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    November 28

    继续专贴冷笑话。。。

    两只番茄过马路,一辆汽车飞驰而过,其中一只闪避不及被压扁,另一个番茄指着被压扁的番茄大笑道:挖哈哈哈,番茄酱…

    石头和年糕打架,石头飞起一脚就把年糕踢进了大海…………
    从前有一对恋人私定终生,但是男生需要服兵役,便和女生定下誓言,给了女生一枚钻石戒,并许诺在三年后的今天与那女生碰面,到时候,那枚戒指作为婚戒. 好不容易3年过去了,女生一直在等男生,却一直等不到,她伤心过度,绝望的她把钻戒扔入大海,远走他乡.可是,那男生其实也一直在等那女孩,可是,女孩误解了约会地点,于是便永远的成为了遗憾.男生伤心欲绝…过了几年,男生出外钓鱼,猜猜看他钓到了什么?
    年糕!!!

    水饺是男生还是女生
    答案 男生 因为水饺有包皮

    阿松和阿柏无事闲聊互道岁月不饶人。
    阿松:“回忆儿童时代,过的最快乐的是儿童节。”
    阿柏:“过了十年就是青年节。”
    阿松:“再过十年就是父亲节。”
    阿柏:“再过几十年就是老人节了。”
    阿松:“又再过几十年.”
    阿柏:“清明节。”

    开心词典节目主持人王小丫现场采访一位节目观众,问:“在你心目中你最崇拜哪个女主持人?”观众说:“是你呀。”王小丫问:“为何如此说?”观众说:“因为你长得有点像杨澜!”

    玻璃杯和咖啡杯一起過馬路,忽然有人大喊:車子來啦!
    結果玻璃杯被車子撞到了,咖啡杯却沒事,請問爲什麽?
    咖啡杯有耳朵啊!

    芹菜走着走着,突然觉得肚子很痛,接着他”卟”的一声,你说他拉出什么来了~~??那就是芹粪(勤奋)咯!!!芹(菜)粪 是什么颜色的??????
    答案:黄色
    因为 : 秦始皇 (芹屎黄)


    有一个胖子….
    从二十楼顶往下跳….
    结果变成了…..
    死胖子!!

    一个德国人、法国人、及一个日本人要到矿场工作。
    老板是美国人,他对德国人说:「你体格不错,你负责苦力。」
    对法国人说:「你说你是工程师,你负责采矿的计划。」
    而对日本人他说:「你很瘦小。你负责supplies(补给)。」
    然后隔周,他们开始上工。
    几天后德国人及法国人发现日本人不见了,找了很久后他们决定还是先回头工作。
    德国人开始工作的时候,日本人突然跳了出来,
    大声叫到:
    「Surprise!」

    有一只北极熊和一只企鹅在一起耍,企鹅把身上的毛一根一根地拔了下来,拔完之后,对北极熊说:“好冷哦!”
    北极熊听了,也把自己身上的毛一根一根地拔了下来,转头对企鹅说:“果然很冷!”


    有一家卖水饺的小吃店都没生意,
    于是她就去请问师公要怎么办,
    师公说:你要去找一个新鲜的尸体把它的肉包成水饺,
    然后卖出去这样生意就会很好了,但是嘱咐他们家的人千万不能吃这种水饺,不然就会有很KB的事情发生。
    老板试了试效果果真很好,
    于是她就再去找尸体。
    隔天她的儿子要带便当,
    可是却找不到于是他就去冰箱找看看,
    结果发现了一个便当盒他以为是他的就带走了,
    没想到盒子里是爸爸卖剩的饺子,
    他中午时掀开来看下了一跳,
    早上饺子明明是10个为何马上就变成5个,
    他又试了试把盖子盖起来再掀开又变成2个了!
    知道为甚么吗?
    .
    .
    .
    .
    .
    .
    .
    .
    因为饺子黏在盖子上了。


    从前,有一只马!它跑着跑着就掉进海里。
    所以,它变成了一只“海马”!
    这只马的另外一只马朋友,为了要去找掉到海里的马,结果却掉到河里。后来,他就就变成“河马”。
    第三只马是只白马。它为了要找失踪的两个朋友,来到了交通混乱的城市。
    它连续被好几台车子给辗过,使得身上出现好几条黑条纹。
    结果,它变成“斑马”了!
    第四只马为了找寻前面三个的同伴,有一天,它来到一间工厂,结果被改造成“铁马”。
    但后来,那些马还是难逃被吃的命运,通通被作成了“沙其马”,肆虐所及,所有马儿无一幸免,成了一个无马的世界……
    然后,有一群人看到这篇笑话后忍不住的说:“马的~真冷”。
    最后,为了纪念这个笑话,有人将它编订成课,我们叫它“马赛课”!


    一个软件公司正在招聘
    这天,一只狗跑来应聘,主管觉得很郁闷,想把狗赶出去。狗拿出一张纸跟一支笔,很工整地写了几个字:请不要歧视动物。
    总管知道这不是普通的狗,出于好奇,他决定试一下。
    总管拿出应聘的条件,上面写着:1。必须会打字。2。必须会编程。3。至少精通一门外语。
    于是狗来到计算机前,很熟练地打了一篇文章,有编写了一个很复杂的程序。然后来到主管前面,对着主管说:喵!!

