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▁not ▁all ▁of ▁them ; ▁being ▁autonom ous , ▁they ▁can ▁choose ▁their ▁own ▁scale . ▁Private ▁univers ities ▁have ▁their ▁own ▁rank ▁in ▁each ▁case , ▁sometimes ▁based ▁on ▁the ▁public ▁university ▁system , ▁although ▁as ▁a ▁general ▁rule ▁they ▁have ▁less ▁ranks ▁or ▁hold ▁a ▁higher ▁ranking ▁as ▁the ▁starting ▁point ▁for ▁a ▁teaching ▁career ▁( dev oting ▁auxili ar ▁ranks ▁to ▁under grad u ates ). ▁ ▁Main ▁professor ships ▁ ▁Honor ary ▁degree , ▁the ▁professor ▁has ▁the ▁same ▁duties ▁and ▁rights ▁as ▁a ▁Professor ▁Tit ular . ▁▁ ▁( The ▁lowest ▁rank ▁for ▁a ▁professor ▁to ▁be ▁head ▁of ▁a ▁teaching ▁team , ▁a ▁" c át ed ra "). ▁ ▁Other ▁professor ships ▁ ▁a ▁retired ▁ten ured ▁fac ulty ▁member ▁who ▁demonstrated ▁excell ency ▁in ▁both ▁teaching ▁and ▁research ing ▁ ▁also ▁a ▁retired ▁fac ulty ▁member ▁who ▁provides ▁assistance ▁in ▁specific ▁knowledge ▁areas ▁ ▁an ▁honor ary ▁mostly ▁cer emon ial ▁position ▁Prof ess ors ▁holding ▁these ▁positions ▁usually ▁teach ▁upper ▁classes , ▁gradu ate ▁classes , ▁or ▁do ▁not ▁teach ▁( working ▁as ▁research ers ▁or ▁research ▁advis ors ). ▁ ▁Tem por ary ▁professor ships ▁▁ ▁( for ▁a ▁certain ▁period ) ▁ ▁Te aching ▁auxili aries ▁or ▁assist ants ▁ ▁( In ▁many ▁univers ities , ▁holding ▁a ▁doctor ate ▁is ▁now ▁a ▁non - form al ▁requirement ▁for ▁this ▁post .) ▁ ▁( Gr adu ated ) ▁ ▁( Gr adu ated ) ▁▁▁ ▁( for ▁under grad uate ▁students ) |
▁ ▁See ▁also ▁List ▁of ▁academic ▁ranks ▁ ▁Category : A cadem ic ▁ranks ▁Category : E ducation ▁in ▁Argentina ▁R anks <0x0A> </s> ▁Dieu ▁a ▁bes oin ▁des ▁hommes ▁( G od ▁Ne eds ▁Man ) ▁is ▁a ▁ 1 9 5 0 ▁French ▁drama ▁film ▁directed ▁by ▁Jean ▁Del anno y . ▁At ▁the ▁ 1 st ▁Berlin ▁International ▁Film ▁Festival ▁it ▁won ▁the ▁Special ▁Prize ▁for ▁an ▁Ex cell ent ▁Film ▁Ach iev ement . ▁ ▁Cast ▁ ▁Antoine ▁Bal p ê tr é ▁- ▁Le ▁père ▁G our ven ne c , ▁un ▁p ê che ur ▁ ▁Luci enne ▁B oga ert ▁- ▁Ana ï s ▁Le ▁Ber re ▁ ▁Charles ▁Bou illa ud ▁- ▁Le ▁g endar me ▁ ▁Jean ▁Bro ch ard ▁- ▁L ' abb é ▁Ker her vé , ▁le ▁rect eur ▁de ▁Les co ff ▁ ▁Jean ▁Car met ▁- ▁Y von ▁ ▁And r ée ▁Cl ément ▁- ▁Sch ol ast ique ▁K erne is ▁ ▁Marcel ▁D ela ître ▁- ▁M . ▁K erne is ▁ ▁Jean ▁d ' Y d ▁- ▁Cor entin ▁G our ven ne c ▁ ▁Pierre ▁F res n ay ▁- ▁Thomas ▁G our ven ne c ▁ ▁Daniel ▁Gé lin ▁- ▁Joseph ▁le ▁Ber re ▁ ▁Marc elle ▁Gé ni at ▁- ▁La ▁mère ▁G our ven ne c ▁ ▁René ▁Gén in ▁- ▁Le ▁père ▁d ' Y von ▁ ▁J ér ôme ▁G oul ven ▁- ▁Le ▁brig ad ier ▁ ▁Daniel ▁I |
vern el ▁- ▁François ▁Gu ill en ▁ ▁Ger ma ine ▁Ker je an ▁- ▁M me ▁K erne is ▁ ▁C é cy l ▁Mar cy l ▁- ▁La ▁vie ille ▁ ▁Albert ▁Michel ▁- ▁Le ▁B ail ▁ ▁Jean - Pierre ▁Mock y ▁- ▁Pierre ▁ ▁Pierre ▁Mon cor b ier ▁- ▁Un ▁p ê che ur ▁ ▁Rap ha ël ▁P ator ni ▁- ▁Jules ▁ ▁Fern and ▁René ▁- ▁Y ves ▁L ann uz el ▁ ▁Made le ine ▁Robinson ▁- ▁Jean ne ▁G our ven ne c ▁ ▁References ▁ ▁External ▁links ▁ ▁Category : 1 9 5 0 ▁films ▁Category : F rench ▁films ▁Category : F rench - language ▁films ▁Category : 1 9 5 0 ▁drama ▁films ▁Category : Fil ms ▁directed ▁by ▁Jean ▁Del anno y ▁Category : F rench ▁black - and - white ▁films ▁Category : Fil ms ▁with ▁screen plays ▁by ▁Jean ▁A uren che ▁Category : Fil ms ▁with ▁screen plays ▁by ▁Pierre ▁B ost <0x0A> </s> ▁The ▁Sent encing ▁Act ▁of ▁ 1 9 8 7 ▁( Pub . L . ▁ 1 0 0 - 1 8 2 ) ▁en act ed ▁some ▁changes ▁to ▁the ▁federal ▁sent encing ▁regime ▁in ▁the ▁United ▁States . ▁The ▁legisl ation ▁am ended ▁ ▁to ▁permit ▁express ly ▁depart ures ▁based ▁on ▁circumstances ▁of ▁an ▁exception al ▁" kind " ▁or ▁" deg ree ". ▁The ▁insert ion ▁of ▁this ▁new ▁language ▁was ▁described ▁by ▁the ▁manager ▁of ▁the ▁House ▁bill |
, ▁Represent ative ▁John ▁Con y ers , ▁as ▁" clar ifying " ▁in ▁nature ▁because ▁it ▁simply ▁made ▁explicit ▁in ▁the ▁law ▁that ▁which ▁was ▁previously ▁described ▁in ▁the ▁Senate ▁Committee ▁Report ▁as ▁implicit ▁and ▁intended . ▁There ▁is ▁evidence ▁that ▁under ▁the ▁new ▁regime , ▁sentences ▁may ▁not ▁have ▁increased ▁as ▁much ▁as ▁expected : ▁although ▁the ▁average ▁prison ▁term ▁for ▁trial ▁sentences ▁increases ▁post ‐ re forms , ▁there ▁is ▁no ▁system atic ▁increase ▁in ▁the ▁average ▁length ▁of ▁the ▁ple as . ▁ ▁Additionally , ▁the ▁law ▁abol ished ▁par ole ▁for ▁federal ▁prisoners ▁and ▁created ▁the ▁United ▁States ▁Sent encing ▁Commission . ▁The ▁commission ▁makes ▁the ▁gu idel ines ▁used ▁by ▁federal ▁jud ges ▁when ▁sent encing ▁people ▁conv icted ▁of ▁a ▁federal ▁crime . ▁ ▁The ▁bill ▁was ▁introduced ▁by ▁Senator ▁Joseph ▁B iden ▁on ▁October ▁ 2 7 , ▁ 1 9 8 7 . ▁It ▁was ▁signed ▁into ▁law ▁on ▁December ▁ 7 , ▁ 1 9 8 7 , ▁by ▁President ▁Ron ald ▁Re agan . ▁ ▁References ▁ ▁Category : 1 9 8 7 ▁in ▁law ▁Category : Un ited ▁States ▁sent encing ▁law <0x0A> </s> ▁In vis i Cl ues ▁were ▁hint ▁book lets ▁sold ▁by ▁Info com ▁to ▁help ▁players ▁solve ▁puzz les ▁in ▁their ▁interactive ▁fiction ▁computer ▁games . ▁ ▁Before ▁Info com ' s ▁games ▁expl oded ▁in ▁popular ity , ▁players ▁could ▁request ▁hints ▁by ▁mail ▁and ▁receive ▁a ▁type - written ▁sheet ▁in ▁response . ▁ ▁When |
▁the ▁number ▁of ▁requests ▁proved ▁un man age able , ▁the ▁Z ork ▁Users ▁Group ▁began ▁a ▁pay - per - h int ▁tele phone ▁system . ▁The ▁in vention ▁of ▁In vis i Cl ues ▁replaced ▁this ▁system ▁and ▁was ▁revolution ary : ▁a ▁player ▁could ▁often ▁buy ▁a ▁hint ▁book ▁at ▁the ▁same ▁time ▁and ▁at ▁the ▁same ▁location ▁as ▁the ▁game ▁itself . ▁ ▁Question s ▁rel ating ▁to ▁the ▁game ▁were ▁printed ▁in ▁the ▁book , ▁for ▁example , ▁the ▁In vis i Cl ues ▁for ▁Z ork ▁I < ref >[ http :// www . cs d . u wo . ca / Info com / In vis ic l ues / z ork 1 / ▁In vis i Cl ues ▁for ▁Z ork ▁I ] ▁ ▁from ▁the ▁Info com ▁Home page </ ref > ▁contained ▁the ▁question ▁" How ▁can ▁I ▁kill ▁the ▁song bird ?" ▁ ▁A ▁series ▁of ▁" empty " ▁boxes ▁located ▁below ▁or ▁following ▁the ▁text ▁contained ▁the ▁answers , ▁printed ▁in ▁invisible ▁in k . ▁The ▁contents ▁of ▁each ▁box ▁could ▁be ▁revealed ▁by ▁using ▁a ▁high l ighter - like ▁marker ▁that ▁came ▁with ▁the ▁book . ▁ ▁Over ▁time , ▁the ▁in k ▁de grad ed ▁and ▁the ▁text ▁revert ed ▁to ▁in visibility . ▁ ▁To ▁disc ou rage ▁players ▁from ▁accident ally ▁learning ▁what ▁await ed ▁by ▁reading ▁all ▁the ▁questions , ▁each ▁book let ▁contained ▁a ▁number ▁of ▁pla us ible - s ounding ▁" f |
ake " ▁questions . ▁Re ve aling ▁these ▁answers ▁usually ▁resulted ▁in ▁a ▁m ild ▁sc old ing . ▁Several ▁" non - p uzz les " ▁also ▁had ▁questions , ▁such ▁as ▁the ▁song bird ▁example ▁used ▁above . ▁The ▁answer ▁to ▁these ▁was ▁usually ▁a ▁t ers ely - word ed ▁statement ▁saying ▁" You ▁can ' t ▁do ▁that ", ▁often ▁followed ▁by ▁one ▁or ▁more ▁items ▁reading ▁" This ▁space ▁intention ally ▁left ▁blank " ▁or , ▁on ▁occasion , ▁showed ▁false ▁cl ues ▁such ▁as ▁" How ▁Do ▁I ▁get ▁off ▁the ▁roof ▁of ▁the ▁House ?" ▁the ▁clue ▁being ▁" How ▁did ▁you ▁get ▁up ▁there ? ". ▁Even ▁the ▁answers ▁to ▁real ▁questions ▁began ▁with ▁vague ▁hints , ▁so ▁a ▁player ▁could ▁choose ▁to ▁stop ▁short ▁of ▁getting ▁explicit ▁solutions ▁to ▁the ▁puzz les . ▁ ▁The ▁In vis i Cl ues ▁books ▁were ▁very ▁popular . ▁By ▁late ▁ 1 9 8 4 ▁Info com ▁had ▁sold ▁more ▁than ▁ 5 0 0 , 0 0 0 ▁copies ▁at ▁$ 9 . 9 5 ▁each ▁for ▁its ▁games , ▁including ▁about ▁ 2 0 0 , 0 0 0 ▁for ▁the ▁Z ork ▁I ▁book . ▁Richard ▁E . ▁S ny der ▁of ▁Simon ▁& ▁Sch uster ▁amaz ed ▁In vis i Cl ues ▁author ▁Mike ▁D orn bro ok ▁by ▁stating ▁that ▁such ▁volumes ▁made ▁him ▁" one ▁of ▁the ▁best s elling ▁authors ▁on ▁the ▁planet ▁... ▁In ▁terms ▁of ▁dollars ▁you ' |
re ▁at ▁Stephen ▁King ▁level !" ▁ ▁For ▁a ▁short ▁time , ▁The ▁Status ▁Line , ▁the ▁Info com ▁Game ▁news letter , ▁included ▁" Vis ic l ues ". ▁ ▁These ▁were ▁just ▁select ▁In vis i Cl ues ▁questions ▁from ▁a ▁couple ▁of ▁newer ▁games , ▁with ▁answers ▁written ▁in ▁a ▁simple ▁crypt ogram . ▁ ▁In vis i Cl ues ▁books ▁were ▁almost ▁always ▁pack aged ▁with ▁the ▁navigation ▁map ▁for ▁the ▁same ▁given ▁game . ▁▁ ▁Though ▁In vis i Cl ues , ▁like ▁the ▁games ▁themselves , ▁are ▁no ▁longer ▁available , ▁a ▁few ▁Internet ▁sites ▁have ▁re created ▁the ▁book lets . ▁Typ ically , ▁either ▁all ▁the ▁answers ▁are ▁printed ▁normally ▁on ▁the ▁site ▁or ▁the ▁user ▁must ▁" highlight " ▁a ▁section ▁by ▁clicking ▁and ▁drag ging ▁the ▁mouse ▁to ▁reve al ▁the ▁hints . ▁ ▁The ▁In vis i Cl ues ▁were ▁included ▁in ▁a ▁hint ▁book let ▁pack aged ▁with ▁The ▁Lost ▁Tre asures ▁of ▁Info com . ▁However , ▁the ▁In vis i Cl ues ▁pack aged ▁with ▁the ▁Tre asures ▁were ▁not ▁produced ▁to ▁Info com ' s ▁high ▁standards : ▁ ▁The ▁cl ues ▁were ▁not ▁written ▁in ▁invisible ▁in k , ▁which ▁made ▁it ▁easy ▁to ▁accident ally ▁getting ▁answers ▁to ▁puzz les . ▁ ▁Some ▁of ▁the ▁hints ▁were ▁missing ▁ ▁There ▁were ▁many ▁errors , ▁such ▁as ▁miss p ell ings , ▁mis - cap ital izations , ▁formatting ▁issues , ▁and ▁pun ctu ation ▁errors . |
▁ ▁The ▁cl ues ▁were ▁not ▁included ▁with ▁The ▁Lost ▁Tre asures ▁of ▁Info com ▁II ' '. ▁However , ▁there ▁was ▁a ▁pay - per - min ute ▁card ▁included . ▁ ▁In ▁the ▁Sol id ▁Gold ▁line , ▁typing ▁" H INT " ▁twice ▁would ▁allow ▁you ▁to ▁access ▁In vis ic l ues ▁from ▁in - game . ▁ ▁References ▁ ▁External ▁links ▁ ▁The ▁Info com ▁Gallery , ▁a ▁site ▁with ▁In vis i Cl ues ▁to ▁some ▁Info com ▁games ▁ ▁Category : Video ▁game ▁culture ▁Category : Info com <0x0A> </s> ▁William ▁Joseph ▁Jenkins ▁( 1 8 ▁May ▁ 1 8 8 5 ▁- ▁ 2 3 ▁December ▁ 1 9 5 6 ) ▁was ▁a ▁Wel sh ▁international ▁forward ▁who ▁played ▁club ▁rugby ▁for ▁C anton ▁R FC ▁and ▁Card iff . ▁He ▁won ▁four ▁caps ▁for ▁Wales ▁ending ▁on ▁the ▁losing ▁side ▁just ▁once . ▁ ▁Personal ▁history ▁Jenkins ▁was ▁born ▁in ▁Card iff ▁in ▁ 1 8 8 5 . ▁His ▁younger ▁brother ▁Edd ie ▁became ▁a ▁football ▁player ▁of ▁note ▁in ▁the ▁association ▁game ▁winning ▁a ▁single ▁cap ▁for ▁Wales ▁in ▁ 1 9 2 5 . ▁Out side ▁of ▁rugby ▁Jenkins ▁worked ▁in ▁the ▁building ▁trade . ▁ ▁Rugby ▁career ▁Jenkins ▁joined ▁Card iff ▁in ▁the ▁ 1 9 0 9 - 1 0 ▁season , ▁but ▁it ▁wasn ' t ▁until ▁ 1 9 1 2 ▁that ▁he ▁was ▁selected ▁to ▁play ▁for ▁Wales . ▁He ▁turned ▁out ▁on ▁ 9 |
▁March ▁ 1 9 1 2 ▁against ▁Ireland ▁at ▁the ▁Bal m oral ▁Show ground s , ▁in ▁a ▁match ▁Wales ▁lost ▁ 1 2 - 5 . ▁Two ▁weeks ▁later ▁he ▁played ▁his ▁second ▁game ▁for ▁Wales , ▁this ▁time ▁at ▁Rod ney ▁Par ade ▁against ▁France . ▁Although ▁Wales ▁won ▁the ▁game , ▁eight ▁members ▁of ▁the ▁Wel sh ▁team ▁played ▁their ▁last ▁Five ▁Nations ▁Championship ▁game . ▁Jenkins ▁was ▁one ▁of ▁the ▁seven ▁players ▁to ▁surv ive ▁this ▁c ull ▁and ▁he ▁was ▁chosen ▁to ▁play ▁in ▁two ▁matches ▁the ▁next ▁season ▁in ▁the ▁ 1 9 1 3 ▁Five ▁Nations ▁Championship . ▁ ▁Jenkins ▁was ▁also ▁selected ▁to ▁play ▁for ▁inv it ational ▁tour ing ▁team , ▁the ▁Barb ari ans . ▁He ▁played ▁twice ▁for ▁the ▁Barb ari ans , ▁a ▁war - time ▁match ▁against ▁the ▁SA ▁Services ▁in ▁ 1 9 1 5 ▁and ▁later ▁against ▁Ne ath ▁in ▁ 1 9 2 1 . ▁ ▁International ▁matches ▁played ▁Wales ▁▁▁ 1 9 1 2 ▁▁▁ 1 9 1 2 , ▁ 1 9 1 3 ▁▁▁ 1 9 1 3 ▁ ▁Bibli ography ▁ ▁References ▁ ▁Category : Wel sh ▁rugby ▁union ▁players ▁Category : R ugby ▁union ▁fl ank ers ▁Category : 1 8 8 5 ▁birth s ▁Category : 1 9 5 6 ▁death s ▁Category : Card iff ▁R FC ▁players ▁Category : Bar bar ian ▁F . C . ▁players ▁Category : W ales ▁international ▁rugby ▁union ▁players ▁Category : R |
ugby ▁union ▁players ▁from ▁Card iff ▁Category : C anton ▁R FC ▁players <0x0A> </s> ▁Ast ro bi ology ▁Science ▁and ▁Technology ▁for ▁Ex pl oring ▁Plan ets ▁( AST EP ) ▁is ▁a ▁program ▁established ▁by ▁NASA ▁to ▁spons or ▁research ▁projects ▁that ▁advance ▁the ▁technology ▁and ▁techniques ▁used ▁in ▁planet ary ▁expl oration . ▁ ▁The ▁objective ▁is ▁to ▁enable ▁the ▁study ▁of ▁ast ro bi ology ▁and ▁to ▁aid ▁the ▁planning ▁of ▁extr ater rest rial ▁expl oration ▁miss ions ▁while ▁prior it izing ▁science , ▁technology , ▁and ▁field ▁campaign s . ▁ ▁Over view ▁A ST EP ▁is ▁one ▁of ▁four ▁elements ▁of ▁NASA ' s ▁Ast ro bi ology ▁Program , ▁which ▁falls ▁under ▁the ▁Planet ary ▁Science ▁Division ▁of ▁the ▁Science ▁Mission ▁Director ate . ▁According ▁to ▁the ▁formal ▁description ▁from ▁NASA , ▁" The ▁A ST EP ▁program ▁spons ors ▁the ▁development ▁of ▁techn ologies ▁that ▁enable ▁remote ▁searches ▁for , ▁and ▁identification ▁of , ▁life ▁in ▁extreme ▁environments , ▁including ▁planet ary ▁surfaces ▁and ▁sub sur faces ." ▁A ST EP ▁is ▁concerned ▁with ▁discover ing ▁techn ologies ▁which ▁will ▁enable ▁scient ists ▁to ▁study ▁ast ro bi ology ▁both ▁on ▁the ▁surface ▁of ▁the ▁Earth ▁and ▁on ▁extr ater rest rial ▁bodies . ▁A ▁central ▁focus ▁of ▁A ST EP ' s ▁research ▁is ▁terrest rial ▁field ▁campaign s , ▁or ▁long - duration ▁exped itions ▁where ▁research ers ▁live ▁in ▁the ▁same ▁region ▁they ▁are ▁studying . ▁These ▁are ▁conducted ▁on ▁Earth |
▁in ▁remote ▁or ▁host ile ▁locations , ▁such ▁as ▁Ant arct ica ▁or ▁the ▁bottom ▁of ▁the ▁ocean . ▁Through ▁understanding ▁complex ▁and ▁ex otic ▁life ▁on ▁Earth , ▁such ▁as ▁extrem oph iles , ▁scient ists ▁hope ▁to ▁better ▁define ▁the ▁characteristics ▁they ▁should ▁look ▁for ▁and ▁the ▁locations ▁they ▁should ▁seek ▁when ▁attempting ▁to ▁discover ▁extr ater rest rial ▁life . ▁ ▁Method ology ▁A ST EP ▁fund ed ▁projects ▁typically ▁perform ▁research ▁by ▁searching ▁for ▁and ▁studying ▁extrem oph ile ▁bi ology ▁in ▁Earth ' s ▁har sh est ▁environments ▁through ▁the ▁use ▁of ▁field ▁research ▁campaign s . ▁The ▁environments ▁where ▁this ▁research ▁is ▁conducted ▁is ▁meant ▁to ▁simulate ▁the ▁expected ▁conditions ▁on ▁extr ater rest rial ▁world s ▁in ▁the ▁Sol ar ▁System . ▁Past ▁field ▁work ▁has ▁typically ▁target ed ▁two ▁regions . ▁Ar ctic ▁and ▁Ant ar ctic ▁clim ates ▁simulate ▁the ▁low ▁temper atures ▁expected ▁on ▁many ▁other ▁plan ets ▁such ▁as ▁Mars , ▁near ▁ro ver ▁landing ▁sites . ▁Under water ▁regions ▁are ▁also ▁an ▁area ▁of ▁study ▁because ▁they ▁simulate ▁high ▁pressure , ▁low ▁light ▁and ▁variable ▁temperature ▁conditions . ▁This ▁region ▁sim ul ates ▁proposed ▁miss ions ▁to ▁explore ▁the ▁vast ▁liquid ▁water ▁ocean ▁that ▁is ▁expected ▁to ▁res ide ▁under ▁Jup iter ' s ▁moon , ▁Europa . ▁ ▁A ST EP ▁prom otes ▁the ▁development ▁of ▁new ▁expl oration ▁techn ologies ▁and ▁techniques ▁that ▁can ▁search , ▁identify , ▁and ▁study ▁life ▁in ▁extreme ▁conditions ▁in ▁locations ▁that ▁are ▁difficult |
▁to ▁access . ▁There ▁is ▁a ▁broad ▁range ▁of ▁things ▁that ▁can ▁fall ▁into ▁this ▁category . ▁Pre vious ▁examples ▁include ▁labor ator ies ▁such ▁as ▁the ▁Mars ▁Science ▁Labor atory , ▁sampling ▁techniques , ▁the ▁Mars ▁ro vers , ▁the ▁T itan ▁land er ▁( H u yg ens ), ▁and ▁subm ers ibles . ▁Aut onom ous ▁systems ▁are ▁preferred ▁because ▁data ▁can ▁be ▁collected ▁without ▁the ▁presence ▁of ▁humans ▁near ▁the ▁test ▁area . ▁The ▁field ▁campaign s ▁are ▁used ▁as ▁a ▁proof - of - con cept ▁for ▁the ▁proposed ▁techn ologies ▁as ▁well ▁as ▁a ▁demonstr ation . ▁They ▁are ▁generally ▁tested ▁with ▁mock - mission ▁where ▁conditions ▁and ▁challeng es ▁simulate ▁those ▁that ▁might ▁be ▁experienced ▁on ▁an ▁actual ▁mission . ▁This ▁helps ▁identify ▁their ▁strength s ▁and ▁weak ness es ▁in ▁the ▁technology ' s ▁mission ▁execution ▁and ▁struct ural ▁end urance . ▁ ▁Bey ond ▁the ▁practice ▁of ▁new ▁techn ologies , ▁A ST EP ▁st riv es ▁to ▁learn ▁more ▁about ▁ast ro bi ology ▁through ▁observation ▁and ▁study ▁on ▁the ▁field ▁campaign s . ▁Anal yz ing ▁the ▁collected ▁samples ▁helps ▁research ers ▁determine ▁the ▁thermal , ▁phot onic , ▁pressure , ▁and ▁chemical ▁boundary ▁conditions ▁for ▁living ▁organ isms . ▁Under standing ▁how ▁these ▁organ isms ▁adapt ▁and ▁evol ve ▁in ▁these ▁extreme ▁conditions ▁may ▁be ▁similar ▁to ▁the ▁methods ▁used ▁by ▁extr ater rest rial ▁organ isms , ▁and ▁thus ▁offers ▁cl ues ▁about ▁where ▁life ▁may ▁be ▁found . ▁Another |
