ZOJ10,11月赛

Ant

Time Limit: 1 Second      Memory Limit: 32768 KB

There is an ant named Alice. Alice likes going hiking very much. Today, she wants to climb a cuboid. The length of cuboid's longest edge is n, and the other edges are all positive integers. Alice's starting point is a vertex of this cuboid, and she wants to arrive at the opposite vertex. The opposite vertex means the vertex which has no common planes or edges with the starting point. Just like the picture below:

mtz9548_ant_pic1.gif
Alice is very clever, she always walks on the shortest path. But she can only walk on the surface of the cuboid. Now, Alice only knows the length of cuboid's longest edge is n, and doesn't know the length of other edges. Suppose the L is the length of shortest path of a cuboid. Alice wants to compute the sum of L2 for every possible cuboid.

Input

The first line of input contains an integer T(T ≤ 100) . is the number of the cases. In the following T lines, there are a positive integer n(1≤n≤1014) in each line. n is the longest edge of the cuboid.

Output

For each test case, output the sum of L2 for every possible cuboid in a line. L is the length of shortest path of a cuboid. It may be very large, so you must output the answer modulo 1000000007.

Sample Input
2
3
4
Sample Output
160
440
Hint

(3,2,1) and (3,1,2) are regrad as the same cuboids.

#include<bits/stdc++.h>
using namespace std;
#define ll unsigned long long
#define MAXN 205
#define M 100005
#define MOD 1000000007
int x,y,rev;
int n2,n6;
void extend_Euclid(int a,int b){
    if(b==0) {
        x=1;
        y=0;
        return ;
    }
    extend_Euclid(b,a%b);
    int t=x;
    x=y;
    y=t-a/b*y;
}
void init(){
    extend_Euclid(2,MOD);
    n2=(x%MOD+MOD)%MOD;
    extend_Euclid(6,MOD);
    n6=(x%MOD+MOD)%MOD;
}
int main()
{
    init();
    int T;
    scanf("%d",&T);
    while(T--){
        ll n,ans=0;
        scanf("%llu",&n);
        ll t = ((((n%MOD)*((n+1)%MOD))%MOD)*n2)%MOD;
        ans = (ans+(t*t)%MOD)%MOD;

        t = (((((((n%MOD)*((n+1)%MOD))%MOD)*((2*n+1)%MOD))%MOD)%MOD)*n6);
        ans = (ans + ((t%MOD)*((n+2)%MOD))%MOD) % MOD;

        ans = (ans + ((((((((n%MOD)*(n%MOD))%MOD)*((1+n)%MOD))%MOD)*(n%MOD))%MOD)*n2)%MOD)%MOD;
        printf("%llu\n", ans);
    }
    return 0;
}



Author: MU, Tongzhou
内容概要:本文档详细介绍了基于MATLAB实现的多头长短期记忆网络(MH-LSTM)结合Transformer编码器进行多变量时间序列预测的项目实例。项目旨在通过融合MH-LSTM对时序动态的细致学习和Transformer对全局依赖的捕捉,显著提升多变量时间序列预测的精度和稳定性。文档涵盖了从项目背景、目标意义、挑战与解决方案、模型架构及代码示例,到具体的应用领域、部署与应用、未来改进方向等方面的全面内容。项目不仅展示了技术实现细节,还提供了从数据预处理、模型构建与训练到性能评估的全流程指导。 适合人群:具备一定编程基础,特别是熟悉MATLAB和深度学习基础知识的研发人员、数据科学家以及从事时间序列预测研究的专业人士。 使用场景及目标:①深入理解MH-LSTM与Transformer结合的多变量时间序列预测模型原理;②掌握MATLAB环境下复杂神经网络的搭建、训练及优化技巧;③应用于金融风险管理、智能电网负荷预测、气象预报、交通流量预测、工业设备健康监测、医疗数据分析、供应链需求预测等多个实际场景,以提高预测精度和决策质量。 阅读建议:此资源不仅适用于希望深入了解多变量时间序列预测技术的读者,也适合希望通过MATLAB实现复杂深度学习模型的开发者。建议读者在学习过程中结合提供的代码示例进行实践操作,并关注模型训练中的关键步骤和超参数调优策略,以便更好地应用于实际项目中。
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