System Relationships GDTR IDTR Global Descriptor Table descriptor descriptor descriptor descriptor descriptor descriptor descriptor Interrupt Descriptor Table descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor
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System Relationships TR LDTR GDTR Global Descriptor Table descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor descriptor Task State Segment Local Descriptor Table descriptor descriptor descriptor descriptor descriptor descriptor
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COLORADO DEPARTMENT OF CORRECTIONS POLYGRAPH SANCTIONS GRID FORM ADMISSIONS PRIOR TO PRE-TEST ADMISSIONS DURING PRE-TEST 1 2 (7/1998) ADMISSIONS TO NONDECPT DURING POST-TEST 3 ADMISSIONS TO DECPT DURING POST-TEST 4 NO ADMISSIONS TO DECPT 5 Old Offenses & Old High Risk Behaviors A Old refers to behaviors which happened before being placed on parole, community, or at Phase II TC. NONE NONE LOW LOW MODERATE MODERATE MODERATE MODERATE MODERATE MODERATE LOW LOW MODERATE HIGH MODERATE HIGH MODERATE HIGH HIGH New High Risk Behaviors & New Behavioral Lapses B New refers to behaviors which happened after being placed on parole, community, or at Phase II TC, or since the last polygraph. LOW LOW LOW LOW New Major Violations C MODERATE MODERATE HIGH MODERATE MODERATE HIGH MODERATE MODERATE HIGH HIGH HIGH SEVERE HIGH SEVERE HIGH SEVERE HIGH SEVERE SEVERE New Offenses D © 2005 Kim English, Peggy Heil SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE SEVERE
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VOMmean on WINE150hl(FA,FS,TS,AL) 0 1 1 4 2 4 3 5 4 3 5 8 6 7 7 1 8 7 _________[0 9 2 . 10) 42 low 0 high CLUS_1.1.1.1.1.1 _________[0 . 19) 56 low 10 5 [10,16) 15 low 12 high CLUS 1.1.1.1.1.2 [19,22) 1 low 12 high 11 4 But no algorithm would pick 19 as a cut! ___12 _____[0 . 13) 56 low 5 1 low 12 high But no alg would pick a 13 cut! 13 7 [13,16) 14 3 _________[0 15 3 . 16) 57 low 12 high CLUS 1.1.1.1.1 35 high CLUS 1.1.1.1.2 17 2 [16,31) 0 low 18 5 19 4 20 3 21 1 22 4 23 4 24 5 25 1 26 2 27 1 29 2 30 1 31 1 _________[0 . 31) 57 low 47 high CLUS_1.1.1.1.1 32 1 28 high CLUS 1.1.1.1.2 34 1 [31,58) 0 low 36 1 37 3 38 1 39 2 40 1 43 6 44 4 46 2 47 1 48 1 50 1 52 1 _________[0 .58) 57 low 75 high CLUS_1.1.1.1 56 1 5 high CLUS 1.1.1.2 60 1 [58,70) 0 low 63 1 65 2 _________[0 .70) 57 low 80 high CLUS_1.1.1 67 1 [70,78) 0 low 2 high CLUS 1.1.2 72 1 _________[0 .78) 57 low 82 high CLUS_1.1 74 1 82 1 7 high CLUS1.2 83 1 [78,94) 0 low 85 1 86 1 87 2 _________[0.94) 57 low 89 high CLUS_1 88 1 99 1 0 low 4 high CLUS_2 105 1 113 1 119 1 d=VOMMEAN DPP on WINE_150_HL_(FA,FSO2,TSO2,ALCOHOL). Some agglomeration required: CLUS1.1.1.1.1.1 is LOW_Quality F[0,10], else HIGH Quality F[13,119] with 15 LOW error. Classification accuracy = 90% (if it had been cut 13, 99.3% accuracy!) 7 1 8 4 STDs=(1.9,9,23,1.2) 9 4 maxSTD=23 for 10 5 11 4 d=e TS on WN150hl(FA,FS,TS,AL 12 7 13 7 14 8 _________[0 CLUS 1.1.1.1.1.1 15 2 . 16) 42 low 12 high CLUS 1.1.1.1.1.2 16 5 [16,22) 15 low 17 4 18 5 . 19 7 20 3 _________[0 . 22) 57 low 12 high CLUS_1.1.1.1.1 21 3 32 high CLUS 1.1.1.1.2 23 2 [22,33) 0 low 24 9 25 3 26 1 27 4 28 4 29 5 30 1 31 2 _________[0 . 33) 57 low 44 high CLUS_1.1.1.1.1 32 1 31 high CLUS 1.1.1.1.2 34 2 [33,60) 0 low 35 1 36 1 37 1 39 1 41 1 42 3 43 1 44 2 45 1 47 6 48 4 49 2 50 1 51 1 53 1 55 1 _________[0 .60) 57 low 75 high CLUS_1.1.1.1 59 1 5 high CLUS 1.1.1.2 63 1 [60,72) 0 low 65 1 67 2 _________[0.72) 57 low 89 high CLUS1.1.1 69 1 74 1 [72,80] high CLUS1.1 CLUS1.1.2 _________[0.80) 57 low 892 high 75 1 84 1 7 high CLUS1.2 85 1 [80,95] 86 1 87 1 88 2 _________[0.95) 57 low 89 high CLUS1 89 1 100 1 4 high CLUS2 106 1 113 1 119 1 Identical cuts and accuracy! Tells us that d=eTotal_SO2 is responsible for all separation. WINE
