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... 0 1 9 Session 3: Biodiversity and agricultural sustainability 9.00-9.30 Claire Chenu* Managing ... ). Preliminary Program Note: *indicates keynote speaker. T h u rs d a y , 1 4 th N o v e m b e r, 2 0 1 9 Session ... 0 1 9 Session 3: Biodiversity and agricultural sustainability 9.00-9.30 Claire Chenu* Managing ... ). Preliminary Program Note: *indicates keynote speaker. T h u rs d a y , 1 4 th N o v e m b e r, 2 0 1 9 Session ... 1 ...
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... T I O N NFPA CODES Health 3 Fire 1 Reactivity 0 Specific Hazard Corrosive REVISION SUMMARY Rev. M ... 1 Material Safety EKC Technology Data Sheet EKC Technology 2520 Barrington Court Hayward, CA 94545 ... T I O N NFPA CODES Health 3 Fire 1 Reactivity 0 Specific Hazard Corrosive REVISION SUMMARY Rev. M ... 1 ... 1 ...
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... 180 180 180 … Voting Array for Q 0 … 101 102 103 104 105 … Inverted File Index Query image Q Visual ... Slide 1 Lecture 12 Recognition Davide Scaramuzza http://rpg.ifi.uzh.ch/ Institute of Informatics ... 180 180 180 … Voting Array for Q 0 … 101 102 103 104 105 … Inverted File Index Query image Q Visual ... Slide 1 ... Slide 1 ...
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... 0 1 2 3 4 Years after AVR 0.8 1 0.6 0.4 0.2 Su rv iv al (% ) 0 Type of LV hypertrophic remodeling ... (pumping dysfunction - HFrEF 0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 < 1 0 10 - 1 4 15 - 1 9 20 -2 4 25 -2 9 30 - 3 ... 0 1 2 3 4 Years after AVR 0.8 1 0.6 0.4 0.2 Su rv iv al (% ) 0 Type of LV hypertrophic remodeling ... (pumping dysfunction - HFrEF 0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 < 1 0 10 - 1 4 15 - 1 9 20 -2 4 25 -2 9 30 - 3 ... Folie 1 ...
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... Slide 1 1 Novel Three-Phase 2/ 3-Modulated Buck-Boost Current Source Inverter System Employing Dual ... , 0, 0] [bb] iph = [ 0, 0, 0] [cc] iph = [ 0, 0, 0] 3-φ Load Current Waveforms Conventional Pulse ... Slide 1 1 Novel Three-Phase 2/ 3-Modulated Buck-Boost Current Source Inverter System Employing Dual ... , 0, 0] [bb] iph = [ 0, 0, 0] [cc] iph = [ 0, 0, 0] 3-φ Load Current Waveforms Conventional Pulse ... Slide 1 ...
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... 0 0x20f8 0x12 r/w 1 0x0001 0x05 read Process ID VPN PPN acce ss Core 1 TLB: Core 2 TLB: Core 3 TLB ... 0 0x20f8 0x12 r/w 1 0x0001 0x05 read Process ID VPN PPN acce ss Core 1 TLB: Core 2 TLB: Core 3 TLB ... 0 0x20f8 0x12 r/w 1 0x0001 0x05 read Process ID VPN PPN acce ss Core 1 TLB: Core 2 TLB: Core 3 TLB ... 0 0x20f8 0x12 r/w 1 0x0001 0x05 read Process ID VPN PPN acce ss Core 1 TLB: Core 2 TLB: Core 3 TLB ... Slide 1 ...
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... (RFX) 8 Subject 1 Subject 2 Subject 3 Subject N … Modelling all subjects at once Simple model Lots ... Within-subject Variance Fixed effects 11 • N subjects = 12 with each 50 scans = 600 scans c = [4, 3, 2, 1 ... (RFX) 8 Subject 1 Subject 2 Subject 3 Subject N … Modelling all subjects at once Simple model Lots ... Within-subject Variance Fixed effects 11 • N subjects = 12 with each 50 scans = 600 scans c = [4, 3, 2, 1 ... Slide 1 ...
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... System 1. Collect all words within query region 2. Inverted file index to find relevant frames 3. Compare ... multiple images. 101 103 105 105 180 180 180 … Voting Array for Q 0 … 101 102 103 104 105 … Inverted File ... System 1. Collect all words within query region 2. Inverted file index to find relevant frames 3. Compare ... multiple images. 101 103 105 105 180 180 180 … Voting Array for Q 0 … 101 102 103 104 105 … Inverted File ... Slide 1 ...
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... 4 h 7 2 h 1 44 h IC 5 0 = 8 3 . 3 M IC 5 0 = 4 7 .9 M IC 5 0 = 2 5 . 0 M 0 . 1 1 1 0 1 0 0 0 5 0 1 0 ... in culture 4 7 1 1 1 4 1 8 2 1 4 7 1 1 1 4 1 8 2 1 0 1 0 2 0 3 0 4 0 5 0 6 0 A T P ( p m o l/ M T ) T ... 4 h 7 2 h 1 44 h IC 5 0 = 8 3 . 3 M IC 5 0 = 4 7 .9 M IC 5 0 = 2 5 . 0 M 0 . 1 1 1 0 1 0 0 0 5 0 1 0 ... in culture 4 7 1 1 1 4 1 8 2 1 4 7 1 1 1 4 1 8 2 1 0 1 0 2 0 3 0 4 0 5 0 6 0 A T P ( p m o l/ M T ) T ... Slide 1 ...
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... … Voting Array for Q 0 … 101 102 103 104 105 … Inverted File DB Query image Q Visual words in Q + 1 + 1 + 1 ... -car Classifier Feature extraction Training examples 1. Obtain training data 2. Define features 3 ... … Voting Array for Q 0 … 101 102 103 104 105 … Inverted File DB Query image Q Visual words in Q + 1 + 1 + 1 ... -car Classifier Feature extraction Training examples 1. Obtain training data 2. Define features 3 ... Slide 1 ...
