general topics for chapter 1
TOPICS for Inter Econ Assignment 2013
... attractive countries for FDI for past two decades All the three PICs were among the top 10 destinations for FDI in developing AsiaPacific region (Table 1) Table 1: Top 10 Destinations for FDI in Developing ... 1) Table 1: Top 10 Destinations for FDI in Developing Asia: 19 91- 1993 and 19 98-2000 ( Average Inflows per capita in US$) Insert Table1 here Source: Asian Development Bank (2006) Past trends ... Mello, Jr., L.R., (19 97) “Foreign Direct Investment in Developing Countries: A Selective Survey”, The Journal of Development Studies, 34 (1) : 1- 34 Elek, A., H Hill, and Tabor, S., (19 93) “Liberalization...
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A general framework for studying class consciousness and class formation
... analysis A general framework 19 1 Class formation I will use the expression ``class formation'' either to designate a process (the process of class formation) or an outcome (a class formation) ... causal models of class consciousness and class formation A general framework 19 9 Figure 10 .4 Forms of determination: limits, selects, transforms 10 .3 The micro-model If class consciousness is ... ``weak'' class formations; unitary or fragmented class formations; revolutionary, counterrevolutionary or reformist class formations Typically, class formations involve creating formal organizations...
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Excersices for chapter 4 accrual accounting concepts (solution)
... 2009, $15 ,480; 2 010 , $0; 2 011 , $0; 2 010 , $0 B 2009, $5 ,16 0; 2 010 , $5 ,16 0; 2 011 , $5 ,16 0 C 2009, $3,870; 2 010 , $5 ,16 0; 2 011 , $5 ,16 0; 2 012 , $1, 290 D 2009, $0; 2 010 , $0; 2 011 , $0; 2 012 , $15 ,480 E ... has not paid his rent for December 19 In general journal form, record the December 31 adjusting entries for the following transactions and events Assume that December 31 is the end of the annual ... amounts for the December 31 financial statements by completing the following table: 15 A company has 20 employees who each earn $500 per week for a 5-day week that begins on Monday December 31 of...
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Tài liệu Tips, Tools, and General Guidance for Public Speaking pptx
... that reflect your speech LWR: Tips, Tools, and General Guidance for Public Speaking, Page 11 How to Prepare, Practice and Deliver a Great Speech Tip 1- Write and rewrite the outline of your speech ... identification and to make the message easier to understand Less formal LWR: Tips, Tools, and General Guidance for Public Speaking, Page 10 Preparing, “Writing” a Great Speech I don’t know what’s ... to give the speech Also remember to thank the audience for listening LWR: Tips, Tools, and General Guidance for Public Speaking, Page 12 Build a Relationship with Your Audience and Deliver a...
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Tài liệu Standard & Poor''''s - General Criteria For S&p U.s. Index Membership(PDF) pptx
... July 31, 2000, 17 1 of the 600 companies in the S&P SmallCap 600 were below the $300 million lower range and 11 9 companies were above the $1 billion upper range For the S&P MidCap 400, 10 6 of ... appropriate Index for it to be in Here are some general guidelines Standard & Poor’s uses for each of its Indices: For the S&P SmallCap 600, Standard & Poor’s generally looks for companies with ... corporate actions than all of the preceding reasons combined.3 From its inception in 19 26 to September 15 , 2000, 10 01 companies exited the S&P 500, the overwhelming majority as a result of mergers...
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40 topics for TOEFL oral exams
... helps us to search for websites or information that we need and so I I also use this website to find out information for my work and for my relaxation I think it's really helpful for us Topic 39: ... birthday, I was very surprised to get a special gift from one of my former friends who hasn't kept in touch with me for a long 6/8 Topics for oral exams Prepared by Nguyen Viet Thuan- MiHao-Hung Yen ... perhaps, I will never forget this trip Topic 36: Describe something difficult that you did well 7/8 Topics for oral exams Prepared by Nguyen Viet Thuan- MiHao-Hung Yen I will never forget the time...
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Tài liệu Báo cáo khoa học: "Word representations: A simple and general method for semi-supervised learning" doc
... features: wi for i in {−2, 1, 0, +1, +2}, wi ∧ wi +1 for i in { 1, 0} • Tag features: wi for i in {−2, 1, 0, +1, +2}, ti ∧ ti +1 for i in {−2, 1, 0, +1} ti ∧ ti +1 ∧ ti+2 for i in {−2, 1, 0} • Embedding ... 1B All (Brown+C&W+HLBL+Gaz), 37M All+Nonlocal, 37M Lin and Wu (2009), 700B 250 10 10 0 1K 10 K 10 0K 1M Frequency of word in unlabeled data C&W, 50-dim Brown, 10 00 clusters 200 15 0 10 0 50 0 10 10 0 ... uniformly in the range [-0. 01, +0. 01] , not [ -1, +1] For rare words, which are typically updated only 14 3 times per epoch2 , and given that our embedding learning rate was typically 1e-6 or 1e-7,...
