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1,201.0328 | Let us first agree on what the term "semantics" means: An unorthodox
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1,201.0414 | Continuity in Information Algebras | [0.004874754697084, 0.01284213270992, 0.005925842560827, -0.020765714347362, -0.022409724071621003, 0.0006527185905720001, -0.012653475627303, -0.004595138598233, -0.004217825364321, -0.046166993677616, 0.035278808325529, 0.028702773153781003, 0.017747206613421003, 0.012276162393391, 0.006293049082159, -0.011299190111458001, 0.023056546226143, 0.029457399621605002, -0.011319403536617001, -0.004089808091521, -0.020833091810345, -0.0014940937981, -0.026681451126933, -0.020240170881152, -0.0034244563430540003, 0.007579957600682001, 0.021439488977193003, -0.038485970348119, -0.010288529098033001, -0.012303113006055001, 0.019135180860757002, 0.021169979125261, -0.036868914961814006, 0.018043667078018, 0.006232409272342001, 0.0019371001981190001, 0.009453048929572001, -0.015308145433664001, 0.021223880350589003, -0.022342346608638, 0.028999233618378, 0.033446140587329004, 0.005868571344763001, -0.014162729494273002, 0.025401279330253, 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1,201.0478 | Technical Note: Exploring \Sigma^P_2 / \Pi^P_2-hardness for
Argumentation Problems with fixed distance to tractable classes | [0.022391721606254, 0.0009396650712000001, -0.005482975859194001, -0.026948096230626002, 0.015801003202795, 0.032438039779663, -0.015619862824678001, -0.000304150162264, -0.01950741186738, -0.026209600269794003, 0.013905997388064001, 0.016135416924953003, 0.007956237532198, -0.008666864596307, 0.0033545780461280003, 0.022949077188968003, 0.022740067914128, 0.008680798113346001, 0.020301641896367, 0.015689533203840002, -0.026920227333903, 0.000219131805351, -0.003925866447389, -0.011801984161138, 0.0023548232857130004, 0.017194390296936, 0.027129236608743, -0.018587777391076, -0.024997355416417, -0.024788346141576, 0.040101666003465, -0.02204337529838, -0.020761460065841, -0.013822394423186002, -0.001413416466675, -0.008910707198083, -0.002191100269556, -0.007440684363245, 0.01832303404808, -0.007858700118958001, 0.035642828792333006, 0.009098813869059, -0.0036994412075720003, -0.02395231463015, -0.007733295205980001, 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1,201.0564 | The RegularGcc Matrix Constraint | [0.004004224669188, 0.0022163735702630002, -0.005834225565195, -0.014169335365295, 0.0036845891736440005, -0.0029575063381340003, -0.031527999788522006, -0.006508621387183, -0.027425426989793004, -0.018686382099986, 0.020442619919776, 0.009884112514555001, 0.005261691752821001, 0.01098000537604, 0.013136667199432002, 0.017730988562107003, 0.020807918161153002, -0.019824424758553002, 0.011296128854155001, 0.016340047121047002, -0.029673412442207003, -0.007889024913311001, -0.032848693430423, -0.028549419716000002, 0.0042992727831000005, 0.02257820777595, 0.030656905844807004, -0.02269060537219, -0.009413440711796001, -0.012040773406624001, 0.014267684891819002, -0.025458438321948003, -0.010200235061347, 0.000300975429126, -0.020063273608684002, -0.010460158810019, 0.010607683099806, -0.007650176528841, 0.002595721045508, 0.008956817910075, 0.038890153169631, -0.0017017954960460002, 0.006059024017304001, -0.0032156733796000004, 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1,201.061 | Random Forests for Metric Learning with Implicit Pairwise Position
Dependence | [-0.024644168093800004, -0.004090247210115, -0.01429361756891, -0.009152569808065001, -6.743987614754587e-05, 0.024657858535647, 0.01146638393402, -0.0039499122649430006, -0.024096518754959002, -0.013095636852085, 0.017250917851924, 0.019578421488404, -0.006119968369603, -0.004038905259221, -0.009932968765497001, 0.007194727659225001, 0.032283861190080004, -0.007673920132219, 0.004329843446612001, -0.004398299381136, -0.022042838856577002, 0.017428902909159, -0.031270708888769004, -0.020126070827245, 0.006342450622469, -0.0008865054696790001, 0.036555513739585, -0.019030774012207003, 0.0072426469996570006, -0.007927207276225, 0.015963943675160002, -0.020290365442633, 0.002271028468385, -0.010213638655841, -0.033926803618669, -0.019783789291977, 0.026026979088783, -0.023699475452303, 0.008981429971754001, -0.02725918777287, 0.017196152359247003, -0.002753643551841, -0.014471602626144001, -0.020440967753529, 0.016552666202187, 0.0035768272355190004, 0.027204422280192, 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1,201.0782 | Umgebungserfassungssystem fuer mobile Roboter (environment logging
