# 名古屋大集中講義 iwatawiki lec01 s

Free

0
2
12
2 years ago
Preview
Full text
(1) 1 2016/11/29 13:00-14:30 • • • • → 2

(2) … → h.p://www.new-fukushima.jp/archives/4992.html • “On Growth and Form” D’Arcy Thompson – – – 8

(3) … t y • → = → N 9 # 2nπt 2nπt & y(t) = ∑% c n cos + dn sin ( \$ T T ' i=0 f ( x) = a0 ∞ + ∑ (an cos nx + bn sin nx ) 2 n =1 an = bn = 10 11 1 π∫ 2π 0 1 π∫ 2π 0 f ( x) cos nxdx f ( x) sin nxdx 12

(4) 4 f(x) 0.0 -1.0 6 8 10 f1(x)+f2(x) 2 8 10 0 6 8 1.0 -1.0 0.0 4 6 8 10 2 4 6 8 10 N = 20 N = 40 N = 80 x 2 4 6 8 10 6 8 10 f1(x)+f2(x)+f3(x)+f4(x) f3(x) 2 10 -1.0 0.0 1.0 x f4(x) N = 10 -1.0 0.0 1.0 4 f1(x)+f2(x)+f3(x) 2 0 0 4 sin 7 x 7π N=5 x x 4 sin 5 x 5π N=1 -1.0 0.0 1.0 f2(x) -1.0 0.0 1.0 4 sin 3x 3π 0 f 4 ( x) = 6 x x f 3 ( x) = 4 -1.0 0.0 1.0 0 f 2 ( x) = 2 ● sin x f1(x) 4 π ● 0 -1.0 0.0 1.0 f1 ( x) = 1.0 ⎧ 1 (0 ≤ x < π ) f ( x) = ⎨ ⎩− 1 (π ≤ x < 2π ) f ( x + 2π ) = f ( x) 0 2 4 6 8 10 x 0 2 4 x ∞ 4 4 4 4 4 f (x) = sin x + sin 3x + sin5x + sin 7x + ... = ∑ sin(2k −1)π π 3π 5π 7π k =1 (2k −1)π  ( 2 ( 2 2 8 15 ● ● y(t) = c1 cos 2πt 2πt + d1 sin T T 2πt 2πt + d1 sin T T 10πt 10πt +....+ c 5 cos + d5 sin T T y(t) = c1 cos 2πt 2πt + d1 sin T T 20πt 20πt +....+ c10 cos + d10 sin T T y(t) = c1 cos 2πt 2πt + c1 sin T T 80πt 80πt +....+ c 40 cos + d40 sin T T y(t) = c1 cos  2 =7 2 3 a1=1, b1=0, c116 =0

(5) PC2 cn PC1 an PC1=1.2 (  bn PC2=0.8 17 19 PC2 cn an PC1 bn Pictured by Dr. Satoshi Niikura 77 PC1=1.2 PC2=0.8 18 20

(6) → p t p = ∑ Δt i i =1 T = tK 2ntπ 2ntπ ⎞ ⎛ x(t ) = ∑ ⎜ an cos + bn sin ⎟ T T ⎠ i =0 ⎝ N 2ntπ 2ntπ ⎞ ⎛ y (t ) = ∑ ⎜ cn cos + d n sin ⎟ T T ⎠ i =0 ⎝ N ∞ 2nπ t 2nπ t ⎞ ⎛ y (t ) = ∑ ⎜ cn cos + d n sin ⎟ T T ⎠ n =1 ⎝ cn = dn = SHAPE h.p://lbm.ab.a.u-tokyo.ac.jp/~iwata/shape/ 6×6 T 2n 2π 2 T 2n 2π 2 21 K Δy p ∑ Δt p =1 K 2nπ t p T p Δy p ∑ Δt p =1 (cos (sin 2nπ t p p T − cos − sin 2nπ t p −1 T ) ⎡ 2(a b + c d ) ⎤ θ1 = arctan⎢ 2 1 21 21 1 2 ⎥ 2 ⎣ a1 + c1 − b1 − d1 ⎦ 1 ⎡a1* ⎢ * ⎣b1 1 2 ) 1 256 T Kuhl and Giardina (1982)23 ... 1 RGB 2nπ t p −1 E* c1* ⎤ ⎡ cosθ1 sin θ1 ⎤ ⎡a1 c1 ⎤ ⎥=⎢ ⎥⎢ ⎥ d1* ⎦ ⎣− sin θ1 cosθ1 ⎦ ⎣b1 d1 ⎦ ψ 1 = arctan c1* a1* E* = a1*2 + c1*2 ⎡an** cn** ⎤ 1 ⎡ cosψ 1 sinψ 1 ⎤ ⎡an = ⎢ ** ⎢ ⎥⎢ ** ⎥ ⎣bn d n ⎦ E * ⎣− sinψ 1 cosψ 1 ⎦ ⎣bn 22 ... cn ⎤ ⎡ cos nθ1 sin nθ1 ⎤ d n ⎥⎦ ⎢⎣− sin nθ1 cos nθ1 ⎥⎦ 24

