The reporter interviewed in depth and detail, In-depth interviews were conducted with several current and former middle-level and senior officials of the securities and communication company. Shareholders, partners and customers have dug up a large number of exclusive internal information and latest developments of the company. They have a thorough understanding of the company's strategic layout, business development and personnel game. The structural layout and language of the article are also polished clearly and concisely. It is a high-quality company report.
《大敦煌》采用宋代、清末和民国三个历史阶段的创作形式，以一部金字大藏经的命运为贯穿，反映了千年敦煌的形成、发展、辉煌、衰败、重生的过程。
苏岸笃定道：将军放心好，一定妥当。
林大爷急了，小声道：姑娘，有人来了……兰儿和小草根本不理他，只顾侧耳凝听曲子。
这么一想，她心里就轻松起来。
主要讲述六个女兵、一个男伤员和一群保育院的孩子，在日军大扫荡的重重封锁线面前，他们踏上了前途未卜的转移之旅。
倚天之锋锐，天下莫有能挡。
该剧是讲述在聚光灯背后操纵这个世界的真实政治玩家们的危险赌局，将描写指向权力顶点的超级辅佐官张泰俊的炙热的生存故事。
赵文华解开袋子。
不久后，星愿公司的第一个委托案上门，韦笑宝完美完成任务。
三年后，小四喜也离开了渔村，只有阿水一个人生活。阿水终于发现了那幅雯雯在墙上画给他的画。此刻，对雯雯万分思念的阿水终于决定去香港找她。然而意外发生了。
这是对未来充满心意的歌和直斗的对手的一句话。从SNS诞生的奇迹和恋爱？
苦情励志大戏《养女》是一部以写实的手法，描述上世纪90年代至今，西安底层文化和真实生活的电视剧，也讲述了一个牵动着两代养女命运的家庭辛酸情感故事。通过刻画一群淳朴善良的西安市民形象，反映改革开放后西安的巨大变化，同时将西安风土人情尽情展现。
故事发生在雍正皇帝（陈建斌 饰）在位期间，盛大的皇宫选秀仪式上，本不愿入宫的大理寺少卿甄远道长女甄嬛（孙俪 饰），因某种原因被皇帝一眼相中，从而和沈眉庄（斓曦 饰）、安陵容（陶昕然 饰）等两个初相识便情投意合的好姐们进入了暗流涌动的深宫内院。后宫之中，看似娴熟温良的皇后（蔡少芬 饰）滴水不漏，城府颇深；众妃之首的华妃（蒋欣 饰）则仰仗哥哥年羹尧的重臣地位和皇帝的宠幸而飞扬跋扈，对异己肆意打击倾轧。身处钩心斗角、以血洗血的残酷乱局之中，甄嬛和姐妹们都无法独善其身，她们或主动、或被动地卷入了裹挟着爱情、友情、金钱、权力的残酷战场……
在这部充满娱乐效果又富含教育意义的喜剧特辑中，凯文·哈特点出黑人历史中若干无名英雄的出色贡献
阿布隐藏的身份以及来到地球的真正目的到底是什么？神秘的高科技军团背后有何故事？自暴自弃的熊二能否振作起来实现他的英雄梦？他们能否挽救地球的危机？
在20世纪30年代的德州，政府依旧沿用着自19世纪70年代开始制定的对黑人实行种族隔离或种族歧视的法律——黑人被剥夺选举权，并在学校、住区、公共交通、公共场所以及就业、司法、军役、婚姻等各方面，受到残酷的隔离和歧视。马文·托尔森作为一名有知识有头脑的黑人非常希望通过自己的
食神传人杨一恒这次被老鬼指派到了1945年初的大上海，此刻，抗战正值黎明前的黑暗，杨一恒需要完成的任务险峻无比。这次他得到了许多正义之士的协助，也牺牲了很多。仙医神厨再次发挥超能力，智斗日本侵略者，势要破坏日本人在撤退投降前部署的狡诈阴谋！
2012年，美国南部边陲路易斯安那州的警探搭档Rust和Martin回到了一处荒败之地，重访他们1995年经手的一桩古怪仪式杀人案件。随着调查的展开，过往经历浮现，两人不得不重新面对内心深处多年未愈的伤口。然而当记忆细节与新的线索交叠之时，他们再度深陷当年的泥沼 。在追寻真相的过程中，两人意识到黑暗远远不只存在于罪恶的一侧。
For codes of the same length, theoretically, the further the coding distance between any two categories, the stronger the error correction capability. Therefore, when the code length is small, the theoretical optimal code can be calculated according to this principle. However, it is difficult to effectively determine the optimal code when the code length is slightly larger. In fact, this is an NP-hard problem. However, we usually do not need to obtain theoretical optimal codes, because non-optimal codes can often produce good enough classifiers in practice. On the other hand, it is not that the better the theoretical properties of coding, the better the classification performance, because the machine learning problem involves many factors, such as dismantling multiple classes into two "class subsets", and the difficulty of distinguishing the two class subsets formed by different dismantling methods is often different, that is, the difficulty of the two classification problems caused by them is different. Therefore, one theory has a good quality of error correction, but it leads to a difficult coding for the two-classification problem, which is worse than the other theory, but it leads to a simpler coding for the two-classification problem, and it is hard to say which is better or weaker in the final performance of the model.