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1,201.0856 | Complexity Classification in Infinite-Domain Constraint Satisfaction | [0.025557048618793002, 0.021900214254856002, 0.009216850623488001, -0.024387948215007, -0.01460014283657, 0.023042125627398, -0.013397058472037001, -0.001176745747216, -0.022294446825981, -0.053071655333042006, 0.038145251572132, 0.011018078774213002, -0.0019609597511580003, 0.018284164369106, 0.019888276234269, 0.018882308155298, 0.027800086885690002, 0.005804712418466001, 0.021927403286099, 0.0016992718446990002, -0.040537826716899004, -0.006389262154698001, -0.010725803673267, -0.030804399400949003, -0.006260117515921, 0.0049890619702630005, 0.01060345582664, -0.018392918631434, -0.0009295016643590001, -0.020812680944800002, 0.01948045194149, -0.016938341781497, -0.023939341306686002, -0.007300071418285, -0.019004656001925, -0.0033798515796660002, 0.016272226348519003, -0.024958904832601003, -0.000893816992174, 0.012024046853184001, 0.025842525064945002, 0.013900043442845001, -0.012064829468727, 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1,201.0959 | Constrained variable clustering and the best basis problem in functional
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1,201.0979 | Sciduction: Combining Induction, Deduction, and Structure for
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1,201.1192 | Formalization of semantic network of image constructions in electronic
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1,201.1216 | Probabilistic Motion Estimation Based on Temporal Coherence | [-0.017591938376426003, -0.008189815096557, 0.020285956561565, -0.032247398048639, 0.010035217739641, 0.026334028691053002, 0.001602098927833, 0.005852754693478001, -0.013584586791694001, -0.044262718409299004, -0.013369065709412, 0.013928074389696002, 0.005098429508507, 0.017820930108428, -0.00321261677891, -0.007920413278043001, 0.032274339348077004, 0.018696486949920002, 0.0032968048471950003, -0.012109612114727001, -0.002283180365338, 0.010829953476786001, -0.006586874835193, -0.024488626047968, 0.013874193653464002, -0.0033254288136950003, 0.025512352585792, -0.024488626047968, 0.008304310962557, -0.0064959516748780005, 0.028233312070369002, -0.007671216968446, -0.0028405054472380002, -0.0033692065626380004, -0.02120192348957, -0.024461686611175003, 0.019625922664999, -0.0012333552585910002, 0.020919051021337003, -0.012938022613525, 0.0071054729633030006, -0.0020912317559120003, -0.007563456427305001, 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1,201.1384 | A Thermodynamical Approach for Probability Estimation | [-0.0041188839823, -0.015109338797628002, -0.006154654547572001, -0.033843837678432, -0.0023401214275500003, 0.016556696966290002, -0.000610392948146, 0.006212143227458, -0.030272783711552002, -0.023577060550451, 0.008136318065226, 0.031327866017818, -0.0027476137038320004, 0.016570223495364002, -0.0005334597663020001, 0.018815658986568, 0.041689328849315005, 0.025822496041655003, 2.146573206118773e-05, -0.004967685323208001, -0.007216501515358, 0.027621548622846003, -0.003953182138502, -0.03343803435564, 0.009759523905813, 0.021669793874025, 0.035737577825784, -0.023063046857714, -0.007392348721623001, -0.004386036656796001, 0.037144355475902, 0.015406926162540002, -0.040120232850313006, -0.009448409080505002, -0.007182684727013001, -0.019762527197599, 0.023171259090304003, 0.007013600785285, 0.023482374846935, -0.031409028917551, 0.012694820761680001, 0.021575108170509002, 0.004703914746642, -0.013946041464805001, 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1,201.145 | The Interaction of Entropy-Based Discretization and Sample Size: An
Empirical Study | [-0.018099870532751, -0.004418214317411, 0.040166836231946, -0.034271292388439005, -0.004139278084039, 0.032893829047679006, 0.013444043695926002, -0.0039843134582040005, -0.024918315932154003, -0.040910668671131, 0.019780376926064002, 0.038789372891187, 0.010158794000744, 0.0038431233260780005, -0.014353170059621001, 0.027025833725929004, 0.024546399712562002, 0.033499911427497, 0.019463559612631, -0.008368090726435, -0.040524978190660005, 0.019270714372396, -0.014821507968008001, -0.019559981301426003, -0.00543753709644, 0.005344558507204, 0.041847340762615, -0.030744986608624004, -0.007624260615557, -0.006660036277025, 0.026915637776255, 0.0015823612920930002, -0.022370006889104, -0.026226906105875, -0.027177356183528, -0.0037983555812380002, 0.033196870237588, -0.000685288105159, 0.035428363829851005, -0.02471169643104, 0.018554434180259, -0.008712456561625, -0.0052997907623640005, -3.198836202500388e-05, 0.031268421560525, 0.031929604709148005, 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1,201.1571 | A United Image Force for Deformable Models and Direct Transforming
