Problem: Given integers c_{0}, c_{1}, …, c_{k-1} we can define the cost of a number 0 ≤ x < 2^{k} as p(x) = ∑_{i=0}^{k-1} \left( \left⌊ \frac{x}{2^{i}} \right⌋ mod 2 \right) ⋅ c_{i}. In other words, the cost of number x is the sum of c_{i} over the bits of x which are equal to one.

Let's define the cost of array a of length n ≥ 2 with elements from [0, 2^{k}) as follows: cost(a) = ∑_{i=1}^{n - 1} p(a_{i} ⊕ a_{i+1}), where ⊕ denotes [bitwise exclusive OR](https://en.wikipedia.org/wiki/Exclusive_or) operation.

You have to construct an array of length n with minimal cost, given that each element should belong to the given segment: l_{i} ≤ a_{i} ≤ r_{i}.

Input

The first line contains two integers n and k (2 ≤ n ≤ 50, 1 ≤ k ≤ 50) — the size of an array and bit length of the numbers in question.

Next n lines contain the restrictions for elements of the array: the i-th line contains two integers l_{i} and r_{i} (0 ≤ l_{i} ≤ r_{i} < 2^{k}).

The last line contains integers c_{0}, c_{1}, …, c_{k-1} (0 ≤ c_{i} ≤ 10^{12}).

Output

Output one integer — the minimal cost of an array satisfying all the restrictions.

Examples

Input


4 3
3 3
5 5
6 6
1 1
5 2 7


Output


30


Input


3 3
2 2
3 4
4 6
1 10 100


Output


102

Note

In the first example there is only one array satisfying all the restrictions — [3, 5, 6, 1] — and its cost is equal to cost([3, 5, 6, 1]) = p(3 ⊕ 5) + p(5 ⊕ 6) + p(6 ⊕ 1) = p(6) + p(3) + p(7) = (c_{1} + c_{2}) + (c_{0} + c_{1}) + (c_{0} + c_{1} + c_{2}) = (2 + 7) + (5 + 2) + (5 + 2 + 7) = 30.

In the second example the only optimal array is [2, 3, 6].

Solution (in CPP):

Solution:
#include <bits/stdc++.h>

template <typename T, int NDIMS> struct tensor_view {
	static_assert(NDIMS >= 0, "NDIMS must be nonnegative");

protected:
	std::array<int, NDIMS> shape;
	std::array<int, NDIMS> strides;
	T* data;

	tensor_view(std::array<int, NDIMS> shape_, std::array<int, NDIMS> strides_, T* data_) : shape(shape_), strides(strides_), data(data_) {}

public:
	tensor_view() : shape{0}, strides{0}, data(nullptr) {}

protected:
	int flatten_index(std::array<int, NDIMS> idx) const {
		int res = 0;
		for (int i = 0; i < NDIMS; i++) { res += idx[i] * strides[i]; }
		return res;
	}
	int flatten_index_checked(std::array<int, NDIMS> idx) const {
		int res = 0;
		for (int i = 0; i < NDIMS; i++) {
			assert(0 <= idx[i] && idx[i] < shape[i]);
			res += idx[i] * strides[i];
		}
		return res;
	}

public:
	T& operator[] (std::array<int, NDIMS> idx) const {
		return data[flatten_index(idx)];
	}
	T& at(std::array<int, NDIMS> idx) const {
		return data[flatten_index_checked(idx)];
	}

	template <int D = NDIMS>
	typename std::enable_if<(0 < D), tensor_view<T, NDIMS-1>>::type operator[] (int idx) const {
		std::array<int, NDIMS-1> nshape; std::copy(shape.begin()+1, shape.end(), nshape.begin());
		std::array<int, NDIMS-1> nstrides; std::copy(strides.begin()+1, strides.end(), nstrides.begin());
		T* ndata = data + (strides[0] * idx);
		return tensor_view<T, NDIMS-1>(nshape, nstrides, ndata);
	}
	template <int D = NDIMS>
	typename std::enable_if<(0 < D), tensor_view<T, NDIMS-1>>::type at(int idx) const {
		assert(0 <= idx && idx < shape[0]);
		return operator[](idx);
	}

	template <int D = NDIMS>
	typename std::enable_if<(0 == D), T&>::type operator * () const {
		return *data;
	}

	template <typename U, int D> friend struct tensor_view;
	template <typename U, int D> friend struct tensor;
};

template <typename T, int NDIMS> struct tensor {
	static_assert(NDIMS >= 0, "NDIMS must be nonnegative");

protected:
	std::array<int, NDIMS> shape;
	std::array<int, NDIMS> strides;
	int len;
	T* data;

