LEADER 04733nam 2200661 450 001 9910827594203321 005 20230807210047.0 010 $a0-8014-5662-2 010 $a0-8014-5544-8 024 7 $a10.7591/9780801455445 035 $a(CKB)2670000000606923 035 $a(SSID)ssj0001461261 035 $a(PQKBManifestationID)12644009 035 $a(PQKBTitleCode)TC0001461261 035 $a(PQKBWorkID)11470942 035 $a(PQKB)10804023 035 $a(MiAaPQ)EBC3138717 035 $a(OCoLC)1080549244 035 $a(MdBmJHUP)muse58489 035 $a(DE-B1597)480038 035 $a(OCoLC)905902787 035 $a(OCoLC)979740829 035 $a(DE-B1597)9780801455445 035 $a(Au-PeEL)EBL3138717 035 $a(CaPaEBR)ebr11036245 035 $a(CaONFJC)MIL759703 035 $a(OCoLC)922998676 035 $a(EXLCZ)992670000000606923 100 $a20150414h20152015 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOur Lady of the Rock $evision and pilgrimage in the Mojave Desert /$fLisa M. Bitel ; photographs by Matt Gainer 210 1$aIthaca, [New York] ;$aLondon, [England] :$cCornell University Press,$d2015. 210 4$d©2015 215 $a1 online resource (224 pages) $cillustrations (some color), photographs 300 $aIncludes index. 311 0 $a0-8014-4854-9 311 0 $a1-336-28417-X 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tContents --$tPreface: Looking the Wrong Way --$t1. Déjà Vu --$t2. The Desert Is Wide --$t3. The Model Visionary --$t4. Looking Like Pilgrims --$t5. From Witness to Visionary --$t6. Discernment at a Distance --$tConclusion: The Longue Durée of Christian Religious Vision --$tNotes --$tIndex --$tImpressions 330 $aFor more than twenty years, Maria Paula Acuña has claimed to see the Virgin Mary, once a month, at a place called Our Lady of the Rock in the Mojave Desert of California. Hundreds of men, women, and children follow her into the desert to watch her see what they cannot. While she sees and speaks with the Virgin, onlookers search the skies for signs from heaven, snapping photographs of the sun and sky. Not all of them are convinced that Maria Paula can see the Virgin, yet at each vision event they watch for subtle clues to Mary's presence, such as the unexpected scent of roses or a cloud in the shape of an angel. The visionary depends on her audience to witness and authenticate her visions, while observers rely on Maria Paula and the Virgin to create a sacred space and moment where they, too, can experience firsthand one of the oldest and most fundamental promises of Christianity: direct contact with the divine. Together, visionary and witnesses negotiate and enact their monthly liturgy of revelations. Our Lady of the Rock, which features text by Lisa M. Bitel and more than sixty photographs by Matt Gainer, shows readers what happens in the Mojave Desert each month and tells us how two thousand years of Christian revelatory tradition prepared Maria Paula and her followers to meet in the desert. Based on six years of observation and interviews, chapters analyze the rituals, iconographies, and physical environment of Our Lady of the Rock. Bitel and Gainer also provide vivid portraits of the pilgrims-who they are, where they come from, and how they practice the traditional Christian discernment of spirits and visions. Our Lady of the Rock follows three pilgrims as they return home with relics and proofs of visions where, out of Maria Paula's sight, they too have learned to see the Virgin. The book also documents the public response from the Catholic Church and popular news media to Maria Paula and other contemporary visionaries. Throughout, Our Lady of the Rock locates Maria Paula and her followers in the context of recent demographic and cultural shifts in the American Southwest, the astonishing increase in reported apparitions and miracles from around the world, the latest developments in communications and visual technologies, and the never-ending debate among academics, faith leaders, scientists, and citizen observers about sight, perception, reason, and belief. 606 $aElectronic books 606 $aRELIGION / Christianity / Catholic$2bisacsh 615 0$aElectronic books. 615 7$aRELIGION / Christianity / Catholic. 676 $a232.91/70979495 700 $aBitel$b Lisa M.$f1958-$01098855 702 $aGainer$b Matt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910827594203321 996 $aOur Lady of the Rock$94076504 997 $aUNINA LEADER 02642nam 2200505Ia 450 001 9910438057103321 005 20200520144314.0 010 $a3-642-38652-0 024 7 $a10.1007/978-3-642-38652-7 035 $a(OCoLC)847735622 035 $a(MiFhGG)GVRL6WIW 035 $a(CKB)2670000000371295 035 $a(MiAaPQ)EBC1317208 035 $a(EXLCZ)992670000000371295 100 $a20130314d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aDimensionality reduction with unsupervised nearest neighbors /$fOliver Kramer 205 $a1st ed. 2013. 210 $aDordrecht $cSpringer$d2013 215 $a1 online resource (xviii, 130 pages) $cillustrations (some color) 225 0 $aIntelligent systems reference library ;$v51 300 $a"ISSN: 1868-4394." 311 $a3-642-38651-2 320 $aIncludes bibliographical references and index. 327 $aPart I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions. 330 $aThis book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.  . 410 0$aIntelligent systems reference library ;$vv. 51. 606 $aDimensions 606 $aData mining 615 0$aDimensions. 615 0$aData mining. 676 $a006.31 676 $a519.5/36 700 $aKramer$b Oliver$0761919 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438057103321 996 $aDimensionality Reduction with Unsupervised Nearest Neighbors$92513616 997 $aUNINA