I thought this thing was the same as before, Is to rush up and bite people, So we're just shooting with guns, When the distance is close, throw it with a grenade. At the most dangerous time, even mines are thrown out, At that time, the 'round head' anti-infantry mine (still referring to Type 72 anti-infantry mine) had a 'bolt' (i.e. Safety ring) under it. Just pull it out, Throw it out like a grenade, But more powerful than a grenade, As soon as a large area is blown up, But because it is heavier, More than three grenades, Therefore, those with weak throwing ability generally dare not use it like this. Because I'm afraid I can't throw it out of the safe range and blow it up to myself, But fortunately, whatever it is, As long as you want to attack, It must be an attack on the ground, We have a great advantage from a high position. Throwing explosives from high to low can increase the throwing distance, Even if you can't throw it far away, After landing, you can also roll down a little further distance. Therefore, some comrades who throw grenades not far away can also throw mines away from a safe distance to strike the enemy. Then there is the explosive cartridge, The vast majority of people can't throw out three pieces of explosive cartridges in one go like a company commander. And it is too wasteful to throw them all like that, So he threw the explosive cartridges out one by one, When the "big grenade" is used, The reason why I dare to do this and make such "rich" moves is because there are not many others in our positions, that is, grenades, mines and explosive cartridges are plenty of explosives, and many to "tube enough", thanks to these, otherwise in the period of time without artillery support, the Vietnamese army's offensive forces increased several times, and the guns in hand alone could not hold up completely.
What will this book tell us//010
《福尔摩斯：基本演绎法第五季》该剧根据著名的《福尔摩斯》系列改编，讲述了Sherlock Holmes一位苏格兰警视厅的前顾问，因为药瘾问题来到纽约的康戒中心修养，在生活重新回到正轨后和一名叫Joan Watson的前急救医师生活在布鲁克林。
5 fixed-term imprisonment: whoever commits the crime of causing traffic accidents shall be sentenced to fixed-term imprisonment of not more than 3 years or criminal detention; If a person dies due to escape from a traffic accident, he shall be sentenced to not less than 7 years but not more than 15 years.
The panel probability of most poisoning classes is 100% +, which makes the applicability of Gemini poison greatly increased.
兵士虽然不情愿，也只得同往。
全职妈妈沈彗星曾是顶尖大学高材生，婚后她选择相夫教子，把才能用在了经营家庭上。丈夫盛江川在职场打拼，工作异常繁忙无暇陪伴妻女，家庭责任全部落在了沈彗星身上。女儿年纪渐长，沈彗星觉得时机成熟，决定重返职场，却与盛江川任职的公司产生竞争关系。而她进入职场后，要求盛江川分担更多家庭责任，又导致二人摩擦不断。一次意外事件的发生放大了沈彗星和盛江川之间的裂痕，触发了两人间的离婚大战。其后，沈彗星渐渐在职场重获认可，盛江川也在和女儿的相处中理解了妻子的付出，两人渐渐找回对彼此曾经的爱恋。这对年轻夫妻在职场交锋和情感碰撞中各自成长，最终学会了以更成熟的方式面对家庭和事业。他们选择相信彼此，携手成长，事业上共同奋战，家庭里分担责任。
其实月下也想过，要为你们一个个单独加更，只是……月下太渣，一直没有做到。
我们是来看武侠的，不是来等女主角的。
她才华横溢，精于琴箫，她容颜秀丽，倾国倾城。
黄国，一个黑道江湖的小混混，欠下一屁股债，成天想着从姐姐黄奇和姐姐男友张兴那里捞多点钱，这天，黄国又找到张兴要钱，张不肯，两人便起了争执，拉扯中黄国无意中刺伤了张兴，不久，张兴却离奇死亡。黄国十分紧张想要跑路，却苦于没有路费，为了得到钱，他向姐姐黄奇谎称自己在械斗中杀了人，黄奇信以为真了拿了钱让黄国赶紧逃命，却不知死的正是自己的男友张兴。当黄奇知道真相后，态度大为转变，立即向警方指证是黄国杀害了张兴。黄国在车站正欲逃跑时被警察逮个正着，而黄国此时却并不承认自己杀了人，但人证物证俱在，不容黄国狡辩。在审讯当中，狡猾的黄国找了个机会打晕警察逃了出去，当他找到姐姐时发现姐姐正同前男友李荣发生争执，黄奇大发雷霆，将黄国赶出去。之后警察发现李荣也离奇死在黄奇的家里，黄奇却不知所踪，而黄国却一反常态地承认自己是杀人凶手，张兴的死亡和李荣的死亡究竟有什么联系，究竟谁才是真正的杀人凶手？
讲述了一对年轻的中国情侣陈峰和梁小艺在东南亚阿尼国旅行过程中，意外卷入了一个贩毒案，二人被当地警方列为贩毒嫌疑犯，陈峰和梁小艺遭到当地警方追捕和贩毒集团的追杀，而梁小艺也被毒贩抓走生死未卜。走投无路的陈锋找到了好兄弟善控，两人决定找到贩毒集团的贩毒证据，并闯毒窝将陈锋女友救出……
11年前，司法实习生奥森黎（福士苍汰 饰）还是中学三年级学生，他为了保护遭受家暴的母亲（铃木保奈美 饰）而把父亲（堀部圭亮 饰）杀死，母子合力把遗体埋在庭园，再把其车陈驶到海中，伪装成失踪的样子。7年后，黎的父亲作为确定获得在法律上被视为死亡的失踪宣判。一直掩盖着自己罪行的黎与立花爽订了婚，就在他们沉浸于幸福生活中时，打算爆出黎的秘密的人出现了.....
尹旭？萧何点头道：不错，是他，听闻项羽当时专门问过英布和他的意思。
小姨子顾芸结婚，开哥去岳母家送亲途中遇到追债人，慌不择路的他误入顾芸的婚纱之内，丢尽颜面。谷小燕的父亲谷八抬担任婚庆司仪，却因找不到电动车钥匙而错过了时辰，婆家人无奈之下邀请能说会道的开哥前去救场。由于紧张，开哥误将“二拜高堂”说成了“二拜灵堂”，惹怒婆家人。同时，他的救场行为也被谷八抬误认为是故意抢生意。
唐顺之闻言起身，走到房间角落，抽了一把椅子：既然如此，你坐在这里即可，你可以与我同时看到军报。
阎王宝藏一案之后，梅雨墨继承帝位，白雪晴逃离了京城。几年过去，阳城爆发瘟疫，蔓延至京城，梅雨墨带上佟安出宫微服私访，二人身陷黑店之际，被一日本女孩晴明所救。另一边，白雪晴同秦三川进京调查瘟疫的原因。机缘巧合之下，白雪晴与梅雨墨匆匆相遇......此时皇城深宫中，权倾朝野的六贝勒与仁贵妃似乎又有着不可告人的预谋。风雨欲来，随着帝国主义势力的渗透，大清江山摇摇欲坠......
从“仙境”回到现实世界的爱丽丝·金斯利(米娅·华希科沃斯卡饰)，对自己曾经有过的奇幻遭遇早就忘了个一
The obvious key difficulty is that you do not have past data to train your classifier. One way to alleviate this problem is to use migration learning, which allows you to reuse data that already exists in one domain and apply it to another domain.