▁area ▁of ▁study ▁is ▁the ▁environmental ▁foot print ▁that ▁extrem oph ile ▁life ▁leaves ▁behind , ▁bi om ole cules ▁or ▁b ios ign atures ▁such ▁as ▁chemical ▁tra ils , ▁ge ological ▁form ations , ▁etc . ▁Ident ifying ▁these ▁cl ues ▁often ▁insp ires ▁new ▁bi ology ▁searching ▁techniques , ▁and ▁simpl ifies ▁mission ▁planning . ▁ ▁Past ▁projects ▁▁ 2 0 0 7 ▁The ▁Deep ▁Ph re atic ▁Th erm al ▁Explorer ▁( DE P TH X ) ▁The ▁Ar ctic ▁G ak kel ▁V ents ▁Ex ped ition ▁( AG AV E ) ▁The ▁Ar ctic ▁Mars ▁Anal og ue ▁S val b ard ▁Ex ped ition ▁( A MA SE ) ▁The ▁Mon ter ey ▁Bay ▁Aqu arium ▁Research ▁Institute ’ s ▁Environment al ▁Sample ▁Process or ▁( ES P ) ▁▁ 2 0 0 8 ▁The ▁Environment ally ▁Non - Dist urb ing ▁Under - I ce ▁Rob otic ▁Ant ar ctic ▁Explorer ▁( END UR ANCE ) ▁Ar ctic ▁Mars ▁Anal og ue ▁S val b ard ▁Ex ped ition ▁( A MA SE ) ▁Sample ▁Return ▁O ases ▁for ▁Life ▁and ▁Pre - Bi otic ▁Chem istry : ▁H ydro ther mal ▁Ex pl oration ▁Using ▁Advanced ▁Under water ▁Rob ot ics ▁Ice B ite : ▁An ▁aug er ▁and ▁sampling ▁system ▁for ▁ground ▁ice ▁on ▁Mars ▁ ▁VAL K Y RI E : ▁Very - deep ▁Aut onom ous ▁Las er - power ed ▁Kil ow att - class ▁Y o - yo ing |
▁Rob otic ▁Ice ▁Explorer ▁ ▁Aut onom ous ▁Ex pl oration , ▁Disc overy , ▁and ▁Sam pling ▁of ▁Life ▁in ▁Deep ▁Sea ▁Ext reme ▁En viron ments ▁ ▁Deep ▁Dr illing ▁and ▁Sam pling ▁Via ▁Comp act ▁Low - M ass ▁Rot ary - H ammer ▁Auto - G opher ▁ ▁Ex pl oration ▁of ▁Deep - Se a ▁H ydro ther mal ▁Vent ▁Micro b ial ▁Commun ities ▁using ▁the ▁Environment al ▁Sample ▁Process or ▁( ES P ) ▁▁ 2 0 1 1 ▁The ▁ 2 0 1 1 ▁projects ▁included : ▁Mars ▁M eth ane ▁Pl ume ▁T rac er ▁Planet ary ▁Lake ▁Land er ▁Sh allow - B ore hole ▁Array ▁for ▁Me as uring ▁Green land ▁Em ission ▁of ▁Trace ▁G ases ▁as ▁an ▁Anal og ue ▁for ▁M eth ane ▁on ▁Mars ▁( GET G AM M ) ▁VAL K Y RI E : ▁Ph ase ▁ 2 ▁Rob otic ▁Investig ation ▁of ▁Sub sur face ▁Life ▁in ▁the ▁At ac ama ▁Des ert ▁ ▁Other ▁projects ▁St rom atol ite ▁building ▁provides ▁important ▁ge ological ▁information ▁on ▁the ▁history ▁of ▁micro organ isms ▁d ating ▁back ▁to ▁over ▁a ▁billion ▁years ▁ago . ▁In ▁recent ▁years , ▁A ST EP ▁has ▁been ▁research ing ▁how ▁these ▁layer ed ▁foss ils ▁could ▁have ▁formed ▁by ▁studying ▁modern ▁day ▁micro b ial ▁m ats , ▁which ▁leave ▁st rom atol ite ▁similar ▁to ▁their ▁ancest ors . ▁ ▁A ST EP ' s ▁instrument ▁development ▁program ▁is |
▁currently ▁working ▁on ▁a ▁prototype ▁to ▁detect ▁the ▁presence ▁of ▁DNA ▁on ▁the ▁Mart ian ▁surface . ▁The ▁prototype ▁will ▁rep licate ▁any ▁DNA ▁found ▁in ▁Mart ian ▁ice ▁or ▁reg ol ith ▁using ▁polym er ase ▁chain ▁reaction ▁ampl ification ▁techniques . ▁ ▁The ▁Ice B ite ▁Project ▁involves ▁testing ▁dr ills ▁for ▁future ▁Mart ian ▁miss ions ▁where ▁ice ▁will ▁need ▁to ▁be ▁pen etr ated . ▁The ▁research ▁is ▁being ▁conducted ▁in ▁high ▁alt itude ▁Ant ar ctic ▁valle ys ▁which ▁closely ▁res emble ▁the ▁Phoenix ▁landing ▁site ▁in ▁ge olog ic ▁composition . ▁As ▁of ▁ 2 0 0 9 , ▁the ▁scient ists ▁have ▁successfully ▁completed ▁the ▁first ▁phase ▁of ▁the ▁three - year ▁mission , ▁which ▁was ▁to ▁pro be ▁the ▁region , ▁install ▁scientific ▁ ▁instruments , ▁and ▁determine ▁the ▁future ▁testing ▁sites . ▁ ▁A ▁team ▁of ▁A ST EP ▁scient ists ▁are ▁expl oring ▁the ▁Mid - C ay man ▁Sp reading ▁Center , ▁a ▁wide ▁r idge ▁at ▁the ▁western most ▁region ▁of ▁the ▁C ay man ▁Tr ough . ▁Ocean ic ▁life ▁reaches ▁the ▁extrem es ▁at ▁the ▁depth s , ▁where ▁the ▁pressure ▁is ▁the ▁greatest ▁and ▁under water ▁sea ▁v ents ▁p ump ▁hot ▁and ▁min eral - rich ▁water ▁into ▁the ▁ocean . ▁Project ▁research ers ▁think ▁extr ater rest rial ▁life ▁could ▁be ▁similar ▁to ▁the ▁ex otic ▁life ▁forms ▁found ▁near ▁these ▁v ents . ▁The ▁subm ers ible ▁N ere us ▁was ▁developed ▁by ▁A ST |
EP ▁to ▁autonom ously ▁survey ▁the ▁hydro ther mal ▁vent ▁systems ▁at ▁the ▁depth s ▁of ▁the ▁Mid - C ay man ▁Sp reading ▁Center . ▁ ▁Public ity ▁In ▁order ▁to ▁raise ▁aw aren ess ▁about ▁the ▁research ▁being ▁conducted ▁under ▁the ▁aus p ices ▁of ▁A ST EP , ▁scient ists ▁have ▁been ▁increasing ly ▁using ▁blog s ▁as ▁a ▁way ▁to ▁convey ▁information ▁about ▁their ▁studies , ▁typically ▁when ▁they ▁are ▁performing ▁science ▁at ▁a ▁remote ▁location ▁on ▁a ▁terrest rial ▁field ▁test . ▁Scient ists ▁have ▁also ▁begun ▁contact ing ▁museum s ▁via ▁satellite ▁u pl ink ▁to ▁discuss ▁ast ro bi ology ▁with ▁the ▁public . ▁The ▁most ▁prominent ▁blog ▁is ▁produced ▁by ▁NASA ' s ▁Ice B ite ▁team , ▁which ▁performs ▁annual ▁exped itions ▁to ▁Ant arct ica . ▁ ▁See ▁also ▁▁ ▁Ab i ogen esis ▁ ▁NASA ▁Ast ro bi ology ▁Institute ▁ ▁N ex us ▁for ▁Ex op lan et ▁System ▁Science ▁ ▁References ▁ ▁Works ▁c ited ▁Bill ings , ▁L . ▁( 2 0 0 8 , ▁ 0 1 ▁ 2 2 ). ▁About ▁A ST EP . ▁Retrieved ▁from ▁Ast ro bi ology : ▁https :// web . archive . org / web / 2 0 1 0 0 5 2 8 0 9 1 4 0 3 / http :// astro bi ology . n asa . gov / aste p / about / ▁Bill ings , ▁L . ▁( 2 0 0 8 , ▁ 0 2 |
▁ 0 6 ). ▁NASA ▁Ast ro bi ology ▁Road map ▁ 2 0 0 8 . ▁Retrieved ▁from ▁Ast ro bi ology : ▁https :// web . archive . org / web / 2 0 1 0 0 2 1 9 0 9 3 3 0 2 / http :// astro bi ology . n asa . gov / road map / ▁Com mod ore , ▁J . ▁( 2 0 1 0 , ▁ 0 2 ). ▁N RA ▁Pro pos ers ▁Guide book ▁- ▁Final . ▁Retrieved ▁from ▁NASA : ▁http :// www . h q . n asa . gov / office / proc ure ment / n rag u ide book / ▁German , ▁C . ▁( 2 0 0 9 , ▁ 1 0 ▁ 2 1 ). ▁Ast ro bi ology ▁Magazine . ▁Retrieved ▁from ▁O ases ▁for ▁Life ▁on ▁the ▁Mid - C ay men ▁R ise : ▁http :// www . ast rob io . net / press release / 3 2 8 7 / o ases - for - life - on - the - mid - c ay men - r ise ▁Mar in ova , ▁M . ▁( 2 0 1 0 , ▁ 0 2 ▁ 0 1 ). ▁Ast ro bi ology ▁Magazine . ▁Retrieved ▁from ▁Ice B ite ▁B log : ▁Say ing ▁F are well ▁to ▁a ▁Fro zen ▁World : ▁http :// www . ast rob io . net / index . php ? option = |
com _ exp ed ition & task = detail & id = 3 3 8 8 & type = blog & pid = 1 9 ▁Peter ▁Dor an , ▁P . ▁C . ▁( 2 0 1 0 ). ▁R ES ULT S ▁FROM ▁A ST EP ▁AND ▁O T HER ▁A ST RO B IO LOG Y ▁FI ELD ▁CA MP A IG NS ▁II . ▁Sch ir ber , ▁M . ▁( 2 0 1 0 , ▁ 0 1 ▁ 0 3 ). ▁Ast ro bi ology ▁Magazine . ▁Retrieved ▁from ▁First ▁F oss il - M akers ▁in ▁Hot ▁Water : ▁http :// www . ast rob io . net / ex clus ive / 3 4 1 8 / first - f oss il - m akers - in - hot - water ▁Sch ir ber , ▁M . ▁( 2 0 1 0 , ▁ 0 2 ▁ 1 5 ). ▁Ast ro bi ology ▁Magazine . ▁Retrieved ▁from ▁Det ect ing ▁Our ▁Mart ian ▁C ous ins : ▁http :// www . ast rob io . net / ex clus ive / 3 4 0 1 / det ect ing - our - mart ian - c ous ins ▁ ▁Category : A st ro bi ology ▁Category : N AS A ▁programs <0x0A> </s> ▁Rob ust ▁statistics ▁are ▁statistics ▁with ▁good ▁performance ▁for ▁data ▁drawn ▁from ▁a ▁wide ▁range ▁of ▁probability ▁distributions , ▁especially ▁for ▁distributions ▁that ▁are ▁not ▁normal . ▁ ▁Rob ust ▁statistical |
▁methods ▁have ▁been ▁developed ▁for ▁many ▁common ▁problems , ▁ ▁such ▁as ▁estim ating ▁location , ▁scale , ▁and ▁regression ▁parameters . ▁One ▁motiv ation ▁is ▁to ▁produce ▁statistical ▁methods ▁that ▁are ▁not ▁und uly ▁affected ▁by ▁out liers . ▁Another ▁motiv ation ▁is ▁to ▁ ▁provide ▁methods ▁with ▁good ▁performance ▁when ▁there ▁are ▁small ▁depart ures ▁from ▁paramet ric ▁distribution . ▁For ▁example , ▁robust ▁methods ▁work ▁well ▁for ▁mi xt ures ▁of ▁two ▁normal ▁distributions ▁with ▁different ▁standard - de vi ations ; ▁under ▁this ▁model , ▁non - rob ust ▁methods ▁like ▁a ▁t - test ▁work ▁poor ly . ▁ ▁Introduction ▁ ▁Rob ust ▁statistics ▁seek ▁to ▁provide ▁methods ▁that ▁em ulate ▁popular ▁statistical ▁methods , ▁but ▁which ▁are ▁not ▁und uly ▁affected ▁by ▁out liers ▁or ▁other ▁small ▁depart ures ▁from ▁model ▁assumptions . ▁ ▁In ▁statistics , ▁classical ▁estimation ▁methods ▁rely ▁heavily ▁on ▁assumptions ▁which ▁are ▁often ▁not ▁met ▁in ▁practice . ▁In ▁particular , ▁it ▁is ▁often ▁assumed ▁that ▁the ▁data ▁errors ▁are ▁normally ▁distributed , ▁at ▁least ▁approximately , ▁or ▁that ▁the ▁central ▁limit ▁theorem ▁can ▁be ▁re lied ▁on ▁to ▁produce ▁normally ▁distributed ▁estimates . ▁Unfortunately , ▁when ▁there ▁are ▁out liers ▁in ▁the ▁data , ▁classical ▁estim ators ▁often ▁have ▁very ▁poor ▁performance , ▁when ▁jud ged ▁using ▁the ▁break down ▁point ▁and ▁the ▁influence ▁function , ▁described ▁below . ▁ ▁The ▁practical ▁effect ▁of ▁problems ▁seen ▁in ▁the ▁influence ▁function ▁can ▁be ▁studied ▁empir ically ▁by ▁exam ining ▁the ▁sampling ▁distribution ▁of |
▁proposed ▁estim ators ▁under ▁a ▁mixture ▁model , ▁where ▁one ▁mix es ▁in ▁a ▁small ▁amount ▁( 1 – 5 % ▁is ▁often ▁sufficient ) ▁of ▁cont am ination . ▁For ▁instance , ▁one ▁may ▁use ▁a ▁mixture ▁of ▁ 9 5 % ▁a ▁normal ▁distribution , ▁and ▁ 5 % ▁a ▁normal ▁distribution ▁with ▁the ▁same ▁mean ▁but ▁significantly ▁higher ▁standard ▁deviation ▁( re present ing ▁out liers ). ▁ ▁Rob ust ▁paramet ric ▁statistics ▁can ▁proceed ▁in ▁two ▁ways : ▁by ▁design ing ▁estim ators ▁so ▁that ▁a ▁pre - selected ▁behaviour ▁of ▁the ▁influence ▁function ▁is ▁achieved ▁ ▁by ▁replacing ▁estim ators ▁that ▁are ▁optimal ▁under ▁the ▁assumption ▁of ▁a ▁normal ▁distribution ▁with ▁estim ators ▁that ▁are ▁optimal ▁for , ▁or ▁at ▁least ▁derived ▁for , ▁other ▁distributions : ▁for ▁example ▁using ▁the ▁t - distribution ▁with ▁low ▁degrees ▁of ▁freedom ▁( high ▁k urt osis ; ▁ ▁degrees ▁of ▁freedom ▁between ▁ 4 ▁and ▁ 6 ▁have ▁often ▁been ▁found ▁to ▁be ▁useful ▁in ▁practice ▁) ▁or ▁with ▁a ▁mixture ▁of ▁two ▁or ▁more ▁distributions . ▁ ▁Rob ust ▁estimates ▁have ▁been ▁studied ▁for ▁the ▁following ▁problems : ▁estim ating ▁location ▁parameters ▁ ▁estim ating ▁scale ▁parameters ▁ ▁estim ating ▁regression ▁coefficients ▁ ▁estimation ▁of ▁model - states ▁in ▁models ▁expressed ▁in ▁state - space ▁form , ▁for ▁which ▁the ▁standard ▁method ▁is ▁equivalent ▁to ▁a ▁Kal man ▁filter . ▁ ▁Definition ▁▁ ▁There ▁are ▁various ▁definitions ▁of ▁a ▁" rob ust ▁stat istic ." ▁Str ict ly ▁speaking |
, ▁a ▁robust ▁stat istic ▁is ▁resist ant ▁to ▁errors ▁in ▁the ▁results , ▁produced ▁by ▁devi ations ▁from ▁assumptions ▁( e . g ., ▁of ▁normal ity ). ▁This ▁means ▁that ▁if ▁the ▁assumptions ▁are ▁only ▁approximately ▁met , ▁the ▁robust ▁estim ator ▁will ▁still ▁have ▁a ▁reasonable ▁efficiency , ▁and ▁reason ably ▁small ▁bias , ▁as ▁well ▁as ▁being ▁asympt ot ically ▁un bi ased , ▁meaning ▁having ▁a ▁bias ▁t ending ▁towards ▁ 0 ▁as ▁the ▁sample ▁size ▁tends ▁towards ▁infinity . ▁ ▁One ▁of ▁the ▁most ▁important ▁cases ▁is ▁distribution al ▁robust ness . ▁Class ical ▁statistical ▁procedures ▁are ▁typically ▁sensitive ▁to ▁" long ta iled ness " ▁( e . g ., ▁when ▁the ▁distribution ▁of ▁the ▁data ▁has ▁longer ▁t ails ▁than ▁the ▁assumed ▁normal ▁distribution ). ▁This ▁implies ▁that ▁they ▁will ▁be ▁strongly ▁affected ▁by ▁the ▁presence ▁of ▁out liers ▁in ▁the ▁data , ▁and ▁the ▁estimates ▁they ▁produce ▁may ▁be ▁heavily ▁dist orted ▁if ▁there ▁are ▁extreme ▁out liers ▁in ▁the ▁data , ▁compared ▁to ▁what ▁they ▁would ▁be ▁if ▁the ▁out liers ▁were ▁not ▁included ▁in ▁the ▁data . ▁ ▁By ▁contrast , ▁more ▁robust ▁estim ators ▁that ▁are ▁not ▁so ▁sensitive ▁to ▁distribution al ▁dist ort ions ▁such ▁as ▁long ta iled ness ▁are ▁also ▁resist ant ▁to ▁the ▁presence ▁of ▁out liers . ▁Thus , ▁in ▁the ▁context ▁of ▁robust ▁statistics , ▁distribution ally ▁robust ▁and ▁out lier - res istant ▁are ▁effectively ▁syn onymous . ▁ ▁For ▁one ▁perspective ▁on ▁research |
▁in ▁robust ▁statistics ▁up ▁to ▁ 2 0 0 0 , ▁see ▁. ▁ ▁A ▁related ▁topic ▁is ▁that ▁of ▁resist ant ▁statistics , ▁which ▁are ▁resist ant ▁to ▁the ▁effect ▁of ▁extreme ▁scores . ▁ ▁When ▁considering ▁how ▁robust ▁an ▁estim ator ▁is ▁to ▁the ▁presence ▁of ▁out liers , ▁it ▁is ▁useful ▁to ▁test ▁what ▁happens ▁when ▁an ▁extreme ▁out lier ▁is ▁added ▁to ▁the ▁dataset , ▁and ▁to ▁test ▁what ▁happens ▁when ▁an ▁extreme ▁out lier ▁rep laces ▁one ▁of ▁the ▁existing ▁dat ap oint s , ▁and ▁then ▁to ▁consider ▁the ▁effect ▁of ▁multiple ▁add itions ▁or ▁rep lac ements . ▁ ▁Ex amples ▁ ▁The ▁mean ▁is ▁not ▁a ▁robust ▁measure ▁of ▁central ▁t endency . ▁If ▁the ▁dataset ▁is ▁e . g . ▁the ▁values ▁{ 2 , 3 , 5 , 6 , 9 }, ▁then ▁if ▁we ▁add ▁another ▁dat ap oint ▁with ▁value ▁- 1 0 0 0 ▁or ▁+ 1 0 0 0 ▁to ▁the ▁data , ▁the ▁resulting ▁mean ▁will ▁be ▁very ▁different ▁to ▁the ▁mean ▁of ▁the ▁original ▁data . ▁Similarly , ▁if ▁we ▁replace ▁one ▁of ▁the ▁values ▁with ▁a ▁dat ap oint ▁of ▁value ▁- 1 0 0 0 ▁or ▁+ 1 0 0 0 ▁then ▁the ▁resulting ▁mean ▁will ▁be ▁very ▁different ▁to ▁the ▁mean ▁of ▁the ▁original ▁data . ▁ ▁The ▁median ▁is ▁a ▁robust ▁measure ▁of ▁central ▁t endency . ▁T aking ▁the ▁same ▁dataset ▁{ 2 , 3 , 5 , 6 , 9 }, |
▁if ▁we ▁add ▁another ▁dat ap oint ▁with ▁value ▁- 1 0 0 0 ▁or ▁+ 1 0 0 0 ▁then ▁the ▁median ▁will ▁change ▁slightly , ▁but ▁it ▁will ▁still ▁be ▁similar ▁to ▁the ▁median ▁of ▁the ▁original ▁data . ▁If ▁we ▁replace ▁one ▁of ▁the ▁values ▁with ▁a ▁dat ap oint ▁of ▁value ▁- 1 0 0 0 ▁or ▁+ 1 0 0 0 ▁then ▁the ▁resulting ▁median ▁will ▁still ▁be ▁similar ▁to ▁the ▁median ▁of ▁the ▁original ▁data . ▁ ▁Descri bed ▁in ▁terms ▁of ▁break down ▁points , ▁the ▁median ▁has ▁a ▁break down ▁point ▁of ▁ 5 0 %, ▁while ▁the ▁mean ▁has ▁a ▁break down ▁point ▁of ▁ 1 / N , ▁where ▁N ▁is ▁the ▁number ▁of ▁original ▁dat ap oint s ▁( a ▁single ▁large ▁observation ▁can ▁throw ▁it ▁off ). ▁ ▁The ▁median ▁absolute ▁deviation ▁and ▁inter qu art ile ▁range ▁are ▁robust ▁measures ▁of ▁statistical ▁disp ersion , ▁while ▁the ▁standard ▁deviation ▁and ▁range ▁are ▁not . ▁ ▁Tr im med ▁estim ators ▁and ▁W ins or ised ▁estim ators ▁are ▁general ▁methods ▁to ▁make ▁statistics ▁more ▁robust . ▁L - est im ators ▁are ▁a ▁general ▁class ▁of ▁simple ▁statistics , ▁often ▁robust , ▁while ▁M - est im ators ▁are ▁a ▁general ▁class ▁of ▁robust ▁statistics , ▁and ▁are ▁now ▁the ▁preferred ▁solution , ▁though ▁they ▁can ▁be ▁quite ▁involved ▁to ▁calculate . ▁ ▁Example : ▁speed - of - light ▁data ▁ ▁Gel man ▁et ▁al . ▁in ▁Bay esian |
▁Data ▁Analysis ▁( 2 0 0 4 ) ▁consider ▁a ▁data ▁set ▁rel ating ▁to ▁speed - of - light ▁measurements ▁made ▁by ▁Simon ▁New comb . ▁The ▁data ▁sets ▁for ▁that ▁book ▁can ▁be ▁found ▁via ▁the ▁Classic ▁data ▁sets ▁page , ▁and ▁the ▁book ' s ▁website ▁contains ▁more ▁information ▁on ▁the ▁data . ▁ ▁Although ▁the ▁bulk ▁of ▁the ▁data ▁look ▁to ▁be ▁more ▁or ▁less ▁normally ▁distributed , ▁there ▁are ▁two ▁obvious ▁out liers . ▁These ▁out liers ▁have ▁a ▁large ▁effect ▁on ▁the ▁mean , ▁drag ging ▁it ▁towards ▁them , ▁and ▁away ▁from ▁the ▁center ▁of ▁the ▁bulk ▁of ▁the ▁data . ▁Thus , ▁if ▁the ▁mean ▁is ▁intended ▁as ▁a ▁measure ▁of ▁the ▁location ▁of ▁the ▁center ▁of ▁the ▁data , ▁it ▁is , ▁in ▁a ▁sense , ▁bi ased ▁when ▁out liers ▁are ▁present . ▁ ▁Also , ▁the ▁distribution ▁of ▁the ▁mean ▁is ▁known ▁to ▁be ▁asympt ot ically ▁normal ▁due ▁to ▁the ▁central ▁limit ▁theorem . ▁However , ▁out liers ▁can ▁make ▁the ▁distribution ▁of ▁the ▁mean ▁non - normal ▁even ▁for ▁fairly ▁large ▁data ▁sets . ▁Besides ▁this ▁non - normal ity , ▁the ▁mean ▁is ▁also ▁in efficient ▁in ▁the ▁presence ▁of ▁out liers ▁and ▁less ▁variable ▁measures ▁of ▁location ▁are ▁available . ▁ ▁Est imation ▁of ▁location ▁ ▁The ▁plot ▁below ▁shows ▁a ▁density ▁plot ▁of ▁the ▁speed - of - light ▁data , ▁together ▁with ▁a ▁rug ▁plot ▁( panel ▁( a )). ▁Also ▁shown ▁is ▁a ▁normal ▁Q |
– Q ▁plot ▁( panel ▁( b )). ▁The ▁out liers ▁are ▁clearly ▁visible ▁in ▁these ▁plots . ▁ ▁Pan els ▁( c ) ▁and ▁( d ) ▁of ▁the ▁plot ▁show ▁the ▁bootstrap ▁distribution ▁of ▁the ▁mean ▁( c ) ▁and ▁the ▁ 1 0 % ▁trim med ▁mean ▁( d ). ▁The ▁trim med ▁mean ▁is ▁a ▁simple ▁robust ▁estim ator ▁of ▁location ▁that ▁delet es ▁a ▁certain ▁percentage ▁of ▁observations ▁( 1 0 % ▁here ) ▁from ▁each ▁end ▁of ▁the ▁data , ▁then ▁comput es ▁the ▁mean ▁in ▁the ▁usual ▁way . ▁The ▁analysis ▁was ▁performed ▁in ▁R ▁and ▁ 1 0 , 0 0 0 ▁bootstrap ▁samples ▁were ▁used ▁for ▁each ▁of ▁the ▁raw ▁and ▁trim med ▁means . ▁ ▁The ▁distribution ▁of ▁the ▁mean ▁is ▁clearly ▁much ▁wider ▁than ▁that ▁of ▁the ▁ 1 0 % ▁trim med ▁mean ▁( the ▁plots ▁are ▁on ▁the ▁same ▁scale ). ▁Also ▁whereas ▁the ▁distribution ▁of ▁the ▁trim med ▁mean ▁appears ▁to ▁be ▁close ▁to ▁normal , ▁the ▁distribution ▁of ▁the ▁raw ▁mean ▁is ▁quite ▁ske wed ▁to ▁the ▁left . ▁So , ▁in ▁this ▁sample ▁of ▁ 6 6 ▁observations , ▁only ▁ 2 ▁out liers ▁cause ▁the ▁central ▁limit ▁theorem ▁to ▁be ▁in app lic able . ▁ ▁Rob ust ▁statistical ▁methods , ▁of ▁which ▁the ▁trim med ▁mean ▁is ▁a ▁simple ▁example , ▁seek ▁to ▁out perform ▁classical ▁statistical ▁methods ▁in ▁the ▁presence ▁of ▁out liers , ▁or , ▁more ▁generally , ▁when ▁underlying ▁paramet ric ▁assumptions |
