28 1.87 1,143 16 1.38 1,132 32 1.86 1,149 22 1.63 1,147 35 1.77 1,147 8 1.10 1,092 27 1.68 1,133 12 1.28 1,110 21 1.53 1,100 5 0.90 1,084 16 1.44 1,100 28 1.68 1,132 8 1.06 1,091 4 0.66 1,091 43 1.90 980 [...] d 37 1.34 1,396 56 1.37 1,427 9 0.83 1,334 5 0.64 1,319 35 1.35 1,359 22 1.18 1,364 22 1.18 1,366 25 1.19 1,384 34 1.32 1,388 18 1.10 1,307 Ostdeutschland 28 1.89 546 58 2.06 568 10 1.34 519 7 1.19 515 [...] nd 1.7 0.03 1,964 1.5 0.03 1,967 1.6 0.03 1,969 1.2 0.02 1,969 1.4 0.02 1,967 1.3 0.02 1,965 1.7 0.04 1,954 1.6 0.04 1,961 Deutschland 1.7 0.02 7,218 1.5 0.02 7,220 1.6 0.02 7,213 1.2 0.01 7,224 1.5 0 …

Name Vorname 1 Abbas Sermad 1 Alper Hatice 1 Ehrenberg Nikolas 1 Fongang Narcisse 1 Kerschke Pascal 1 Meinke Stefan 1 Monthe Gleyne Gregory 1 Ndzelen Sammy 1 Sladek Stephan 1 Szugat Sebastian 1 Uerek Sinan [...] ppen finden freitags von 12.30 - 14.00 Uhr (s.t.) (Gruppe 1) und von 14.00 - 15.30 Uhr (s.t.) (Gruppe 2) in Raum M/E 27 im Mathetower statt. 1 …

×ψα1 1 (x1)A/α1β1(x1)ψ β1 1 (x1) · · · ψ̄αn n (xn)A/αnβn(xn)ψβn n (xn) ] = ∞∑ n=0 (−1)n n! ∫ dx1 · · · dxn∑ P εP ∑ α1,...,αn (−ie) ( A/α1β1(x1)S β1αP1 F (x1 − xP1) ) · · · (−ie) ( A/α1β1(x1)S β1αP1 F (x1 [...] m,n=0 (−1)m m!n! ( i~ 2 )m+n (∂m p ∂ n q f)(∂n p ∂ m q g) = 1 (2π)2 ∫ du1dv1du2dv2dq1dp1dq2dp2 e iu2qeiv2pe i~ 2 (v1(u2−u1)−u1(v2−v1)) ×e−iu1q1−iv1p1f(q1, p1)e−i(u2−u1)q2−i(v2−v1)p2g(q2, p2) = 1 (2π)2 ∫ [...] .γµ1 ∗C γµ1 . . .γµr (4.127) = (−1)(d−r)r+r(r−1)/2 1 (d− r)! εµ1...µd γµr+1 . . .γµdγµr . . .γµ1 ∗C γµ1 . . .γµr (4.128) = (−1)(d−r)r+r(r−1)/2gµ1ν1g µ1µ1 . . . gµrνrg µrµr 1 (d− r)! εν1...νr µr+1...µd …

Vorlesung Wissensentdeckung - SVM -- SMO, Kernfunktionen, Anwendungen

K2(~xi, ~xj) = (~xi ∗ ~xj)2 = ((xi1 , xi2) ∗ (xj1 , xj2))2 = (xi1xj1 + xi2xj2)2 = x2 i1x 2 j1 + 2xi1xj1xi2xj2 + x2 i2x 2 j2 = (x2 i1 , √ 2xi1xi2 , x 2 i2) ∗ (x2 j1 , √ 2xj1xj2 , x 2 j2) =: φ(~xi) ∗ φ( ~xj) [...] α2 Sei α = (α1, . . . , αN ) eine Lösung des Optimierungsproblems. Wir wählen zum update: α̂2 = α2 + y2 ((f(x1)− y1)− (f(x2)− y2)) K(x1, x1)− 2K(x1, x2) +K(x2, x2) Optimales α̂1 = α1 + y1y2 (α2 − α̂2) [...] −αold1 )H = min(C,C+αold2 −αold1 ) Ableiten des Dualen Problems nach α2 ergibt das Optimum für αnew2 αnew2 = αold2 + y2((f( ~x1)−y1)−(f( ~x2)−y2)) η = αold2 + y2(E1−E2) η η = xT1 x1 + xT2 x2 − 2xT1 x2 9 …

Vorlesung Wissensentdeckung - Häufige Mengen in Datenströmen

6)} ∗ 1.∗ 1.8.∗ 1.8.9.∗ 1.8.9.7 4 1.2.∗ 1.2.3.∗ 1.2.3.4 2 1.2.5.∗ 1.2.5.6 6 l = 2 ≤ h HHH2 = {(1.2.5.6)} ∗ 1.∗ 1.8.∗ 1.8.9.∗ 1.8.9.7 4 1.2.∗ 1.2.3.∗ 1.2.3.4 2 1.2.5.∗ 1.2.5.6 6 l = 1 ≤ h HHH1 = {(1.2.5.6) [...] 5 ∗ 1.∗ 1.8.∗ 1.8.9.∗ 1.8.9.7 4 1.2.∗ 1.2.3.∗ 1.2.3.4 2 1.2.5.∗ 1.2.5.6 6 l = h = 4 HHH4 = {(1.2.5.6)} ∗ 1.∗ 1.8.∗ 1.8.9.∗ 1.8.9.7 4 1.2.∗ 1.2.3.∗ 1.2.3.4 2 1.2.5.∗ 1.2.5.6 6 l = 3 ≤ h HHH3 = {(1.2.5.6)} [...] 5.∗ 1.∗ ∗ ∗ 5.6.∗ 1.∗ 5.∗ 1.2.∗ ∗ ∗ 5.6.7.∗ 1.∗ 5.6.∗ 1.2.∗ 5.∗ 1.2.3.∗ ∗ ∗ 5.6.7.8 1.∗ 5.6.7.∗ 1.2.∗ 5.6.∗ 1.2.3.∗ 5.∗ 1.2.3.4 ∗ 1.∗ 5.6.7.8 1.2.∗ 5.6.7.∗ 1.2.3.∗ 5.6.∗ 1.2.3.4 5.∗ 1.2.∗ 5.6.7.8 1.2.3 …