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1,201.1613 | Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict
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1,201.167 | Customers Behavior Modeling by Semi-Supervised Learning in Customer
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1,201.2004 | Optimal Fuzzy Model Construction with Statistical Information using
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1,201.21 | Biologically inspired design framework for Robot in Dynamic Environments
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1,201.2173 | Automatic Detection of Diabetes Diagnosis using Feature Weighted Support
Vector Machines based on Mutual Information and Modified Cuckoo Search | [-0.009357430972158002, 0.014745042659342001, 0.026276422664523003, -0.027127098292112004, -0.024710098281502002, 0.011862197890877, -0.015190634876489001, -0.037699777632951, -0.022657675668597003, -0.036997634917497, 0.020092146471142002, 0.037537746131420004, -0.016716448590159, 0.006585984025150001, -0.016176337376236, -0.012847901321947, 0.029355056583881003, 0.004584196023643, 0.002597598591819, -0.036079443991184006, -0.020362202078104002, 0.026384444907307, -0.006960686296224001, -0.013948378153145001, 0.010390394367277001, 0.021955531090497003, 0.005944601725786001, -0.028220823034644002, -0.023859422653913002, 0.00733876414597, 0.011328837834298, -0.024872131645679002, -0.020740279927849003, -0.008385229855775, -0.027154102921485002, 0.006805404089391001, 0.023845920339226, -0.014880070462822002, 0.0028997233603140003, -0.038915030658245, 0.010376891121268, -0.000531672209035, 0.008344721980392001, 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1,201.2241 | Distance-Based Bias in Model-Directed Optimization of Additively
Decomposable Problems | [-0.016203792765736, -0.003295567585155, 0.009795354679226001, -0.014854648150503, -0.0032780005130910003, 0.034965340048074, 0.0050206463783970005, -0.002842339221388, -0.033925373107194005, -0.044184498488903004, -0.0015467735938720002, 0.023792732506990003, -0.009619684889912002, 0.015037344768643001, 0.002394380979239, 0.004012300632894, 0.026982899755239, -0.0067562647163860005, 0.025858610868453, 0.014278450980782, -0.041233241558074, -0.006724644452333, -0.011369356885552, -0.02573212981224, -0.011425571516156, 0.010659649968147, 0.043060209602117004, -0.020363656803965003, -0.008839709684252, -0.005449280608445, 0.021979819983243002, -0.016245953738689003, -0.024720270186662, -0.006492760032415001, -0.013090921565890002, 0.0013061058707530002, 0.008151084184646001, -0.002417217940092, 0.017314026132225, -0.022120356559753, -0.00026196776889200003, -0.0014852890744800002, 0.0043776943348340005, -0.045646071434020004, 0.033419445157051, 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1,201.2395 | Polynomial Regression on Riemannian Manifolds | [0.005912883207201, 0.0153803313151, 0.022967962548136003, -0.015571731142699002, -0.002399331657215, 0.015858830884099, 0.000258688640315, 0.015913516283035, -0.011190045624971001, -0.020165324211120002, 0.007669658865779001, 0.010916617698967, 0.019085282459855, 0.021710192784667, -0.013944833539426, -0.026057699695229003, 0.02757522650063, 0.009303391911089, 0.0015286341076710002, 0.0022643266711380003, -0.006234161090105, 0.022803904488682, 0.016419358551502002, -0.032975431531667, -0.008435257710516, 0.007327873725444, 0.046892922371625005, -0.028709951788187003, 0.021327393129467003, -0.031280174851417, 0.018306013196706, -0.006986088585108001, -0.006685317493975, -0.024430802091956003, -0.015052217990159001, 0.008756535127758001, 0.010964467190206, -0.003141005523502, -0.000292653538053, 0.008011443540453, 0.027821311727166002, -0.008736028335988001, -0.00728685921058, -0.031116120517253, 0.013931162655353001, 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1,201.243 | A Well-typed Lightweight Situation Calculus | [0.011855884455144001, -0.002426013816148, 0.012022477574646001, -0.020574262365698003, -0.0051886849105350005, 0.029264876618981, -0.01963023468852, 0.0019227634184060002, -0.0042724218219510005, -0.037899956107139005, 0.015645880252122, 0.0017926124855870001, -0.010453724302351001, 0.019782945513725003, 0.012057184241712001, -0.013098391704261001, 0.033346410840749005, 0.010911855846643, 0.031763773411512, 0.004147476982325001, -0.005195626057684, -0.008628140203654001, -0.011487990617752, -0.0037795836105940003, -0.0008216863498090001, -0.002771347761154, 0.012904033064842, -0.031347289681434, 0.00693791359663, -0.022143015637993, 0.024808507412672, -0.017256280407309, -0.025683121755719, 0.00040802324656300003, -0.022476201876997, 0.014139600098133, -0.003472427371889, 0.007316219154745, -0.0034672215115270004, -0.019352579489350003, 0.033152051270008004, 0.034123845398426, 0.012189070694148001, -0.021768180653452002, 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1,201.2542 | An efficient FPGA implementation of MRI image filtering and tumor
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1,201.2575 | Joint Approximation of Information and Distributed Link-Scheduling
Decisions in Wireless Networks | [-0.009330756962299, 0.015389245934784001, 0.011569317430257001, 0.005055586341768, -0.014882660470902, 0.0025705790612840004, 0.0066198403947050006, 0.018291844055056003, -0.017908481881022002, -0.040389925241470004, -0.002411415567621, 0.028670001775026002, -0.009043236263096001, 0.020701548084616002, -0.012979541905224, 0.005805196240544001, 0.022289762273430002, -0.002674976829439, 0.016867928206920003, 0.002635613782331, -0.027040712535381, -0.010542455129325001, -0.005295187700539001, -0.043703269213438006, 0.030586810782551002, 0.018634131178259003, 0.023138634860515, -0.015402937307953002, 0.009009007364511, -0.00030955625697900004, 0.032038111239671, 0.0035495213232930004, -0.008687256835401001, -0.007112734485417001, -0.015457703731954, -0.011405020020902, 0.027971735224127003, -0.038034986704587, 0.020290803164243, -0.038445733487606, 0.022330837324261003, 0.0019373472314320002, -0.004282016772776, -0.007256494835019001, 0.021208133548498, 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1,201.2605 | Autonomous Cleaning of Corrupted Scanned Documents - A Generative
Modeling Approach | [-0.01495218463242, 0.030742026865482, 0.0045338207855820005, -0.009786630980670001, -0.021122926846146, 0.027866069227457, -0.013877191580832001, 0.0061951745301480005, -0.027405358850955002, -0.025004073977470002, 0.021248577162623003, 0.025297254323959004, -0.016278475522994003, 0.024794658645987, -0.014896340668201, 0.0009676688350730001, 0.029541384428739003, 0.012229798361659001, 0.000123030928079, 0.014212254434823001, -0.034706939011812, 0.026693349704146004, -0.022770320996642, -0.009242153726518001, 0.006216116249561, 0.019880402833223003, 0.022002467885613, -0.034734860062599, -0.0045582526363430005, -0.01353514753282, 0.008948973380029, -0.018051518127322003, -0.009137446060776001, -0.014365824870765001, -0.022937852889299, -0.004013774916529001, 0.023482330143451, -0.025311214849352, 0.0023105384316290003, 0.0014554297085850002, 0.014177351258695, 0.021653443574905, 0.014149430207908001, -0.014938224107027002, 0.015008028596639002, 0.004792098421603001, 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1,201.263 | Hybrid GPS-GSM Localization of Automobile Tracking System | [-0.014744296669960001, 0.029170084744691002, -0.006091477815061, 0.0048439856618640005, -0.009827318601310002, 0.001060036593116, -0.019720993936061002, -0.007438504137098001, -0.01763741672039, -0.024684419855475002, 0.01540785562247, 0.025401065126061002, -0.039495073258876, 0.015168974176049002, -0.016774788498878, 0.005510863382369001, 0.020995028316974, -0.005895727779716001, -0.0016058144392440002, 0.0033327285200350004, -0.008294495753943, 0.003410696750506, 0.006546016316860001, -0.029647847637534003, -0.0013088714331380002, 0.007916267029941, 0.031824324280023006, -0.020251842215657002, 0.007717199157923, -0.0038254216779020004, 0.0059488127008080005, -0.009707878343760001, -0.021273722872138003, 2.1604570065392185e-05, -0.012667354196310002, 0.013735685497522, 0.019269773736596, -0.025878828018903004, 0.008659454062581, 0.019017620012164, 0.017305636778473, 0.008991234004497, 0.004326409194618, 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1,201.3107 | Tacit knowledge mining algorithm based on linguistic truth-valued