▁are ▁not ▁quite ▁correct . ▁ ▁Wh ilst ▁the ▁trim med ▁mean ▁performs ▁well ▁relative ▁to ▁the ▁mean ▁in ▁this ▁example , ▁better ▁robust ▁estimates ▁are ▁available . ▁In ▁fact , ▁the ▁mean , ▁median ▁and ▁trim med ▁mean ▁are ▁all ▁special ▁cases ▁of ▁M - est im ators . ▁Details ▁appear ▁in ▁the ▁sections ▁below . ▁ ▁Est imation ▁of ▁scale ▁▁ ▁The ▁out liers ▁in ▁the ▁speed - of - light ▁data ▁have ▁more ▁than ▁just ▁an ▁ad verse ▁effect ▁on ▁the ▁mean ; ▁the ▁usual ▁estimate ▁of ▁scale ▁is ▁the ▁standard ▁deviation , ▁and ▁this ▁quantity ▁is ▁even ▁more ▁badly ▁affected ▁by ▁out liers ▁because ▁the ▁squares ▁of ▁the ▁devi ations ▁from ▁the ▁mean ▁go ▁into ▁the ▁calculation , ▁so ▁the ▁out liers ' ▁effects ▁are ▁ex ac erb ated . ▁ ▁The ▁plots ▁below ▁show ▁the ▁bootstrap ▁distributions ▁of ▁the ▁standard ▁deviation , ▁the ▁median ▁absolute ▁deviation ▁( MA D ) ▁and ▁the ▁R ous see uw – Cr oux ▁( Q n ) ▁estim ator ▁of ▁scale . ▁The ▁plots ▁are ▁based ▁on ▁ 1 0 , 0 0 0 ▁bootstrap ▁samples ▁for ▁each ▁estim ator , ▁with ▁some ▁Gaussian ▁noise ▁added ▁to ▁the ▁res ample d ▁data ▁( sm ooth ed ▁bootstrap ). ▁P anel ▁( a ) ▁shows ▁the ▁distribution ▁of ▁the ▁standard ▁deviation , ▁( b ) ▁of ▁the ▁M AD ▁and ▁( c ) ▁of ▁Q n . ▁ ▁The ▁distribution ▁of ▁standard ▁deviation ▁is ▁err atic ▁and ▁wide , ▁a ▁result ▁of ▁the |
▁out liers . ▁The ▁M AD ▁is ▁better ▁behav ed , ▁and ▁Q n ▁is ▁a ▁little ▁bit ▁more ▁efficient ▁than ▁M AD . ▁This ▁simple ▁example ▁demonstr ates ▁that ▁when ▁out liers ▁are ▁present , ▁the ▁standard ▁deviation ▁cannot ▁be ▁recommended ▁as ▁an ▁estimate ▁of ▁scale . ▁ ▁Man ual ▁screen ing ▁for ▁out liers ▁ ▁Trad itionally , ▁statist icians ▁would ▁manually ▁screen ▁data ▁for ▁out liers , ▁and ▁remove ▁them , ▁usually ▁checking ▁the ▁source ▁of ▁the ▁data ▁to ▁see ▁whether ▁the ▁out liers ▁were ▁err one ously ▁recorded . ▁Indeed , ▁in ▁the ▁speed - of - light ▁example ▁above , ▁it ▁is ▁easy ▁to ▁see ▁and ▁remove ▁the ▁two ▁out liers ▁prior ▁to ▁proceed ing ▁with ▁any ▁further ▁analysis . ▁However , ▁in ▁modern ▁times , ▁data ▁sets ▁often ▁consist ▁of ▁large ▁numbers ▁of ▁variables ▁being ▁measured ▁on ▁large ▁numbers ▁of ▁experimental ▁units . ▁Therefore , ▁manual ▁screen ing ▁for ▁out liers ▁is ▁often ▁imp ract ical . ▁ ▁Out liers ▁can ▁often ▁interact ▁in ▁such ▁a ▁way ▁that ▁they ▁mask ▁each ▁other . ▁As ▁a ▁simple ▁example , ▁consider ▁ ▁a ▁small ▁un ivari ate ▁data ▁set ▁containing ▁one ▁mod est ▁and ▁one ▁large ▁out lier . ▁The ▁estimated ▁standard ▁deviation ▁will ▁be ▁gross ly ▁infl ated ▁by ▁the ▁large ▁out lier . ▁The ▁result ▁is ▁that ▁the ▁mod est ▁out lier ▁looks ▁relatively ▁normal . ▁As ▁soon ▁as ▁the ▁large ▁out lier ▁is ▁removed , ▁the ▁estimated ▁standard ▁deviation ▁shr inks , ▁and ▁the ▁mod est |
▁out lier ▁now ▁looks ▁unusual . ▁ ▁This ▁problem ▁of ▁mask ing ▁gets ▁worse ▁as ▁the ▁complexity ▁of ▁the ▁data ▁increases . ▁For ▁example , ▁in ▁regression ▁problems , ▁di agnostic ▁plots ▁are ▁used ▁to ▁identify ▁out liers . ▁However , ▁it ▁is ▁common ▁that ▁once ▁a ▁few ▁out liers ▁have ▁been ▁removed , ▁others ▁become ▁visible . ▁The ▁problem ▁is ▁even ▁worse ▁in ▁higher ▁dimensions . ▁ ▁Rob ust ▁methods ▁provide ▁automatic ▁ways ▁of ▁detect ing , ▁down weight ing ▁( or ▁removing ), ▁and ▁flag ging ▁out liers , ▁largely ▁removing ▁the ▁need ▁for ▁manual ▁screen ing . ▁▁ ▁Care ▁must ▁be ▁taken ; ▁initial ▁data ▁showing ▁the ▁o zone ▁hole ▁first ▁appearing ▁over ▁Ant arct ica ▁were ▁rejected ▁as ▁out liers ▁by ▁non - human ▁screen ing . ▁ ▁Vari ety ▁of ▁applications ▁ ▁Although ▁this ▁article ▁de als ▁with ▁general ▁principles ▁for ▁un ivari ate ▁statistical ▁methods , ▁robust ▁methods ▁also ▁exist ▁for ▁regression ▁problems , ▁generalized ▁linear ▁models , ▁and ▁parameter ▁estimation ▁of ▁various ▁distributions . ▁ ▁Me asures ▁of ▁robust ness ▁ ▁The ▁basic ▁tools ▁used ▁to ▁describe ▁and ▁measure ▁robust ness ▁are , ▁the ▁break down ▁point , ▁the ▁influence ▁function ▁and ▁the ▁sens itivity ▁curve . ▁ ▁Break down ▁point ▁ ▁Int uit ively , ▁the ▁break down ▁point ▁of ▁an ▁estim ator ▁is ▁the ▁proportion ▁of ▁incorrect ▁observations ▁( e . g . ▁arbitr arily ▁large ▁observations ) ▁an ▁estim ator ▁can ▁handle ▁before ▁giving ▁an ▁incorrect ▁( e . g ., ▁arbitr arily |
▁large ) ▁result . ▁For ▁example , ▁given ▁ ▁independent ▁random ▁variables ▁ ▁and ▁the ▁corresponding ▁realiz ations ▁, ▁we ▁can ▁use ▁ ▁to ▁estimate ▁the ▁mean . ▁Such ▁an ▁estim ator ▁has ▁a ▁break down ▁point ▁of ▁ 0 ▁because ▁we ▁can ▁make ▁ ▁arbitr arily ▁large ▁just ▁by ▁changing ▁any ▁of ▁ ▁. ▁ ▁The ▁higher ▁the ▁break down ▁point ▁of ▁an ▁estim ator , ▁the ▁more ▁robust ▁it ▁is . ▁Int uit ively , ▁we ▁can ▁understand ▁that ▁a ▁break down ▁point ▁cannot ▁exceed ▁ 5 0 % ▁because ▁if ▁more ▁than ▁half ▁of ▁the ▁observations ▁are ▁cont amin ated , ▁it ▁is ▁not ▁possible ▁to ▁distinguish ▁between ▁the ▁underlying ▁distribution ▁and ▁the ▁cont amin ating ▁distribution ▁. ▁Therefore , ▁the ▁maximum ▁break down ▁point ▁is ▁ 0 . 5 ▁and ▁there ▁are ▁estim ators ▁which ▁achieve ▁such ▁a ▁break down ▁point . ▁For ▁example , ▁the ▁median ▁has ▁a ▁break down ▁point ▁of ▁ 0 . 5 . ▁The ▁X % ▁trim med ▁mean ▁has ▁break down ▁point ▁of ▁X %, ▁for ▁the ▁chosen ▁level ▁of ▁X . ▁ ▁and ▁ ▁contain ▁more ▁details . ▁The ▁level ▁and ▁the ▁power ▁break down ▁points ▁of ▁tests ▁are ▁investig ated ▁in ▁. ▁ ▁Statistics ▁with ▁high ▁break down ▁points ▁are ▁sometimes ▁called ▁resist ant ▁statistics . ▁ ▁Example : ▁speed - of - light ▁data ▁ ▁In ▁the ▁speed - of - light ▁example , ▁removing ▁the ▁two ▁lowest ▁observations ▁causes ▁the ▁mean ▁to ▁change ▁from ▁ 2 6 . 2 |
▁to ▁ 2 7 . 7 5 , ▁a ▁change ▁of ▁ 1 . 5 5 . ▁The ▁estimate ▁of ▁scale ▁produced ▁by ▁the ▁Q n ▁method ▁is ▁ 6 . 3 . ▁We ▁can ▁divide ▁this ▁by ▁the ▁square ▁root ▁of ▁the ▁sample ▁size ▁to ▁get ▁a ▁robust ▁standard ▁error , ▁and ▁we ▁find ▁this ▁quantity ▁to ▁be ▁ 0 . 7 8 . ▁Thus , ▁the ▁change ▁in ▁the ▁mean ▁resulting ▁from ▁removing ▁two ▁out liers ▁is ▁approximately ▁twice ▁the ▁robust ▁standard ▁error . ▁ ▁The ▁ 1 0 % ▁trim med ▁mean ▁for ▁the ▁speed - of - light ▁data ▁is ▁ 2 7 . 4 3 . ▁Rem oving ▁the ▁two ▁lowest ▁observations ▁and ▁re comput ing ▁gives ▁ 2 7 . 6 7 . ▁Clear ly , ▁the ▁trim med ▁mean ▁is ▁less ▁affected ▁by ▁the ▁out liers ▁and ▁has ▁a ▁higher ▁break down ▁point . ▁ ▁If ▁we ▁replace ▁the ▁lowest ▁observation , ▁− 4 4 , ▁by ▁− 1 0 0 0 , ▁the ▁mean ▁becomes ▁ 1 1 . 7 3 , ▁whereas ▁the ▁ 1 0 % ▁trim med ▁mean ▁is ▁still ▁ 2 7 . 4 3 . ▁In ▁many ▁areas ▁of ▁applied ▁statistics , ▁it ▁is ▁common ▁for ▁data ▁to ▁be ▁log - transform ed ▁to ▁make ▁them ▁near ▁symmet rical . ▁Very ▁small ▁values ▁become ▁large ▁negative ▁when ▁log - transform ed , ▁and ▁zero es ▁become ▁neg atively ▁infinite . ▁Therefore , ▁this ▁example ▁is ▁of ▁practical ▁interest . ▁ |
▁Emp ir ical ▁influence ▁function ▁▁▁▁▁ ▁The ▁empir ical ▁influence ▁function ▁is ▁a ▁measure ▁of ▁the ▁dependence ▁of ▁the ▁estim ator ▁on ▁the ▁value ▁of ▁one ▁of ▁the ▁points ▁in ▁the ▁sample . ▁It ▁is ▁a ▁model - free ▁measure ▁in ▁the ▁sense ▁that ▁it ▁simply ▁re lies ▁on ▁calculating ▁the ▁estim ator ▁again ▁with ▁a ▁different ▁sample . ▁On ▁the ▁right ▁is ▁Tu key ' s ▁bi weight ▁function , ▁which , ▁as ▁we ▁will ▁later ▁see , ▁is ▁an ▁example ▁of ▁what ▁a ▁" good " ▁( in ▁a ▁sense ▁defined ▁later ▁on ) ▁empir ical ▁influence ▁function ▁should ▁look ▁like . ▁ ▁In ▁mathematical ▁terms , ▁an ▁influence ▁function ▁is ▁defined ▁as ▁a ▁vector ▁in ▁the ▁space ▁of ▁the ▁estim ator , ▁which ▁is ▁in ▁turn ▁defined ▁for ▁a ▁sample ▁which ▁is ▁a ▁subset ▁of ▁the ▁population : ▁▁ ▁is ▁a ▁probability ▁space , ▁ ▁is ▁a ▁measure ▁space ▁( state ▁space ), ▁ ▁is ▁a ▁parameter ▁space ▁of ▁dimension ▁, ▁ ▁is ▁a ▁measure ▁space , ▁ ▁For ▁example , ▁▁ ▁is ▁any ▁probability ▁space , ▁, ▁ ▁, ▁ ▁The ▁definition ▁of ▁an ▁empir ical ▁influence ▁function ▁is : ▁Let ▁ ▁and ▁ ▁are ▁i . i . d . ▁and ▁ ▁is ▁a ▁sample ▁from ▁these ▁variables . ▁ ▁is ▁an ▁estim ator . ▁Let ▁. ▁The ▁empir ical ▁influence ▁function ▁ ▁at ▁observation ▁ ▁is ▁defined ▁by : ▁▁▁▁ ▁What ▁this ▁actually ▁means ▁is ▁that ▁we ▁are ▁replacing ▁the ▁i - th ▁value ▁in ▁the ▁sample |
▁by ▁an ▁arbitrary ▁value ▁and ▁looking ▁at ▁the ▁output ▁of ▁the ▁estim ator . ▁Alternatively , ▁the ▁E IF ▁is ▁defined ▁as ▁the ▁( scale d ▁by ▁n + 1 ▁instead ▁of ▁n ) ▁effect ▁on ▁the ▁estim ator ▁of ▁adding ▁the ▁point ▁ ▁to ▁the ▁sample . ▁ ▁In flu ence ▁function ▁and ▁sens itivity ▁curve ▁ ▁Instead ▁of ▁re lying ▁sole ly ▁on ▁the ▁data , ▁we ▁could ▁use ▁the ▁distribution ▁of ▁the ▁random ▁variables . ▁The ▁approach ▁is ▁quite ▁different ▁from ▁that ▁of ▁the ▁previous ▁paragraph . ▁What ▁we ▁are ▁now ▁trying ▁to ▁do ▁is ▁to ▁see ▁what ▁happens ▁to ▁an ▁estim ator ▁when ▁we ▁change ▁the ▁distribution ▁of ▁the ▁data ▁slightly : ▁it ▁assumes ▁a ▁distribution , ▁and ▁measures ▁sens itivity ▁to ▁change ▁in ▁this ▁distribution . ▁By ▁contrast , ▁the ▁empir ical ▁influence ▁assumes ▁a ▁sample ▁set , ▁and ▁measures ▁sens itivity ▁to ▁change ▁in ▁the ▁samples . ▁ ▁Let ▁ ▁be ▁a ▁convex ▁subset ▁of ▁the ▁set ▁of ▁all ▁finite ▁signed ▁measures ▁on ▁. ▁We ▁want ▁to ▁estimate ▁the ▁parameter ▁ ▁of ▁a ▁distribution ▁ ▁in ▁. ▁Let ▁the ▁functional ▁ ▁be ▁the ▁asympt otic ▁value ▁of ▁some ▁estim ator ▁sequence ▁. ▁We ▁will ▁suppose ▁that ▁this ▁functional ▁is ▁Fish er ▁consistent , ▁i . e . ▁. ▁This ▁means ▁that ▁at ▁the ▁model ▁, ▁the ▁estim ator ▁sequence ▁asympt ot ically ▁measures ▁the ▁correct ▁quantity . ▁ ▁Let ▁ ▁be ▁some ▁distribution ▁in ▁. ▁What ▁happens ▁when ▁the ▁data ▁doesn ' t ▁follow ▁the ▁model ▁ |
▁exactly ▁but ▁another , ▁slightly ▁different , ▁" going ▁towards " ▁? ▁ ▁We ' re ▁looking ▁at : ▁, ▁ ▁which ▁is ▁the ▁one - s ided ▁Gate aux ▁derivative ▁of ▁ ▁at ▁, ▁in ▁the ▁direction ▁of ▁. ▁ ▁Let ▁. ▁ ▁is ▁the ▁probability ▁measure ▁which ▁gives ▁mass ▁ 1 ▁to ▁. ▁We ▁choose ▁. ▁The ▁influence ▁function ▁is ▁then ▁defined ▁by : ▁ ▁It ▁describes ▁the ▁effect ▁of ▁an ▁inf init es imal ▁cont am ination ▁at ▁the ▁point ▁ ▁on ▁the ▁estimate ▁we ▁are ▁seeking , ▁standard ized ▁by ▁the ▁mass ▁ ▁of ▁the ▁cont am ination ▁( the ▁asympt otic ▁bias ▁caused ▁by ▁cont am ination ▁in ▁the ▁observations ). ▁For ▁a ▁robust ▁estim ator , ▁we ▁want ▁a ▁bounded ▁influence ▁function , ▁that ▁is , ▁one ▁which ▁does ▁not ▁go ▁to ▁infinity ▁as ▁x ▁becomes ▁arbitr arily ▁large . ▁ ▁Des irable ▁properties ▁▁ ▁Properties ▁of ▁an ▁influence ▁function ▁which ▁best ow ▁it ▁with ▁des irable ▁performance ▁are : ▁Fin ite ▁re jection ▁point ▁, ▁Small ▁gross - error ▁sens itivity ▁, ▁Small ▁local - shift ▁sens itivity ▁. ▁ ▁Re jection ▁point ▁ ▁G ross - error ▁sens itivity ▁ ▁Local - shift ▁sens itivity ▁▁ ▁This ▁value , ▁which ▁looks ▁a ▁lot ▁like ▁a ▁Li ps ch itz ▁constant , ▁represents ▁the ▁effect ▁of ▁sh ifting ▁an ▁observation ▁slightly ▁from ▁ ▁to ▁a ▁neighbour ing ▁point ▁, ▁i . e ., ▁add ▁an ▁observation ▁at ▁ ▁and ▁remove ▁one ▁at ▁. ▁ ▁M - |
est im ators ▁▁ ▁( The ▁mathematical ▁context ▁of ▁this ▁paragraph ▁is ▁given ▁in ▁the ▁section ▁on ▁empir ical ▁influence ▁functions .) ▁ ▁Histor ically , ▁several ▁approaches ▁to ▁robust ▁estimation ▁were ▁proposed , ▁including ▁R - est im ators ▁and ▁L - est im ators . ▁However , ▁M - est im ators ▁now ▁appear ▁to ▁domin ate ▁the ▁field ▁as ▁a ▁result ▁of ▁their ▁general ity , ▁high ▁break down ▁point , ▁and ▁their ▁efficiency . ▁See ▁. ▁ ▁M - est im ators ▁are ▁a ▁general ization ▁of ▁maximum ▁lik elihood ▁estim ators ▁( M LE s ). ▁What ▁we ▁try ▁to ▁do ▁with ▁M LE ' s ▁is ▁to ▁maxim ize ▁ ▁or , ▁equival ently , ▁minim ize ▁. ▁In ▁ 1 9 6 4 , ▁H uber ▁proposed ▁to ▁general ize ▁this ▁to ▁the ▁minim ization ▁of ▁, ▁where ▁ ▁is ▁some ▁function . ▁M LE ▁are ▁therefore ▁a ▁special ▁case ▁of ▁M - est im ators ▁( h ence ▁the ▁name : ▁" Max imum ▁lik elihood ▁type " ▁estim ators ). ▁ ▁Min im izing ▁ ▁can ▁often ▁be ▁done ▁by ▁differenti ating ▁ ▁and ▁solving ▁, ▁where ▁ ▁( if ▁ ▁has ▁a ▁derivative ). ▁ ▁Several ▁choices ▁of ▁ ▁and ▁ ▁have ▁been ▁proposed . ▁The ▁two ▁figures ▁below ▁show ▁four ▁ ▁functions ▁and ▁their ▁corresponding ▁ ▁functions . ▁ ▁For ▁squ ared ▁errors , ▁ ▁increases ▁at ▁an ▁acceler ating ▁rate , ▁whilst ▁for ▁absolute ▁errors , ▁it ▁increases ▁at ▁a ▁constant |
▁rate . ▁When ▁W ins or izing ▁is ▁used , ▁a ▁mixture ▁of ▁these ▁two ▁effects ▁is ▁introduced : ▁for ▁small ▁values ▁of ▁x , ▁ ▁increases ▁at ▁the ▁squ ared ▁rate , ▁but ▁once ▁the ▁chosen ▁threshold ▁is ▁reached ▁( 1 . 5 ▁in ▁this ▁example ), ▁the ▁rate ▁of ▁increase ▁becomes ▁constant . ▁ ▁This ▁W ins or ised ▁estim ator ▁is ▁also ▁known ▁as ▁the ▁H uber ▁loss ▁function . ▁ ▁Tu key ' s ▁bi weight ▁( also ▁known ▁as ▁bis quare ) ▁function ▁behav es ▁in ▁a ▁similar ▁way ▁to ▁the ▁squ ared ▁error ▁function ▁at ▁first , ▁but ▁for ▁larger ▁errors , ▁the ▁function ▁tap ers ▁off . ▁ ▁Properties ▁of ▁M - est im ators ▁ ▁M - est im ators ▁do ▁not ▁necessarily ▁relate ▁to ▁a ▁probability ▁density ▁function . ▁Therefore , ▁off - the - sh elf ▁approaches ▁to ▁inference ▁that ▁arise ▁from ▁lik elihood ▁theory ▁can ▁not , ▁in ▁general , ▁be ▁used . ▁ ▁It ▁can ▁be ▁shown ▁that ▁M - est im ators ▁are ▁asympt ot ically ▁normally ▁distributed , ▁so ▁that ▁as ▁long ▁as ▁their ▁standard ▁errors ▁can ▁be ▁computed , ▁an ▁approximate ▁approach ▁to ▁inference ▁is ▁available . ▁ ▁Since ▁M - est im ators ▁are ▁normal ▁only ▁asympt ot ically , ▁for ▁small ▁sample ▁sizes ▁it ▁might ▁be ▁appropriate ▁to ▁use ▁an ▁alternative ▁approach ▁to ▁inference , ▁such ▁as ▁the ▁bootstrap . ▁However , ▁M - est im ates ▁are ▁not ▁necessarily ▁unique ▁( i . e ., |
▁there ▁might ▁be ▁more ▁than ▁one ▁solution ▁that ▁satisfies ▁the ▁equations ). ▁Also , ▁it ▁is ▁possible ▁that ▁any ▁particular ▁bootstrap ▁sample ▁can ▁contain ▁more ▁out liers ▁than ▁the ▁estim ator ' s ▁break down ▁point . ▁Therefore , ▁some ▁care ▁is ▁needed ▁when ▁design ing ▁bootstrap ▁schemes . ▁ ▁Of ▁course , ▁as ▁we ▁saw ▁with ▁the ▁speed - of - light ▁example , ▁the ▁mean ▁is ▁only ▁normally ▁distributed ▁asympt ot ically ▁and ▁when ▁out liers ▁are ▁present ▁the ▁approximation ▁can ▁be ▁very ▁poor ▁even ▁for ▁quite ▁large ▁samples . ▁However , ▁classical ▁statistical ▁tests , ▁including ▁those ▁based ▁on ▁the ▁mean , ▁are ▁typically ▁bounded ▁above ▁by ▁the ▁nom inal ▁size ▁of ▁the ▁test . ▁The ▁same ▁is ▁not ▁true ▁of ▁M - est im ators ▁and ▁the ▁type ▁I ▁error ▁rate ▁can ▁be ▁substantial ly ▁above ▁the ▁nom inal ▁level . ▁ ▁These ▁consider ations ▁do ▁not ▁" in validate " ▁M - est imation ▁in ▁any ▁way . ▁They ▁merely ▁make ▁clear ▁that ▁some ▁care ▁is ▁needed ▁in ▁their ▁use , ▁as ▁is ▁true ▁of ▁any ▁other ▁method ▁of ▁estimation . ▁ ▁In flu ence ▁function ▁of ▁an ▁M - est im ator ▁ ▁It ▁can ▁be ▁shown ▁that ▁the ▁influence ▁function ▁of ▁an ▁M - est im ator ▁ ▁is ▁proportional ▁to ▁, ▁which ▁means ▁we ▁can ▁derive ▁the ▁properties ▁of ▁such ▁an ▁estim ator ▁( such ▁as ▁its ▁re jection ▁point , ▁gross - error ▁sens itivity ▁or ▁local - shift ▁sens itivity ) ▁when ▁we |
▁know ▁its ▁ ▁function . ▁▁▁▁ ▁with ▁the ▁ ▁given ▁by : ▁ ▁Cho ice ▁of ▁ ψ ▁and ▁ ρ ▁ ▁In ▁many ▁practical ▁situations , ▁the ▁choice ▁of ▁the ▁ ▁function ▁is ▁not ▁critical ▁to ▁gain ing ▁a ▁good ▁robust ▁estimate , ▁and ▁many ▁choices ▁will ▁give ▁similar ▁results ▁that ▁offer ▁great ▁improvements , ▁in ▁terms ▁of ▁efficiency ▁and ▁bias , ▁over ▁classical ▁estimates ▁in ▁the ▁presence ▁of ▁out liers . ▁ ▁The oret ically , ▁ ▁functions ▁are ▁to ▁be ▁preferred , ▁and ▁Tu key ' s ▁bi weight ▁( also ▁known ▁as ▁bis quare ) ▁function ▁is ▁a ▁popular ▁choice . ▁ ▁recommend ▁the ▁bi weight ▁function ▁with ▁efficiency ▁at ▁the ▁normal ▁set ▁to ▁ 8 5 %. ▁ ▁Rob ust ▁paramet ric ▁approaches ▁ ▁M - est im ators ▁do ▁not ▁necessarily ▁relate ▁to ▁a ▁density ▁function ▁and ▁so ▁are ▁not ▁fully ▁paramet ric . ▁Ful ly ▁paramet ric ▁approaches ▁to ▁robust ▁model ing ▁and ▁inference , ▁both ▁Bay esian ▁and ▁lik elihood ▁approaches , ▁usually ▁deal ▁with ▁heavy ▁ta iled ▁distributions ▁such ▁as ▁Student ' s ▁t - distribution . ▁ ▁For ▁the ▁t - distribution ▁with ▁ ▁degrees ▁of ▁freedom , ▁it ▁can ▁be ▁shown ▁that ▁▁▁▁ ▁For ▁, ▁the ▁t - distribution ▁is ▁equivalent ▁to ▁the ▁C auch y ▁distribution . ▁The ▁degrees ▁of ▁freedom ▁is ▁sometimes ▁known ▁as ▁the ▁k urt osis ▁parameter . ▁It ▁is ▁the ▁parameter ▁that ▁controls ▁how ▁heavy ▁the ▁t ails ▁are . ▁In ▁principle , ▁ ▁can ▁be ▁estimated |