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Tài liệu Báo cáo khoa học: "Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification" pptx
... JST [YE10] [LI10] 82.53 83.76 94.98 91. 78 - Book 79.96 84.32 89.95 82.75 79.49 MDS DVD Elec 81. 32 83. 61 85.62 85.4 91. 7 88.25 82.85 84.55 81. 65 83.64 Kitch 85.82 87.68 89.85 87.9 85.65 10 0 Table ... (%) ccuracy 95 90 85 80 75 10 15 30 50 10 0 15 0 200 No of Topics Figure 2: Classification accuracy vs no of topics The only parameter we need to set is the number of topics T It has to be noted ... framework for transfer learning In ICML, pages 19 3–200 H Daum´ III and D Marcu 2006 Domain adaptation e for statistical classifiers Journal of Artificial Intelligence Research, 26 (1) :10 1 12 6 H Daum´...
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Tài liệu Báo cáo khoa học: "Context Management with Topics for Spoken Dialogue Systems" pptx
... 214 0975076 916 911 7, c o n t a c t ) , mi (-5 52580 413 14543 815 , iam), mi (-3 8 319 55835588453 ,meals ) , mi (0 ,mix), mi ( m l * 1 ~ ~n a i v e ) mi (-2 72092452 319 9709, paym) , mi (0 96873535 618 814 07 ... FBI-HH-B -19 1/96, University of Hamburg M Nagata and T Morimoto 19 94 An informationtheoretic model of discourse for next utterance type prediction In Transactions of Information 637 pages 11 6 -12 1 M ... Language Generalion, pages 10 -26 Pinter Publishers, London K W Church and W A Gale 19 91 Probability scoring for spelling correction Statistics and Computing, (1) :93 -10 3 H H Clark and S E Haviland 19 77...
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Reproductive System Structure, Development and Function in Cephalopods with a New General Scale for Maturity Stages pot
... with species For example in Sthenoteuthis pteropus the main part of stage III falls into substage 11 1 -1 (Burukovksy et al., 19 77), and in IIlex il/ecebrosus into substages 11 1-2 and -11 1-3 (Burukovsky ... Zool Zh., 66(8): 11 55 -11 63 SANZO, L 19 29 Nidamento pelagico, uova e larvi di Thysanoteuthis rhombus Troschel Mem R Gomm talassogr ital., 16 1: 1- 10 SHIMAMURA, S., and H FUKATAKI 19 57 Squids In: ... Fish Bull Calif Dep Fish Game, 13 1: 1- 108 FIORONI, P 19 78 Cephalopoda, Tintenfische In: Morphologie der Tiere VEB Gustav Fischer Verlag, Jena, 18 1 p FROERMAN, Yu M MS 19 85 Ecology and mechanism of...
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt
... disk (TB) since 19 95 1, 000,000 Number of analysts 500,000 19 95 19 96 19 97 19 98 19 99 © Tan,Steinbach, KumarKamath, V Kumar, “Data Mining for Mining and Engineering Applications” From: R Grossman, ... No 60K 10 Yes Divorced 220K No No Single 85K Yes No Married 75K No 10 No Single 90K Yes Test Set 10 © Tan,Steinbach, Kumar Training Set Introduction to Data Mining Learn Classifier Model 11 Classification: ... discover useful information Much of the data is never analyzed at all 4,000,000 3,500,000 The Data Gap 3,000,000 2,500,000 2,000,000 1, 500,000 Total new disk (TB) since 19 95 1, 000,000 Number of...
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Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx
... − qk ) k =1 Introduction to Data Mining 49 Euclidean Distance point p1 p2 p3 p4 p1 p3 p4 p2 0 y 1 p1 p1 p2 p3 p4 x 2.828 3 .16 2 5.099 p2 2.828 1. 414 3 .16 2 p3 3 .16 2 1. 414 p4 5.099 3 .16 2 Distance ... 12 5K No No Married 10 0K No No Single 70K No Yes Married 12 0K No No Divorced 95K Yes No Married No Yes Divorced 220K No No Single 85K Yes No Married 75K No 10 No Single 90K Yes 60K 10 © Tan,Steinbach, ... describe an object Single 12 5K No No Married 10 0K No No Single 70K No Yes Married 12 0K No No Divorced 95K Yes No Married No Yes Divorced 220K No No Single 85K Yes No Married 75K No 10 – Object is also...
