Interesting letter[转]

一名公司员工因生活所需向老板提出加薪请求,老板回复表示理解但鉴于公司现状及经济不确定性无法满足其要求。

One day an employee sends a letter to his boss asking for an increase in his salary!
Dear Bo$$
In thi$ life, we all need $ome thing mo$t de$perately. I think you $hould be under$tanding of the need$ of u$ worker$ who have given $o much $upport including $weat and $ervice to your company.
I am$ure you will gue$$ what I mean and re$pond $oon.
Your$ $incerely,
Norman $oh
一名公司雇员某天给老板写信请求加薪。
亲爱的老板:
人的一生我们都迫切一种东西。大家一直为公司挥汗付出,我想你应该理解我们的需要。
我相信你会猜到我的意思并能及时回复。
诚挚的
诺曼.
The next day, the employee received this letter of reply:
Dear NOrman,
I kNOw you have been working very hard. NOwadays, NOthing much has changed. You must have NOticed that our company is NOt doing NOticeably well as yet.
NOw the newspaper are saying the world’s leading ecoNOmists are NOt sure if the <st1:country-region w:st="on"><st1:place w:st="on">United States</st1:place></st1:country-region> may go into aNOther recession. After the NOvember presidential elections things may turn bad.
I have NOthing more to add NOw. you kNOw what I mean.<o:p></o:p>

第二天,雇员收到了回信:
亲爱的诺曼,
我知道你一直工作努力。当前,情况没有多大改观。你肯定也注意到了我们公司业绩平平。
报纸上说,世界知名经济学家们对美国是否会再度进入经济衰退还难下定论。也许过了十一月份的总统选举后情况会更糟。
其它不多说了。你现在也该明白我的意思了
As we all know the fact, you can use several Source Words to compose an Interesting Word. Little Gyro has already learnt this fantastic idea from one of his best friends Brother Yu. On Little Gryo's birthday, fortunately, Little Gryo received a gift from Brother Yu. The gift consists of n Interesting Words S 1 ​ ,S 2 ​ ,...,S n ​ . Little Gyro also thought those words were really fantastic. So he decided to play with these words. For each Interesting Word S i ​ , Little Gyro will select a period of consecutive letters P i ​ , and splice into a new word T within the given order, and then he defined these kind of words "Fascinating Words". It means you can take apart a Fascinating Word T and form n period of consecutive letters P 1 ​ +P 2 ​ +...+P n ​ from the given Interesting Words. Specially, if Little Gyro considers the Interesting Word really useless, he'll not choose any letter from this Interesting Word either. For example, supposed that there are some Interesting Words: S 1 ​ ="telephone", S 2 ​ ="microscope". Little Gyro may select a period of consecutive letters from each given word such as: P 1 ​ ="tele", P 2 ​ ="scope". And then form the Fascinating Word T=P 1 ​ +P 2 ​ ="telescope". Specially, Little Gyro also can only select P 2 ​ ="scope" and form the Fascinating Word T=P 1 ​ +P 2 ​ ="scope", if he dislikes the first Interesting Word. Now given all the Interesting Words that Little Gyro has received on his birthday, Little Gyro wants to know the total number of the different Fascinating Words that he can generate. Input Specification: Each input file only contains one test case. The first line contains an integer n (1 ≤ n ≤ 2000), indicating the number of the Interesting Words. Then, the following n lines, each line contains an Interesting Word S i ​ (1 ≤ ∑ i=1 n ​ ∣S i ​ ∣ ≤ 5×10 4 ). It's guaranteed that the Interesting Words can only be made up by the lowercase letters, and the sum of the length of the Interesting Words S i ​ of all test cases will not exceed 5×10 4 . Output Specification: For each test case, output the number of the different Fascinating Words that Little Gyro can generate. Because the number may be very large, just output the number mod 10 9 +7. Sample Input 1: 2 aa ab Sample Output 1: 8 Sample Input 2: 3 abb aca aba Sample Output 2: 139 Hint: For the first sample, Little Gyro can generate 8 different Fascinating Words in total, including "a", "b", "aa", "ab", "aaa", "aab", "aaab" and the empty word.
10-01
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内容概要:文章围绕智能汽车新一代传感器的发展趋势,重点阐述了BEV(鸟瞰图视角)端到端感知融合架构如何成为智能驾驶感知系统的新范式。传统后融合与前融合方案因信息丢失或算力需求过高难以满足高阶智驾需求,而基于Transformer的BEV融合方案通过统一坐标系下的多源传感器特征融合,在保证感知精度的同时兼顾算力可行性,显著提升复杂场景下的鲁棒性与系统可靠性。此外,文章指出BEV模型落地面临大算力依赖与高数据成本的挑战,提出“数据采集-模型训练-算法迭代-数据反哺”的高效数据闭环体系,通过自动化标注与长尾数据反馈实现算法持续进化,降低对人工标注的依赖,提升数据利用效率。典型企业案例进一步验证了该路径的技术可行性与经济价值。; 适合人群:从事汽车电子、智能驾驶感知算法研发的工程师,以及关注自动驾驶技术趋势的产品经理和技术管理者;具备一定自动驾驶基础知识,希望深入了解BEV架构与数据闭环机制的专业人士。; 使用场景及目标:①理解BEV+Transformer为何成为当前感知融合的主流技术路线;②掌握数据闭环在BEV模型迭代中的关键作用及其工程实现逻辑;③为智能驾驶系统架构设计、传感器选型与算法优化提供决策参考; 阅读建议:本文侧重技术趋势分析与系统级思考,建议结合实际项目背景阅读,重点关注BEV融合逻辑与数据闭环构建方法,并可延伸研究相关企业在舱泊一体等场景的应用实践。
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