▁from ▁the ▁data ▁in ▁the ▁same ▁way ▁as ▁any ▁other ▁parameter . ▁In ▁practice , ▁it ▁is ▁common ▁for ▁there ▁to ▁be ▁multiple ▁local ▁maxim a ▁when ▁ ▁is ▁allowed ▁to ▁vary . ▁As ▁such , ▁it ▁is ▁common ▁to ▁fix ▁ ▁at ▁a ▁value ▁around ▁ 4 ▁or ▁ 6 . ▁The ▁figure ▁below ▁displays ▁the ▁- function ▁for ▁ 4 ▁different ▁values ▁of ▁. ▁ ▁Example : ▁speed - of - light ▁data ▁ ▁For ▁the ▁speed - of - light ▁data , ▁allowing ▁the ▁k urt osis ▁parameter ▁to ▁vary ▁and ▁maxim izing ▁the ▁lik elihood , ▁we ▁get ▁▁▁▁ ▁Fix ing ▁ ▁and ▁maxim izing ▁the ▁lik elihood ▁gives ▁ ▁Rel ated ▁concepts ▁ ▁A ▁p iv otal ▁quantity ▁is ▁a ▁function ▁of ▁data , ▁whose ▁underlying ▁population ▁distribution ▁is ▁a ▁member ▁of ▁a ▁paramet ric ▁family , ▁that ▁is ▁not ▁dependent ▁on ▁the ▁values ▁of ▁the ▁parameters . ▁An ▁anc ill ary ▁stat istic ▁is ▁such ▁a ▁function ▁that ▁is ▁also ▁a ▁stat istic , ▁meaning ▁that ▁it ▁is ▁computed ▁in ▁terms ▁of ▁the ▁data ▁alone . ▁Such ▁functions ▁are ▁robust ▁to ▁parameters ▁in ▁the ▁sense ▁that ▁they ▁are ▁independent ▁of ▁the ▁values ▁of ▁the ▁parameters , ▁but ▁not ▁robust ▁to ▁the ▁model ▁in ▁the ▁sense ▁that ▁they ▁assume ▁an ▁underlying ▁model ▁( param et ric ▁family ), ▁and ▁in ▁fact ▁such ▁functions ▁are ▁often ▁very ▁sensitive ▁to ▁viol ations ▁of ▁the ▁model ▁assumptions . ▁Thus ▁test ▁statistics , ▁frequently ▁constructed ▁in ▁terms ▁of ▁these ▁to ▁not ▁be |
▁sensitive ▁to ▁assumptions ▁about ▁parameters , ▁are ▁still ▁very ▁sensitive ▁to ▁model ▁assumptions . ▁ ▁Rep la cing ▁out liers ▁and ▁missing ▁values ▁▁ ▁Rep la cing ▁missing ▁data ▁is ▁called ▁im putation . ▁If ▁there ▁are ▁relatively ▁few ▁missing ▁points , ▁there ▁are ▁some ▁models ▁which ▁can ▁be ▁used ▁to ▁estimate ▁values ▁to ▁complete ▁the ▁series , ▁such ▁as ▁replacing ▁missing ▁values ▁with ▁the ▁mean ▁or ▁median ▁of ▁the ▁data . ▁Simple ▁linear ▁regression ▁can ▁also ▁be ▁used ▁to ▁estimate ▁missing ▁values . ▁In ▁addition , ▁out liers ▁can ▁sometimes ▁be ▁accommod ated ▁in ▁the ▁data ▁through ▁the ▁use ▁of ▁trim med ▁means , ▁other ▁scale ▁estim ators ▁apart ▁from ▁standard ▁deviation ▁( e . g ., ▁M AD ) ▁and ▁W ins or ization . ▁In ▁calculations ▁of ▁a ▁trim med ▁mean , ▁a ▁fixed ▁percentage ▁of ▁data ▁is ▁dropped ▁from ▁each ▁end ▁of ▁an ▁ordered ▁data , ▁thus ▁elimin ating ▁the ▁out liers . ▁The ▁mean ▁is ▁then ▁calculated ▁using ▁the ▁remaining ▁data . ▁W ins or izing ▁involves ▁accommod ating ▁an ▁out lier ▁by ▁replacing ▁it ▁with ▁the ▁next ▁highest ▁or ▁next ▁smallest ▁value ▁as ▁appropriate . ▁ ▁However , ▁using ▁these ▁types ▁of ▁models ▁to ▁predict ▁missing ▁values ▁or ▁out liers ▁in ▁a ▁long ▁time ▁series ▁is ▁difficult ▁and ▁often ▁un re li able , ▁particularly ▁if ▁the ▁number ▁of ▁values ▁to ▁be ▁in - filled ▁is ▁relatively ▁high ▁in ▁comparison ▁with ▁total ▁record ▁length . ▁The ▁accuracy ▁of ▁the ▁estimate ▁depends ▁on ▁how ▁good ▁and ▁representative ▁the |
▁model ▁is ▁and ▁how ▁long ▁the ▁period ▁of ▁missing ▁values ▁extends . ▁The ▁in ▁a ▁case ▁of ▁a ▁dynamic ▁process , ▁so ▁any ▁variable ▁is ▁dependent , ▁not ▁just ▁on ▁the ▁historical ▁time ▁series ▁of ▁the ▁same ▁variable ▁but ▁also ▁on ▁several ▁other ▁variables ▁or ▁parameters ▁of ▁the ▁process . ▁In ▁other ▁words , ▁the ▁problem ▁is ▁an ▁exercise ▁in ▁mult ivari ate ▁analysis ▁rather ▁than ▁the ▁un ivari ate ▁approach ▁of ▁most ▁of ▁the ▁traditional ▁methods ▁of ▁estim ating ▁missing ▁values ▁and ▁out liers ; ▁a ▁mult ivari ate ▁model ▁will ▁therefore ▁be ▁more ▁representative ▁than ▁a ▁un ivari ate ▁one ▁for ▁predict ing ▁missing ▁values . ▁The ▁ ▁Koh onen ▁self ▁organ ising ▁map ▁( K SO M ) ▁offers ▁a ▁simple ▁and ▁robust ▁mult ivari ate ▁model ▁for ▁data ▁analysis , ▁thus ▁providing ▁good ▁possibilities ▁to ▁estimate ▁missing ▁values , ▁taking ▁into ▁account ▁its ▁relationship ▁or ▁correlation ▁with ▁other ▁pert inent ▁variables ▁in ▁the ▁data ▁record . ▁ ▁Standard ▁Kal man ▁filters ▁are ▁not ▁robust ▁to ▁out liers . ▁To ▁this ▁end ▁ ▁have ▁recently ▁shown ▁that ▁a ▁modification ▁of ▁Mas rel iez ' s ▁theorem ▁can ▁deal ▁with ▁out liers . ▁ ▁One ▁common ▁approach ▁to ▁handle ▁out liers ▁in ▁data ▁analysis ▁is ▁to ▁perform ▁out lier ▁detection ▁first , ▁followed ▁by ▁an ▁efficient ▁estimation ▁method ▁( e . g ., ▁the ▁least ▁squares ). ▁While ▁this ▁approach ▁is ▁often ▁useful , ▁one ▁must ▁keep ▁in ▁mind ▁two ▁challeng es . ▁First , ▁an ▁out lier ▁detection ▁method ▁that |
▁re lies ▁on ▁a ▁non - rob ust ▁initial ▁fit ▁can ▁suffer ▁from ▁the ▁effect ▁of ▁mask ing , ▁that ▁is , ▁a ▁group ▁of ▁out liers ▁can ▁mask ▁each ▁other ▁and ▁escape ▁detection . ▁Second , ▁if ▁a ▁high ▁break down ▁initial ▁fit ▁is ▁used ▁for ▁out lier ▁detection , ▁the ▁follow - up ▁analysis ▁might ▁inherit ▁some ▁of ▁the ▁in e ffic ien cies ▁of ▁the ▁initial ▁estim ator . ▁ ▁See ▁also ▁Rob ust ▁confidence ▁intervals ▁Rob ust ▁regression ▁Unit - weight ed ▁regression ▁ ▁Notes ▁ ▁References ▁. ▁. ▁Repub lished ▁in ▁paper back , ▁ 2 0 0 5 . ▁. ▁. ▁. ▁ 2 nd ▁ed ., ▁C RC ▁Press , ▁ 2 0 1 1 . ▁. ▁Repub lished ▁in ▁paper back , ▁ 2 0 0 4 . ▁ 2 nd ▁ed ., ▁W iley , ▁ 2 0 0 9 . ▁. ▁. ▁. ▁. ▁. ▁. ▁. ▁Repub lished ▁in ▁paper back , ▁ 2 0 0 3 . ▁. ▁Pre print ▁. ▁. ▁. ▁. ▁. ▁ ▁External ▁links ▁ ▁Brian ▁Ri ple y ' s ▁ ▁robust ▁statistics ▁course ▁notes . ▁Nick ▁F i eller ' s ▁course ▁notes ▁on ▁Statist ical ▁Mod elling ▁and ▁Comput ation ▁contain ▁material ▁on ▁robust ▁regression . ▁David ▁O live ' s ▁site ▁contains ▁course ▁notes ▁on ▁robust ▁statistics ▁and ▁some ▁data ▁sets . ▁Online ▁experiments ▁using ▁R ▁and ▁JS X Graph <0x0A> </s> ▁The ▁contrast ▁of ▁white ▁and ▁black ▁( light ▁and ▁darkness , |
▁day ▁and ▁night ) ▁has ▁a ▁long ▁tradition ▁of ▁met ap hor ical ▁usage , ▁trace able ▁to ▁the ▁An cient ▁Near ▁East , ▁and ▁explicitly ▁in ▁the ▁P yth ag ore an ▁Table ▁of ▁O pp os ites . ▁In ▁Western ▁culture ▁as ▁well ▁as ▁in ▁Conf u cian ism , ▁the ▁contrast ▁symbol izes ▁the ▁moral ▁dich ot omy ▁of ▁good ▁and ▁evil . ▁ ▁Description ▁ ▁Day , ▁light , ▁and ▁good ▁are ▁often ▁linked ▁together , ▁in ▁opposition ▁to ▁night , ▁darkness , ▁and ▁evil . ▁These ▁contrast ing ▁met aph ors ▁may ▁go ▁back ▁as ▁far ▁as ▁human ▁history , ▁and ▁appear ▁in ▁many ▁cult ures , ▁including ▁both ▁the ▁ancient ▁Chinese ▁and ▁the ▁ancient ▁Pers ians . ▁The ▁philosophy ▁of ▁ne op lat on ism ▁is ▁strongly ▁im bu ed ▁with ▁the ▁met ap hor ▁of ▁good ness ▁as ▁light . ▁ ▁Ex amples ▁ ▁Religion ▁and ▁myth ology ▁▁▁ ▁The ▁Gen esis ▁creation ▁narr ative ▁has ▁God ▁" separ ate ▁light ▁from ▁darkness " ▁on ▁the ▁First ▁Day . ▁ ▁The ▁Bible ▁associ ates ▁light ▁with ▁God , ▁truth , ▁and ▁virtue ; ▁darkness ▁is ▁associated ▁with ▁sin ▁and ▁the ▁Dev il . ▁P ain ters ▁such ▁as ▁Rem brand t ▁port rayed ▁divine ▁light ▁ill umin ating ▁an ▁otherwise ▁dark ▁world . ▁War ▁of ▁the ▁S ons ▁of ▁Light ▁Again st ▁the ▁S ons ▁of ▁Dark ness , ▁part ▁of ▁the ▁Dead ▁Sea ▁Scroll s . ▁The ▁under world ▁( H ades , ▁T art |
arus ) ▁was ▁imag ined ▁as ▁a ▁ch th onic ▁place ▁of ▁darkness , ▁contrast ing ▁with ▁the ▁cel est ial ▁real m ▁of ▁the ▁gods . ▁Christian ▁not ions ▁of ▁heaven ▁and ▁hell ▁inherit ▁this ▁conception , ▁as ▁do ▁the ▁" dark ▁ang els " ▁vs . ▁the ▁unf allen ▁ang els , ▁often ▁with ▁a ure ola ▁( hal os ), ▁in ▁Christian ▁myth ology . ▁Day ▁and ▁night ▁are ▁person ified ▁as ▁de ities ▁in ▁various ▁myth ologies ▁( e . g . ▁Nor se ▁D agr ▁and ▁N ó tt , ▁Greek ▁Hem era ▁and ▁Ny x , ▁et ▁c eter a ). ▁ ▁D ress ▁▁▁ ▁White ▁often ▁represents ▁pur ity ▁or ▁innoc ence ▁in ▁Western ▁culture , ▁particularly ▁as ▁white ▁cl othing ▁or ▁objects ▁can ▁be ▁st ained ▁easily . ▁ ▁In ▁most ▁Western ▁countries ▁white ▁is ▁the ▁color ▁worn ▁by ▁br ides ▁at ▁wed d ings . ▁ ▁Ang els ▁are ▁typically ▁dep icted ▁as ▁cloth ed ▁in ▁white ▁ro bes . ▁In ▁many ▁Hollywood ▁Western s , ▁bad ▁cow bo ys ▁wear ▁black ▁h ats ▁while ▁the ▁good ▁ones ▁wear ▁white . ▁ ▁Mel od rama ▁villa ins ▁are ▁dressed ▁in ▁black ▁and ▁hero ines ▁in ▁white ▁dress es . ▁ ▁This ▁can ▁be ▁revers ed ▁as ▁a ▁deliber ate ▁play ▁on ▁convent ions , ▁by ▁having ▁the ▁evil ▁character ▁dress ▁in ▁white , ▁as ▁a ▁symbol ▁of ▁their ▁hyp oc ris y ▁or ▁ar rog ance . ▁For ▁example , ▁Don ▁F an ucci |
▁in ▁The ▁God father , ▁Part ▁II ▁is ▁an ▁evil ▁character , ▁but ▁we ars ▁an ▁expensive ▁all - white ▁suit ▁as ▁a ▁sign ▁of ▁his ▁este em , ▁power ▁and ▁prest ige . ▁Sometimes ▁protagon ists ▁can ▁wear ▁black ▁too , ▁as ▁in ▁Return ▁of ▁the ▁J edi , ▁where in ▁Luke ▁Sky walk er ▁we ars ▁black ▁during ▁the ▁final ▁battle . ▁This ▁may ▁symbol ize ▁the ▁danger ▁of ▁Luke ▁turning ▁to ▁the ▁dark ▁side , ▁but ▁once ▁he ▁has ▁pre va iled ▁( in ▁the ▁scene ▁where ▁he ▁removes ▁Dar th ▁V ader ' s ▁hel met ), ▁his ▁jack et ▁has ▁opened ▁up ▁to ▁reve al ▁that ▁it ▁has ▁a ▁l ighter ▁color ▁in ▁the ▁inside , ▁as ▁if ▁to ▁indicate ▁that ▁Luke ▁" on ▁the ▁inside " ▁was ▁always ▁good . ▁Dar th ▁V ader ▁himself , ▁while ▁still ▁in ▁the ▁g rip ▁of ▁the ▁dark ▁side , ▁dress es ▁all ▁in ▁black ▁and ▁may ▁be ▁regarded ▁as ▁a ▁science ▁fiction ▁version ▁of ▁a ▁Dark ▁Knight . ▁The ▁chief ▁ant agon ist ▁of ▁the ▁Star ▁Wars ▁franch ise , ▁the ▁evil ▁Emperor ▁Pal pat ine , ▁we ars ▁a ▁black ▁clo ak . ▁In ▁computer ▁security , ▁a ▁black ▁hat ▁is ▁an ▁attack er ▁with ▁evil ▁intent ions , ▁while ▁a ▁white ▁hat ▁be ars ▁no ▁such ▁ill ▁will . ▁ ▁( This ▁is ▁derived ▁from ▁the ▁Western ▁movie ▁convention .) ▁ ▁Magic ▁▁ ▁He aling ▁or ▁" good " ▁par an ormal ▁magic ▁is ▁called ▁White ▁magic |
. ▁Black ▁magic ▁is ▁a ▁destruct ive ▁or ▁evil ▁form ▁of ▁magic . ▁ ▁A ▁Tre at ise ▁on ▁White ▁Magic ▁is ▁a ▁book ▁by ▁Alice ▁Ba iley , ▁a ▁The osoph ist . ▁ ▁White ▁w itch . ▁ ▁Ev il ▁w itch es ▁are ▁s tere ot yp ically ▁dressed ▁in ▁black ▁and ▁good ▁f ai ries ▁in ▁white . ▁ ▁In ▁popular ▁culture ▁▁ ▁The ▁to pos ▁of ▁" light ▁and ▁darkness " ▁is ▁also ▁reflected ▁in ▁numerous ▁titles ▁in ▁popular ▁culture , ▁such ▁as ▁Heart ▁of ▁Dark ness ▁( 1 8 9 9 ), ▁Light ▁in ▁My ▁Dark ness ▁( 1 9 2 7 ), ▁Dark ness ▁and ▁the ▁Light ▁( 1 9 4 2 ), ▁Cre atures ▁of ▁Light ▁and ▁Dark ness ▁( 1 9 6 9 ), ▁From ▁Dark ness ▁to ▁Light ▁( 1 9 7 3 ), ▁Dark ness ▁and ▁Light ▁( 1 9 8 9 ), ▁The ▁Lord ▁of ▁the ▁Light ▁and ▁of ▁the ▁Dark ness ▁( 1 9 9 3 ), ▁the ▁Star ▁Tre k : ▁Deep ▁Space ▁ 9 ▁episode ▁" The ▁Dark ness ▁and ▁the ▁Light " ▁( 1 9 9 7 ), ▁the ▁Bab yl on ▁ 5 ▁episode ▁" B et ween ▁the ▁Dark ness ▁and ▁the ▁Light " ▁( 1 9 9 7 ), ▁and ▁Out ▁of ▁the ▁Dark ness , ▁In to ▁the ▁Light ▁( 1 9 9 8 ). ▁ ▁In ▁works ▁of ▁fant asy ▁fiction , ▁the ▁main ▁ant agon ist ▁is ▁often ▁called ▁a ▁" |
D ark ▁Lord ", ▁for ▁example ▁Sa ur on ▁in ▁The ▁Lord ▁of ▁the ▁R ings . ▁ ▁The ▁space - opera ▁franch ise ▁Star ▁Wars ▁also ▁dep ict s ▁Light ▁and ▁Dark ▁aspects ▁in ▁the ▁form ▁of ▁the ▁fict ional ▁energy ▁field ▁called ▁The ▁Force ▁where ▁there ▁are ▁two ▁sides , ▁light ▁side ▁and ▁dark ▁side ▁where in ▁the ▁protagon ists , ▁the ▁J edi ▁practice ▁and ▁propag ate ▁the ▁use ▁of ▁the ▁former ▁and ▁the ▁ant agon ists , ▁the ▁S ith ▁use ▁the ▁latter . ▁▁ ▁George ▁Or well ▁makes ▁a ▁bitter ly ▁ir onic ▁use ▁of ▁the ▁" light ▁and ▁darkness " ▁to pos ▁in ▁his ▁Nin ete en ▁E ight y ▁Four . ▁In ▁the ▁early ▁part ▁of ▁the ▁book ▁the ▁protagon ist ▁gets ▁a ▁promise ▁that ▁" We ▁will ▁meet ▁in ▁the ▁place ▁where ▁there ▁is ▁no ▁darkness " ▁– ▁which ▁he ▁interpre ts ▁as ▁referring ▁to ▁a ▁place ▁where ▁the ▁opp ress ive ▁total itar ian ▁state ▁does ▁not ▁rule . ▁But ▁the ▁man ▁who ▁made ▁the ▁promise ▁was ▁in ▁fact ▁an ▁agent ▁of ▁the ▁Th ought ▁Police ▁– ▁and ▁they ▁eventually ▁meet ▁as ▁prisoner ▁and ▁inter rog ator ▁where ▁there ▁is ▁indeed ▁no ▁darkness , ▁in ▁det ention ▁cells ▁where ▁the ▁light ▁remains ▁on ▁perman ently , ▁day ▁and ▁night , ▁as ▁an ▁additional ▁means ▁of ▁tort uring ▁d eta ine es . ▁ ▁The ▁Dark ▁Cry stal ▁explains ▁the ▁two ▁split ▁hal ves ▁of ▁a ▁bal anced ▁whole , ▁reflect ing ▁the ▁impos s |
ibility ▁of ▁acknowled ging ▁any ▁met ap hor ical ▁divine ▁balance ▁without ▁the ▁combination ▁of ▁both ▁the ▁light ( the ▁Myst ics ) ▁and ▁the ▁dark ( the ▁Sk esis ). ▁ ▁Other ▁examples ▁▁ ▁The ▁Dark ▁A ges ▁vs . ▁the ▁Age ▁of ▁En light en ment . ▁ ▁" Black ▁and ▁white ▁thinking " ▁is ▁the ▁false ▁dich ot omy ▁of ▁assuming ▁anything ▁not ▁good ▁is ▁evil ▁and ▁vice ▁vers a . ▁ ▁See ▁also ▁ ▁Black ▁and ▁white ▁thinking ▁ ▁Dia lect ics ▁of ▁Nature ▁ ▁Fant asy ▁trop es ▁and ▁convent ions ▁ ▁Table ▁of ▁O pp os ites ▁ ▁References ▁ ▁Ar min ▁L ange , ▁Eric ▁M . ▁Mey ers ▁( eds .), ▁Light ▁Again st ▁Dark ness : ▁D ual ism ▁in ▁An cient ▁Mediter rane an ▁Religion ▁and ▁the ▁Contempor ary ▁World , ▁ ▁V anden ho e ck ▁& ▁R up recht ▁( 2 0 1 1 ). ▁Font aine , ▁Pet rus ▁Francis cus ▁Maria , ▁The ▁Light ▁and ▁the ▁Dark : ▁A ▁Cultural ▁History ▁of ▁D ual ism , ▁ 2 1 ▁volumes ▁( 1 9 8 6 ). ▁ ▁Category : Met aph ors ▁Category : D ual ism ▁Category : D ich ot om ies <0x0A> </s> ▁Tur bin ic arp us ▁al onso i ▁is ▁a ▁species ▁of ▁plant ▁in ▁the ▁family ▁C act aceae . ▁It ▁is ▁en demic ▁to ▁Mexico . ▁ ▁Its ▁natural ▁habitat ▁is ▁hot ▁desert s . ▁ ▁Cult iv ation ▁Tur bin ic |
arp us ▁al onso i ▁is ▁easily ▁grown ▁in ▁cultiv ation , ▁however ▁due ▁to ▁its ▁large ▁tap root , ▁it ▁requires ▁por ous ▁soil ▁with ▁plenty ▁of ▁in organ ic ▁material ▁such ▁as ▁stones ▁and ▁d ries ▁as ▁quickly ▁as ▁possible . ▁Water ▁inf re qu ently ▁and ▁only ▁when ▁it ▁is ▁dry . ▁Full ▁sun ▁to ▁part ▁sh ade ▁is ▁preferred , ▁as ▁it ▁will ▁encou rage ▁slow , ▁compact ▁and ▁steady ▁growth ▁during ▁spring ▁and ▁summer ▁months . ▁ ▁During ▁its ▁winter ▁qu ies cent ▁period , ▁keep ▁dry ▁to ▁prevent ▁rot . ▁ ▁References ▁ ▁External ▁links ▁▁▁▁▁▁▁ ▁al onso i ▁Category : C act i ▁of ▁Mexico ▁Category : End em ic ▁fl ora ▁of ▁Mexico ▁Category : C rit ically ▁end anger ed ▁plants ▁Category : End anger ed ▁bi ota ▁of ▁Mexico ▁Category : T ax onomy ▁articles ▁created ▁by ▁Pol bot <0x0A> </s> ▁The ▁ 1 9 1 5 ▁R ice ▁O w ls ▁football ▁team ▁was ▁an ▁American ▁football ▁team ▁that ▁represented ▁R ice ▁University ▁as ▁a ▁member ▁of ▁the ▁South west ▁Conference ▁( SW C ) ▁during ▁the ▁ 1 9 1 5 ▁college ▁football ▁season . ▁In ▁its ▁fourth ▁season ▁under ▁head ▁coach ▁Philip ▁Ar bu ck le , ▁the ▁team ▁compiled ▁a ▁ 5 – 3 ▁record ▁( 1 – 2 ▁against ▁SW C ▁oppon ents ) ▁and ▁was ▁out sc ored ▁by ▁a ▁total ▁of ▁ 1 4 3 ▁to ▁ 1 2 2 . ▁ ▁Sch edule ▁ ▁References |
▁ ▁R ice ▁Category : R ice ▁O w ls ▁football ▁seasons ▁R ice <0x0A> </s> ▁ 5 - I od ow ill ardi ine ▁is ▁a ▁select ive ▁ag on ist ▁for ▁the ▁k ain ate ▁re ceptor , ▁with ▁only ▁limited ▁effects ▁at ▁the ▁A MP A ▁re ceptor . ▁It ▁is ▁select ive ▁for ▁k ain ate ▁re cept ors ▁composed ▁of ▁G lu R 5 ▁sub un its . ▁It ▁is ▁an ▁exc it ot ox ic ▁ne uro to x in ▁in ▁v ivo , ▁but ▁has ▁proved ▁highly ▁useful ▁for ▁character ising ▁the ▁sub types ▁and ▁function ▁of ▁the ▁various ▁k ain ate ▁re cept ors ▁in ▁the ▁brain ▁and ▁sp inal ▁cord . ▁ ▁References ▁ ▁Category : Ne uro to x ins ▁Category : K ain ate ▁re ceptor ▁ag on ists ▁Category : P yr im id ines ▁Category : A min o ▁acid ▁derivatives ▁Category : I odo aren es <0x0A> </s> ▁Pub lius ▁Corn el ius ▁R util us ▁C oss us ▁was ▁a ▁states man ▁and ▁military ▁commander ▁from ▁the ▁early ▁Roman ▁Republic ▁who ▁served ▁as ▁D ict ator ▁in ▁ 4 0 8 ▁BC . ▁ ▁Family ▁ ▁C oss us ▁belonged ▁to ▁the ▁gens ▁Corn elia , ▁one ▁of ▁the ▁most ▁important ▁pat ric ian ▁g entes ▁of ▁the ▁Republic . ▁ ▁His ▁father ▁was ▁named ▁Marcus , ▁and ▁his ▁grand father ▁Lu cius , ▁but ▁no ▁mag ist racy ▁is ▁recorded ▁for ▁them . ▁ ▁He ▁was ▁however |
