import numpy as np
from scipy.optimize import LinearConstraint
from scipy.optimize import Bounds
from scipy.optimize import milp

# N = 2048
N = 50
M = 256

def read_ints():
	return list(map(int, input().split()))
	
A_src = [[0]*(M+M) for _ in range(N+M)]

for i in range(M):
	A_src[N+i][i*2] = 1
	A_src[N+i][i*2+1] = 1
	
for i in range(N):
	a_i,b_i,c_i,p_i,q_i,r_i = read_ints()
	A_src[i][a_i*2+p_i] = 1
	A_src[i][b_i*2+q_i] = 1
	A_src[i][c_i*2+r_i] = 1

A = np.array(A_src)

b_u = np.array([3 if i < N else 1 for i in range(N+M)])
b_l = np.array([1 if i < N else 1 for i in range(N+M)])

constraints = LinearConstraint(A, b_l, b_u)

# ub = np.array([1]*(M+M))
# lb = np.array([0]*(M+M))

bounds = Bounds(lb=0, ub=1)

c = np.array([-1]*(M+M))

integrality = np.ones_like(c)
# integrality = np.zeros_like(c)

res = milp(c=c,bounds=bounds,integrality=integrality,constraints=constraints)

s = ["0"]*M
for i in range(M):
	if res.x[i*2] < res.x[i*2+1]:
		s[M-1-i] = "1"

print(*s,sep="")