6918 - val_auxilliary_output_1_loss: 0.5229 - val_auxilliary_output_2_loss: 0.6048 - val_output_accuracy: 0.8438 - val_auxilliary_output_1_accuracy: 0.8301 - val_auxilliary_output_2_accuracy: 0.8361\nEpoch 00035: LearningRateScheduler reducing learning rate to 0.008493465599999998.\n50000/50000 [==============================] - 148s 3ms/sample - loss: 0.1580 - output_loss: 0.0586 - auxilliary_output_1_loss: 0.2283 - auxilliary_output_2_loss: 0.1032 - output_accuracy: 0.9805 - auxilliary_output_1_accuracy: 0.9208 - auxilliary_output_2_accuracy: 0.9654 - val_loss: 1.0190 - val_output_loss: 0.6755 - val_auxilliary_output_1_loss: 0.5559 - val_auxilliary_output_2_loss: 0.5889 - val_output_accuracy: 0.8474 - val_auxilliary_output_1_accuracy: 0.8270 - val_auxilliary_output_2_accuracy: 0.8434\nRendered (Tue, 26 May 2020 11:03:53 UTC)


Valencia CF vs Real Madrid football predictions, statistics - 15 Dec 2019\n7ᵗʰ placeSpain La Liga2ⁿᵈ place\nValencia CFReal Madrid\nPreview 15/12/2019 22:00 GMT 27 33 40 2 2 - 3 3.03 11° 1.79\n4.33 4.10 1.79 1 1 -1\nPreview15/12/2019 22:00 GMT 39 61 Over 3.03 11° 1.61\nPreview15/12/2019 22:00 GMT 15 46 39 X 2 11° 5.00\nPreview15/12/2019 22:00 GMT 18 82 Yes 2 - 3 3.03 11° 1.57\nPreview 15/12/2019 22:00 GMT\nPreview15/12/2019 22:00 GMT 40% Away 0 2 - 3 3.03 11° 1.53\nPreview15/12/2019 22:00 73% 2X 11° 1.20\n1\t 4.33 4.20 1.75\n2\t 4.20 4.10 1.79\n3\t 4.10 3.90 1.74\n4\t 4.30 4.25 1.79\n5\t 4.38 4.08 1.79\nEzequiel Garay 15 Uncertain\nFerran Torres 19 Likely to play\nGoncalo Guedes 6 Will not play\nLee Kang-in 9 Likely to play\nRodrigo 17 Will not play\n3. F. Torres 4 4 -\n5. D. Cheryshev 1 5 -\n6. Lee Kangin 1 2 1\n7. R. Sobrino 1 - -\n8. C. Soler 1 3 -\n9. M. Vallejo 1 - -\n10. D. Wass 1 3 -\n11. E. Garay - 2 -\n12. F. Coquelin - 4 1\n13. G. Kondogbia - 2 -\n14. Gabriel - 2 -\n15. J. Cillessen - 1 -\n16. J. Gaya - 2 -\n17. M. Diakhaby - 1 -


RMG38B0K–CARL VON LINNE known as LINNAEUS Swedish naturalist and botanist. Date: 1707-1778\nRME5K43C–Carl Linnaeus, or Carl von Linné, 1707-1778, a Swedish botanist, physician, and zoologist,\nRME1GNNJ–Carl von Linne (1707-1778) known as Linnaeus, Swedish physician, botanist, and zoologist. Binomial classification. Engraving, Paris, 1874.\nRMED5D22–Carl Linnaeus, or Carl von Linné, 1707-1778, a Swedish botanist, physician, and zoologist, Carl von Linné, oder Carl Nilsson Li\nRMFR9KHH–A 1610 portrait of Carl von Linné by Alexander Roslin in 1775, oil on canvas. Carl Linnaeus (1707 – 10 January 1778), also known after his ennoblement as Carl von Linné was a Swedish botanist, physician, and zoologist, who formalised the modern system of naming organisms called binomial nomenclature. He is known by the epithet 'father of modern taxonomy'\nRMDGTKTX–Carl Linnaeus, or Carl von Linné, 1707-1778, a Swedish botanist, physician, and zoologist,\nRM2CATT03–Carl Linnaeus [Linneo] Carl Linnaeus (23 May 1707 – 10 January 1778), also known after his ennoblement as Carl von Linné. was a Swedish botanist, zoologist, and physician who formalised binomial nomenclature, the modern system of naming organisms. He is known as the 'father of modern taxonomy'. Many of his writings were in Latin, and his name is rendered in Latin as Carolus Linnæus (Later as Carolus A Linné). From the book La ciencia y sus hombres : vidas de los sabios ilustres desde la antigüedad hasta el siglo XIX T. 3 [Science and its men: lives of the illustrious sages from antiquity to t\nRMHRKMW0–Carl Linnaeus, Swedish Botanist\nRMHGNH45–Carl Linnaeus, or Carl von Linné, 1707-1778, a Swedish botanist, physician, and zoologist, Carl von Linné, oder Carl Nilsson Linnæus; 1707-1778, ein\nRMB7AR2K–Carl Linnaeus (Latinized as Carolus Linnaeus, also known after his ennoblement as Carl von Linné (help·info), May 23 [O.S. May\nRMRYA6RK–Linnaeus. Carl Linnaeus, Swedish Original Name Carl Nilsson Linnæus, 23 May 1707 – 10 January 1778 Also Known after His Ennoblement As V-Carl Von Linné, Was a Swedish Botanist, Physician, and Zoologist\nRMRKD525–Carl Linnaeus. Carl Linnaeus ( 23 May 1707 – 10 January 1778), also known after his ennoblement as Carl von Linné was a Swedish botanist, physician,\nRM2B02W1W–Carl Linnaeus, also known after his ennoblement as Carl von Linné, was a Swedish botanist, physician, and zoologist, who laid the foundations for the modern biological naming scheme of binomial nomenclature. He is known as the father of modern taxonomy, and is also considered one of the fathers of modern ecology.


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