Wissensentdeckung in Datenbanken - Übersicht, Literaturhinweise

Models, Exponential Families, and Variational Inference; 2.5.1, 3.1, 3.2, 3.3 Proximaler Gradientenabstieg Proximal Algorithms; 1.1–1.3, 4.2, 6.1.3 4 von 9 Informatik—Künstliche Intelligenz Computergestützte [...] Models, Exponential Families, and Variational Inference; 2.5.1, 3.1, 3.2, 3.3 Proximaler Gradientenabstieg Proximal Algorithms; 1.1–1.3, 4.2, 6.1.3 4 von 9 Informatik—Künstliche Intelligenz Computergestützte [...] Models, Exponential Families, and Variational Inference; 2.5.1, 3.1, 3.2, 3.3 Proximaler Gradientenabstieg Proximal Algorithms; 1.1–1.3, 4.2, 6.1.3 4 von 9 Informatik—Künstliche Intelligenz Computergestützte …

DeepLearning on FPGAs - Introduction to Artificial Neural Networks

linear classifier f : Rd → {0, 1} with f̂(~x) = { +1 if ∑d i=1wi · xi ≥ b 0 else Linear function in d = 2: y = mx+ b̃ Perceptron: w1 · x1 + w2 · x2 ≥ b⇔ x2 = b w2 − w1 w2 x1 Obviously: A perceptron is a [...] linear classifier f : Rd → {0, 1} with f̂(~x) = { +1 if ∑d i=1wi · xi ≥ b 0 else Linear function in d = 2: y = mx+ b̃ Perceptron: w1 · x1 + w2 · x2 ≥ b⇔ x2 = b w2 − w1 w2 x1 Obviously: A perceptron is a [...] linear classifier f : Rd → {0, 1} with f̂(~x) = { +1 if ∑d i=1wi · xi ≥ b 0 else Linear function in d = 2: y = mx+ b̃ Perceptron: w1 · x1 + w2 · x2 ≥ b⇔ x2 = b w2 − w1 w2 x1 Obviously: A perceptron is a …

Introduction

Medical Genetics, Hokkaido University, Japan ABSTRACT Expressions of protein phosphatases 1α (PP1α), and 1γ1 (PP1γ1) was determined in cultured rat ascites hepatoma cell lines (AH-13, AH-109A) and in the [...] conditions. Therefore, in an attempt to further understand role(s) of PP1α and PP1γ1 in hepatomas, the mRNA levels of PP1α and PP1γ1 in ascites hepatoma cells were examined under in vitro conditions. 1105 [...] of PP1α, PP1γ1, PP2Aα, and PP2Cα. RESULTS Figure 1 shows that the PP1α mRNA was higher (about 3 to 4 folds) in both cultured hepatomas than those of the normal liver up to day 3. mRNA levels of PP1α were …

Anticancer platinum complex

expression of P-glycoprotein/MDR1 in porcine kidney epithelial LLC-PK1 cells Noriaki Kitada1, Kohji Takara1,*, Hisato Kishi1, Toshiyuki Sakaeda2, Noriaki Ohnishi1, Teruyoshi Yokoyama1 1Department of Hospital Pharmacy [...] substrate for MDR1, was evaluated in LLC-PK1 cells pretreated with oxaliplatin by using a WST-1 colorimetric assay (Takara et al., 2002a, 2005). LLC-PK1 cells were pretreated with or without 0.1 or 1 µM oxaliplatin [...] 182 Table 1: IC50 values for paclitaxel in LLC-PK1 cells pretreated with or without oxaliplatin for 48 h Treatment IC50 (nM) RRa) Control 2.33 ± 0.31 － + 0.1 µM oxaliplatin 3.52 ± 1.12 1.51 + 1 µM oxaliplatin …

0 0 0 0 0 0 0 0 0 0 1 1 6 "nonspam" 1.11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.22 0 3.33 0 0 0 2.22 1.11 2.22 1.11 1.11 1.11 1.11 1.11 0 1.11 1.11 1.11 1.11 0 1.11 1.11 0 0 1.11 3.33 1.11 0 0 0 0 0.353 0 [...] 0 2.08 0 0 0 0 0 0 0 0 1.181 3 13 "nonspam" 0 0 0 0 0 0 0 0 0 0 0 1.31 0 0 0 0 0 0 1.31 0 0 0 0 0 2.63 2.63 2.63 1.31 1.31 1.31 1.31 1.31 0 1.31 1.31 1.31 1.31 0 1.31 1.31 0 0 1.31 0 2.63 0 0 0 0 0.407 [...] 01 0.052 0 0.01 0.167 0 1.733 12 442 "spam" 0.1 0.1 0.73 0 0.2 0.1 0.2 0.62 0.1 0.31 0.31 1.04 0 0 0 0.1 1.14 0.31 2.4 0.93 2.92 0 0 0.2 0.1 0.1 0 0 0 0 0 0 0 0 0 0 0.1 0 0.1 0.1 0 0 0 0 0 0 0 0 0 0.163 …