(7) h.p://lbm.ab.a.u-tokyo.ac.jp/~iwata/shape/ 25 27 26 28 73.9% 78.9% 14.2% 10.3% 3.9% 5.6% 1.9% 3.8% h.p://lbm.ab.a.u-tokyo.ac.jp/~iwata/shape/

(8) … T ... C ......... T ... A ...... G ... C ..... T ... … A ... G ......... C ... A ...... G ... T ..... T ... … T ... C ......... C ... A ...... A ... C ..... G ... … A ... C ......... C ... A ...... G ... C ..... G ... … T ... C ......... T ... C ...... A ... C ..... G ... ↓ DNA ↓ … T ... C ......... T ... C ...... A ... T ..... T ... … A ... C ......... C ... C ...... G ... C ..... T ... … A ... G ......... T ... C ...... A ... C ..... T ... 2 29 31 0.01 0.02 0.00 ) PC2 0 8 • −0.01 2 2 2 −0.02 2 DNA • 0 −0.20 −0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 PC1 2 =7 30 ( 2 2 32

(9) 1 y1 x1 DNA 36,901 x2 t1 y2 s1 77 x3 t2 y3 s2 x4 y4 PLS DNA DNA 33 GS PLS input space) y = f (x) 223 273 173 142 373 234 138 304 423 203 133 x feature space) y y1 φ1 x x T yi = x i w + ei φ2 x t1 s1 y2 φ3 x t2 s2 y3 x φ x yi = φ (x i )T w + ei 77 y4 φ4 x y = \\\f (x) ∞ x PLS

(10) (Goto et al. 2005) → 39 • • 8.3 m tower HP (Goto (Yoshioka 2004) 2005) 38 40

(11) Table 4. Estimation of parameters in regresssion models. Variables Estimate SE P (Prob>|t|) t AP3 -21.214 5.549 -3.820 0.0003 AP5 -41.990 14.138 -2.970 0.0040 BP3 -38.009 18.649 -2.040 0.0452 Area 0.013 0.002 5.250 <0.0001 -16.181 5.998 -2.700 0.0087 Weight 3 • 3 4 AP3 AP5 BP3 • 70% AP1 AP2 BP3 1 9.5% 2 AP3, AP5, • 41 43 CG -2 s.d. PC1: PC2 (37.81%) PC : +2 s.d. PC1 (42.09%) CG 42 44

(12) PC1: mean-2s.d. vs. mean+2s.d. Contour density PC2: mean-2s.d. vs. mean+2s.d. Contour density Training Training 100 100 / Dafni and Neal, 1997; Dafni et al., 1997 0 0 P = 0.31, n = 24 P < 0.02, n = 45 PC1: mean-4s.d. vs. mean+4s.d. Training 100 100 Training 100 100 0 0 P = 0.57, n = 28 P = 0.33, n = 26 0 ±2 95 PC1 45 PC2: mean-4s.d. vs. mean+4s.d. Training 100 100 0 0 P < 0.02, n = 38 P = 1, n = 28 100 100 0 0 P < 0.04, n = 25 P = 0.52, n = 22 ±4 PC1 PC2 Results of two-tailed binomial tests 7.468 1.876 Contour Density Results of two-tailed binomial tests Training P < 0.04, n = 25 Contour density PC2 PC1: mean-4s.d. vs. mean+4s.d. 0 P < 0.02, n = 38 46 47

(13)
Free

## Tags

Qtl解析 Iwatawiki Lec01 S 講義資料 経済学演習（成城大学・2015年度） Hiroshi Morita S Homepage 講義資料 経済学演習（成城大学・2014年度） Hiroshi Morita S Homepage 講義資料2 経済学演習（成城大学・2015年度） Hiroshi Morita S Homepage 講義資料1 経済学演習（成城大学・2015年度） Hiroshi Morita S Homepage 講義資料 Dbms講義 講義 Junichi Nishimura 講義資料 Dbms講義 08 09 Sql