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Comparative Table for the Five Techniques/Methods of Reducing Multi-Path Fading Method/Technique Time Domain Clipping (for multi-carrier modulation systems) Sector Beamspace Adaptive Diversity Combiner (for general wireless communication) Simple Multi-path Delay Spread Estimation (for mobile/portable/indoor or personal communication systems) Directive Antenna or Sector Antenna Diversity (for mobile/portable/indoor or personal communication systems) Quasi-Optical Receiver with Angle Diversity (for general communication systems) Functionability Ease of Application Advantages Disadvantages Reliability high (2) very high (3) very high (3) low (2) high (2) very high (3) high (2) very high (3) very low (3) high (2) high (2) very high (3) high (2) low (2) high (2) very high (3) high (2) very high (3) low (2) very high (3) average (2) high (2) high (2) high (2) very low (3) high (2) average (2) Cost average (2) Complexity Effectiveness very low (3) Remarks high (2) 19 below average very low (3) (3) very high (3) 22 (Best Method) below average very low (3) (3) high (2) 19 low (2) very high (3) 20 low (2) high (2) 17
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Scoring Descriptions Impact High Impact Moderate Impact Low Impact Score Descriptor 1 Exceptional 2 Outstanding 3 Excellent 4 Very Good 5 Good 6 Satisfactory 7 Fair 8 Marginal 9 Poor Strengths/Weaknesses Weaknesses
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Motor Controls Actions Left Forward Left Reverse Left PWM Left Motion Low Low Low Low High High High Low Low High High Low Low High Low High Low High Low High Low High High High Coasting Coasting Coasting Reverse Coasting Forward Coasting Active Braking Right Forward Right Reverse Right PWM Low Low Low Low High High High Low Low High High Low Low High Low High Low High Low High Low High High High Right Motion Coasting Coasting Coasting Reverse Coasting Forward Coasting Active Braking 13
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Output: A Decision Tree for “buys_computer” age? <=30 student? 30..40 overcast yes >40 age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent credit rating? no yes excellent fair no yes no yes 10/18/2006 Classification I buys_computer no no yes yes yes no yes no yes yes yes yes yes no Mining Biological Data KU EECS 800, Luke Huan, Fall’06 slide10
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VOMmean w F=(DPP-MN)/4 Concrete4150(C, W, FA, Ag) 0 1 1 1 5 1 6 1 7 1 _________[0,9) 8 4 9 1 [9,14) 10 1 11 2 12 1 13 5 14 1 [14,18) 15 3 16 3 17 4 18 1 [18,23) 19 3 20 9 21 4 22 3 23 7 24 2 [23,31) 25 4 26 8 27 7 28 7 29 10 30 3 31 1 [31,36) 32 3 33 6 34 4 35 5 37 2 [36,39) 38 2 40 1 [39,52) 42 3 43 1 44 1 45 1 46 4 ______ 49 1 56 1 [52,90) 58 1 61 1 65 1 66 1 69 1 71 1 77 1 80 1 83 1 _________[0.90) 86 1 100 1 [90,113) 103 1 105 1 108 2 112 1 e4 accuracy rate = 104/150= 69% 2 Low 1 Low 5 Medium 3 Medium 5 High 5 High CLUS_1.1.1.1.1.1.1.1.1 CLUS_1.1.1.1.1.1.1.1.2 5 Low 2 Medium 4 High 6 Low 1 Medium 13 High CLUS_1.1.1.1.1.1.2 25 Low 4 Medium 19 High CLUS_1.1.1.1.1.2 3 Low 7 Medium 11 High CLUS_1.1.1.1.2 1 Low 1 Medium 2 High 0 Low 12 Medium 1 High 0 Low 11 Medium CLUS_1.1.1.1.1.1.1.2 . 