· FDA Establishment Type: Public School Lunchrooms No. of Risk Factor ... Compliance Status Su...

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Page 1:  · FDA Establishment Type: Public School Lunchrooms No. of Risk Factor ... Compliance Status Su rvision .2652 OUT DI R VR .2653 1 0.5 0 .2656,.2657 1 0.5 0 1 0.5 0 1 0.5 0 pasteurized
Page 2:  · FDA Establishment Type: Public School Lunchrooms No. of Risk Factor ... Compliance Status Su rvision .2652 OUT DI R VR .2653 1 0.5 0 .2656,.2657 1 0.5 0 1 0.5 0 1 0.5 0 pasteurized
Page 3:  · FDA Establishment Type: Public School Lunchrooms No. of Risk Factor ... Compliance Status Su rvision .2652 OUT DI R VR .2653 1 0.5 0 .2656,.2657 1 0.5 0 1 0.5 0 1 0.5 0 pasteurized

Machine(Learning(&(DataMining...Recall:(BiasCVariance(Decomposi5on(0 20 40 60 80 100 −1 −0.5 0 0.5 1 1.5 0 20 40 60 80 100 0 0.5 1 1.5 0 20 40 60 80 100 −1 −0.5 0 0.5 1 1.5

Machine(Learning(&(DataMining...Recall:(BiasCVariance(Decomposi5on(0 20 40 60 80 100 −1 −0.5 0 0.5 1 1.5 0 20 40 60 80 100 0 0.5 1 1.5 0 20 40 60 80 100 −1 −0.5 0 0.5 1 1.5

TBA Equations for Minimal Surfaces in AdSito/osaka1011.pdfWilson Loop in AdS r=0 vev of Wilson loop = area of minimal surface-1-0.5 0 0.5 1 y1-1-0.5 0 0.5 1 y2-1-0.5 0 0.5 1 y0 Katsushi

TBA Equations for Minimal Surfaces in AdSito/osaka1011.pdfWilson Loop in AdS r=0 vev of Wilson loop = area of minimal surface-1-0.5 0 0.5 1 y1-1-0.5 0 0.5 1 y2-1-0.5 0 0.5 1 y0 Katsushi

GROUP SUPE RVISION - bma.bm · Insurance Digest. 2 INSURANCE GROUP SUPE RVISION Bermuda’s group supervision framework reflects inter-national developments and principles adopted

GROUP SUPE RVISION - bma.bm · Insurance Digest. 2 INSURANCE GROUP SUPE RVISION Bermuda’s group supervision framework reflects inter-national developments and principles adopted

GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization ... · Cam0 2.2 0.0 1.3 8 0 0 Cam1 2.2 0.5 1.3 8 0 0 Cam2 2.2 -0.5 1.3 8 0 0 Cam3 2.5 0.5 2.3 4 -20 0 Cam4 2.6 -0.7

GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization ... · Cam0 2.2 0.0 1.3 8 0 0 Cam1 2.2 0.5 1.3 8 0 0 Cam2 2.2 -0.5 1.3 8 0 0 Cam3 2.5 0.5 2.3 4 -20 0 Cam4 2.6 -0.7

P14 P12 P1 - epa.govt.nz · pauatahan ui i n l et 58 0-0.5 -1 -1.5-2-2.5-2-1.5-1-0.5-0.5-0.5-0.5-0.5-0.5 -1-1.5-1-0.5 0 0-0.5-1-1.5-2 kak hor rat pau duc p1 p2 p3 p4 p5 p6 p7 p8 p9

P14 P12 P1 - epa.govt.nz · pauatahan ui i n l et 58 0-0.5 -1 -1.5-2-2.5-2-1.5-1-0.5-0.5-0.5-0.5-0.5-0.5 -1-1.5-1-0.5 0 0-0.5-1-1.5-2 kak hor rat pau duc p1 p2 p3 p4 p5 p6 p7 p8 p9

Local and Global Optimizationweb.maths.unsw.edu.au/~rsw/lgopt.pdf · Local and Global Optimization Formulation, Methods and Applications 1 0.5 0 0.5 1 1.5 2 1 0.5 0 0.5 1 1.5 2 2.5

Local and Global Optimizationweb.maths.unsw.edu.au/~rsw/lgopt.pdf · Local and Global Optimization Formulation, Methods and Applications 1 0.5 0 0.5 1 1.5 2 1 0.5 0 0.5 1 1.5 2 2.5

1 Coordinate descent methods for composite functionsverify that the point (0.5, 0.5) is minimized along both coordinate axis, while it is not an optimum point. 1 0.5 0-0.5-1-0.5 0

1 Coordinate descent methods for composite functionsverify that the point (0.5, 0.5) is minimized along both coordinate axis, while it is not an optimum point. 1 0.5 0-0.5-1-0.5 0

Riemann Solvers - TIFR CAM Bangaloremath.tifrbng.res.in/~praveen/notes/acfd2013/fds.pdf · 11. The Riemann Solver of Roe I \ L 0 0 0.5 1 Position i 01 0 0.5 I Position 3.8 I I 0 0.5

Riemann Solvers - TIFR CAM Bangaloremath.tifrbng.res.in/~praveen/notes/acfd2013/fds.pdf · 11. The Riemann Solver of Roe I \ L 0 0 0.5 1 Position i 01 0 0.5 I Position 3.8 I I 0 0.5

Debugging the Machine Learning Pipelinepages.cs.wisc.edu/~jerryzhu/machineteaching/pub/debugML.pdfHarry Potter Toy Example 0 0.5 1 magical heritage 0 0.5 1 education 0 0.5 1 magical