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1,201.3116 | Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using
Functional Data Analysis | [-0.008561305701732, 0.020336657762527, 0.008582638576626, -0.015032060444355, -0.013460591435432, 0.027660697698593, -0.032993737608194004, -0.012017115950584, -0.014107666909694, -0.032225780189037004, 0.015487146563827001, 0.031315606087446005, 0.012877512723207, 0.020251328125596, -0.018089668825268003, -0.008355095051229, 0.022299215197563, 0.0025171947199850004, -0.0019358933204780002, -0.01945492811501, -0.004287763498723, 0.038682311773300004, -0.029523706063628002, -0.0013181496178730002, -0.039706256240606, 0.01710839010775, 0.033562593162059, -0.04172569885849901, -0.019142055884003, -0.002600745530799, 0.013794795610010001, -0.0008021779940460001, -0.001278151758015, 0.009016391821205, -0.015273825265467, -0.020535757765173003, 0.001673685503192, -0.013965453021228001, 0.016795519739389, -0.013467702083289, 0.005692130886018, -0.028613533824682003, 0.006790736690163001, -0.025584368035197, -0.0033242611680170003, -0.006534751038998, 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1,201.3133 | Fractal Descriptors in the Fourier Domain Applied to Color Texture
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1,201.3153 | Fractal and Multi-Scale Fractal Dimension analysis: a comparative study
of Bouligand-Minkowski method | [-0.0020853071473530002, 0.027626913040876003, 0.020784925669431003, -0.022188758477568002, -0.0035606960300350004, 0.008974998258054001, -0.009111293591558, -0.023769775405526, -0.011414672248065002, -0.036690503358840006, 0.011155712418258001, 0.016096390783786, 0.014951515942811001, 0.017418447881937003, -0.028676381334662004, 0.004504536278545, 0.033010549843311005, 0.013438646681606001, 0.004473869688808, -0.00619458919391, -0.01278443261981, 0.030175622552633, -0.017445707693696, -0.032547146081924, -0.022229647263884003, 0.045604169368743, 0.034291718155145, -0.036063548177480004, -0.014910628087818002, -0.013833900913596, 0.0080073075369, -0.011966665275394, -0.012211995199322001, -0.0028485569637260004, -0.021984316408634002, 0.011114823631942002, 0.027422470971941, -0.012416437268257, 0.008048195391893, -0.015115070156753, -0.0024856724776320003, -0.008838703855872, -0.004971344955265001, -0.011728148907423, -0.0009131737169800001, 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1,201.3204 | Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy | [-0.014026982709765, 0.009925153106451002, 0.000303962238831, -0.035733383148908004, 0.005996889434754, 0.03587507084012, -0.026892134919762, -0.0027434085495760003, -0.030972711741924, -0.037433624267578, 0.005111347418278, 0.023704184219241003, -0.004742961842566, 0.002332517178729, -0.015373006463050001, 0.006751371081918, 0.021706402301788, -0.0073464550077910005, 0.007608575280755, 0.016109777614474, -0.023420810699462002, -0.013963223434984, 0.00794862397015, -0.018660137429833003, -0.011965441517531001, -0.00282842060551, 0.046019837260246006, -0.017866691574454002, -0.017087414860725, 0.004502094816416001, 0.03414649143815, -0.023902544751763, -0.005408889614045001, -0.021323846653103003, -0.006829298567026001, -0.003666143165901, 0.022754883393645002, -0.019566932693123002, 0.019071029499173, -0.000175669367308, 0.0029949024319640004, -0.027983121573925, 0.005415973719209001, 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1,201.3337 | A New Color Feature Extraction Method Based on Dynamic Color
Distribution Entropy of Neighborhoods | [-0.006634262856096001, 0.029794113710522003, 0.013455406762659002, -0.018634669482707002, -0.004815513733774, 0.036762092262506006, -0.013709030114114002, -0.001950566773302, -0.0067944461479780005, -0.040473006665706, -0.011426416225731002, 0.027551546692848, 0.00098529481329, 0.018381046131253003, -0.026256730780005, -0.00273312954232, 0.035053469240665006, 0.018060678616166, -0.007315042428672001, -0.011119398288428001, -0.012594420462846001, 0.030408151447772, -0.001731983153149, -0.021184256300330002, 0.010425270535051, 0.024681594222784, 0.03692227602005, -0.030755214393138, 0.009223895147442, -0.012314099818468, 0.018127422779798, 0.0013932619476690002, -0.008062564767897, -0.016699120402336003, -0.026577098295092003, -0.001550942542962, 0.024574805051088004, -0.019262054935097, 