0 High CLUS_1.1.1.2 CLUS_1.1.2 CLUS_1.2 43 Low 46 Medium 55 High CLUS_1 0 Low 6 Medium 0 High CLUS_2 d=e4 Conc4150 ________=0 0 17 ________=1 1 11 ________=3 3 12 ________=6 6 35 _______ ________=13 13 25 _______ 22 25 =22 24 8 =24 44 7 ________=44 67 ________=67 4 89 2 ________=89 91 4 13 Lo 2 Lo 12 Lo 13 Lo 3 Lo 0 Lo 0 Lo 0 Lo 0 Lo 0 Lo 4 Med 9 Med 0 Med 5 Med 3 Med 6 Med 8 Med 7 Med 4 Med 4 Med 0 4 STD=(101,28,99,81) d=e1 Conc4150 6 3 7 2 8 Low 7 Medium 0 High CLUS_1.1.1.1.1.2 12 10 [9,16) 13 2 14 3 CLUS_1.1.1.1.2 18 9 [16,31) 18 Low 11 Medium 0 High 20 4 22 5 23 3 24 5 27 3 1 Low 3 Medium 3 High CLUS_1.1.1.2 31 3 [31,39) . 36 4 5 Low 5 Medium 7 High CLUS_1.1.2 41 2 [39,52) 42 1 43 4 44 3 46 3 48 2 ______ 49 2 55 13 4 Low 17 Medium 38High CLUS_1.2 58 8 [52,80) 60 6 Entirely inconclusive using e ! 62 5 1 65 4 71 16 72 4 ________[0.80) 43Low 46Medium 55High CLUS_1 74 3 7 High CLUS_2 82 4 [80,101) 0 Low 7 Medium 83 7 97 2 100 1 VOM MN 0 Hi 0 Hi 0 Hi 17 Hi 19 Hi 19 Hi 0 Hi 0 Hi 0 Hi 0 Hi CLUS_11 CLUS_10 CLUS_9 CLUS_8 CLUS_7 . CLUS_6 . CLUS_5 CLUS_3 CLUS_1 CLUS_2 d=e3 Conc4150 0 15 3 2 ________ 5 4 17 3 19 8 21 1 29 3 ________ 41 28 46 3 47 8 48 3 52 4 ________ 53 15 58 3 62 4 63 4 64 1 65 7 67 3 69 4 72 3 73 12 75 2 78 5 83 1 100 4 2,4 [0.9) [9,32) 3 Low 1 Low 16 Medium 8 Medium 2 High CLUS_1.1 6 High CLUS_1.2 [0.32) 4 Low 24 Medium 8 High CLUS_1 [32,101) 39 Low 28 Medium 47 High CLUS_2 [32,55) 21 Low 12 Medium 28 High CLUS_2.1 [55,101) 1 Low 8 Medium 6 High CLUS_2.2 0 2 3 4 ________[0,5) 3 Lo 1 Med 2 Hi CLUS_2 8 4 ________=8 0 Lo 1 Med 3 Hi CLUS_3 10 2 ________=10 0 Lo 2 Med 0 Hi CLUS_4 12 8 ________[10,14) 1 Lo 4 Med 9 Hi CLUS_5 13 4 15 4 16 14 17 3 18 1 19 3 ________[14,21) 9 Lo 9 Med 15 Hi CLUS_6 20 8 22 15 ________[21,25) 3 Lo 1 Med 15 Hi CLUS_7 24 4 ________[25,28) 5 Lo 1 Med 3 Hi CLUS_8 27 9 29 3 e2 accuracy= 30 6 31 3 Inconclusive on e2! 93/150=62% 32 3 33 7 34 4 d=e2 ________[28,36) 14 Lo 12 Med 8 Hi CLUS_9 35 8 0 Hi CLUS_10 5 [36.40) 7 Lo 1 Med Conc4150 37 38 3 0 1 2 3 4 5 6 7 8 9 10 11 12 13 18 19 20 21 29 30 31 32 34 35 36 62 64 93 121 125 2 5 6 12 2 1 6 6 1 4 12 11 5 3 2 9 10 4 1 4 9 4 9 4 1 2 5 4 2 4 2,4 =0 2L 0M 0H C14 =1 1L 4M 0H C13 [2,4) 11L 6M 1H C12 =4 0L 2M 0H C11 [5,9) 14L 0M 0H C10 [9,11) 3L 0 M 13H C9 =11 =12 =13 [15,25) 5L 5L 0L 2L 2 M 4H 0 M 0H 3 M 0H 4 M 19H C8 C7 C6 C5 [25,33) 0L 0 M 18H C4 [33,50) 0L 14 M 0H C3 [50,93) 0L 7 M 0H C2 [93,m) 0L 10M 0H C1 Accuracy= 127/150=85% CONCRETE
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Training dataset age Class: <=30 C1:buys_computer= <=30 ‘yes’ 30…40 C2:buys_computer= >40 >40 ‘no’ >40 31…40 Data sample <=30 X =(age<=30, Income=medium, <=30 >40 Student=yes <=30 Credit_rating= 31…40 Fair) 31…40 >40 UIC - CS 594 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent B. Liu buys_computer no no yes yes yes no yes no yes yes yes yes yes no 33
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Intel Memory Management 3.5 SYSTEM DESCRIPTOR TYPES When the S (descriptor type) flag in a segment descriptor is clear, the descriptor type is a system descriptor. The processor recognizes the following types of system descriptors: • Local descriptor-table (LDT) segment descriptor. • Task-state segment (TSS) descriptor. • Call-gate descriptor. • Interrupt-gate descriptor. • Trap-gate descriptor. • Task-gate descriptor. These descriptor types fall into two categories: system-segment descriptors and gate descriptors. System-segment descriptors point to system segments (LDT and TSS segments). Gate descriptors are in themselves “gates,” which hold pointers to procedure entry points in code segments (call, interrupt, and trap gates) or which hold segment selectors for TSS’s (task gates). Table 3-2 shows the encoding of the type field for system-segment descriptors and gate descriptors. 9.1: Intel Memory 22