▁the ▁brother ▁of ▁the ▁more ▁famous ▁A ulus ▁Corn el ius ▁C oss us , ▁one ▁of ▁the ▁only ▁three ▁Rom ans ▁awarded ▁the ▁sp olia ▁op ima ▁for ▁having ▁killed ▁the ▁king ▁of ▁Ve ii ▁Lars ▁Tol umn ius ▁in ▁single ▁combat . ▁ ▁A ulus ▁was ▁then ▁cons ul ▁in ▁ 4 2 8 , ▁and ▁cons ular ▁trib une ▁in ▁ 4 2 6 . ▁ ▁Pub lius ' ▁had ▁at ▁least ▁two ▁nep he ws : ▁G na eus , ▁cons ular ▁trib une ▁in ▁ 4 1 4 ▁and ▁cons ul ▁in ▁ 4 0 9 , ▁and ▁Pub lius , ▁cons ular ▁trib une ▁in ▁ 4 0 8 . ▁ ▁A ulus , ▁dict ator ▁in ▁ 3 8 5 ▁and ▁perhaps ▁cons ul ▁in ▁ 4 1 3 , ▁may ▁have ▁also ▁been ▁his ▁nep hew . ▁ ▁The ▁Corn el ii ▁Cos si ▁were ▁thus ▁among ▁the ▁for em ost ▁families ▁of ▁the ▁Republic ▁at ▁the ▁end ▁of ▁the ▁ 5 th ▁century ▁BC . ▁ ▁Career ▁In ▁ 4 0 8 ▁BC , ▁a ▁large ▁army ▁compr ising ▁mainly ▁Vol sci ▁and ▁A equ i ▁assemble d ▁at ▁Ant ium . ▁When ▁news ▁of ▁this ▁reached ▁Rome , ▁the ▁Senate , ▁thinking ▁the ▁situation ▁to ▁be ▁a ▁dangerous ▁one , ▁called ▁for ▁the ▁appointment ▁of ▁a ▁dict ator ▁to ▁lead ▁the ▁war ▁effort . ▁ ▁This ▁caused ▁const ern ation ▁among ▁two ▁of ▁the ▁three ▁Cons ular ▁trib unes , ▁G ai us |
▁Julius ▁I ulus ▁and ▁Pub lius ▁Corn el ius ▁C oss us , ▁who ▁wanted ▁the ▁command ▁to ▁stay ▁with ▁them . ▁ ▁The ▁disag re ement ▁st oked ▁the ▁existing ▁t ensions ▁in ▁Rome ▁during ▁the ▁Conf lict ▁of ▁the ▁Or ders , ▁but ▁Liv y ' s ▁narr ative ▁is ▁confused ▁on ▁these ▁events . ▁ ▁The ▁situation ▁was ▁only ▁resolved ▁when ▁the ▁third ▁trib une , ▁G ai us ▁Serv il ius ▁Struct us ▁Ah ala , ▁seeing ▁that ▁I ulus ▁and ▁Corn el ius ▁could ▁not ▁be ▁persu aded , ▁rose ▁to ▁nomin ate ▁R util us ▁C oss us , ▁Corn el ius ' ▁uncle . ▁ ▁R util us ▁C oss us ▁then ▁appointed ▁Ah ala ▁as ▁his ▁mag ister ▁equ it um , ▁which ▁is ▁doubt less ▁the ▁result ▁of ▁a ▁power - sh aring ▁neg ot iation ▁between ▁the ▁cons ular ▁trib unes . ▁ ▁R util us ▁C oss us ▁and ▁Ah ala ▁then ▁led ▁the ▁army ▁out ▁to ▁Ant ium . ▁They ▁defeated ▁the ▁Vol sci an ▁coal ition ▁in ▁one ▁battle ▁before ▁lay ing ▁waste ▁to ▁the ▁coun tr ys ide ▁and ▁storm ing ▁the ▁Vol sci an ▁for tr ess ▁at ▁Lake ▁F uc inus . ▁As ▁many ▁as ▁ 3 . 0 0 0 ▁Vol sci ▁were ▁taken ▁prisoner . ▁ ▁When ▁C oss us ▁returned ▁to ▁the ▁city , ▁he ▁lay ▁down ▁the ▁office ▁of ▁dict ator ▁and , ▁according ▁to ▁Liv y , ▁did ▁not ▁receive ▁much ▁acc |
laim ▁for ▁his ▁success . ▁ ▁Indeed , ▁according ▁to ▁the ▁Fast i ▁Tri umph ales , ▁R util us ▁C oss us ▁was ▁not ▁awarded ▁a ▁triumph . ▁▁ ▁R util us ▁C oss us ▁was ▁elected ▁as ▁one ▁of ▁the ▁cons ular ▁trib unes ▁for ▁the ▁year ▁ 4 0 6 ▁BC , ▁alongside ▁G na eus ▁Corn el ius ▁C oss us , ▁his ▁distant ▁cousin , ▁N umer ius ▁Fab ius ▁Amb ust us , ▁and ▁Lu cius ▁Val er ius ▁Pot it us . ▁ ▁The ▁Senate ▁ordered ▁a ▁new ▁war ▁on ▁Ve ii , ▁but ▁the ▁cons ular ▁trib unes ▁opposed ▁it , ▁arg uing ▁that ▁the ▁war ▁against ▁the ▁Vol sci ▁was ▁not ▁over . ▁ ▁R util us ▁C oss us ▁was ▁given ▁the ▁command ▁against ▁the ▁city ▁of ▁E c etra , ▁while ▁Fab ius ▁took ▁An x ur . ▁ ▁The ▁cons ular ▁trib unes ▁then ▁shared ▁the ▁boot y ▁with ▁the ▁soldiers , ▁which ▁improved ▁the ▁relations ▁between ▁ple be ians ▁and ▁patr icians . ▁ ▁The ▁Senate ▁followed ▁and ▁ordered ▁that ▁citizens ▁must ▁be ▁paid ▁while ▁serving , ▁whereas ▁they ▁had ▁to ▁cover ▁their ▁own ▁exp enses ▁before . ▁ ▁References ▁ ▁Bibli ography ▁ ▁An cient ▁sources ▁▁▁ ▁Liv y , ▁Ab ▁Ur be ▁Cond ita . ▁ ▁John ▁the ▁L yd ian , ▁de ▁magistr at ibus . ▁ ▁Fast i ▁Cons ular es . ▁Fast i ▁Tri umph ales . ▁ ▁Modern ▁sources ▁▁ ▁T . ▁Robert ▁S |
. ▁Br ought on , ▁The ▁Mag istr ates ▁of ▁the ▁Roman ▁Republic , ▁American ▁Phil ological ▁Association , ▁ 1 9 5 2 – 1 9 8 6 . ▁ ▁Category : An cient ▁Roman ▁dict ators ▁Category : 5 th - century ▁BC ▁Rom ans <0x0A> </s> ▁George ▁Washington ▁Don ag he y ▁( J uly ▁ 1 , ▁ 1 8 5 6 ▁– ▁December ▁ 1 5 , ▁ 1 9 3 7 ) ▁was ▁the ▁ 2 2 nd ▁Governor ▁of ▁the ▁U . S . ▁state ▁of ▁Ark ansas ▁from ▁ 1 9 0 9 ▁to ▁ 1 9 1 3 . ▁ ▁Early ▁life ▁and ▁education ▁Don ag he y ▁was ▁born ▁as ▁the ▁oldest ▁of ▁five ▁children ▁to ▁Christopher ▁Columb us ▁and ▁Elizabeth ▁( née ▁In gram ) ▁Don ag he y , ▁in ▁the ▁Oak land ▁Community ▁in ▁Union ▁Par ish ▁in ▁north ▁Louisiana . ▁His ▁father ' s ▁family ▁was ▁from ▁Ireland ▁and ▁his ▁mother ' s ▁from ▁Scotland . ▁His ▁father ▁Christopher ▁was ▁a ▁far mer ▁who ▁moved ▁from ▁Alabama ▁to ▁northern ▁Louisiana , ▁purch asing ▁land ▁there , ▁and ▁later ▁moved ▁to ▁Ark ansas ▁where ▁he ▁served ▁in ▁the ▁Confeder ate ▁Army . ▁ ▁In ▁ 1 8 7 5 , ▁without ▁letting ▁his ▁family ▁know , ▁Don ag he y ▁moved ▁to ▁Texas ▁where ▁he ▁worked ▁as ▁a ▁cow boy ▁on ▁the ▁Ch ish ol m ▁Tra il ▁and ▁far mer , ▁but ▁later ▁moved ▁again ▁to ▁Ark ansas ▁in |
▁ 1 8 7 6 ▁due ▁to ▁cow boy ▁l ifest yle ▁and ▁health ▁issues . ▁From ▁ 1 8 8 2 ▁to ▁ 1 8 8 3 , ▁he ▁attended ▁the ▁University ▁of ▁Ark ansas ▁at ▁F ay ette ville . ▁He ▁was ▁a ▁school ▁teacher ▁and ▁car p enter , ▁and ▁studied ▁both ▁architecture ▁and ▁struct ural ▁engineering . ▁In ▁ 1 8 8 3 , ▁Don ag he y ▁established ▁his ▁residence ▁at ▁Con way , ▁Ark ansas , ▁and ▁adopted ▁that ▁city ▁as ▁his ▁h omet own . ▁There , ▁he ▁later ▁met ▁his ▁wife ▁Lou ven ia ▁Wal lace ; ▁they ▁had ▁no ▁children . ▁One ▁of ▁the ▁major ▁streets ▁there ▁be ars ▁his ▁name . ▁He ▁served ▁one ▁term ▁as ▁town ▁mar shal ▁and ▁was ▁an ▁un success ful ▁prohib ition ▁candidate ▁for ▁mayor ▁in ▁ 1 8 8 5 . ▁ ▁Having ▁himself ▁lack ed ▁a ▁formal ▁education , ▁Don ag he y ▁worked ▁dil ig ently ▁to ▁bring ▁institutions ▁of ▁higher ▁learning ▁to ▁Con way . ▁He ▁served ▁on ▁the ▁bo ards ▁of ▁Phil ander ▁Smith ▁College ▁in ▁Little ▁Rock , ▁Hend rix ▁College ▁( to ▁which ▁he ▁don ated ▁$ 7 5 , 0 0 0 ▁in ▁ 1 9 1 0 ), ▁the ▁University ▁of ▁Central ▁Ark ansas , ▁State ▁Normal ▁School ▁( where ▁he ▁was ▁the ▁principal ▁speaker ▁for ▁its ▁ 1 9 0 8 ▁dedic ation ) ▁and ▁Little ▁Rock ▁Junior ▁College ▁( both ▁now ▁part ▁of ▁University ▁of ▁Central ▁Ark |
ansas ) ▁in ▁Con way , ▁where ▁his ▁service ▁extended ▁from ▁ 1 9 0 6 ▁until ▁his ▁death . ▁Additionally , ▁he ▁gave ▁gener ously ▁to ▁both ▁institutions . ▁ ▁Business ▁Don ag he y ▁entered ▁business ▁as ▁a ▁contract or ▁and ▁constructed ▁cour th ouses ▁in ▁Texas ▁and ▁Ark ansas , ▁including ▁the ▁first ▁bank ▁building ▁in ▁Con way ▁in ▁ 1 8 9 0 . ▁Short ly ▁after ward , ▁he ▁det oured ▁into ▁the ▁merc ant ile ▁business — for ▁his ▁contract ing ▁business ▁was ▁not ▁prof itable ▁in ▁its ▁early ▁years — and ▁suffered ▁significant ▁losses ▁after ▁building ▁the ▁second ▁Fa ulk ner ▁County ▁cour th ouse . ▁When ▁he ▁returned , ▁he ▁re construct ed ▁the ▁Ark ansas ▁Ins ane ▁As yl um ▁after ▁a ▁torn ado ▁in ▁ 1 8 9 4 . ▁He ▁built ▁ice ▁plants ▁and ▁roads ▁in ▁Ark ansas , ▁and ▁water ▁t anks ▁and ▁rail road ▁stations ▁for ▁the ▁Cho ct aw , ▁Oklahoma ▁and ▁G ulf ▁Rail road , ▁and ▁often ▁inv ested ▁in ▁farm ▁and ▁tim ber ▁land . ▁ ▁In ▁ 1 8 9 9 , ▁Don ag he y ▁was ▁appointed ▁to ▁the ▁commission ▁task ed ▁with ▁construct ing ▁the ▁new ▁state ▁capit ol . ▁The ▁project ▁was ▁not ▁complete ▁until ▁a ▁dozen ▁years ▁later ; ▁during ▁much ▁of ▁that ▁time ▁Jefferson ▁" J eff " ▁Davis ▁was ▁state ▁governor ▁and ▁firm ly ▁opposed ▁all ▁the ▁new ▁plans . ▁This ▁obst ruction ▁imp elled ▁Don ag he y |
▁to ▁enter ▁politics ; ▁eventually ▁in ▁ 1 9 0 7 ▁he ▁sought ▁the ▁nom ination ▁for ▁governor , ▁in ▁the ▁teeth ▁of ▁opposition ▁from ▁Davis ▁( who ▁had ▁been ▁elected ▁U . S . ▁sen ator ▁for ▁Ark ansas ) ▁and ▁Davis ' s ▁al ly ▁William ▁F . ▁Kir by . ▁ ▁As ▁governor ▁In ▁ 1 9 0 8 , ▁Don ag he y ▁won ▁a ▁three - way ▁primary ▁election ▁that ▁broke ▁the ▁hold ▁of ▁Jeff ▁Davis ▁on ▁the ▁Ark ansas ▁Democratic ▁Party . ▁He ▁then ▁att ained ▁an ▁easy ▁victory ▁in ▁the ▁g ubern atorial ▁general ▁election ▁with ▁ 1 0 6 , 5 1 2 ▁votes , ▁over ▁Republican ▁John ▁I . ▁Wor thing ton ▁( 4 2 , 9 7 9 ) ▁and ▁Social ist ▁J . ▁Sam ▁Jones ▁( 6 , 5 3 7 ). ▁Wor thing ton ▁had ▁also ▁run ▁in ▁ 1 9 0 6 ▁against ▁Davis . ▁Don ag he y ▁had ▁to ▁wait ▁ten ▁months ▁to ▁take ▁office . ▁In ▁the ▁meant ime ▁he ▁tra ve led ▁the ▁country , ▁and ▁as ▁professor ▁Cal vin ▁Led bet ter , ▁Jr . ▁of ▁the ▁University ▁of ▁Ark ansas ▁at ▁Little ▁Rock ▁points ▁out ▁in ▁his ▁book ▁The ▁Car p enter ▁from ▁Con way , ▁Don ag he y ▁educated ▁himself ▁for ▁the ▁political ▁office ▁which ▁await ed ▁him . ▁In ▁June ▁ 1 9 0 9 , ▁he ▁appointed ▁the ▁fourth ▁and ▁final ▁state ▁capit ol ▁commission ▁and ▁h ired ▁Cass ▁Gilbert |
▁for ▁the ▁architecture ▁project . ▁ ▁Don ag he y ▁was ▁re elect ed ▁in ▁ 1 9 1 0 , ▁defe ating ▁another ▁Republican , ▁Andrew ▁L . ▁Roland , ▁by ▁ 1 0 1 , 6 1 2 ▁votes ▁to ▁ 3 8 , 8 7 0 . ▁Another ▁ 9 , 1 9 6 ▁ball ots ▁were ▁cast ▁for ▁the ▁Social ist ▁candidate , ▁Dan ▁Hog an . ▁That ▁same ▁year ▁he ▁negoti ated ▁with ▁the ▁Southern ▁Regional ▁Education ▁Board ▁to ▁bring ▁its ▁campaign ▁to ▁Ark ansas , ▁which ▁had ▁successful ▁results ▁in ▁the ▁state , ▁and ▁he ▁also ▁supported ▁four ▁agricult ural ▁high ▁schools ▁that ▁later ▁formed ▁into ▁Ark ansas ▁Te ch ▁University , ▁Ark ansas ▁State ▁University , ▁Southern ▁Ark ansas ▁University ▁and ▁the ▁University ▁of ▁Ark ansas ▁at ▁Mont ic ello . ▁His ▁actions ▁in ▁ 1 9 1 0 ▁also ▁included ▁helping ▁to ▁create ▁the ▁Bo one ville ▁T uber cul osis ▁San ator ium , ▁thus ▁impro ving ▁public ▁health ; ▁he ▁later ▁also ▁negoti ated ▁with ▁the ▁Rock ef eller ▁San it ary ▁Commission ▁to ▁er ad icate ▁hook w orm . ▁During ▁his ▁term , ▁Ark ansas ▁was ▁the ▁first ▁state ▁in ▁the ▁country ▁to ▁require ▁small po x ▁v acc in ations ▁for ▁all ▁school children ▁and ▁school ▁personnel , ▁and ▁the ▁C ros sett ▁mal aria ▁control ▁experiment ▁campaign ed ▁against ▁the ▁mos qu itos . ▁Don ag he y ' s ▁achiev ements ▁included ▁establishment ▁of ▁a ▁new ▁state ▁board |
▁of ▁education , ▁support ▁for ▁high ▁schools , ▁and ▁the ▁passage ▁of ▁a ▁law ▁making ▁cons olid ation ▁easier . ▁ ▁Although ▁several ▁of ▁the ▁prisoners ▁he ▁p ardon ed ▁from ▁the ▁conv ict ▁le ase ▁program ▁were ▁black , ▁Don ag he y ▁still ▁supported ▁seg reg ation . ▁In ▁ 1 9 1 0 ▁at ▁the ▁state ▁Bapt ist ▁Col ored ▁Convention ▁in ▁Little ▁Rock , ▁he ▁said ▁" It ▁is ▁not ▁for ▁any ▁political ▁purpose ▁that ▁I ▁come ▁to ▁talk ▁to ▁you . ▁It ▁is ▁not ▁for ▁the ▁purpose ▁of ▁getting ▁your ▁votes , ▁this ▁you ▁know ▁as ▁well ▁as ▁I ▁do , ▁because ▁your ▁people ▁don ' t ▁vote ▁much . ▁This , ▁perhaps , ▁is ▁best ▁for ▁you . ▁The ▁greatest ▁man ▁in ▁your ▁race ▁[ Book er ▁T . ▁Washington ] ▁has ▁said ▁that ▁you ▁should ▁keep ▁out ▁of ▁politics ▁and ▁in ▁this ▁I ▁agree ▁with ▁him . ▁I ▁think ▁it ▁is ▁best ▁that ▁you ▁stay ▁out ▁of ▁politics ▁and ▁look ▁after ▁the ▁condition ▁of ▁your ▁people , ▁and ▁in ▁this ▁you ▁have ▁as ▁much ▁as ▁you ▁can ▁do ". ▁In ▁aut umn ▁ 1 9 1 1 , ▁he ▁appeared ▁with ▁Book er ▁T . ▁Washington ▁at ▁the ▁National ▁Neg ro ▁Business ▁League ▁and ▁said ▁to ▁an ▁audience ▁of ▁one ▁thousand ▁black ▁men ▁to ▁" not ▁waste ▁their ▁time ▁running ▁around ▁begg ing ▁for ▁social ▁equality ". ▁The ▁Chicago ▁Def ender ▁quoted ▁him ▁as ▁saying ▁" You ▁must ▁ride ▁in ▁the ▁last ▁two ▁seats ▁in ▁our |
▁street ▁cars ; ▁you ▁must ▁not ▁sit ▁in ▁a ▁P ull man ▁car ; ▁you ▁must ▁not ▁ride ▁on ▁the ▁same ▁deck , ▁nor ▁eat ▁in ▁the ▁same ▁restaurant , ▁nor ▁drink ▁in ▁the ▁same ▁sal oon ▁as ▁me ... You ▁are ▁a ▁race ▁of ▁deg ener ates , ▁your ▁women ▁are ▁le wd ▁and ▁we ▁cannot ▁afford ▁to ▁have ▁our ▁white ▁women ▁and ▁children ▁associate ▁with ▁you ". ▁ ▁Don ag he y ' s ▁progress ive ▁st ance ▁proc ured ▁passage ▁of ▁the ▁In iti ative ▁and ▁Refer endum ▁Act ▁by ▁which ▁Ark ans ans ▁can ▁take ▁government al ▁matters ▁into ▁their ▁own ▁hands ▁and ▁by pass ▁the ▁state ▁legisl ature . ▁He ▁rec ru ited ▁William ▁Jenn ings ▁Bry an ▁to ▁help ▁campaign ▁for ▁the ▁am end ment ' s ▁ad option ▁in ▁ 1 9 1 0 . ▁Ark ansas ▁is ▁the ▁only ▁state ▁in ▁the ▁American ▁South ▁to ▁grant ▁its ▁citizens ▁such ▁power . ▁The ▁initi ative , ▁which ▁began ▁in ▁South ▁Dak ota , ▁is ▁otherwise ▁particularly ▁known ▁in ▁California ▁and ▁Colorado . ▁ ▁The ▁Don ag he y ▁administration ▁focused ▁on ▁roads , ▁public ▁health , ▁and ▁rail ro ads . ▁Don ag he y ▁was ▁veh ement ly ▁opposed ▁to ▁the ▁use ▁of ▁prisoners ▁for ▁contract - leased ▁labor , ▁especially ▁for ▁building ▁rail ro ads . ▁He ▁particularly ▁learned ▁about ▁conv ict ▁le ase ▁while ▁at ▁a ▁Southern ▁govern ors ' ▁conference ▁in ▁West ▁Virginia ▁in ▁aut umn ▁ 1 9 1 2 . |
▁Unable ▁to ▁get ▁the ▁legisl ature ▁to ▁abol ish ▁the ▁practice , ▁he ▁prior ▁to ▁leaving ▁office ▁p ardon ed ▁ 3 6 0 ▁prisoners , ▁ 4 4 ▁in ▁country ▁far ms ▁and ▁ 3 1 6 ▁out ▁of ▁ 8 5 0 ▁in ▁pen it enti aries ▁and ▁ 3 7 ▁percent ▁of ▁the ▁in car cer ated ▁population . ▁This ▁left ▁the ▁le ase ▁system ▁with ▁ins u fficient ▁available ▁prisoners ▁for ▁util ization ▁in ▁construction . ▁In ▁ 1 9 1 3 , ▁a ▁year ▁after ▁Don ag he y ▁left ▁office , ▁the ▁legisl ature ▁finally ▁ended ▁the ▁practice ▁and ▁a ▁new ▁prison ▁board ▁was ▁formed . ▁ ▁In ▁ 1 9 1 2 , ▁he ▁was ▁eager ▁for ▁a ▁third ▁term , ▁hoping ▁to ▁take ▁care ▁of ▁state wide ▁prohib ition ▁and ▁the ▁much - ne eded ▁tax ▁reform , ▁but ▁the ▁legisl ature ▁rejected ▁his ▁re forms ▁and ▁the ▁elect or ate ▁rejected ▁his ▁prohib ition ▁plans . ▁During ▁his ▁campaign ▁for ▁the ▁third ▁term ▁the ▁state ▁capit ol ▁project ▁ran ▁out ▁of ▁money , ▁and ▁Don ag he y ' s ▁appropri ation ▁plans ▁were ▁not ▁successful . ▁What ▁also ▁helped ▁bring ▁on ▁his ▁defeat ▁was ▁that ▁former ▁governor ▁Jeff ▁Davis ▁and ▁his ▁al lies ▁also ▁campaign ed ▁for ▁governor , ▁along ▁with ▁emer ging ▁power bro ker ▁Joseph ▁Taylor ▁Robinson . ▁ ▁Don ag he y ▁was ▁the ▁first ▁Ark ansas ▁governor ▁who ▁could ▁ind is put ably ▁be ▁l abeled ▁' progress ive |
' ▁but ▁was ▁also ▁within ▁the ▁southern ▁progress ive ▁tradition , ▁as ▁well ▁as ▁the ▁first ▁business man ▁to ▁become ▁governor ▁of ▁Ark ansas . ▁ ▁After ▁being ▁governor ▁After ▁his ▁bid ▁for ▁a ▁third ▁term ▁as ▁governor ▁was ▁defeated ▁by ▁Joseph ▁Taylor ▁Robinson ▁in ▁ 1 9 1 2 , ▁Don ag he y ▁pers isted ▁in ▁his ▁quest ▁to ▁complete ▁the ▁Capit ol . ▁A ▁critical ▁year ▁was ▁ 1 9 1 3 . ▁Senator ▁Jeff ▁Davis ▁died ▁two ▁days ▁into ▁the ▁year . ▁Robinson , ▁by ▁this ▁time ▁state ▁governor , ▁was ▁named ▁by ▁the ▁legisl ature ▁as ▁Davis ' s ▁successor . ▁J . ▁M . ▁Fut tre ll , ▁president ▁of ▁the ▁Ark ansas ▁Senate , ▁became ▁acting ▁governor . ▁The ▁result ▁was ▁that ▁Fut tre ll ▁and ▁the ▁Capit ol ▁Building ▁Commission ▁asked ▁Don ag he y ▁to ▁become ▁a ▁commission ▁member ▁and ▁take ▁charge ▁of ▁comple ting ▁construction , ▁which ▁he ▁did . ▁The ▁Capit ol , ▁val ued ▁at ▁more ▁than ▁$ 3 0 0 ▁million ▁today , ▁was ▁completed ▁in ▁ 1 9 1 7 ▁for ▁$ 2 . 2 ▁million , ▁ending ▁an ▁ 1 8 - year ▁effort . ▁As ▁a ▁hall mark ▁to ▁completion , ▁Don ag he y ▁personally ▁built ▁the ▁governor ' s ▁conference ▁table , ▁which ▁sets ▁today ▁as ▁the ▁center pie ce ▁of ▁the ▁governor ' s ▁conference ▁room ▁in ▁the ▁north ▁wing ▁of ▁the ▁Capit ol . ▁ ▁As ▁a ▁former ▁governor , ▁Don ag he |
y ▁served ▁on ▁a ▁number ▁of ▁bo ards ▁and ▁comm issions ▁responsible ▁for ▁a ▁variety ▁of ▁tasks ▁such ▁as ▁constru ctions , ▁education , ▁and ▁char ities . ▁He ▁p enn ed ▁the ▁book ▁Build ▁a ▁State ▁Capit ol , ▁which ▁details ▁the ▁construction ▁of ▁the ▁Ark ansas ▁capit ol ▁building . ▁ ▁Don ag he y ▁died ▁from ▁a ▁heart ▁attack ▁in ▁Little ▁Rock ▁in ▁ 1 9 3 7 , ▁and ▁is ▁inter red ▁there ▁at ▁the ▁Ros ela wn ▁Memorial ▁Park ▁C emetery . ▁His ▁estate ▁is ▁managed ▁by ▁George ▁W . ▁Don ag he y ▁Foundation ▁in ▁Little ▁Rock . ▁ ▁Form er ▁Ark ansas ▁Governor ▁( 1 9 4 9 - 1 9 5 3 ) ▁Sid ▁Mc Math ▁said ▁in ▁his ▁mem oir ▁Prom ises ▁Ke pt : ▁a ▁Mem oir ▁that ▁Don ag he y ▁was ▁" without ▁a ▁doubt , ▁one ▁of ▁the ▁great ▁govern ors ▁of ▁Ark ansas ▁and ▁served ▁as ▁an ▁insp iration ▁to ▁my ▁administration ▁and ▁to ▁others , ▁particularly ▁in ▁the ▁continu ing ▁struggle ▁for ▁human ▁rights , ▁and ▁I ▁decided ▁to ▁continue ▁what ▁he ▁had ▁begun ". ▁One ▁book ▁called ▁him ▁" arg u ably ▁one ▁of ▁the ▁best ▁and ▁most ▁influ ential ▁govern ors ▁and ▁phil anth rop ists ▁in ▁Ark ansas ▁history ". ▁ ▁In ▁ 1 9 9 9 , ▁the ▁Log ▁Cab in ▁Dem ocrat ▁named ▁him ▁one ▁of ▁the ▁ten ▁most ▁influ ential ▁people ▁in ▁Fa ulk ner ▁County ' s ▁history . ▁ ▁Don |