0.009804559871554, -0.023827282711863, -0.0027047635521730003, -0.021931778639554003, -0.008529766462743001, -0.032570626586675006, 0.006167061161249001, 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1,201.3408 | The computation of first order moments on junction trees | [-0.004002385772764, 0.000500298221595, -0.012462601996958, -0.005134094972163001, -0.009916256181895, 0.019832512363791, -0.023848699405789, -0.024221334606409003, -0.012635118328034002, -0.031991485506296005, 0.014201569370925001, 0.021392062306404003, -0.018065942451357002, 0.010295792482793002, 0.008839752525091001, 0.002815471496433, 0.017486285418272, 0.009516017511487, -0.001326652942225, 0.009591924957931, -0.016630603000521, 0.019942922517657002, -0.008694838732481001, -0.02669177390635, 0.019128644838929003, 0.012283184565603001, 0.025739481672644, -0.014933039434254001, -0.007176692131906, -0.017541490495204003, 0.016520192846655003, -0.008701738901436001, -0.012635118328034002, -0.008239394053816, -0.036932360380887, 0.012172773480415, 0.026443349197506003, -0.021157439798116, 0.01613375544548, -0.0005382518866090001, 0.028154714033007, -0.006531479768455001, -0.007169791497290001, 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1,201.3612 | Spatiotemporal Gabor filters: a new method for dynamic texture
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1,201.3674 | On the Lagrangian Biduality of Sparsity Minimization Problems | [0.010382064618170001, 0.026509819552302003, 0.016582857817411003, -0.021418340504169003, -0.008725200779736, 0.023366754874587004, -0.008590091019868, -0.00816343165934, -0.030491979792714, -0.055750261992216006, 0.00893853046, 0.034843914210796, -0.00948607828468, 0.033307936042547004, 0.0030470646452150003, 0.015487764030694, 0.030520424246788004, -0.002670181682333, 0.029297331348061003, 0.019640589132905003, -0.031800404191017005, 0.0008746533421790001, -0.021290343254804, -0.022356992587447003, -0.0019573036115610003, -6.227682752069086e-05, 0.04815571010112701, -0.004725261591374001, -0.00104887294583, -0.007210556883364001, 0.018630826845765003, -0.007743882015347, -0.020735684782266003, 0.000614657299593, -0.007039892952889, -0.013390018604695, 0.018403274938464, -0.00044377101585200003, 0.018744602799415002, -0.008526092395186001, 0.023053871467709, 6.17212790530175e-05, 0.003223062027245, 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1,201.372 | A Multimodal Biometric System Using Linear Discriminant Analysis For
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1,201.3803 | Image Labeling and Segmentation using Hierarchical Conditional Random
Field Model | [-0.006065451540052001, 0.00702140154317, -0.019132319837808002, -0.001459738705307, 0.004003664478659, 0.011531352996826, -0.0017403615638610002, 0.012330752797424, -0.025514200329780003, -0.022423185408115002, -0.000600383093114, 0.006428512744605001, -0.017333667725324003, -0.003587309969589, 0.006331918295472001, 0.002479807008057, 0.038344580680131, 0.008953286334872001, -0.007261221297085001, -0.030537100508809003, -0.029631113633513003, 0.031496379524469, -0.0057023903355, -0.032189194113016004, 0.007088018115609, 0.010691981762647, 0.042714636772871004, -0.026793241500854003, 0.004942960105836, 0.005276043433696, 0.002654675859957, 0.014202683232724, 0.009259723126888001, -0.014855526387691002, -0.013023567385971001, 0.0028894997667520002, 0.014762262813746001, -0.017426932230591, -0.001407278003171, -0.0015421768184750002, 0.029311353340744, -0.010345575399696001, -0.015894746407866003, -0.019785163924098, 0.021836956962943, -0.005998834967613001, 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1,201.3868 | A Dichotomy for 2-Constraint Forbidden CSP Patterns | [0.0014177920529610001, 0.029515691101551004, 0.0040274541825050005, -0.013050090521574001, -0.010794431902468002, 0.031536534428596004, -0.005745882168412, 0.011961396783590001, -0.033528912812471, -0.043718516826629, 0.012345641851425001, 0.037172123789787, 0.001773574156686, 0.009478037245571001, 0.017760647460818003, 0.052399601787328005, 0.028263337910175, 0.008318187668919001, 0.013185287825763002, 0.006083875428885, -0.051801886409521006, 0.018401054665446, -0.009648812934756001, -0.038822952657938004, -0.005361637566238001, 0.005817038938403, 0.00875935703516, -0.009549194015562, -0.016095586121082, -0.003472434123978, 0.026185568422079003, -0.005393657833337001, -0.026925595477223, -0.006635337602347, -0.004390352405607, -0.003290985245257, 0.00136709306389, -0.002659471938386, 0.016209436580538, 0.018799530342221, 0.022812753915786, 0.016977926716208, -0.017675258219242002, -0.011022131890058, 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1,201.388 | Modelling and simulation of complex systems: an approach based on
