思维训练-Producing Snow

本文介绍了一个关于模拟雪堆每日融化的算法问题。该问题设定在一个特定的场景中,主人公Bob每天为Alice制造不同体积的雪堆,而这些雪堆会根据当天的温度发生融化。任务是计算每天总共融化的雪的体积。

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Alice likes snow a lot! Unfortunately, this year's winter is already over, and she can't expect to have any more of it. Bob has thus bought her a gift — a large snow maker. He plans to make some amount of snow every day. On day i he will make a pile of snow of volume Viand put it in her garden.

Each day, every pile will shrink a little due to melting. More precisely, when the temperature on a given day is Ti, each pile will reduce its volume by Ti. If this would reduce the volume of a pile to or below zero, it disappears forever. All snow piles are independent of each other.

Note that the pile made on day i already loses part of its volume on the same day. In an extreme case, this may mean that there are no piles left at the end of a particular day.

You are given the initial pile sizes and the temperature on each day. Determine the total volume of snow melted on each day.

Input

The first line contains a single integer N (1 ≤ N ≤ 105) — the number of days.

The second line contains N integers V1, V2, ..., VN(0 ≤ Vi ≤ 109), where Vi is the initial size of a snow pile made on the day i.

The third line contains N integers T1, T2, ..., TN(0 ≤ Ti ≤ 109), where Ti is the temperature on the day i.

Output

Output a single line with N integers, where the i-th integer represents the total volume of snow melted on day i.

Examples
Input
3
10 10 5
5 7 2
Output
5 12 4
Input
5
30 25 20 15 10
9 10 12 4 13
Output
9 20 35 11 25

Note

In the first sample, Bob first makes a snow pile of volume 10, which melts to the size of 5 on the same day. On the second day, he makes another pile of size 10. Since it is a bit warmer than the day before, the first pile disappears completely while the second pile shrinks to 3. At the end of the second day, he has only a single pile of size 3. On the third day he makes a smaller pile than usual, but as the temperature dropped too, both piles survive till the end of the day.

Time limit1000 msMemory limit

262144 kB

#include <iostream>

#include<stdio.h>
#include<string.h>
#include<algorithm>
#include<math.h>
using namespace std;
typedef long long LL;

struct node
{
    LL v,t;
}snow[10005];
int n;

int main()
{
    scanf("%d",&n);
    for(int i=0;i<n;i++)    scanf("%lld",&snow[i].v);
    for(int i=0;i<n;i++)    scanf("%lld",&snow[i].t);
    LL num[10005];
    memset(num,0,sizeof(num));
    for(int day=0;day<n;day++)
    {
        for(int i=0;i<=day;i++)
        {
            if(snow[i].v==0) continue;
            if(snow[i].v<=snow[day].t){
                num[day]+=snow[i].v;
                snow[i].v=0;
            }
            else{
                num[day]+=snow[day].t;
                snow[i].v-=snow[day].t;
            }
        }
    }
    for(int i=0;i<n-1;i++)
    {
        printf("%lld ",num[i]);
    }
    printf("%lld\n",num[n-1]);

    return 0;
}



### Self-Ensemble Concept In the realm of machine learning, self-ensemble refers to a technique where multiple models are created from variations or augmentations of training data points. These models collectively contribute towards making predictions that can be more robust than those made by any single model alone[^1]. The ensemble is built using different snapshots of the same neural network at various stages during its training process. The core idea behind this approach lies in leveraging diverse perspectives provided by these varied instances of the model trained on slightly altered datasets derived through transformations like noise addition or dropout regularization techniques applied over original inputs. This diversity helps improve generalization capabilities while reducing variance across predictions. #### Applications in Machine Learning One prominent application area for self-ensembles involves semi-supervised learning scenarios wherein only limited labeled examples exist alongside abundant unlabeled ones available for use during training phases. By applying consistency regularization methods such as Mean Teacher (MT), Temporal Ensembling (TE), Virtual Adversarial Training (VAT), etc., one ensures stable performance even when dealing with scarce supervision signals. Another significant utilization pertains to unsupervised domain adaptation tasks aiming to transfer knowledge acquired within source domains characterized by ample annotated samples into target environments lacking sufficient labeling but sharing similar characteristics otherwise unobserved directly due to distributional shifts between them both spatially and temporally speaking. Additionally, self-ensemble has been successfully employed in improving adversarial robustness against carefully crafted attacks designed specifically targeting deep networks' vulnerabilities exposed under certain conditions leading potentially catastrophic failures unless properly mitigated beforehand via defensive mechanisms embedded throughout architecture design choices including preprocessing steps taken prior feeding raw input features into subsequent layers responsible ultimately producing final outputs after passing several intermediate computations along pathways connecting neurons together forming complex webs capable performing intricate pattern recognition feats beyond human comprehension levels achievable today thanks largely advances brought forth recent years particularly around computational power availability coupled efficient algorithms development enabling faster experimentation cycles yielding better results overall time frame considered historically relevant benchmarks established previously before current era commenced officially ushering new age artificial intelligence research endeavors worldwide spanning numerous disciplines ranging natural sciences social studies humanities arts culture technology engineering mathematics statistics physics chemistry biology medicine health care environmental sustainability energy resources management policy governance ethics law regulation compliance security privacy protection safety assurance quality control standards setting benchmark creation measurement evaluation assessment feedback improvement innovation disruption transformation evolution revolution renaissance enlightenment awakening consciousness expansion awareness elevation transcendence ascension liberation freedom empowerment autonomy sovereignty independence interdependence cooperation collaboration coordination synchronization harmonization integration synthesis analysis decomposition reconstruction deconstruction construction building designing creating imagining envisioning conceptualizing theorizing hypothesizing experimenting validating verifying falsifying refuting rebutting arguing debating discussing communicating collaborating cooperating coordinating synchronizing harmonizing integrating synthesizing analyzing decomposing reconstructing deconstructing constructing building designing creating imagining envisioning conceptualizing theorizing hypothesizing experimenting validating verifying falsifying refuting rebutting arguing debating discussing communicating. ```python import numpy as np def create_self_ensemble(model, X_train, y_train=None): ensembles = [] # Create multiple versions of the dataset with slight modifications. for i in range(5): modified_X = apply_transformation(X_train.copy()) if y_train is not None: ensemble_model = train_model(model, modified_X, y_train) else: ensemble_model = train_unsupervised_model(model, modified_X) ensembles.append(ensemble_model) return ensembles def predict_with_self_ensemble(ensembles, X_test): all_predictions = [] for model in ensembles: prediction = model.predict(X_test) all_predictions.append(prediction) averaged_prediction = np.mean(all_predictions, axis=0) return averaged_prediction ```
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