ag he y ' s ▁Monument ▁In ▁ 1 9 3 1 , ▁Don ag he y , ▁who ▁felt ▁a ▁kin ship ▁to ▁both ▁Ark ansas ▁and ▁Louisiana , ▁established ▁a ▁monument ▁at ▁the ▁Union ▁Par ish / Union ▁County ▁state ▁line ▁near ▁his ▁birth place . ▁The ▁Art ▁Dec o - style ▁monument ▁contains ▁intr icate ▁car v ings ; ▁it ▁includes ▁references ▁to ▁transport ation ▁in ▁ 1 8 3 1 ▁and ▁ 1 9 3 1 , ▁and ▁mentions ▁Governor ▁H ue y ▁P . ▁Long , ▁Jr ., ▁whose ▁educational ▁program ▁Don ag he y ▁adm ired . ▁The ▁land ▁was ▁not ▁registered ▁with ▁state ▁par ks ▁offices ▁in ▁either ▁state , ▁tim ber ▁companies ▁cut ▁trees ▁around ▁it , ▁and ▁the ▁marker ▁was ▁forgotten . ▁ ▁In ▁ 1 9 7 5 , ▁an ▁employee ▁of ▁the ▁Louisiana ▁Department ▁of ▁Transport ation ▁came ▁across ▁the ▁abandoned ▁monument ▁and ▁informed ▁then - State ▁Represent ative ▁Louise ▁B . ▁Johnson ▁of ▁Bern ice ▁of ▁his ▁discovery . ▁In ▁an ▁article ▁in ▁the ▁North ▁Louisiana ▁Historical ▁Association ▁Journal ▁( after ward ▁North ▁Louisiana ▁History ), ▁Johnson ▁explained ▁that ▁she ▁asked ▁the ▁O link raft ▁Tim ber ▁Company ▁of ▁West ▁Mon roe , ▁Louisiana , ▁to ▁ce ase ▁cutting ▁trees ▁on ▁the ▁property ▁and ▁to ▁help ▁with ▁the ▁rest oration ▁of ▁the ▁monument . ▁She ▁introduced ▁a ▁bill ▁to ▁c ede ▁the ▁state ' s ▁part ▁of ▁the ▁property ▁to ▁the ▁state ▁par ks ▁system . ▁Governor ▁Ed win ▁Washington ▁Edwards |
▁signed ▁what ▁became ▁Act ▁ 7 3 4 ▁of ▁ 1 9 7 5 , ▁and ▁a ▁re - ded ic ation ▁ceremony ▁was ▁held ▁in ▁which ▁he ▁and ▁Johnson ▁plant ed ▁a ▁tree . ▁Month s ▁later , ▁according ▁to ▁the ▁Ark ansas ▁Historic ▁Pres ervation ▁Program , ▁Ark ansas ▁sold ▁its ▁part ▁of ▁the ▁land ▁to ▁O lin ▁Math ies on ▁Chem ical ▁Corporation . ▁Since ▁that ▁time , ▁ch unks ▁of ▁the ▁monument ▁have ▁been ▁lost ▁or ▁sp ray - p aint ed ▁by ▁v and als . ▁Rest oration ▁efforts ▁were ▁un ve iled ▁in ▁ 2 0 0 9 . ▁ ▁The ▁Monument ▁was ▁dedicated ▁in ▁ 1 9 3 3 ; ▁Don ag he y ▁died ▁four ▁years ▁later . ▁At ▁one ▁time ▁there ▁were ▁plans ▁for ▁a ▁Don ag he y ▁State ▁Park , ▁but ▁these ▁were ▁never ▁implemented . ▁ ▁See ▁also ▁ ▁References ▁ ▁External ▁links ▁ ▁Encyclopedia ▁of ▁Ark ansas ▁ ▁Ark ansas ▁Secretary ▁of ▁State ▁ ▁Category : G overn ors ▁of ▁Ark ansas ▁Category : Ar k ansas ▁Democr ats ▁Category : Pe ople ▁from ▁Union ▁Par ish , ▁Louisiana ▁Category : Pe ople ▁from ▁Con way , ▁Ark ansas ▁Category : Pol it icians ▁from ▁Little ▁Rock , ▁Ark ansas ▁Category : 1 8 5 6 ▁birth s ▁Category : 1 9 3 7 ▁death s ▁Category : Univers ity ▁of ▁Ark ansas ▁al umn i ▁Category : Univers ity ▁of ▁Ark ansas ▁at ▁Little ▁Rock ▁people ▁Category : American |
▁real ▁estate ▁business people ▁Category : Bus iness people ▁from ▁Little ▁Rock , ▁Ark ansas ▁Category : D em ocr atic ▁Party ▁state ▁govern ors ▁of ▁the ▁United ▁States <0x0A> </s> ▁The ▁pe ach - th ro ated ▁monitor ▁( Var anus ▁job i ensis ), ▁also ▁known ▁commonly ▁as ▁the ▁Sep ik ▁monitor , ▁is ▁a ▁species ▁of ▁monitor ▁l izard ▁in ▁the ▁family ▁Var an idae . ▁The ▁species ▁is ▁native ▁to ▁New ▁Guinea . ▁ ▁Tax onomy ▁V . ▁job i ensis ▁belongs ▁to ▁the ▁sub gen us ▁E up re pi osa urus , ▁which ▁includes ▁species ▁such ▁as ▁the ▁blue - ta iled ▁monitor ▁and ▁mang ro ve ▁monitor , ▁both ▁of ▁which ▁it ▁is ▁sym pat ric ▁with ▁in ▁much ▁of ▁its ▁range . ▁ ▁It ▁is ▁likely ▁that ▁this ▁species ▁is ▁actually ▁a ▁species ▁complex ▁of ▁multiple ▁different ▁species ▁that ▁have ▁been ▁diver ging ▁since ▁the ▁P li oc ene , ▁and ▁diver ged ▁from ▁the ▁V . ▁indic us ▁species ▁complex ▁ 4 . 7 ▁million ▁years ▁ago . ▁ ▁Distribution ▁V . ▁job i ensis ▁is ▁en demic ▁to ▁New ▁Guinea ▁and ▁surrounding ▁islands ▁such ▁as ▁B iak , ▁Sal aw ati , ▁Y ap en , ▁Norman by , ▁and ▁Wa ige o . ▁It ▁occurs ▁in ▁rain for ests ▁at ▁alt itudes ▁of ▁. ▁ ▁Description ▁V . ▁job i ensis ▁grows ▁up ▁to ▁ ▁in ▁total ▁length ▁( including ▁tail ). ▁The ▁colour ▁of ▁the ▁thro at ▁is ▁white - yellow |
▁to ▁red , ▁to ▁which ▁one ▁of ▁its ▁common ▁names ▁refers . ▁ ▁Diet ▁V . ▁job i ensis ▁primarily ▁e ats ▁insect s , ▁and ▁sometimes ▁fro gs , ▁but ▁may ▁also ▁take ▁fresh water ▁fish ▁and ▁small ▁m amm als . ▁ ▁As ▁food ▁V . ▁job i ensis ▁is ▁h unted ▁for ▁human ▁consumption ▁in ▁New ▁Guinea . ▁ ▁Re production ▁V . ▁job i ensis ▁is ▁ov ip ar ous . ▁ ▁Et ym ology ▁The ▁specific ▁name , ▁job i ensis , ▁which ▁is ▁Latin , ▁means ▁" from ▁J obi ". ▁J obi ▁is ▁the ▁island ▁also ▁known ▁as ▁Y ap en , ▁which ▁is ▁the ▁type ▁local ity ▁of ▁this ▁species . ▁ ▁The ▁junior ▁syn onym , ▁Var anus ▁kar l sch mid ti , ▁was ▁named ▁in ▁honor ▁of ▁American ▁her pet ologist ▁Karl ▁P atter son ▁Schmidt . ▁ ▁References ▁ ▁Further ▁reading ▁A hl ▁E ▁( 1 9 3 2 ). ▁" E ine ▁neue ▁E ide ch se ▁und ▁zwei ▁neue ▁Fr ö sche ▁von ▁der ▁Insel ▁J obi ▁". ▁Mit teil ungen ▁aus ▁dem ▁Zo olog ischen ▁Museum ▁in ▁Berlin ▁ 1 7 : ▁ 8 9 2 – 8 9 9 . ▁( Var anus ▁indic us ▁job i ensis , ▁new ▁sub species , ▁p . ▁ 8 9 2 ). ▁( in ▁German ). ▁M ert ens ▁R ▁( 1 9 5 1 ). ▁" A ▁New ▁L izard ▁of ▁the ▁Gen us ▁Var anus ▁from ▁New |
▁Guinea ". ▁Field iana ▁Zo ology ▁ 3 1 ▁( 4 3 ): ▁ 4 6 7 – 4 7 1 . ▁( Var anus ▁kar l sch mid ti , ▁new ▁species ). ▁Z ieg ler ▁T , ▁Sch mit z ▁A , ▁Koch ▁A , ▁B öh me ▁W ▁( 2 0 0 7 ). ▁" A ▁review ▁of ▁the ▁sub gen us ▁E up re pi osa uras ▁of ▁Var anus ▁( S qu am ata : ▁Var an idae ): ▁morph ological ▁and ▁mole cular ▁ph y log eny , ▁distribution ▁and ▁zo oge ography , ▁with ▁an ▁identification ▁key ▁for ▁members ▁of ▁the ▁V . ▁indic us ▁and ▁V . ▁pr as inus ▁species ▁groups ". ▁Z oot ax a ▁ 1 4 7 2 : ▁ 1 - 2 8 . ▁ ▁External ▁links ▁Ph oto ▁at ▁Var anus . net ▁Care ▁of ▁Var anus ▁job i ensis ▁at ▁Re pt icz one . com ▁ ▁Category : Mon itor ▁l iz ards ▁Category : Re pt iles ▁described ▁in ▁ 1 9 3 2 <0x0A> </s> ▁The ▁Im m igration ▁Department ▁of ▁Malays ia ▁() ▁is ▁a ▁department ▁of ▁the ▁Federal ▁Government ▁of ▁Malays ia ▁which ▁provides ▁services ▁to ▁Malays ian ▁Cit iz ens , ▁P erman ent ▁Res idents ▁and ▁Foreign ▁Vis itors . ▁ ▁The ▁functions ▁of ▁the ▁department ▁are ▁as ▁follows :- ▁▁ 1 . ▁Iss uing ▁of ▁pass ports ▁and ▁travel ▁documents ▁to ▁Malays ian ▁Cit iz ens ▁and ▁P erman ent |
▁Res idents . ▁▁ 2 . ▁Iss uing ▁of ▁vis as , ▁passes ▁and ▁perm its ▁to ▁Foreign ▁National s ▁entering ▁Malays ia . ▁▁ 3 . ▁Admin ister ing ▁and ▁man aging ▁the ▁movement ▁of ▁people ▁at ▁author ised ▁entry ▁and ▁exit ▁points . ▁▁ 4 . ▁En for cing ▁the ▁Im m igration ▁Act ▁ 1 9 5 9 / 6 3 , ▁Im m igration ▁Reg ulations ▁ 1 9 6 3 ▁and ▁Pass port ▁Act ▁ 1 9 6 6 . ▁ ▁The ▁department ▁is ▁a ▁section ▁of ▁the ▁Ministry ▁of ▁Home ▁Affairs . ▁ME SB E H ▁AR 6 8 6 1 3 9 ▁ ▁History ▁ 1 . ▁ ▁In ▁the ▁early ▁years ▁before ▁World ▁War ▁II , ▁the ▁Im m igration ▁Department ▁conducted ▁surve ill ance ▁and ▁ins pection ▁work ▁involving ▁the ▁ins pection ▁of ▁trav ellers ▁and ▁travel ▁documents ▁at ▁entry ▁points . ▁▁ 2 . ▁ ▁Im m igration ▁matters ▁were ▁admin ister ed ▁by ▁a ▁Senior ▁Officer ▁of ▁the ▁Mal ay an ▁Civil ▁Service ▁who ▁bore ▁the ▁title ▁of ▁‘ Im m igration ▁Officer ▁of ▁the ▁Stra its ▁S ett lement ▁and ▁Feder ated ▁Mal ay ▁States ’ . ▁He ▁was ▁assist ed ▁by ▁the ▁Deput y ▁Im m igration ▁Officer , ▁who ▁was ▁actually ▁a ▁police ▁officer , ▁tempor arily ▁second ed ▁to ▁the ▁post . ▁They ▁were ▁based ▁in ▁Pen ang ▁which ▁was ▁the ▁main ▁entry ▁point ▁into ▁Mal aya . ▁Other ▁entry ▁points ▁were ▁Ch ang lo on , ▁Pad |
ang ▁Bes ar , ▁K ro h ▁and ▁Port ▁Sw etten ham . ▁The ▁administrative ▁centre ▁was ▁based ▁in ▁Singapore . ▁▁ 3 . ▁ ▁After ▁World ▁War ▁II , ▁the ▁Im m igration ▁Department ▁was ▁known ▁as ▁The ▁Ref uge es ▁and ▁Dis pos al ▁Pers ons ▁Bureau ▁which ▁was ▁based ▁in ▁K ual a ▁L ump ur ▁and ▁led ▁by ▁a ▁British ▁Military ▁Administration ▁Officer . ▁Its ▁main ▁role ▁was ▁to ▁bring ▁people ▁str and ed ▁in ▁other ▁countries ▁due ▁to ▁World ▁War ▁II ▁back ▁to ▁Malays ia . ▁▁ 4 . ▁ ▁The ▁first ▁imm igration ▁law ▁was ▁the ▁Pass enger ▁Rest riction ▁Ord in ance ▁ 1 9 2 2 , ▁which ▁was ▁enfor ced ▁on ▁ 2 1 ▁July ▁ 1 9 2 2 ▁to ▁reg ulate ▁entries ▁into ▁this ▁country . ▁In ▁ 1 9 3 0 , ▁the ▁Ali ens ▁Im m igration ▁Rest riction ▁Ord in ance ▁was ▁en act ed ▁to ▁reg ulate ▁the ▁arrival s ▁and ▁to ▁monitor ▁the ▁labour ers ▁especially ▁those ▁from ▁China ▁where ▁the ▁qu ota ▁system ▁was ▁used . ▁A ▁review ▁of ▁the ▁law ▁was ▁done ▁as ▁a ▁step ▁to ▁increase ▁the ▁control . ▁The ▁Ali ens ▁Ord in ance ▁ 1 9 3 2 ▁took ▁effect ▁on ▁ 1 ▁April ▁ 1 9 3 3 . ▁▁ 5 . ▁ ▁A ▁treat y ▁on ▁the ▁formation ▁of ▁Feder ated ▁Mal ay ▁States ▁and ▁the ▁Dec laration ▁of ▁Emer gency ▁in ▁ 1 9 4 8 ▁led ▁to ▁a |
▁better ▁Im m igration ▁and ▁Pass port ▁Law ▁which ▁compr ises ▁the ▁following : ▁ ▁The ▁Emer gency ▁( Tra vel ▁Rest riction ) ▁Reg ulation ▁ 1 9 4 8 ▁The ▁Pass port ▁Ord in ance ▁ 1 9 4 9 ▁The ▁Pass port ▁Reg ulations ▁ 1 9 4 9 ▁and ▁The ▁Emer gency ▁( Entry ▁By ▁Land ▁From ▁Th ailand ) ▁Reg ulations ▁ 1 9 4 9 ▁ 6 . ▁ ▁The ▁imm igration ▁laws ▁used ▁during ▁the ▁State ▁of ▁Emer gency ▁were ▁replaced ▁by ▁The ▁Im m igration ▁Ord in ance ▁ 1 9 5 2 . ▁It ▁became ▁the ▁main ▁imm igration ▁law ▁used ▁to ▁reg ulate ▁and ▁monitor ▁the ▁entries ▁of ▁all ▁British ▁national s , ▁people ▁under ▁the ▁British ▁col ony ▁and ▁‘ ali ens ’ ▁to ▁the ▁Feder ated ▁Mal ay ▁States . ▁The ▁law ▁was ▁also ▁enfor ced ▁in ▁Singapore . ▁▁ 7 . ▁ ▁The ▁Im m ig rat ation ▁Department ▁was ▁then ▁placed ▁under ▁the ▁administration ▁of ▁the ▁Ministry ▁of ▁Foreign ▁Affairs . ▁Besides ▁being ▁responsible ▁for ▁the ▁control ▁of ▁entry , ▁the ▁Im m igration ▁Department ▁was ▁also ▁responsible ▁for ▁the : ▁ ▁Iss uing ▁of ▁pass ports ▁at ▁the ▁pass port ▁issu ing ▁offices ▁in ▁Singapore , ▁Pen ang , ▁Res idents ’ ▁Off ices ▁and ▁the ▁office ▁of ▁the ▁British ▁ad visor ; ▁Iss u ance ▁of ▁vis as ▁and ▁citizens hip ▁applications ▁for ▁Commonwealth ▁countries ▁on ▁beh alf ▁of ▁the ▁British ▁government ▁ 8 . ▁ ▁After |
▁independence , ▁The ▁Im m igration ▁Ord in ance ▁ 1 9 5 9 , ▁The ▁Im m igration ▁Reg ulations ▁ 1 9 5 9 ▁and ▁The ▁Pass port ▁Ord in ance ▁ 1 9 6 0 ▁were ▁introduced ▁to ▁replace ▁The ▁Im m igration ▁Ord in ance ▁ 1 9 4 9 . ▁These ▁laws ▁provided ▁greater ▁power ▁for ▁reg ulating ▁the ▁entry ▁of ▁foreign ers ▁and ▁visitors ▁into ▁the ▁Feder ated ▁Mal ay ▁States . ▁▁ 9 . ▁ ▁The ▁formation ▁of ▁Malays ia ▁in ▁ 1 9 6 3 ▁had ▁extended ▁the ▁imm igration ▁requirements ▁to ▁the ▁states ▁of ▁Sab ah ▁and ▁Sar aw ak . ▁The ▁Im m igration ▁( Trans itional ▁Pro vis ions ) ▁Act ▁ 1 9 6 3 ▁was ▁en act ed ▁to ▁protect ▁the ▁interests ▁of ▁both ▁States . ▁A part ▁from ▁reg ulating ▁and ▁cont rolling ▁the ▁entry ▁and ▁exit ▁of ▁non ▁citizens , ▁the ▁Sab ah ▁and ▁Sar aw ak ' s ▁imm igration ▁office ▁also ▁controlled ▁the ▁entry ▁of ▁Malays ian ▁citizens ▁origin ating ▁from ▁Pen ins ular ▁Malays ia ▁( West ▁Malays ia ). ▁▁ 1 0 . ▁In ▁ 1 9 6 4 , ▁the ▁management ▁of ▁imm igration ▁matters ▁was ▁placed ▁under ▁the ▁Ministry ▁of ▁Home ▁Affairs . ▁The ▁administration ▁was ▁handed ▁over ▁to ▁a ▁Malays ian . ▁Mr . ▁I bra him ▁bin ▁Ali ▁was ▁appointed ▁as ▁the ▁first ▁National ▁Im m igration ▁Controller . ▁The ▁appointment ▁took ▁place ▁on ▁ 1 ▁January ▁ 1 9 |
6 7 . ▁Starting ▁from ▁ 1 3 ▁April ▁ 1 9 6 5 , ▁the ▁imm igration ▁head ▁office ▁was ▁located ▁at ▁J al an ▁T ug u , ▁K ual a ▁L ump ur . ▁▁ 1 1 . ▁On ▁ 1 ▁December ▁ 1 9 7 1 , ▁imm igration ▁administrative ▁matters ▁of ▁the ▁Mal ay ▁States ▁came ▁under ▁the ▁Malays ian ▁Im m igration ▁Head quarters . ▁The ▁imm igration ▁laws ▁enfor ced ▁at ▁that ▁time ▁were ▁review ed ▁and ▁in ▁ 1 9 7 4 , ▁a ▁special ▁provision ▁for ▁the ▁states ▁of ▁Sab ah ▁and ▁Sar aw ak ▁was ▁included . ▁The ▁Im m igration ▁Act ▁ 1 9 5 9 / 6 3 ▁( Act ▁No . ▁ 1 5 5 ) ▁and ▁the ▁Pass port ▁Act ▁ 1 9 6 6 ▁( Act ▁No . ▁ 1 5 0 ) ▁were ▁used ▁nation wide . ▁These ▁Act s ▁were ▁re vised ▁and ▁am ended ▁from ▁time ▁to ▁time ▁according ▁to ▁the ▁current ▁situation ▁and ▁need . ▁The ▁title , ▁Im m igration ▁Controller ▁was ▁replaced ▁with ▁the ▁Director ▁General ▁of ▁Im m igration ▁in ▁ 1 9 6 9 . ▁▁ 1 2 . ▁Since ▁its ▁establishment ▁in ▁ 1 9 4 7 , ▁the ▁Head quarters ▁of ▁the ▁Im m igration ▁Department ▁of ▁Malays ia ▁was ▁in ▁Pen ang . ▁On ▁ 1 3 ▁April ▁ 1 9 6 5 , ▁the ▁Im m igration ▁Head quarters ▁was ▁transferred ▁to ▁J al an ▁T |
ug u , ▁K ual a ▁L ump ur . ▁In ▁January ▁ 1 9 8 1 , ▁the ▁office ▁moved ▁to ▁B UK OT A ▁Building , ▁J al an ▁P ant ai ▁Bah aru , ▁K ual a ▁L ump ur , ▁before ▁moving ▁to ▁P us at ▁Band ar ▁Dam ans ara , ▁K ual a ▁L ump ur ▁in ▁ 1 9 8 8 . ▁Now , ▁the ▁headquarters ▁of ▁the ▁Im m igration ▁Department ▁of ▁Malays ia ▁are ▁located ▁at ▁Put raj aya . ▁The ▁move ▁of ▁prem ises ▁started ▁in ▁September ▁ 2 0 0 4 ▁and ▁it ▁was ▁done ▁in ▁stages ▁to ▁ensure ▁that ▁the ▁quality ▁of ▁services ▁to ▁the ▁public ▁was ▁maintained . ▁ ▁History ▁of ▁Cor ruption ▁ ▁The ▁Im m igration ▁Department ▁of ▁Malays ia ▁has ▁a ▁long standing ▁history ▁of ▁cor ruption , ▁allowing ▁for ▁thre ats ▁of ▁terror ism ▁and ▁human ▁tra ff ick ing ▁to ▁become ▁significant ▁problems ▁for ▁the ▁country . ▁Despite ▁the ▁Malays ian ▁government ' s ▁desire ▁to ▁promote ▁an ▁image ▁of ▁Malays ia ▁as ▁a ▁progress ive ▁nation , ▁w ides p read ▁ab uses ▁of ▁imm igration ▁controls ▁since ▁at ▁least ▁the ▁ 2 0 0 0 s ▁have ▁sul lied ▁that ▁image . ▁In ▁ 2 0 1 7 , ▁government ▁minister ▁Dat uk ▁Ser i ▁Id ris ▁Har on ▁named ▁the ▁Department ▁of ▁Im m igration ▁in ▁Mal ac ca ▁as ▁the ▁most ▁cor rupt ▁civil ▁service ▁department ▁in ▁the ▁state . ▁After |
▁being ▁promoted ▁as ▁head ▁of ▁Malays ia ' s ▁Department ▁of ▁Im m igration ▁in ▁ 2 0 1 7 , ▁Dat uk ▁Ser i ▁Must af ar ▁Ali ▁revealed ▁that ▁an ▁internal ▁aud it ▁and ▁an ▁investigation ▁by ▁the ▁Malays ian ▁Anti - Cor ruption ▁Commission ▁( MA CC ) ▁had ▁un cover ed ▁a ▁pass port ▁fra ud ▁scheme ▁being ▁committed ▁by ▁officers ▁in ▁Sel ang or ▁since ▁ 2 0 1 4 ▁that , ▁according ▁to ▁MAC C ▁deput y ▁commission er ▁Dat uk ▁Az am ▁B aki , ▁" could ▁be ▁happening ▁at ▁most ▁Im m igration ▁offices ▁nation wide ." ▁In ▁ 2 0 1 6 , ▁massive ▁cor ruption ▁was ▁discovered ▁involving ▁the ▁dis abling ▁of ▁the ▁national ▁electronic ▁security ▁system ▁at ▁Malays ia ' s ▁international ▁air ports ▁by ▁imm igration ▁officers ▁prof iting ▁from ▁b rib es ▁by ▁human ▁tra ff ick ing ▁synd ic ates ▁to ▁allow ▁illegal ▁passage ▁of ▁migr ants ▁into ▁the ▁country , ▁raising ▁questions ▁about ▁the ▁system ' s ▁effect iveness ▁at ▁keeping ▁terror ists ▁from ▁streaming ▁into ▁Malays ia . ▁ ▁Un iform s ▁Since ▁the ▁ 1 9 6 0 s , ▁imm igration ▁officials ▁use ▁white ▁uniform ▁and ▁dark ▁blue ▁uniform ▁color . ▁In ▁early ▁ 2 0 1 3 ▁a ▁new ▁color ▁uniform ▁imm igration ▁officers ▁have ▁been ▁converted ▁to ▁black ▁overall . ▁Fe atures ▁new ▁uniform s ▁are ▁black ▁ber et , ▁bad ge ▁over ▁the ▁left ▁shoulder ▁and ▁right , ▁tags ▁and ▁bad ges |
▁on ▁the ▁ch est ▁to ▁the ▁right ▁service . ▁They ▁have ▁also ▁introduced ▁digital ▁uniform s ▁for ▁enfor cement ▁duties ▁and ▁tasks ▁in ▁the ▁imm igration ▁det ention ▁centre . ▁The ▁need ▁to ▁change ▁uniform s ▁was ▁de emed ▁necessary ▁as ▁the ▁previous ▁white ▁and ▁dark ▁blue ▁uniform s ▁have ▁remained ▁in ▁service ▁since ▁the ▁ 1 9 6 0 s . ▁In ▁addition , ▁the ▁application ▁ranks ▁were ▁changed ▁to ▁avoid ▁confusion ▁with ▁the ▁ranks ▁of ▁other ▁ag encies . ▁ ▁We ap on ry ▁and ▁equipment ▁Hi att ▁speed c uff s , ▁T - bat on , ▁LED ▁flash light , ▁ri ot ▁shield s , ▁hel m ets ▁and ▁walk ie - t alk ies ▁are ▁supplied ▁to ▁the ▁officer ▁when ▁on ▁duty ▁to ▁en force ▁imm igration ▁law . ▁The ▁need ▁for ▁better ▁weapons ▁is ▁necessary ▁to ▁ensure ▁the ▁safety ▁of ▁officers ▁during ▁the ▁operation ▁and ▁control ▁of ▁d eta ine es ▁in ▁imm igration ▁det ention . ▁ ▁The ▁Department ▁of ▁Im m igration ▁bear ▁fire ar ms , ▁but ▁not ▁all ▁imm igration ▁officers ▁are ▁supplied ▁with ▁them . ▁Im m igration ▁officers ▁are ▁lic ensed ▁by ▁the ▁particular ▁State ▁Im m igration ▁Director ▁to ▁carry ▁fire ar ms ▁in ▁the ▁possession ▁of ▁imm igration ▁like ▁the ▁standard ▁issued ▁ones : ▁▁ ▁Smith ▁& ▁W ess on ▁Model ▁ 3 8 ▁ ▁H K ▁US P ▁Comp act ▁ ▁Ve ktor ▁SP 1 ▁▁ ▁Fran chi ▁SP AS - 1 2 ▁ ▁Rem ington ▁Model ▁ |