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1,201.3883 | Dynamic Shared Context Processing in an E-Collaborative Learning
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1,201.3972 | A Novel Approach to Fast Image Filtering Algorithm of Infrared Images
based on Intro Sort Algorithm | [0.008864382281899001, 0.024072337895631003, -0.021606931462883002, -0.013608211651444001, 0.00027484778547600004, 0.022811932489275003, -0.020263422280550003, -0.038753971457481, -0.035679139196872, -0.019764801487326, 0.018836811184883003, 0.009730044752359, -0.005720296408981001, -0.005806862842291, 0.0016560120275240001, -0.0059349806979290005, 0.014238413423299002, 0.007756334263831001, -0.006811031140387, 0.012964158318936, -0.046067085117101, 0.027216423302888003, -2.2737161998520602e-05, 0.000533680838998, -0.012811802327632, 0.008386536501348001, 0.026288433000445, -0.03750741481781, -0.030388209968805, -0.017216291278600002, 0.022978140041232, -0.0032912481110540003, -0.021856240928173003, -0.029141655191779, -0.018878363072872002, -0.018933765590190003, 0.009432256221771, -0.012209300883114001, 0.013220394961535, -0.030803726986050002, 0.043047655373811, -0.016191348433494002, -0.004501443821936, -0.013372750952839001, 0.0029934602789580002, 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1,201.4002 | Adaptive Policies for Sequential Sampling under Incomplete Information
and a Cost Constraint | [-0.0025966467801480002, -0.010657360777258, 0.004037300124764, -0.028632543981075002, -0.0026990547776220004, 0.004707290325313001, -0.004315016325563001, 0.016565773636102, -0.031187534332275002, -0.008157914504408, 0.020051112398505003, 0.037463922053575, 0.01966230943799, 0.007130364421755001, 0.0053043803200120004, 0.0019058276666320002, 0.030298842117190004, -0.004481645766645, 0.0008713346906000001, 0.013330379500985002, -0.030159983783960002, 0.010310215875506, -0.005887584295123, -0.013913583941757, 0.011303051374852002, -0.01385804079473, 0.027077334001660004, -0.019842825829982, 0.011309994384646001, -0.003988699521869, 0.043906938284635, -0.011816825717687001, -0.013878868892788, -0.029826724901795002, -0.0012705518165600001, -0.01630194298923, 0.005991728045046001, -0.015732625499367003, 0.031298622488975004, -0.026438586413860002, 0.013691410422325, 0.0023501736577600002, 0.0021280006039880004, -0.007907969877123, 0.02113420702517, 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1,201.408 | Progress in animation of an EMA-controlled tongue model for
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1,201.4139 | Image decomposition with anisotropic diffusion applied to leaf-texture
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1,201.421 | Collaborative Personalized Web Recommender System using Entropy based
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1,201.4342 | A Pareto-metaheuristic for a bi-objective winner determination problem
in a combinatorial reverse auction | [0.0034380897413930005, 0.009888466447591001, 0.01589192636311, 0.014976980164647002, -0.018721224740147, 0.019256116822361002, -0.016426818445324003, -0.005475606303662, -0.021015631034970002, -0.04433974251151, 0.0016301893629130002, 0.043354414403438006, -0.008326018229126, -0.008804606273770001, 0.02186019718647, 0.0049547902308400005, 0.025168083608150003, 0.017792202532291003, -0.010402244515717, -0.0028521716594690004, -0.036006685346364004, -0.009599906392395, -0.014582849107682, -0.011289039626717, 0.008741263300180001, 0.017510680481791, 0.046507462859153005, -0.013773472979664001, 0.001921388902701, -0.016497200354933, 0.029334612190723003, -0.0036386742722240004, -0.013006324879825, -0.002602320630103, -0.0019407435320310002, -0.008762378245592001, 0.017285462468862003, 0.005926041863858001, 0.028687110170722, -0.001817577634938, 0.020522966980934, 0.011732436716556001, 0.018003342673182002, 0.016088992357254, 0.000983568141236, 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1,201.4443 | Dynamic behavior analysis for a six axis industrial machining robot | [-0.034883536398410006, 0.009884133003652, -0.008191501721739, -0.009807512164115, -0.031038546934723004, 0.033824771642684, -0.037307552993297, 