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5. Mean projections and mean student scores are calculated. Student Projection1 Student Score 1 Student Projection 2 Student Score 2 Student Projection 3 Student Score 3 Student Projection 4 Student Score 4 Student Projection 5 Your School Student Score 5 Student Projection 6 Student Score 6 Student Projection 7 Student Score 7 Student Projection 8 Student Score 8 Student Projection 9 Student Score 9 Student Projection 10 Student Score 10 Student Projection 11 Student Score 11 Student Projection 12 Student Score 12 Student Projection 13 Student Score 13 Student Projection 14 Student Score 14 Student Projection 15 Student Score 15 Student Projection 16 Student Score 16 Student Projection 17 Student Score 17 Student Projection 18 Student Score 18 Student Projection 19 Student Score 19 Student Projection 20 Student Score 20 Mean Projected Score Mean Student Score Copyright © 2003. Battelle for Kids
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Decision Tree Training Dataset 10/18/2006 Classification I age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent Mining Biological Data KU EECS 800, Luke Huan, Fall’06 buys_computer no no yes yes yes no yes no yes yes yes yes yes no slide9
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Building a decision tree: an example training dataset age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 UIC - CS 594 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent B. Liu buys_computer no no yes yes yes no yes no yes yes yes yes yes no 12
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Attribute Selection by info gain     Class P: buys_computer = “yes” Class N: buys_computer = “no” I(p, n) = I(9, 5) =0.940 Compute the entropy for age: age <=30 30…40 >40 age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 pi 2 4 3 ni I(pi, ni) 3 0.971 0 0 2 0.971 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent UIC - CS 594 buys_computer no no yes yes yes no yes no yes yes yes yes yes no 5 4 E ( age)  I ( 2,3)  I (4,0) 14 14 5  I (3,2) 0.694 14 5 I (2,3) means “age <=30” 14 has 5 out of 14 samples, with 2 yes’es and 3 no’s. Hence Gain(age) I ( p, n)  E (age) 0.246 Similarly, Gain(income) 0.029 Gain( student ) 0.151 Gain(credit _ rating ) 0.048 B. Liu 23
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Attribute Selection: Information Gain   Class P: buys_computer = “yes” Class N: buys_computer = “no” Info( D) I (9,5)  9 9 5 5 log 2 ( )  log 2 ( ) 0.940 14 14 14 14 5 4 Infoage ( D )  I (2,3)  I (4,0) 14 14 5  I (3,2) 0.694 14 5 I (2,3)means “age <=30” has 5 out of 14 14 samples, with 2 yes’es and 3 no’s. Hence age <=30 <=30 31…40 >40 >40 >40 31…40 <=30 <=30 >40 <=30 31…40 31…40 >40 income student credit_rating high no fair high no excellent high no fair medium no fair low yes fair low yes excellent low yes excellent medium no fair low yes fair medium yes fair medium yes excellent medium no excellent high yes fair medium no excellent buys_computer no no yes yes yes no yes no yes yes yes yes yes no Gain(age) Info( D)  Infoage ( D) 0.246 Similarly, Gain(income) 0.029 Gain( student ) 0.151 Gain(credit _ rating ) 0.048 12
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