8 7 0 ▁ ▁M oss berg ▁ 5 0 0 ▁ ▁Report ed ▁that ▁Im m igration ▁only ▁have ▁ 1 9 0 ▁of ▁them ▁carry ▁fire ar ms , ▁compare ▁to ▁their ▁ 1 4 , 0 0 0 ▁personnel . ▁Im m igration ▁officers ▁also ▁not ▁supplied ▁with ▁rif les , ▁SM G s ▁and ▁car b ines . ▁ ▁There ▁is ▁no ▁indic ation ▁that ▁imm igration ▁will ▁use ▁better ▁weapons ▁with ▁better ▁fire power ▁such ▁as ▁those ▁used ▁by ▁Royal ▁Malays ian ▁Police ▁or ▁Malays ian ▁Pr ison ▁Department . ▁However , ▁as ▁of ▁ 2 0 1 4 ▁the ▁need ▁for ▁superior ▁weapons ▁and ▁better ▁equipment ▁were ▁planned . ▁ ▁Im m igration ▁Det ention ▁Centre ▁Im migr ants ▁who ▁commit ▁off enses ▁will ▁be ▁held ▁in ▁imm igration ▁det ention ▁facilities ▁are ▁located ▁in ▁every ▁state ▁in ▁Malays ia ▁to ▁further ▁investigation ▁and ▁rep atri ation ▁to ▁the ▁country ▁of ▁origin . ▁Det ention ▁over c row ding ▁in ▁det ention ▁cent res ▁across ▁the ▁country ▁and ▁dil ap id ated ▁buildings ▁has ▁resulted ▁in ▁efforts ▁to ▁combat ▁the ▁problem ▁of ▁illegal ▁imm igr ants ▁to ▁be ▁difficult . ▁The ▁problem ▁is ▁being ▁resolved ▁by ▁upgrad ing ▁det ention ▁and ▁det ention ▁capacity . ▁Cong estion ▁also ▁due ▁to ▁documentation ▁problems ▁involved ▁countries ▁and ▁UN H CR ▁refuge es . ▁Im m igration ▁officers ▁working ▁in ▁det ention ▁cent res ▁receive ▁training ▁tact ics ▁and ▁techniques ▁to ▁control ▁prisoners , ▁un ar med ▁combat , ▁training |
▁T - bat on ▁and ▁so ▁by ▁cert ified ▁train ers ▁from ▁within ▁and ▁outside ▁the ▁department . ▁Im m igration ▁Department ▁has ▁also ▁set ▁up ▁a ▁special ▁anti - ri ot ▁team ▁known ▁as ▁the ▁" P as uk an ▁K aw al an ▁K has ". ME SB E H ▁AR 6 8 6 1 3 9 ▁ ▁Pas uk an ▁K aw al an ▁K has ▁Im m igration ▁Department ▁introduced ▁the ▁Special ▁Control ▁Team ▁( ), ▁which ▁was ▁created ▁to ▁address ▁the ▁threat ▁situation , ▁the ▁ri ot ▁of ▁illegal ▁imm igr ants ▁in ▁the ▁dep ot ▁and ▁the ▁accomp any ing ▁department ▁officials ▁and ▁other ▁VI P s . ▁It ▁is ▁an ▁el ite ▁team ▁and ▁first ▁trained ▁imm igration ▁training ▁modules ▁of ▁the ▁Federal ▁Reserve ▁Unit ▁( FR U ) ▁of ▁the ▁Royal ▁Malays ia ▁Police ▁( R MP ). ▁Stud ents ▁who ▁pract iced ▁the ▁team ▁is ▁divided ▁into ▁two , ▁namely ▁Pre vent ▁Ri ots ▁and ▁Close ▁Qu arter ▁Battle ▁( C Q B ) ▁skills , ▁mart ial ▁arts ▁and ▁un ar med ▁combat ▁situations ▁or ▁dangerous ▁and ▁high - ris k ▁operations . ▁The ▁team ▁is ▁under going ▁training ▁under ▁the ▁super vision ▁of ▁teaching ▁staff ▁is ▁made ▁up ▁of ▁a ▁mix ▁of ▁professional ▁train ers ▁who ▁commission ed ▁from ▁abroad , ▁a ▁former ▁police ▁tra iner ▁and ▁former ▁FR U ▁train ers . ▁The ▁team ▁who ▁received ▁the ▁anti - ri ot ▁training ▁and ▁the ▁skills ▁to ▁use ▁the ▁T - ▁b |
aton , ▁hand c uff s ▁and ▁sp ray ▁te ar ▁from ▁qualified ▁exper ts ▁concerned . ▁The ▁team ▁is ▁equ ipped ▁with ▁digital ▁uniform ▁and ▁gun ▁last ▁for ▁setting ▁up ▁of ▁any ▁unexpected ▁situations ▁occur . ▁The ▁Bra vo ▁pl ato on ▁was ▁assigned ▁as ▁teams ▁Tan dem ▁control ▁involving ▁dign itar ies ▁who ▁faced ▁a ▁high ▁risk ▁situation ▁either ▁from ▁the ▁department ▁or ▁the ▁department ▁yet . ▁For ▁example , ▁the ▁service ▁is ▁required ▁to ▁accomp any ▁the ▁team ▁super iors ▁and ▁operations ▁department ▁following ▁the ▁terror ist ▁attack ▁is ▁likely . ▁ ▁In ▁popular ▁culture ▁ ▁Ger ak ▁K has ▁season ▁ 1 8 ▁( 2 0 1 8 ) ▁co operation ▁with ▁Sk op ▁Production ▁and ▁Royal ▁Malays ia ▁Police ▁which ▁one ▁of ▁the ▁episodes ▁about ▁operation ▁against ▁Human ▁tra ff ick ing ▁and ▁murder ▁Im m igration ▁officer ▁ ▁References ▁ ▁External ▁links ▁▁▁ ▁Im m igration ▁Malays ia ▁Category : Im m igration ▁to ▁Malays ia ▁Category : Min istry ▁of ▁Home ▁Affairs ▁( Mal ays ia ) <0x0A> </s> ▁Sam ▁Jam ie ▁Bird ▁( born ▁ 9 ▁January ▁ 1 9 8 7 ) ▁is ▁a ▁British ▁professional ▁racing ▁driver ▁who ▁currently ▁drives ▁for ▁En vision ▁Virgin ▁Racing ▁in ▁Formula ▁E ▁and ▁for ▁AF ▁Cor se ▁in ▁the ▁FI A ▁World ▁End urance ▁Championship . ▁ ▁Career ▁ ▁Formula ▁B MW ▁Bird ▁made ▁his ▁name ▁in ▁single ▁se ater ▁racing ▁in ▁the ▁Formula ▁B MW ▁category , ▁coming ▁four teenth ▁overall ▁in ▁his ▁debut ▁season ▁and |
▁second ▁in ▁the ▁ro ok ie ▁cup . ▁He ▁came ▁runner ▁up ▁in ▁ 2 0 0 5 , ▁which ▁was ▁only ▁his ▁second ▁season ▁in ▁the ▁category ▁and ▁also ▁came ▁fourth ▁in ▁the ▁Formula ▁B MW ▁world ▁final , ▁the ▁race ▁itself ▁was ▁won ▁by ▁German ▁Marco ▁Hol zer . ▁ ▁Formula ▁Ren ault ▁For ▁ 2 0 0 6 , ▁Bird ▁entered ▁the ▁British ▁Formula ▁Ren ault ▁series , ▁where ▁he ▁won ▁four ▁races ▁and ▁came ▁fourth ▁in ▁the ▁championship , ▁ 1 1 1 ▁points ▁behind ▁series ▁champion ▁Sebastian ▁H oh ent hal . ▁ ▁Formula ▁Three ▁ ▁In ▁ 2 0 0 7 , ▁Bird ▁rac ed ▁in ▁the ▁British ▁Formula ▁ 3 ▁Championship ▁with ▁Car lin ▁Mot ors port , ▁racing ▁in ▁a ▁Mer cedes ▁power ed ▁D all ara . ▁In ▁March ▁ 2 0 0 7 , ▁Bird ▁secured ▁spons or ship ▁from ▁B P , ▁" The ▁brand ▁is ▁already ▁prominent ▁in ▁the ▁World ▁R ally ▁Championship " ▁Mark ▁Re ader , ▁B P ' s ▁UK ▁Fu els ▁Mark eting ▁Manager , ▁commented , ▁" Sam ' s ▁an ▁incred ible ▁prospect ▁and ▁we ' re ▁excited ▁to ▁be ▁getting ▁into ▁a ▁relationship ▁at ▁this ▁stage ▁of ▁his ▁career " ▁he ▁added . ▁Bird ▁was ▁elected ▁to ▁the ▁Motor ▁Sports ▁Association ▁Race ▁El ite ▁Sch eme ▁in ▁April ▁ 2 0 0 7 , ▁along ▁with ▁ 5 ▁other ▁drivers ▁in ▁various ▁British ▁series ▁and ▁also ▁participated ▁in ▁a ▁series ▁of ▁aer od ynamic |
▁tests ▁with ▁the ▁AT & T ▁Williams ▁F 1 ▁Team . ▁ ▁Bird ▁moved ▁to ▁the ▁Man or ▁Mot ors port ▁and ▁the ▁Formula ▁ 3 ▁Euro ▁Series ▁in ▁ 2 0 0 8 ▁and ▁had ▁a ▁testing ▁year , ▁finishing ▁ele vent h ▁in ▁the ▁championship ▁with ▁ 2 3 ▁points – ▁ 1 6 ▁of ▁which ▁came ▁from ▁second ▁places ▁during ▁Saturday ▁races ▁at ▁Catalunya ▁and ▁Le ▁Mans ▁and ▁only ▁picked ▁up ▁points ▁from ▁three ▁other ▁races . ▁For ▁ 2 0 0 9 , ▁he ▁joined ▁Mc L aren ▁Aut os port ▁BR DC ▁Award ▁winner ▁Alexander ▁Sim s , ▁ 2 0 0 8 ▁M ücke ▁driver ▁Christian ▁V iet oris ▁and ▁ 2 0 0 8 ▁Formula ▁B MW ▁Europe ▁runner - up ▁Marco ▁W itt mann ▁at ▁M ücke ▁Mot ors port . ▁He ▁earned ▁his ▁first ▁pole ▁position ▁and ▁fast est ▁la ps , ▁but ▁failed ▁to ▁win ▁a ▁race ▁en ▁route ▁to ▁e ighth ▁in ▁the ▁championship . ▁ ▁GP 2 ▁Series ▁Bird ▁missed ▁the ▁final ▁round ▁of ▁the ▁F 3 ▁Euro series ▁season ▁to ▁join ▁up ▁with ▁the ▁AR T ▁Grand ▁Prix ▁team ▁for ▁a ▁GP 2 ▁Asia ▁Series ▁test ▁at ▁the ▁Y as ▁Marina ▁Circ uit ▁in ▁Ab u ▁D hab i . ▁He ▁rac ed ▁in ▁the ▁ 2 0 0 9 – 1 0 ▁season ▁for ▁the ▁team , ▁where ▁he ▁finished ▁sevent h ▁in ▁the ▁series , ▁with ▁a ▁second ▁place ▁in ▁the ▁final ▁round . ▁ ▁Bird ▁cont |
ested ▁the ▁ 2 0 1 0 ▁GP 2 ▁Series ▁with ▁AR T , ▁having ▁long ▁cov et ed ▁a ▁drive ▁with ▁the ▁French ▁team . ▁He ▁was ▁fast ▁but ▁frequently ▁un l ucky , ▁losing ▁several ▁potential ▁results ▁due ▁to ▁technical ▁issues , ▁engine ▁fail ures ▁and ▁coll isions ▁for ▁which ▁he ▁was ▁not ▁at ▁fault . ▁However , ▁he ▁managed ▁to ▁claim ▁his ▁ma iden ▁series ▁win ▁at ▁the ▁first ▁race ▁at ▁Mon za , ▁as ▁well ▁as ▁claim ing ▁his ▁third ▁fast est ▁lap ▁of ▁the ▁season . ▁ ▁For ▁ 2 0 1 1 , ▁Bird ▁moved ▁to ▁the ▁i S port ▁International ▁team ▁alongside ▁Marcus ▁Eric sson . ▁His ▁GP 2 ▁Asia ▁campaign ▁resulted ▁in ▁three ▁ret ire ments ▁from ▁four ▁races , ▁but ▁after ▁a ▁strong ▁start ▁to ▁the ▁main ▁series ▁season , ▁he ▁was ▁second ▁in ▁the ▁D ri vers ' ▁Championship ▁after ▁four ▁r ounds , ▁with ▁the ▁same ▁number ▁of ▁points ▁as ▁leader ▁Rom ain ▁Gros je an . ▁After ▁this ▁point , ▁however , ▁he ▁gradually ▁sli pped ▁back ▁in ▁the ▁stand ings ▁and ▁finished ▁sixth ▁overall ▁at ▁the ▁end ▁of ▁the ▁season . ▁ ▁Bird ▁competed ▁for ▁the ▁new ▁Russian ▁Time ▁squad ▁in ▁ 2 0 1 3 ▁and ▁enjoyed ▁a ▁h ug ely ▁successful ▁season . ▁The ▁English man ▁took ▁five ▁wins ▁on ▁the ▁way ▁to ▁second ▁place ▁in ▁the ▁championship , ▁having ▁taken ▁the ▁championship ▁race ▁down ▁to ▁the ▁very ▁last ▁week end . ▁Bird ' s ▁performances ▁alongside ▁team |
mate ▁Tom ▁D ill mann ▁secured ▁Russian ▁Time ▁first ▁place ▁in ▁the ▁GP 2 ▁construct ors ' ▁championship . ▁ ▁Formula ▁Ren ault ▁ 3 . 5 ▁ ▁Between ▁his ▁last ▁two ▁seasons ▁in ▁the ▁GP 2 ▁series , ▁Bird ▁competed ▁in ▁the ▁ 2 0 1 2 ▁Formula ▁Ren ault ▁ 3 . 5 ▁season . ▁He ▁won ▁two ▁races ▁and ▁took ▁five ▁further ▁pod ium ▁positions ▁to ▁head ▁into ▁the ▁final ▁round ▁at ▁Catalunya ▁in ▁a ▁three - way ▁battle ▁for ▁the ▁title ▁with ▁Robin ▁F rij ns ▁and ▁Jules ▁Bian chi . ▁He ▁lost ▁out ▁on ▁the ▁title ▁by ▁just ▁ 1 0 ▁points ▁and ▁ended ▁up ▁finishing ▁third ▁in ▁the ▁championship . ▁ ▁Formula ▁One ▁On ▁ 1 6 ▁November ▁ 2 0 1 0 ▁he ▁took ▁part ▁in ▁the ▁young ▁drivers ▁test ▁in ▁Ab u ▁D hab i ▁driving ▁for ▁Mer cedes ▁GP . ▁ ▁World ▁End urance ▁Championship ▁ ▁In ▁ 2 0 1 4 ▁Bird ▁made ▁two ▁guest ▁appearances ▁for ▁the ▁Ferr ari ▁AF ▁Cor se ▁team . ▁The ▁first ▁was ▁at ▁his ▁home ▁race ▁in ▁Britain ▁for ▁the ▁ 6 ▁hours ▁of ▁Silver stone ▁where ▁he ▁came ▁ 3 rd ▁in ▁the ▁G TE ▁Am ateur ▁class . ▁His ▁second ▁appearance ▁was ▁at ▁the ▁legend ary ▁ 2 4 ▁H ours ▁of ▁Le ▁Mans . ▁Bird ▁took ▁pole ▁position ▁in ▁the ▁G TE ▁Am ▁class , ▁ 2 nd ▁overall ▁of ▁all ▁GT ▁cars . ▁He ▁ran ▁in ▁the ▁first ▁st int ▁of ▁the ▁race |
, ▁holding ▁the ▁G TE ▁Am ▁lead ▁until ▁he ▁coll ided ▁with ▁a ▁pair ▁of ▁front - running ▁L MP 1 ▁cars , ▁the ▁number ▁ 3 ▁A udi ▁and ▁a ▁Toy ota , ▁in ▁wet ▁conditions ▁on ▁the ▁M uls anne ▁Stra ight ▁putting ▁him ▁out ▁of ▁the ▁race ▁in ▁the ▁second ▁hour . ▁ ▁Formula ▁E ▁▁ 2 0 1 4 - 1 5 ▁ ▁In ▁the ▁ 2 0 1 4 – 1 5 ▁Formula ▁E ▁season , ▁Bird ▁began ▁driving ▁for ▁Richard ▁Br anson ' s ▁Virgin ▁Racing ▁alongside ▁Ja ime ▁Al gu ers u ari . ▁He ▁claimed ▁third ▁place ▁in ▁the ▁first ▁race , ▁the ▁Be ij ing ▁e P rix , ▁before ▁domin ating ▁the ▁second ▁race ▁of ▁the ▁season ▁in ▁Put raj aya ▁to ▁claim ▁victory ▁from ▁second ▁on ▁the ▁grid . ▁At ▁the ▁following ▁race ▁in ▁P unta ▁del ▁Este , ▁he ▁did ▁not ▁manage ▁to ▁qual ify ▁and ▁so ▁started ▁from ▁ 1 8 th ▁place ▁and ▁soon ▁retired ▁from ▁the ▁race ▁after ▁a ▁collision . ▁The ▁ 2 0 1 5 ▁Long ▁Beach ▁e P rix ▁was ▁another ▁race ▁to ▁forget ▁for ▁Bird : ▁after ▁starting ▁ 1 1 th , ▁he ▁suffered ▁a ▁susp ension ▁failure ▁on ▁lap ▁ 1 1 ▁and ▁retired ▁from ▁the ▁race . ▁Bird ▁managed ▁to ▁avoid ▁the ▁massive ▁first ▁lap ▁collision ▁at ▁the ▁ 2 0 1 5 ▁Mon aco ▁e P rix ▁and ▁went ▁from ▁his ▁qual ifying ▁position ▁of ▁ 1 2 th ▁to |
▁finish ▁fourth . ▁The ▁final ▁round ▁of ▁the ▁season ▁was ▁the ▁ 2 0 1 5 ▁London ▁e P rix ▁where ▁Bird ▁started ▁from ▁fourth ▁and ▁went ▁on ▁to ▁cross ▁the ▁line ▁second ▁at ▁his ▁home ▁e P rix . ▁Race ▁winner ▁St é ph ane ▁Sar raz in ▁received ▁a ▁ 4 9 - second ▁penalty ▁and ▁so ▁Bird ▁was ▁handed ▁the ▁win . ▁He ▁finished ▁the ▁season ▁with ▁ 1 0 3 ▁points ▁and ▁secured ▁ 5 th ▁place ▁in ▁the ▁championship ▁after ▁his ▁home ▁win . ▁▁ 2 0 1 5 - 1 6 ▁At ▁the ▁first ▁race ▁of ▁the ▁season ▁in ▁Be ij ing , ▁Bird ▁only ▁managed ▁to ▁finish ▁ 7 th . ▁In ▁the ▁next ▁race ▁in ▁Put raj aya , ▁Bird ▁took ▁ 2 nd ▁place ▁after ▁Ren ault ▁had ▁a ▁mechanical ▁failure ▁and ▁the ▁two ▁Dragon ▁cars ▁of ▁Lo ic ▁Du val ▁and ▁Jer ome ▁d ' Am b ros io ▁had ▁susp ension ▁failure ▁while ▁running ▁ 2 nd ▁and ▁ 3 rd ▁respectively . ▁In ▁the ▁next ▁race ▁at ▁P unta ▁del ▁Este , ▁Bird ▁finished ▁ 2 nd ▁just ▁behind ▁Sebast ien ▁Bu emi ; ▁in ▁Buenos ▁Aires , ▁Bird ▁won ▁after ▁f ending ▁off ▁Bu emi ▁who ▁started ▁ 1 8 th ▁and ▁last . ▁Further ▁ 6 th ▁places ▁finish es ▁were ▁achieved ▁in ▁Mexico , ▁Long ▁Beach ▁and ▁Paris , ▁before ▁an ▁ 1 1 th ▁place ▁finish ▁in ▁Berlin ▁after ▁contact ▁in ▁the ▁race . ▁At ▁home ▁in |
▁the ▁double - season ▁finale ▁in ▁London , ▁Bird ▁finish ▁ 7 th ▁in ▁the ▁first ▁race , ▁but ▁in ▁next ▁the ▁next ▁race ▁he ▁retired ▁because ▁of ▁thro tt le ▁failure . ▁Bird ▁eventually ▁finished ▁the ▁season ▁ 4 th ▁place ▁with ▁ 8 8 ▁points ▁after ▁being ▁over t aken ▁by ▁Pro st ▁after ▁Pro st ▁won ▁the ▁double - header ▁in ▁London . ▁▁ 2 0 1 6 - 1 7 ▁▁ 2 0 1 7 - 1 8 ▁▁ 2 0 1 8 - 1 9 ▁Sam ' s ▁first ▁pod ium ▁finish ▁of ▁the ▁season ▁came ▁in ▁the ▁ 2 0 1 9 ▁Mar rak esh ▁e P rix , ▁finishing ▁in ▁ 3 rd ▁after ▁taking ▁pole ▁position . ▁In ▁ 2 0 1 9 , ▁Bird ▁became ▁the ▁first ▁Formula ▁E ▁driver ▁to ▁win ▁a ▁race ▁in ▁every ▁Formula ▁E ▁season , ▁after ▁winning ▁the ▁ 2 0 1 9 ▁Santiago ▁e P rix . ▁▁ 2 0 1 9 - 2 0 ▁Bird ▁won ▁the ▁opening ▁race ▁of ▁the ▁season , ▁the ▁ 2 0 1 9 ▁Di ri y ah ▁e P rix , ▁for ▁En vision ▁Virgin ▁Racing . ▁ ▁Personal ▁life ▁He ▁was ▁educated ▁at ▁Mill field ▁School ▁in ▁Som erset . ▁He ▁successfully ▁proposed ▁marriage ▁to ▁his ▁partner , ▁H oll ie , ▁after ▁winning ▁the ▁G TE - Pro ▁class ▁race ▁at ▁the ▁ 2 0 1 7 ▁ 6 ▁H ours ▁of ▁Bah rain . ▁Their ▁wed ding ▁was |
▁held ▁on ▁ 2 3 ▁August ▁ 2 0 1 8 ▁in ▁Lake ▁Como , ▁Italy . ▁ ▁Racing ▁record ▁ ▁Career ▁summary ▁ ▁† ▁As ▁Bird ▁was ▁a ▁guest ▁driver , ▁he ▁was ▁in el ig ible ▁for ▁points . ▁* ▁Season ▁still ▁in ▁progress . ▁ ▁Complete ▁Formula ▁ 3 ▁Euro ▁Series ▁results ▁key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics ▁indicate ▁fast est ▁lap ) ▁ ▁Complete ▁GP 2 ▁Series ▁results ▁( key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics ▁indicate ▁fast est ▁lap ) ▁ ▁Complete ▁GP 2 ▁Asia ▁Series ▁results ▁( key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics ▁indicate ▁fast est ▁lap ) ▁ ▁Complete ▁Formula ▁Ren ault ▁ 3 . 5 ▁Series ▁results ▁( key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics ▁indicate ▁fast est ▁lap ) ▁ ▁Complete ▁FI A ▁World ▁End urance ▁Championship ▁results ▁( key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics ▁indicate ▁fast est ▁lap ) ▁▁ 2 4 ▁H ours ▁of ▁Le ▁Mans ▁results ▁ ▁Complete ▁We ather T ech ▁Sports Car ▁Championship ▁results ▁( key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics ▁indicate ▁fast est ▁lap ) ▁ ▁Complete ▁Formula ▁E ▁results ▁( key ) ▁( R aces ▁in ▁bold ▁indicate ▁pole ▁position ; ▁races ▁in ▁ital ics |
▁indicate ▁fast est ▁lap ) ▁ ▁† ▁Driver ▁did ▁not ▁finish ▁the ▁race , ▁but ▁was ▁class ified ▁as ▁he ▁completed ▁over ▁ 9 0 % ▁of ▁the ▁race ▁distance . ▁* ▁Season ▁still ▁in ▁progress . ▁ ▁References ▁ ▁External ▁links ▁▁▁▁▁▁ ▁Sam ▁Bird ' s ▁ESP NF 1 ▁column ▁ ▁Category : 1 9 8 7 ▁birth s ▁Category : L iving ▁people ▁Category : Pe ople ▁from ▁Ro e ham pton ▁Category : Pe ople ▁educated ▁at ▁Mill field ▁Category : English ▁racing ▁drivers ▁Category : Mc L aren ▁Aut os port ▁BR DC ▁Award ▁nom ine es ▁Category : Form ula ▁B MW ▁UK ▁drivers ▁Category : B rit ish ▁Formula ▁Ren ault ▁ 2 . 0 ▁drivers ▁Category : B rit ish ▁Formula ▁Three ▁Championship ▁drivers ▁Category : Form ula ▁ 3 ▁Euro ▁Series ▁drivers ▁Category : GP 2 ▁Asia ▁Series ▁drivers ▁Category : GP 2 ▁Series ▁drivers ▁Category : World ▁Series ▁Formula ▁V 8 ▁ 3 . 5 ▁drivers ▁Category : 2 4 ▁H ours ▁of ▁Day ton a ▁drivers ▁Category : 2 4 ▁H ours ▁of ▁Le ▁Mans ▁drivers ▁Category : We ather T ech ▁Sports Car ▁Championship ▁drivers ▁Category : FI A ▁World ▁End urance ▁Championship ▁drivers ▁Category : Form ula ▁E ▁drivers <0x0A> </s> ▁Ba idi ▁( , ▁) ▁is ▁a ▁small ▁village ▁in ▁ ▁Ba idi ▁Township , ▁Nag arz ê ▁County , ▁L h oka ▁( Sh ann an ) ▁Pref ect ure , ▁Tib et ▁Aut onom ous ▁Region , ▁China |
