宋家大院的男主人公宋国生临终前，交给大女儿宋雅梅一块黄连，嘴里叨唠着梅正秋的名字，然后撒手人寰。宋家大院办丧事，大姑爷周长山坚持按老爷子生前的即定方针办，给老爷子穿戏服火化，岳母和小舅子另有主张，双方各不相让，发生激烈冲突。周长山是个三轮车夫，脾气耿直，点火就着，岳母张爱琴从心眼不待见他。二姑爷温左强是个书商，文化公司总经理，头脑灵活，做事不择手段。两个姑爷的脾气禀性截然不同，但因为做了宋家的姑爷，两个自然成了担挑。表面上，温左强对周长山客客气气，但从骨子里看不起他。周长山却是实诚人，对温左强这个有本事的大能人打心眼里
秦国，赫赫有名的秦昭襄王寿终正寝，重病中的太子安国君即位称王，并立庶出的公子异人为太子，出身楚国王族的王后华阳夫人执掌了国政大权。她也是异人的嫡母。
5. Hedgehog effect
16.4 Inner ear disease or vertigo is unqualified.
Instead of the eldest man, he entered the society early and achieved success, and took the role of the eldest man in the family. Because of the parents' preference, he lacked the care of his parents when he was young, so he was in a competitive relationship with his brother. As a result, it has become the main reason that affects family disputes. In order to get the care and affirmation of his parents, he longed for success from an early age. From an early age, his nickname was money, and he made money just like his nickname. Now he is engaged in various businesses, and the accumulated cash of the characters' names. As the name implies, (her name means domestic helper in Korean) acts as a domestic helper at home. Although she is the second daughter-in-law, she bears the responsibility and obligation of the eldest daughter-in-law. She feels inferior in her husband's family because her family is poor. He is always jittery at the faces of his husband and mother-in-law. He is a daughter who still bears hardships because of her mother's family after getting married, and he is also a figure who causes entanglements between daughters-in-law. Because of the poor family environment, she started her family plan after graduating from the night-time women's business school. Although she had a brother on it, she actually played the role of head of the family.
而下边还有行字：心想事成。
Weapons captured at the scene
本季将揭开全新篇章，如今是改变和挑战、希望和恐惧、知识和无知并存的时代。罗西南特的船员们接到任务：探索星环之门外的新世界
CS1, …
《魔法少女奈叶第四季 ViVid》以奈叶收养的女孩薇薇欧为主人公。故事讲述了“JS事件”之后，曾经的空中王牌奈叶收起羽翼暂作休息，而刚进入魔法学校初等科的薇薇欧在掌握了魔法基本知识后，收到了奈叶和菲特赠送的专用法器“神圣之心”；而另一边，自称为“霸王”的神秘人物英格威特，正准备掀起一股新的波澜……
罗七摇头道：如果单单是这样，属下绝对不会贸然带他回来，主要是这平武出自于墨者相里氏。
这是泰国版的罗密欧与茱丽叶.剧情集合悬疑,犯罪,浪漫喜剧於一身.Rita和Aun的家族是世仇,不应相爱却偷偷相爱(不过最后没人死,不是悲剧啦).Rita饰演的角色个性前卫,是个从国外回来的女孩,而Aun饰演的角色则恰恰相反,是个非常中规中矩的人.Rita会经常开他的玩笑,逗他玩. ...
"Exposure compensation is also called EV, which is an abbreviation of Exposure Values and a quantity reflecting the amount of exposure. EV +/-commonly known as exposure compensation, it was originally defined as: when the sensitivity is ISO 100, the aperture coefficient is F1, and the exposure time is 1 second, the exposure is defined as 0, the exposure is reduced by one gear (the shutter time is reduced by half or the aperture is reduced by one gear), and the EV value is increased by 1. Increasing and decreasing exposure compensation depends on adjusting the rocker. The shooting environment is relatively dim and needs to increase brightness. When the flash cannot work, the exposure can be compensated and the exposure can be appropriately increased. "
Season 4, Episode 1-2: The Sign of Four《四签名》29 November 1987
According to the exposure, in fact, "Broadwell" will really enter the post-20nm era. In the future, the technology will remain unchanged and the architecture will be innovated by "SkyLake" (another Sky Lake). At that time, it may even integrate the graphics core from Larrabee project, provided, of course, that Intel can really find a way to give full play to the graphics efficiency of x86 architecture. Going back? Then let's give it another name "Skymont". It can be expected that the process will be upgraded again. According to the current preliminary plan, it will be 11nm, but it will have to be in 2016.
要是它背着小主人跑，那肯定要叫人发现。
企业号的老舰长是克里斯托弗·派克（布鲁斯·格林伍德 Bruce Greenwood 饰），舰员则包括医疗官“老骨头”麦考伊（卡尔·厄本 Karl Urban 饰）、总工程师史考特（西蒙·佩吉 Simon Pegg 饰）、通讯官乌瑚拉（佐伊·桑塔纳 Zoe Saldana 饰）、舵手苏鲁（约翰·周 John Cho 饰），以及17岁的领航员——天才少年契柯夫（安东·尤金 Anton Yelchin 饰）。而来自未来的老年史波克（伦纳德·尼莫伊 Leonard Nimoy 饰）因穿越时空，告诉了柯克未来罗慕伦帝国的残余势力尼诺（艾瑞克·巴纳 Eric Bana 饰）也已经回到现在，并率领大队人马进行复仇，尼诺摧毁了史波克的家乡瓦肯星，下一个目标便是地球……
Information Theory: I forget which publishing house it was. It is a very thin book and it is very good. There is a good talk about the measurement of information, the understanding of entropy and the Markov process (there is no such thing in the company now, I'll go back and find it and make it up). Mastering this knowledge, it is good for you to understand the cross entropy and relative entropy, which look similar but easy to confuse. At least you know why many machine learning algorithms like to use cross entropy as cost function ~