public:
	tensor() : shape{0}, strides{0}, len(0), data(nullptr) {}

	explicit tensor(std::array<int, NDIMS> shape_, const T& t = T()) {
		shape = shape_;
		strides[NDIMS-1] = 1;
		for (int i = NDIMS-1; i > 0; i--) {
			strides[i-1] = strides[i] * shape[i];
		}
		len = strides[0] * shape[0];
		data = new T[len];
		std::fill(data, data + len, t);
	}

	tensor(const tensor& o) : shape(o.shape), strides(o.strides), len(o.len), data(new T[len]) {
		for (int i = 0; i < len; i++) {
			data[i] = o.data[i];
		}
	}

	tensor& operator=(tensor&& o) noexcept {
		using std::swap;
		swap(shape, o.shape);
		swap(strides, o.strides);
		swap(len, o.len);
		swap(data, o.data);
		return *this;
	}
	tensor(tensor&& o) : tensor() {
		*this = std::move(o);
	}
	tensor& operator=(const tensor& o) {
		return *this = tensor(o);
	}
	~tensor() { delete[] data; }

	using view_t = tensor_view<T, NDIMS>;
	view_t view() {
		return tensor_view<T, NDIMS>(shape, strides, data);
	}
	operator view_t() {
		return view();
	}

	using const_view_t = tensor_view<const T, NDIMS>;
	const_view_t view() const {
		return tensor_view<const T, NDIMS>(shape, strides, data);
	}
	operator const_view_t() const {
		return view();
	}

	T& operator[] (std::array<int, NDIMS> idx) { return view()[idx]; }
	T& at(std::array<int, NDIMS> idx) { return view().at(idx); }
	const T& operator[] (std::array<int, NDIMS> idx) const { return view()[idx]; }
	const T& at(std::array<int, NDIMS> idx) const { return view().at(idx); }

	template <int D = NDIMS>
	typename std::enable_if<(0 < D), tensor_view<T, NDIMS-1>>::type operator[] (int idx) {
		return view()[idx];
	}
	template <int D = NDIMS>
	typename std::enable_if<(0 < D), tensor_view<T, NDIMS-1>>::type at(int idx) {
		return view().at(idx);
	}

	template <int D = NDIMS>
	typename std::enable_if<(0 < D), tensor_view<const T, NDIMS-1>>::type operator[] (int idx) const {
		return view()[idx];
	}
	template <int D = NDIMS>
	typename std::enable_if<(0 < D), tensor_view<const T, NDIMS-1>>::type at(int idx) const {
		return view().at(idx);
	}

	template <int D = NDIMS>
	typename std::enable_if<(0 == D), T&>::type operator * () {
		return *view();
	}
	template <int D = NDIMS>
	typename std::enable_if<(0 == D), const T&>::type operator * () const {
		return *view();
	}
};

template <typename T> void setmin(T& a, T b) { if (b < a) a = b; }

int main() {
	using namespace std;
	ios_base::sync_with_stdio(false), cin.tie(nullptr);
	int N, K; cin >> N >> K;
	vector<array<int64_t, 2>> bounds(N);
	// make them both exclusive
	for (auto& a : bounds) {
		cin >> a[0] >> a[1];
		a[0]--, a[1]++;
	}
	vector<int64_t> costs(K); for (auto& c : costs) cin >> c;

	const int64_t INF = 1e18;
	vector<array<array<int64_t, 4>, 4>> dp((N+1)*(N+2)/2);
	auto I = [](int i, int j) {
		return j * (j+1) / 2 + (j - i - 1);
	};
	for (int k = K; k >= 0; k--) {
		for (int j = 0; j <= N; j++) {
			for (int i = j-1; i >= -1; i--) {
				for (int mi = 3; mi >= 0; mi--) {
					for (int mj = 3; mj >= 0; mj--) {
						int64_t val = INF;
						if (k == K) {
							if (j - i == 1) setmin(val, int64_t(0));
						} else {
							bool needs = i >= 0 && j <= N-1 && bool((((bounds[i][mi>>1] ^ bounds[j][mj>>1]) >> k) ^ mi ^ mj) & 1);
							setmin(val, dp[I(i,j)][mi&2][mj&2] + (needs * costs[k]));
						}
						if ((mi & 1) && (mj & 1)) {
							// we get to skip this part
						} else {
							for (int md = i+1; md <= j-1; md++) {
								for (int z = 0; z < 2; z++) {
									// we can try using this midpoint
									int64_t mm = (bounds[md][z] >> k) ^ 1;
									if (bounds[md][0] < (mm << k) && ((mm + 1) << k) <= bounds[md][1]) {
										setmin(val, dp[I(i,md)][mi][2*z+1] + dp[I(md,j)][2*z+1][mj]);
									}
								}
							}
						}
						dp[I(i,j)][mi][mj] = val;
					}
				}

			}
		}
	}

	cout << dp.back()[0][0] << '\n';

	return 0;
}