. ▁ ▁It ▁is ▁located ▁at ▁the ▁western ▁end ▁of ▁Y amd rok ▁Lake . ▁Near ▁the ▁village ▁the ▁Y amd rok ▁H yd rop ower ▁Station , ▁the ▁largest ▁power ▁station ▁in ▁Tib et , ▁was ▁completed ▁and ▁dedicated ▁in ▁ 1 9 9 6 . ▁ ▁References ▁ ▁Category : Pop ulated ▁places ▁in ▁Sh ann an , ▁Tib et ▁Category : N ag arz ê ▁County <0x0A> </s> ▁Make ▁Way ▁for ▁a ▁Lady ▁is ▁a ▁ 1 9 3 6 ▁rom antic ▁comedy / ▁drama ▁directed ▁by ▁David ▁Bur ton , ▁st arring ▁Herbert ▁Marshall ▁and ▁Anne ▁Sh ir ley . ▁June ▁D rew ▁( An ne ▁Sh ir ley ) ▁is ▁the ▁te en aged ▁" l ady " ▁based ▁on ▁Elizabeth ▁Jordan ' s ▁novel ▁D addy ▁and ▁I . ▁ ▁Plot ▁ ▁June ▁D rew ▁( An ne ▁Sh ir ley ) ▁is ▁the ▁daughter ▁of ▁wid owed ▁Christopher ▁D rew ▁( Her bert ▁Marshall ), ▁who ▁suff ers ▁in ▁silence ▁as ▁his ▁daughter ▁tries ▁to ▁" match " ▁him ▁with ▁every ▁el ig ible ▁woman ▁in ▁sight . ▁ ▁Cast ▁ ▁Herbert ▁Marshall ▁as ▁Christopher ▁' Chr is ' ▁D rew ▁▁▁ ▁Anne ▁Sh ir ley ▁as ▁June ▁D rew ▁▁▁ ▁Ger tr ude ▁Michael ▁as ▁Miss ▁Ele an or ▁Em erson ▁▁▁ ▁Mar got ▁Gra h ame ▁as ▁Val erie ▁Br ought on ▁▁▁ ▁Taylor ▁Hol mes ▁as ▁George ▁Terry ▁▁▁ ▁Clara ▁B land ick ▁as ▁Mrs . ▁D ell , ▁D rew ' s |
▁Ma id ▁▁▁ ▁Frank ▁C og hl an ▁Jr . ▁as ▁Billy ▁Hop kins ▁▁▁ ▁Max ine ▁Jenn ings ▁as ▁Miss ▁Marian ▁Moore ▁▁▁ ▁Mary ▁Jo ▁Ell is ▁as ▁M ild red ▁Jackson ▁▁▁ ▁Murray ▁K inn ell ▁as ▁Doctor ▁Bar nes ▁ ▁References ▁ ▁External ▁links ▁▁▁▁▁▁▁▁▁▁ ▁Category : 1 9 3 6 ▁films ▁Category : 1 9 3 0 s ▁rom antic ▁comedy - d rama ▁films ▁Category : American ▁rom antic ▁comedy - d rama ▁films ▁Category : American ▁films ▁Category : American ▁black - and - white ▁films ▁Category : English - language ▁films ▁Category : Fil ms ▁based ▁on ▁American ▁nov els <0x0A> </s> ▁V ay app ar app adi ▁is ▁a ▁location ▁in ▁Man jer i ▁Municip ality ▁in ▁Mal app ur am ▁district ▁of ▁Ker ala ▁State ▁of ▁south ▁India . ▁ ▁Culture ▁V ay app ar ap adi ▁village ▁is ▁a ▁pre domin antly ▁Muslim ▁populated ▁area . ▁ ▁H ind us ▁exist ▁in ▁compar atively ▁smaller ▁numbers . ▁ ▁So ▁the ▁culture ▁of ▁the ▁local ity ▁is ▁based ▁upon ▁Muslim ▁trad itions . ▁ ▁D uff ▁M utt u , ▁Kol k ali ▁and ▁Ara van am utt u ▁are ▁common ▁folk ▁arts ▁of ▁this ▁local ity . ▁ ▁There ▁are ▁many ▁libraries ▁attached ▁to ▁mos ques ▁giving ▁a ▁rich ▁source ▁of ▁Islam ic ▁studies . ▁ ▁Most ▁of ▁the ▁books ▁are ▁written ▁in ▁Arab i - Mal ay al am ▁which ▁is ▁a ▁version ▁of ▁the ▁Mal ay al am ▁language ▁written ▁in |
▁Arab ic ▁script . ▁ ▁People ▁gather ▁in ▁mos ques ▁for ▁the ▁evening ▁prayer ▁and ▁continue ▁to ▁sit ▁there ▁after ▁the ▁pray ers ▁discuss ing ▁social ▁and ▁cultural ▁issues . ▁ ▁Business ▁and ▁family ▁issues ▁are ▁also ▁sorted ▁out ▁during ▁these ▁evening ▁meet ings . ▁ ▁The ▁H indu ▁minor ity ▁of ▁this ▁area ▁keeps ▁their ▁rich ▁trad itions ▁by ▁celebr ating ▁various ▁festiv als ▁in ▁their ▁tem ples . ▁ ▁H indu ▁rit uals ▁are ▁done ▁here ▁with ▁a ▁regular ▁dev otion ▁like ▁other ▁parts ▁of ▁Ker ala . ▁ ▁Transport ation ▁V ay app ar ap adi ▁village ▁connect s ▁to ▁other ▁parts ▁of ▁India ▁through ▁Man jer i ▁town . ▁ ▁National ▁highway ▁No . 6 6 ▁passes ▁through ▁Par app an ang adi ▁and ▁the ▁northern ▁stretch ▁connect s ▁to ▁Go a ▁and ▁M umb ai . ▁ ▁The ▁southern ▁stretch ▁connect s ▁to ▁C och in ▁and ▁T riv and rum . ▁▁ ▁National ▁Highway ▁No . 9 6 6 ▁connect s ▁to ▁Pal ak k ad ▁and ▁Co imb atore . ▁ ▁The ▁nearest ▁air port ▁is ▁at ▁K oz h ik ode . ▁ ▁The ▁nearest ▁major ▁railway ▁station ▁is ▁at ▁Tir ur . ▁ ▁References ▁ ▁Category : Man jer i <0x0A> </s> ▁Gu ill ermo ▁Esp in osa ▁Rodríguez ▁is ▁a ▁Cub an ▁nur se , ▁journalist , ▁blog ger ▁and ▁human ▁rights ▁activ ist . ▁In ▁ 2 0 0 6 ▁he ▁was ▁dismiss ed ▁from ▁his ▁job ▁with ▁the ▁public ▁health ▁service |
▁and ▁arrested ▁after ▁reporting ▁on ▁an ▁out break ▁of ▁den gue ▁fe ver . ▁Since ▁then ▁he ▁has ▁been ▁repeatedly ▁arrested ▁for ▁his ▁human ▁rights ▁activities . ▁ ▁Until ▁ 2 0 0 6 ▁Gu ill ermo ▁Esp in osa ▁Rodríguez ▁worked ▁as ▁a ▁nur se ▁in ▁the ▁public ▁health ▁service ▁and ▁as ▁a ▁part - time ▁rep orter . ▁In ▁October ▁ 2 0 0 5 ▁Esp in osa ▁Rodríguez ▁file d ▁a ▁report ▁on ▁an ▁event ▁in ▁Santiago ▁de ▁Cuba ▁attended ▁by ▁ 2 0 , 0 0 0 ▁young ▁people ▁as ▁part ▁of ▁a ▁campaign ▁for ▁the ▁prevent ion ▁of ▁A ID S . ▁Police ▁tried ▁to ▁remove ▁a ▁particip ant , ▁but ▁were ▁forced ▁to ▁retre at ▁when ▁the ▁crowd ▁turned ▁against ▁them . ▁In ▁July ▁ 2 0 0 6 ▁Esp in osa ▁submitted ▁reports ▁on ▁an ▁out break ▁of ▁den gue ▁fe ver ▁in ▁Santiago ▁de ▁Cuba ▁to ▁the ▁independent ▁ag ency ▁A gen cia ▁de ▁Pr ensa ▁Lib re ▁Oriental ▁( AP LO ). ▁ ▁Short ly ▁afterwards ▁he ▁was ▁dismiss ed ▁from ▁his ▁job . ▁Cuba ▁suppress es ▁reports ▁of ▁ep ide m ics ▁to ▁avoid ▁disturb ing ▁the ▁tour ists . ▁Before ▁Esp in osa ▁published ▁his ▁reports , ▁the ▁official ▁media ▁had ▁refused ▁to ▁recognize ▁the ▁existence ▁of ▁den gue ▁fe ver ▁in ▁Cuba . ▁The ▁reports ▁seem ▁to ▁have ▁triggered ▁his ▁arrest . ▁ ▁Esp in osa ▁Rodríguez ▁was ▁arrested ▁in ▁October ▁ 2 0 0 6 ▁along ▁with ▁Arm ando ▁Bet |
anc ourt ▁Re ina ▁and ▁Ray m undo ▁P erd ig on ▁Brit o . ▁In ▁November ▁ 2 0 0 6 ▁he ▁was ▁conv icted ▁under ▁article ▁ 7 2 ▁of ▁the ▁Cub an ▁Pen al ▁Code ▁in ▁a ▁Santiago ▁de ▁Cuba ▁court ▁on ▁grounds ▁of ▁" social ▁dangerous ness ." ▁He ▁was ▁sent enced ▁to ▁two ▁years ▁of ▁home ▁conf in ement . ▁Although ▁he ▁had ▁lost ▁his ▁job ▁with ▁the ▁public ▁health ▁service , ▁Esp in osa ▁was ▁told ▁he ▁should ▁find ▁another ▁job ▁with ▁a ▁government ▁department ▁or ▁he ▁would ▁have ▁to ▁serve ▁his ▁house ▁arrest ▁in ▁j ail . ▁In ▁a ▁call ▁for ▁Esp in osa ' s ▁release ▁on ▁ 8 ▁November ▁ 2 0 0 6 , ▁Re por ters ▁Without ▁B orders ▁noted ▁that ▁" social ▁dangerous ness " ▁meant ▁he ▁might ▁commit ▁a ▁crime ▁although ▁he ▁had ▁not ▁in ▁fact ▁committed ▁any . ▁The ▁authorities ▁could ▁use ▁this ▁charge ▁to ▁imprison ▁anyone ▁they ▁wanted ▁to . ▁In ▁a ▁press ▁release ▁on ▁ 2 9 ▁January ▁ 2 0 0 7 ▁the ▁Office ▁of ▁the ▁Special ▁R app orte ur ▁for ▁Fre edom ▁of ▁Expression ▁of ▁the ▁Inter - American ▁Commission ▁on ▁Human ▁Rights ▁noted ▁that ▁Esp in osa ▁had ▁been ▁ja iled ▁and ▁said ▁it ▁" re iter ates ▁its ▁great ▁concern ▁over ▁the ▁system atic ▁and ▁continuous ▁situation ▁of ▁utter ▁and ▁complete ▁dis res pect ▁for ▁freedom ▁of ▁thought ▁and ▁expression ▁in ▁Cuba ." ▁ ▁In ▁March ▁ 2 0 0 9 ▁Esp |
in osa ▁was ▁working ▁for ▁the ▁Center ▁of ▁App lied ▁Mark eting ▁and ▁Political ▁Public ity ▁in ▁Santiago ▁de ▁Cuba . ▁He ▁was ▁det ained ▁and ▁then ▁placed ▁under ▁house ▁arrest ▁for ▁his ▁activities ▁on ▁the ▁sixth ▁anni versary ▁of ▁the ▁arrest ▁of ▁ 7 5 ▁activ ists ▁in ▁the ▁" Black ▁Spring " ▁of ▁ 2 0 0 3 . ▁In ▁February ▁ 2 0 1 1 ▁Esp in osa ▁was ▁det ained ▁for ▁comm emor ating ▁the ▁death ▁of ▁Or lando ▁Z ap ata , ▁a ▁political ▁prisoner , ▁one ▁year ▁earlier . ▁He ▁was ▁one ▁of ▁many ▁arrested ▁during ▁march es ▁held ▁across ▁the ▁country . ▁In ▁April ▁ 2 0 1 2 ▁during ▁a ▁pap al ▁mass ▁in ▁Santiago ▁de ▁Cuba ▁a ▁diss ident ▁named ▁And rés ▁Car ri ón ▁Al vare z ▁shout ed ▁" down ▁with ▁commun ism " ▁and ▁was ▁prompt ly ▁arrested . ▁A ▁sc uffle ▁broke ▁out , ▁and ▁Car ri ón ▁was ▁attacked ▁by ▁a ▁Red ▁Cross ▁st ret cher ▁bear er . ▁Esp in osa ▁went ▁to ▁Car ri ón ' s ▁aid , ▁and ▁was ▁himself ▁arrested ▁for ▁" cont empt ▁of ▁authority ". ▁At ▁the ▁time , ▁Esp in osa ▁was ▁under ▁house ▁arrest ▁for ▁three ▁years ▁for ▁his ▁pro - dem ocracy ▁activities . ▁ ▁References ▁ ▁Category : C ub an ▁journal ists ▁Category : M ale ▁journal ists ▁Category : C ub an ▁blog gers ▁Category : C ub an ▁male ▁writers ▁Category : L iving ▁people |
▁Category : Pe ople ▁from ▁Santiago ▁de ▁Cuba ▁Category : C ub an ▁n urs es ▁Category : M ale ▁blog gers ▁Category : Year ▁of ▁birth ▁missing ▁( l iving ▁people ) <0x0A> </s> ▁The ▁Al add in ▁Theater ▁( also ▁known ▁as ▁The ▁Historic ▁C ocoa ▁Village ▁Play house ) ▁is ▁an ▁historic ▁the ater ▁in ▁C ocoa , ▁Florida , ▁United ▁States . ▁It ▁is ▁located ▁at ▁ 3 0 0 ▁Bre vard ▁Avenue ▁and ▁originally ▁opened ▁its ▁doors ▁on ▁August ▁ 1 8 , ▁ 1 9 2 4 . ▁On ▁October ▁ 1 7 , ▁ 1 9 9 1 , ▁it ▁was ▁added ▁to ▁the ▁U . S . ▁National ▁Register ▁of ▁Historic ▁Places . ▁ ▁Bre vard ▁Community ▁College ▁owned ▁the ▁the ater ▁from ▁ 1 9 8 5 ▁to ▁ 2 0 1 0 . ▁In ▁the ▁mid - e ight ies , ▁the ▁college ▁had ▁res cu ed ▁the ▁the ater ▁from ▁a ▁dil ap id ated ▁state . ▁In ▁ 2 0 1 0 ▁the ▁college ▁offered ▁ownership ▁to ▁the ▁city ▁of ▁C ocoa . ▁The ▁the ater ▁has ▁its ▁own ▁board ▁of ▁direct ors . ▁ ▁The ▁annual ▁budget ▁for ▁ 2 0 0 9 ▁was ▁about ▁$ 2 6 3 , 0 0 0 . ▁ ▁History ▁In ▁ 1 9 2 4 ▁the ▁Al add in ▁Theater ▁first ▁started ▁showing ▁silent ▁mov ies ▁and ▁live ▁acts . ▁ ▁It ▁was ▁built ▁for ▁$ 8 0 , 0 0 0 . |
▁ ▁The ▁S par ks ▁Theater ▁chain ▁purchased ▁the ▁Al add in ▁in ▁ 1 9 3 9 ▁and ▁changed ▁its ▁name ▁to ▁the ▁" State ▁Theater ." ▁ ▁The ▁Kent ▁Theater ▁Ch ain ▁purchased ▁the ▁building ▁in ▁ 1 9 6 0 ▁and ▁renamed ▁it ▁the ▁Fine ▁Arts ▁Theater . ▁ ▁Sub sequently , ▁the ▁city ▁of ▁C ocoa ▁bought ▁the ▁building ▁and ▁renamed ▁it ▁the ▁C ocoa ▁Village ▁Play house . ▁The ▁city ▁sold ▁it ▁to ▁Bre vard ▁Community ▁College ▁for ▁$ 1 ▁in ▁ 1 9 8 4 . ▁Through ▁don ations , ▁and ▁gr ants , ▁the ▁building ▁was ▁restored ▁from ▁ 1 9 8 5 ▁through ▁ 1 9 8 9 . ▁In ▁ 1 9 9 0 , ▁the ▁play house ▁began ▁st aging ▁community ▁based ▁musical s . ▁ ▁In ▁ 2 0 0 7 , ▁a ▁$ 2 . 8 ▁million ▁an nex ▁was ▁started . ▁ ▁In ▁ 2 0 1 1 , ▁the ▁building ▁was ▁returned ▁to ▁the ▁city ▁of ▁C ocoa . ▁ ▁In ▁ 2 0 1 2 , ▁there ▁were ▁ 5 0 , 0 0 0 ▁pay ing ▁customers ▁ann ually . ▁ ▁References ▁ ▁External ▁links ▁ ▁C ocoa ▁Village ▁Play house ▁( o fficial ▁site ) ▁C ocoa ▁Village ▁Play house ▁( add itional ▁info ▁via ▁C ocoa ▁Village ▁Publishing ) ▁Bre vard ▁County ▁list ings ▁at ▁National ▁Register ▁of ▁Historic ▁Places ▁Florida ' s ▁Office ▁of ▁Cultural ▁and ▁Historical ▁Program s ▁Bre vard ▁County ▁list ings |
▁C ocoa ▁Village ▁Play house ▁C ocoa - R ock ledge ▁Historical ▁Tra il ▁( Arch ived ▁ 2 0 0 9 - 1 0 - 2 4 ) ▁at ▁Historic ▁H ik ing ▁Tra ils ▁( Arch ived ▁ 2 0 0 9 - 1 0 - 2 4 ) ▁ ▁Category : Build ings ▁and ▁structures ▁in ▁Bre vard ▁County , ▁Florida ▁Category : National ▁Register ▁of ▁Historic ▁Places ▁in ▁Bre vard ▁County , ▁Florida ▁Category : The at res ▁on ▁the ▁National ▁Register ▁of ▁Historic ▁Places ▁in ▁Florida ▁Category : T our ist ▁attra ctions ▁in ▁Bre vard ▁County , ▁Florida ▁Category : The at res ▁completed ▁in ▁ 1 9 2 4 ▁Category : C ocoa , ▁Florida ▁Category : 1 9 2 4 ▁establish ments ▁in ▁Florida <0x0A> </s> ▁White ▁Station ▁may ▁refer ▁to : ▁ ▁White ▁Station , ▁Mississippi , ▁an ▁un in cor por ated ▁community ▁located ▁in ▁Clay ▁County ▁White ▁Station , ▁M emph is , ▁Tennessee , ▁an ▁un in cor por ated ▁area ▁in ▁Sh el by ▁County ▁White ▁Station ▁High ▁School , ▁in ▁M emph is , ▁Tennessee ▁White ▁Station ▁Middle ▁School , ▁in ▁M emph is , ▁Tennessee ▁White ▁Station ▁Tower , ▁a ▁high - r ise ▁office ▁building ▁in ▁M emph is , ▁Tennessee <0x0A> </s> ▁The ▁European ▁Athletics ▁U 2 3 ▁Championships ▁is ▁a ▁bien n ial ▁athlet ics ▁competition ▁for ▁European ▁athlet es ▁under ▁the ▁age ▁of ▁ 2 3 , ▁which ▁is ▁organized ▁by ▁the ▁European ▁Athlet |
ic ▁Association . ▁The ▁oldest ▁of ▁the ▁' age - group ' ▁track ▁and ▁field ▁events ▁held ▁by ▁European ▁Athletics ▁- ▁European ▁Athletics ▁U 2 0 ▁Championships ▁( pre viously ▁called ▁' J un ior ▁Championships ') ▁are ▁held ▁in ▁the ▁same ▁odd ▁number ed ▁years , ▁while ▁the ▁European ▁Athletics ▁U 1 8 ▁Championships , ▁previously ▁the ▁' Y outh ▁Championships ' ▁are ▁held ▁in ▁even ▁number ed ▁years . ▁ ▁The ▁event ▁was ▁first ▁held ▁in ▁ 1 9 9 7 ▁and ▁was ▁a ▁replacement ▁for ▁the ▁European ▁Athletics ▁U 2 3 ▁Cup ▁– ▁a ▁bien n ial ▁event ▁which ▁had ▁" A " ▁and ▁" B " ▁level ▁le agues ▁that ▁was ▁held ▁in ▁ 1 9 9 2 ▁and ▁ 1 9 9 4 . ▁ ▁Ed itions ▁ ▁European ▁Athletics ▁U 2 3 ▁Cup ▁ ▁European ▁Athletics ▁U 2 3 ▁Championships ▁ ▁Championships ▁records ▁ ▁Men ▁ ▁Women ▁ ▁All - time ▁medal ▁table ▁Medal ▁table ▁includes ▁ 1 9 9 7 – 2 0 1 7 ▁Championships . ▁ ▁References ▁ ▁External ▁links ▁ ▁European ▁Athlet ic ▁Association ▁ ▁European ▁Athletics ▁U 2 3 ▁Championships ▁– ▁European ▁Athlet ic ▁Association ▁▁▁ ▁U 2 3 ▁Category : Under - 2 3 ▁athlet ics ▁compet itions ▁Category : Cont inental ▁athlet ics ▁champion ships ▁Category : B ienn ial ▁athlet ics ▁compet itions <0x0A> </s> ▁L oss ▁of ▁significance ▁is ▁an ▁und es irable ▁effect ▁in ▁calculations ▁using ▁finite - prec ision ▁arithmetic ▁such ▁as ▁floating - point ▁arithmetic |
. ▁It ▁occurs ▁when ▁an ▁operation ▁on ▁two ▁numbers ▁increases ▁relative ▁error ▁substantial ly ▁more ▁than ▁it ▁increases ▁absolute ▁error , ▁for ▁example ▁in ▁subtract ing ▁two ▁nearly ▁equal ▁numbers ▁( known ▁as ▁cat ast roph ic ▁can cellation ). ▁The ▁effect ▁is ▁that ▁the ▁number ▁of ▁significant ▁digits ▁in ▁the ▁result ▁is ▁reduced ▁un accept ably . ▁W ays ▁to ▁avoid ▁this ▁effect ▁are ▁studied ▁in ▁numerical ▁analysis . ▁ ▁Dem on str ation ▁of ▁the ▁problem ▁The ▁effect ▁can ▁be ▁demonstrated ▁with ▁decimal ▁numbers . ▁The ▁following ▁example ▁demonstr ates ▁loss ▁of ▁significance ▁for ▁a ▁decimal ▁floating - point ▁data ▁type ▁with ▁ 1 0 ▁significant ▁digits : ▁ ▁Consider ▁the ▁decimal ▁number ▁▁▁▁▁ ▁x ▁= ▁ 0 . 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 0 ▁ ▁A ▁floating - point ▁representation ▁of ▁this ▁number ▁on ▁a ▁machine ▁that ▁keeps ▁ 1 0 ▁floating - point ▁digits ▁would ▁be ▁▁▁▁▁ ▁y ▁= ▁ 0 . 1 2 3 4 5 6 7 8 9 0 ▁ ▁which ▁is ▁fairly ▁close ▁when ▁meas uring ▁the ▁error ▁as ▁a ▁percentage ▁of ▁the ▁value . ▁ ▁It ▁is ▁very ▁different ▁when ▁measured ▁in ▁order ▁of ▁precision . ▁The ▁value ▁' x ' ▁is ▁accurate ▁to ▁, ▁while ▁the ▁value ▁' y ' ▁is ▁only ▁accurate ▁to ▁. ▁ ▁Now ▁perform ▁the ▁calculation ▁▁▁▁▁ ▁x ▁- ▁y ▁= ▁ 0 . 1 2 3 4 5 6 7 8 9 1 2 3 |
4 5 6 7 8 9 0 ▁− ▁ 0 . 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 0 0 ▁ ▁The ▁answer , ▁accurate ▁to ▁ 2 0 ▁significant ▁digits , ▁is ▁▁▁▁▁▁ 0 . 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 0 ▁ ▁However , ▁on ▁the ▁ 1 0 - digit ▁floating - point ▁machine , ▁the ▁calculation ▁yields ▁▁▁▁▁▁ 0 . 1 2 3 4 5 6 7 8 9 1 ▁− ▁ 0 . 1 2 3 4 5 6 7 8 9 0 ▁= ▁ 0 . 0 0 0 0 0 0 0 0 0 1 ▁ ▁In ▁both ▁cases ▁the ▁result ▁is ▁accurate ▁to ▁same ▁order ▁of ▁magnitude ▁as ▁the ▁inputs ▁( − 2 0 ▁and ▁− 1 0 ▁respectively ). ▁ ▁In ▁the ▁second ▁case , ▁the ▁answer ▁seems ▁to ▁have ▁one ▁significant ▁digit , ▁which ▁would ▁amount ▁to ▁loss ▁of ▁significance . ▁ ▁However , ▁in ▁computer ▁floating - point ▁arithmetic , ▁all ▁operations ▁can ▁be ▁viewed ▁as ▁being ▁performed ▁on ▁an til og arith ms , ▁for ▁which ▁the ▁rules ▁for ▁significant ▁figures ▁indicate ▁that ▁the ▁number ▁of ▁significant ▁figures ▁remains ▁the ▁same ▁as ▁the ▁smallest ▁number ▁of ▁significant ▁figures ▁in ▁the ▁mant iss as . ▁ ▁The ▁way ▁to ▁indicate ▁this ▁and ▁represent ▁the ▁answer ▁to ▁ 1 0 ▁significant ▁figures ▁is ▁ ▁Work ar ounds ▁It ▁is ▁possible ▁to |
▁do ▁comput ations ▁using ▁an ▁exact ▁fraction al ▁representation ▁of ▁rational ▁numbers ▁and ▁keep ▁all ▁significant ▁digits , ▁but ▁this ▁is ▁often ▁prohib it ively ▁slower ▁than ▁floating - point ▁arithmetic . ▁ ▁One ▁of ▁the ▁most ▁important ▁parts ▁of ▁numerical ▁analysis ▁is ▁to ▁avoid ▁or ▁minim ize ▁loss ▁of ▁significance ▁in ▁calculations . ▁If ▁the ▁underlying ▁problem ▁is ▁well - posed , ▁there ▁should ▁be ▁a ▁stable ▁algorithm ▁for ▁solving ▁it . ▁ ▁L oss ▁of ▁significant ▁bits ▁▁ ▁Let ▁x ▁and ▁y ▁be ▁positive ▁normal ized ▁floating - point ▁numbers . ▁ ▁In ▁the ▁sub tra ction ▁x ▁− ▁y , ▁r ▁significant ▁bits ▁are ▁lost ▁where ▁ ▁for ▁some ▁positive ▁integers ▁p ▁and ▁q . ▁ ▁Inst ability ▁of ▁the ▁quadratic ▁equation ▁▁ ▁For ▁example , ▁consider ▁the ▁quadratic ▁equation ▁ ▁with ▁the ▁two ▁exact ▁solutions : ▁ ▁This ▁formula ▁may ▁not ▁always ▁produce ▁an ▁accurate ▁result . ▁ ▁For ▁example , ▁when ▁ ▁is ▁very ▁small , ▁loss ▁of ▁significance ▁can ▁occur ▁in ▁either ▁of ▁the ▁root ▁calculations , ▁depending ▁on ▁the ▁sign ▁of ▁. ▁ ▁The ▁case ▁, ▁, ▁ ▁will ▁serve ▁to ▁illustrate ▁the ▁problem : ▁ ▁We ▁have ▁ ▁In ▁real ▁arithmetic , ▁the ▁roots ▁are ▁ ▁In ▁ 1 0 - digit ▁floating - point ▁arithmetic : ▁ ▁Notice ▁that ▁the ▁solution ▁of ▁greater ▁magnitude ▁is ▁accurate ▁to ▁ten ▁digits , ▁but ▁the ▁first ▁non zero ▁digit ▁of ▁the ▁solution ▁of ▁less er ▁magnitude ▁is ▁wrong . ▁ ▁Because ▁of ▁the ▁sub tra ction ▁that |
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