have the following maximal values: 0.7402, 0.4421, 0.6291, 0.8583, 0.5385, 0.7407, 0.3982, 1.0000, 0.8218, 0.9874 Clearly, the earlier way to scale the testing set to [0, 1] generates an erroneous set. C [...] example, a three-category attribute such as {red, green, blue} can be represented as (0,0,1), (0,1,0), and (1,0,0). Our experience indicates that if the number of values in an attribute is not too large [...] selection $ python grid.py svmguide3.scale · · · 128.0 0.125 84.8753 (Best C=128.0, γ=0.125 with five-fold cross-validation rate=84.8753%) $ ./svm-train -c 128 -g 0.125 svmguide3.scale $ ./svm-predict svmguide3 …

balance 0.644 0.940 0.727 0.909 0.788 breast 0.907 0.969 0.931 0.956 0.951 diabetes 0.760 0.781 0.701 0.748 0.763 digits 0.993 0.996 0.994 0.993 0.994 liver 0.579 0.695 0.635 0.634 0.631 wine 0.927 0.971 0 [...] disagree τ #features global local combined 0.05 10 0.624 0.670 0.648 0.147 0.05 20 0.624 0.670 0.653 0.201 0.025 10 0.646 0.670 0.642 0.070 0.025 20 0.646 0.670 0.646 0.019 combined model performs better when [...] 0.927 0.943 0.932 business 0.828 0.866 0.828 0.834 0.821 medicine 0.719 0.744 0.749 0.748 0.756 References Aggarwal, C. C., Procopiuc, C., Wolf, J. L., Yu, P. S., & Park, J. S. (1999). Fast algorithms …

Learning Important Features Through Propagating Activation Differences

define them as follows: ∆y+ = 1 2 ( f(x0 + ∆x+)− f(x0) ) + 1 2 ( f(x0 + ∆x− + ∆x+)− f(x0 + ∆x−) ) ∆y− = 1 2 ( f(x0 + ∆x−)− f(x0) ) + 1 2 ( f(x0 + ∆x+ + ∆x−)− f(x0 + ∆x+) ) m∆x+∆y+ = C∆x+y+ ∆x+ = ∆y+ ∆x+ [...] f(x1, x2, ...). Given the reference activations x0 1, x 0 2, ... of the inputs, we can calculate the reference activation y0 of the output as: y0 = f(x0 1, x 0 2, ...) (4) i.e. references for all neurons can [...] function. Assume y = x1 + x2, where x0 1 = x0 2 = 0. When x1 = 50 and x2 = 0, the output o saturates at very close to 1 and the contributions of x1 and x2 are 0.5 and 0 respectively. However, when x1 = 100 …

Bistability and oscillations in cooperative microtubule and kinetochore dynamics in the mitotic spin

positions and “nd state 0: Xk,l = v+ k ck Xk,r Š Xk,l Š d0 > 0, Xk,r = Šv+ k ck Xk,r Š Xk,l Š d0 < 0, state 2: Xk,l = vŠ k ck Xk,r Š Xk,l Š d0 < 0, Xk,r = Šv+ k ck Xk,r Š Xk,l Š d0 < 0, state 2: Xk,l = [...] vk > 0 vŠ (x) < Švk always v+ (x) > 0 forx < xmax xmin = Š xmax = (F+ / c) ln v0 + /v k vk < 0 vŠ (x) < 0 forx > xmin v+ (x) > v k always xmin = (FŠ / c) ln v0 Š /v k xmax = vk = 0 vŠ (x) < 0 always [...] i 0 i (sŠ1) Fi (pN) Catastrophe c 0.0019 Š2.3 [2] Rescue r 0.024 6.4 [2] Detachment in growing state d+ 0.000 11 3.8 [2] Detachment in shrinking state dŠ 0.035 Š4.0 [2] Attachment rate a 1.0 Estimated …