0.008818401955068, -0.0025842236354940004, -0.018932398408651, 0.006673009134829, 0.01879308745265, 0.004806238226592, -0.0018824433209370002, -0.016285484656691, 0.016313347965478002, 0.026831347495317, 0.025173543021082, 0.012238493189215001, 0.007508876267820001, -0.021049929782748, 0.008825368247926001, 0.013032567687332, -0.026831347495317, -0.015783965587615002, 0.006854113657027001, 0.027625421062111, -0.030341990292072, -0.007738740183413001, -0.009319922886788, 0.001733554410748, -0.033128216862678, 0.017469631507992002, -0.007501910906285001, -0.01368036493659, -0.015547135844826001, 0.012057389132678, 0.004639064893126, 0.00772480899468, 0.014766993001103, 0.018361223861575002, 0.010141858831048001, -0.016076518222689, -0.011632489971816, 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1,201.4445 | Experimental Characterization of Robot Arm Rigidity in Order to Be Used
in Machining Operation | [-0.021525386720895, 0.008114172145724, -0.011310664005577, -0.021427033469080002, -0.020598053932189, 0.028606846928596004, -0.028143178671598, 0.007580252364277001, -0.012062366120517, -0.018602879717946, 0.012659513391554002, 0.007042820099741, 0.010130420327186002, -0.0014981360873200001, -0.012259073555469001, 0.016467202454805003, 0.027173694223165002, 0.025431429967284, 0.01394513528794, 0.001409442164003, -0.0024658651091150003, 0.0026028577703980003, 0.001826566876843, -0.023492459207773, -0.011879709549248002, -0.005398910026997001, 0.036615639925003, -0.02544548176229, 0.0016851834952830002, 0.00023798058100500002, 0.022312216460704002, -0.018265668302774003, 0.0059117539785800005, -0.010474658571183001, -0.029196968302130002, -0.009196061640977, 0.014113740995526002, -0.024813206866383, 0.010123395361006001, 0.004640182480216, 0.036643739789724, 0.008641066029667, -0.007559176534414001, -0.015188605524599, -0.014275322668254, 0.018223516643047, 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1,201.4597 | Fractal Descriptors Based on Fourier Spectrum Applied to Texture
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1,201.4733 | Du TAL au TIL | [-0.019296331331133003, 0.015450702048838002, 0.024151097983121, -0.015382517129182002, -0.014373380690813, 0.014018818736076001, -0.015082503668963002, 0.014100641012191, -0.026155734434723, -0.031228691339492003, 0.008148097433149001, 0.022405564785003003, -0.012627846561372, 0.0017966725863510001, 0.009941360913217, -0.0015938224969430002, 0.021628255024552002, 0.0009034501854330001, 0.016855312511324, 0.00025526728131800003, -0.041074592620134, 0.019269058480858, 0.019555434584617, -0.000451725092716, -0.010139097459614001, -0.003756989026442, 0.030628664419054003, -0.037719897925853, 0.0008501806878480001, -0.025460248813033003, 0.009136779233813001, -0.0034723170101640002, -0.004043365828692001, 0.00016257840616100002, -0.015914360061287002, -0.017059866338968003, -0.004667257424443, -0.006279830820858001, -0.0020932769402860004, 0.005332060623914001, 0.019719077274203002, 0.015218873508274002, 0.018982680514454002, -0.019841810688376, 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1,201.4737 | Production System Rules as Protein Complexes from Genetic Regulatory
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1,201.4906 | Adaptive Shortest-Path Routing under Unknown and Stochastically Varying
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1,201.5283 | An Efficient Primal-Dual Prox Method for Non-Smooth Optimization | [-0.021205330267548003, 0.0007171263569030001, 0.009842841885983, -0.019588092342019, -0.00022459212050300001, 0.023770602419972003, 0.004621674306690001, -0.0048726252280170005, -0.025959450751543003, -0.042828910052776004, -0.0008060046820900001, 0.02153993025422, 0.008469584397971, 0.001620722934603, -0.017259828746318002, 0.018807357177138002, 0.027437271550297, 0.001181559287942, 0.016228141263127, 0.034212939441204, -0.042940445244312, 0.00807224586606, -0.008093158714473001, -0.05016224831342601, -0.0038130558095870004, 0.0011170789366580002, 0.037865664809942, -0.007437898311764001, -0.005179342348128, -0.02322687767446, 0.023394176736474002, 0.0068384050391610005, -0.01258238684386, 0.007981624454259, -0.008769331499934, 0.0028754761442540003, 0.024983530864119002, 0.023101400583982003, 0.025178715586662, -0.011773767881095002, 0.016994936391711003, -0.0026593797374510003, 0.018807357177138002, 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