Debugging the Machine Learning Pipelinepages.cs.wisc.edu/~jerryzhu/machineteaching/pub/debugML.pdfHarry Potter Toy Example 0 0.5 1 magical heritage 0 0.5 1 education 0 0.5 1 magical

Meson-baryon interaction in the meson exchange picture · Candlin et al., NPB 226, 1 (1983) Final 3-1 -0.5 0 0.5 1 0 20 40 60 80 100-0.5 0 0.5 1 -0.5 0 0.5 1 -0.5 0 0.5 1 d σ/d Ω

Meson-baryon interaction in the meson exchange picture · Candlin et al., NPB 226, 1 (1983) Final 3-1 -0.5 0 0.5 1 0 20 40 60 80 100-0.5 0 0.5 1 -0.5 0 0.5 1 -0.5 0 0.5 1 d σ/d Ω

The Curie-Weisse Model · The Curie Weiss Model ¡ 1 ¡ 0.5 0 0.5 1 ¡ 1 ¡ 0.5 0 0.5 1 Figure 2.2: The distribution of the magnetization of the Curie Weiss model, ¹ CW N ;¯ ,0

The Curie-Weisse Model · The Curie Weiss Model ¡ 1 ¡ 0.5 0 0.5 1 ¡ 1 ¡ 0.5 0 0.5 1 Figure 2.2: The distribution of the magnetization of the Curie Weiss model, ¹ CW N ;¯ ,0

UX100 - Hantech Automation · 5.8 22 100 0.5 45 0 0.5 0 22.4 48 24 ux101 0.5 45 0 0.5 0 22.4 output1 ssr / scr / ret input + 4 12 a b b 3 5 alarm output1 100 - 240v ~ 50 / 60hz 8va

UX100 - Hantech Automation · 5.8 22 100 0.5 45 0 0.5 0 22.4 48 24 ux101 0.5 45 0 0.5 0 22.4 output1 ssr / scr / ret input + 4 12 a b b 3 5 alarm output1 100 - 240v ~ 50 / 60hz 8va

SESAMEsesame.geopsy.org/Delivrables/D21.07.pdf2 20 0 500 500 0 250 250 0.5 0.5 2 2 0 0 1 25 50 0 800 800 0 400 400 0.5 0.5 2.1 2.1 1000 1000 100 30 30 0 3500 3500 0 1750 1750 0.5 0.5

SESAMEsesame.geopsy.org/Delivrables/D21.07.pdf2 20 0 500 500 0 250 250 0.5 0.5 2 2 0 0 1 25 50 0 800 800 0 400 400 0.5 0.5 2.1 2.1 1000 1000 100 30 30 0 3500 3500 0 1750 1750 0.5 0.5

0 0.5 Mi. UV

0 0.5 Mi. UV

 · 1084 G. NUPUR AND N. NEELA −1 −0.5 0 0.5 1 −0.5 0 0.5 1 1.5 −1 −0.5 0 0.5 1 −0.5 0 0.5 1.5 Regularized Heaviside Function Heaviside Function Figure 2. Regularized

 · 1084 G. NUPUR AND N. NEELA −1 −0.5 0 0.5 1 −0.5 0 0.5 1 1.5 −1 −0.5 0 0.5 1 −0.5 0 0.5 1.5 Regularized Heaviside Function Heaviside Function Figure 2. Regularized

2.0 1.5 1.0 0.5 0

2.0 1.5 1.0 0.5 0

PVC Customer Service Report - business.flydenver.com Dining Bar Aviator's BBQ B-MEZZ score 86 82 83 84 83.8 int points 0 0 0 0 0 ext points 0.5 0.5 0.5 0.5 2 Cantina Grill B-MEZZ score

PVC Customer Service Report - business.flydenver.com Dining Bar Aviator's BBQ B-MEZZ score 86 82 83 84 83.8 int points 0 0 0 0 0 ext points 0.5 0.5 0.5 0.5 2 Cantina Grill B-MEZZ score

Discrete Random Variables - University of Hong Kongelec2844/sp21/lec2.pdfX pX(0) = 0.2 pX(100) = 0.8·0.5 pX(300) = 0.8·0.5 E(X) = 0·0.2+100·0.8·0.5+300·0.8·0.5 = 160 pX(0) =

Discrete Random Variables - University of Hong Kongelec2844/sp21/lec2.pdfX pX(0) = 0.2 pX(100) = 0.8·0.5 pX(300) = 0.8·0.5 E(X) = 0·0.2+100·0.8·0.5+300·0.8·0.5 = 160 pX(0) =

III. The human Turing machine - Collège de France · stages during dual-task processing N1-500 0 500 1000 1500-1-0.5 0 0.5 1 1.5 P3-500 0 500 1000 1500-0.5 0 0.5 1 1.5-500 0 500

III. The human Turing machine - Collège de France · stages during dual-task processing N1-500 0 500 1000 1500-1-0.5 0 0.5 1 1.5 P3-500 0 500 1000 1500-0.5 0 0.5 1 1.5-500 0 500

12 comparison of NN and FS.ppt - Auburn Universitywilambm/nn/cn_org/12 comparison of NN and FS_9.pdfNeural network with one hidden layer 6 0 5 10 1 0.5 1-0.5 0 0.5-1-0.5 0 0 5 10 15

12 comparison of NN and FS.ppt - Auburn Universitywilambm/nn/cn_org/12 comparison of NN and FS_9.pdfNeural network with one hidden layer 6 0 5 10 1 0.5 1-0.5 0 0.5-1-0.5 0 0 5 10 15