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Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx
... single figure © Tan,Steinbach, Kumar Introduction to Data Mining 11 Representation Is the mapping of information to a visual format Data objects, their attributes, and the relationships among ... Tan,Steinbach, Kumar Introduction to Data Mining 10 Example: Sea Surface Temperature The following shows the Sea Surface Temperature (SST) for July 19 82 – Tens of thousands of data points are summarized ... and appealing techniques for data exploration – Humans have a well developed ability to analyze large amounts of information that is presented visually – Can detect general patterns and trends...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... C1 C2 3 Gini=0.500 31 Examples for computing GINI GINI (t ) = − ∑ [ p ( j | t )]2 j C1 C2 P(C1) = 0/6 = C1 C2 P(C1) = 1/ 6 C1 C2 P(C1) = 2/6 © Tan,Steinbach, Kumar P(C2) = 6/6 = Gini = – P(C1)2 ... Mining 42 Examples for Computing Error Error (t ) = − max P (i | t ) i C1 C2 P(C1) = 0/6 = C1 C2 P(C1) = 1/ 6 C1 C2 P(C1) = 2/6 © Tan,Steinbach, Kumar P(C2) = 6/6 = Error = – max (0, 1) = – = P(C2) ... Introduction to Data Mining 38 Examples for computing Entropy Entropy (t ) = − ∑ p ( j | t ) log p ( j | t ) j C1 C2 P(C1) = 0/6 = C1 C2 P(C1) = 1/ 6 C1 C2 P(C1) = 2/6 © Tan,Steinbach, Kumar P(C2)...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... curse of dimensionality – Can produce counter-intuitive results 11 111 111 111 0 vs 10 0000000000 011 111 111 111 0000000000 01 d = 1. 414 2 d = 1. 414 2 • Solution: Normalize the vectors to unit length © Tan,Steinbach, ... conjuncts that maximizes FOIL’s information gain measure: • • • • R0: {} => class (initial rule) R1: {A} => class (rule after adding conjunct) Gain(R0, R1) = t [ log (p1/(p1+n1)) – log (p0/(p0 + n0)) ... R1 p0: number of positive instances covered by R0 n0: number of negative instances covered by R0 p1: number of positive instances covered by R1 n1: number of negative instances covered by R1...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... {(A :1, B :1, C :1) , (A :1, B :1) , (A :1, C :1) , (A :1) , (B :1, C :1) } D :1 Recursively apply FPgrowth on P null A:7 B:5 B :1 C :1 C:3 D :1 D :1 D :1 Frequent Itemsets found (with sup > 1) : AD, BD, CD, ACD, BCD D :1 © Tan,Steinbach, ... D :1 C:3 D :1 C:3 D :1 D :1 D :1 E :1 E :1 E :1 Pointers are used to assist frequent itemset generation Introduction to Data Mining 38 FP-growth C :1 Conditional Pattern base for D: P = {(A :1, B :1, C :1) , ... transaction 1+ 2356 2+ 356 12 + 356 1, 4,7 3+ 56 3,6,9 2,5,8 13 + 56 234 567 15 + 14 5 13 6 345 12 4 457 © Tan,Steinbach, Kumar 12 5 458 15 9 356 357 689 367 368 Match transaction against 11 out of 15 candidates...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... Candidate 3-subsequences: , , …, , , …, , , …, , , … © Tan,Steinbach, ... Data Sequence Database: Object A A A B B B B C Timestamp 10 20 23 11 17 21 28 14 © Tan,Steinbach, Kumar Events 2, 3, 6, 1 4, 5, 7, 8, 1, 1, 1, 8, Introduction to Data Mining 26 Examples of Sequence ... item for both X1 and X2, then σ(X) ≤ σ(X1) + σ(X2) – If σ(X1 ∪ Y1) ≥ minsup, and X is parent of X1, Y is parent of Y1 then σ(X ∪ Y1) ≥ minsup, σ(X1 ∪ Y) ≥ minsup σ(X ∪ Y) ≥ minsup – If conf(X1 ⇒...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... y y 1 0.5 0.5 0.5 0 -2 -1. 5 -1 -0.5 0.5 1. 5 -2 -1. 5 -1 -0.5 x 0.5 1. 5 -2 -1. 5 -1 -0.5 x Iteration 0.5 1. 5 1. 5 x Iteration Iteration 2.5 2.5 2.5 2 1. 5 1. 5 1. 5 y y y 1 0.5 0.5 0.5 0 -2 -1. 5 -1 -0.5 ... Data Mining 21 Two different K-means Clusterings 2.5 Original Points y 1. 5 0.5 -2 -1. 5 -1 -0.5 0.5 1. 5 x 2.5 2.5 2 1. 5 1. 5 y y 1 0.5 0.5 0 -2 -1. 5 -1 -0.5 0.5 1. 5 x -1. 5 -1 -0.5 0.5 1. 5 x Optimal ... -1. 5 -1 -0.5 0.5 1. 5 -2 -1. 5 -1 -0.5 x 0.5 1. 5 x Iteration Iteration Iteration 2.5 2.5 2.5 2 1. 5 1. 5 1. 5 y y y 1 0.5 0.5 0.5 0 -2 -1. 5 -1 -0.5 0.5 x © Tan,Steinbach, Kumar 1. 5 -2 -1. 5 -1 -0.5...
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