# Original solution
from collections import defaultdict
import heapq


class Dijkstra:
    """
    dijkstraの最短経路問題を解く
    input:
        S: start node
        mat : mat[a][b] = edge_weight(a,b)
    """

    def __init__(self, start, num_node, mat):
        """
        mat: mat[a][b] = edge_weight(a,b)
        num_node: num of nodes
        dist: dist from start_node
        prev: 最短経路において、一つ前のノードを記憶。経路復元に使う?
        """
        self.start_node = start
        self.mat = mat
        self.num_node = num_node
        self.dist = [float('inf')]*num_node
        self.prev = defaultdict(lambda: None)

        # dist form S to S = 0
        self.dist[start] = 0

    def calc_dist(self):
        """
        最短経路 distを計算する
        Q: content (dist_from_start,node)
        Qはheapqとして使う。dist_from_startの小さい順になる
        """
        Q = []
        heapq.heappush(Q, (0, self.start_node))
        while Q:
            dist_to_node, node = heapq.heappop(Q)
            # 現在の値よりも大きくて、更新にならない場合
            if self.dist[node] < dist_to_node:
                continue

            # 次のホップを計算する
            for nex in range(self.num_node):
                weight = self.mat[node][nex]
                cand = dist_to_node + weight

                if self.dist[nex] > cand:  # update
                    self.dist[nex] = cand
                    self.prev[nex] = node
                    heapq.heappush(Q, (cand, nex))

    def get_dist(self, d):
        """
            startからdまでの距離を返す
            これの前にcalc()を実行しておく
            たどり着けない場合は、float('inf')がかえる
        """
        return self.dist[d]
    def get_path(self,dst):
        """
        startからdstまでのpathをlistでreturn
        valifyしてないので怪しい
        """
        cur=dst
        ret=[cur]
        while cur!=self.start_node:
            cur=self.prev[cur]
            ret.append(cur)
        return list(reversed(ret))

N,M,S,T = list(map(int,input().split()))

mat_s=[[float('inf')]*N for _ in range(N)]
mat_t=[[float('inf')]*N for _ in range(N)]
for _ in range(M):
    a,b,c,d = list(map(int,input().split()))
    mat_s[a-1][b-1]=c
    mat_s[b-1][a-1]=c
    mat_t[a-1][b-1]=d
    mat_t[b-1][a-1]=d

D_S = Dijkstra(S-1,N,mat_s)
D_T = Dijkstra(T-1,N,mat_t)

D_S.calc_dist()
D_T.calc_dist()

ans=[None]*N

for i in range(N):
    ans[i]= D_S.get_dist(i)+D_T.get_dist(i)

tmp=float("inf")

ans_list=[]
for i in reversed(list(range(N))):
    tmp = min(tmp,ans[i])
    ans_list.append(10**15-tmp)
print(("\n".join(map(str,reversed(ans_list)))))


# Optimized solution
import heapq
N,M,S,T = list(map(int,input().split()))

adj=[[] for _ in range(N)]

for _ in range(M):
    a,b,c,d = list(map(int,input().split()))
    adj[a-1].append((b-1,c,d))
    adj[b-1].append((a-1,c,d))


D_S=[float("inf")]*N
D_T=[float("inf")]*N

D_S[S-1]=0
D_T[T-1]=0

Q=[]
heapq.heappush(Q,S-1)


while Q:
    v = heapq.heappop(Q)
    dist_to_node,node=v//N,v%N
    for nex in adj[node]:
        (next_node,yen,_) = nex
        if dist_to_node+yen<D_S[next_node]:
            D_S[next_node] = dist_to_node+yen
            heapq.heappush(Q,D_S[next_node]*N+next_node)

Q=[]
heapq.heappush(Q,T-1)

while Q:
    v = heapq.heappop(Q)
    dist_to_node,node=v//N,v%N
    if D_T[node]< dist_to_node:
        continue
    for nex in adj[node]:
        (next_node,_,yen) = nex
        if dist_to_node+yen<D_T[next_node]:
            D_T[next_node] = dist_to_node+yen
            heapq.heappush(Q,D_T[next_node]*N+next_node)


tmp_min=float("inf")
ans=[]
for i in reversed(list(range(N))):
    tmp_min = min(tmp_min,D_S[i]+D_T[i])
    ans.append(tmp_min)


for a in reversed(ans):
    print((10**15-a))