    有一天耶稣没事做,就跑到天堂的入口那儿闲晃~ 走啊走的,
    看到有一个在那儿排队的老人非常的眼熟,好像是他的父亲约瑟~
    但是他也不敢确定~于是耶稣便决定过去跟他说说话。
    「老先生,你好,请问你叫什么名字啊?」
    老先生说:「我的名字是约瑟。」
    耶稣想,啊?我父亲也叫约瑟~
    但是还是不敢太肯定~~所以耶稣又问: 「老先生,那么请问你生前是做什么的?」
    「我是个木匠。」老先生回答。
    耶稣吃了一惊,心想怎么这么巧~我的父亲也是个木匠。
    耶稣继续问:「请问一下老先生,您的儿子是不是手脚都被钉子钉过?」
    老先生很吃惊的看着他说:「你怎么知道??」
    耶稣此时已经泪流满面~~跪下来哭说~~~「喔,父亲~~因为我就是你的儿子啊!」
    老先生也开始流眼泪,看着耶稣说:
    .
    .
    .
    「原来是你啊…小木偶~~~」
    November 25

    有史以来最牛的SC REPLAY!

    太 弓虽 了!
    用TANK A ZEALOT来打LURKER!
    不能不看啊!!!
     
    November 24

    号外号外!今天的新闻真真好~不要铜板就有一份报~

    A Smarter Computer to Pick Stock

    Ray Kurzweil, an inventor and new hedge fund manager, is describing the future of stock-picking, and it isn’t human.

    “Artificial intelligence is becoming so deeply integrated into our economic ecostructure that some day computers will exceed human intelligence,” Mr. Kurzweil tells a room of investors who oversee enormous pools of capital. “Machines can observe billions of market transactions to see patterns we could never see.”

    The listeners, attendees of a conference sponsored earlier this month by the Capital Group Companies, are slightly skeptical. Some have heard that Mr. Kurzweil, 58, who takes more than 150 vitamins and supplements a day, believes people will eventually live forever. Others know he has said that in 2045, man and machine will achieve “singularity,” and humans will hold their breath for hours thanks to nanomachines in our bloodstreams.

    But some are aware that a former Microsoft executive and chairman of the Nasdaq stock market, Michael W. Brown, is an investor in Mr. Kurzweil’s new hedge fund, FatKat, and that Bill Gates once described him as “the best person I know at predicting the future of artificial intelligence.”

    More important, many of them have seen Mr. Kurzweil’s ideas used by stock speculators. So, they want to learn more about his brave, new world.

    “These ideas are the future,” said David Atkinson, a private investor who attended another lecture later that day by Mr. Kurzweil. “I’m not really sure I understand them, but they’re making some folks rich.”

    Complicated stock picking methods are nothing new. For decades, Wall Street firms and hedge funds like D. E. Shaw have snapped up math and engineering Ph.D.s and assigned them to find hidden market patterns. When these analysts discover subtle relationships, like similarities in the price movements of Microsoft and I.B.M., investors seek profits by buying one stock and selling the other when their prices diverge, betting historical patterns will eventually push them back into synchronicity.

    Today, such methods have achieved a widespread use unimaginable just five years ago. The Internet has put almost every data source within easy reach. New software programs, like the Apama Algorithmic Trading Platform, have made it possible for day traders to build complicated trading algorithms almost as easily as they drag an icon across a digital desktop.

    “Five years ago it would have taken $500,000 and 12 people to do what today takes a few computers and co-workers,” said Louis Morgan, managing director of HG Trading, a three-person hedge fund in Wisconsin. “I’m executing 1,500 to 2,000 trades a day and monitoring 1,500 pairs of stocks. My software can automatically execute a trade within 20 milliseconds — five times faster than it would take for my finger to hit the buy button.”

    Studies estimate that a third of all stock trades in the United States were driven by automatic algorithms last year, contributing to an explosion in stock market activity. Between 1995 and 2005, the average daily volume of shares traded on the New York Stock Exchange increased to 1.6 billion from 346 million.

    But in recent years, as algorithms and traditional quantitative techniques have multiplied, their successes have slowed.

    “Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”

    So investment firms have increasingly begun exploring mathematics’ furthest edges and turning to people like Mr. Kurzweil, who became an expert in pattern recognition building a reading machine for the blind.

    For years, computer scientists had tried to help machines perform mundane tasks like reading printed words or telling faces apart. With algorithms similar to those used by stock pickers, programmers created millions of rules designed to tell an “A” from an “a.” But no machine could read a page of text as well as the average child.

    So Mr. Kurzweil and others took a different tack: instead of creating sequential rules to instruct a computer to read, they thought, why not create thousands of random rules and let the computer figure out what works?

    The result was nonlinear decision making processes more akin to how a brain operates. So-called “neural networks” and “genetic algorithms” have become common in higher-level computer science. Neural networks permit computers to create new rules and automatically change underlying assumptions by experimenting with thousands of random sequences and processes. Genetic algorithms encourage software to “evolve” by letting different rules compete, and combining the most successful outcomes.

    Wall Street has rushed to mimic the techniques. Because arbitrage opportunities disappear so quickly now, neural networks have emerged that can consider thousands of scenarios at once. It is unlikely, for instance, that Microsoft will begin selling ice-cream or I.B.M. will declare bankruptcy, but a nonlinear system can consider such possibilities, and thousands of others, without overtaxing computers that must be ready to react in milliseconds.

    “Most software fails in pattern recognition because there aren’t enough sequential rules in the world to teach a computer to discern between two faces, or to find almost imperceptible relationships between stocks,” said Orhan Karaali, a computer scientist and director at Advanced Investment Partners, a $1.7 billion hedge fund. “But a machine that can generate complicated rules a person would never have thought of, and that can learn from past mistakes is a powerful tool.”

    Last year, the funds using Mr. Karaali’s model returned in excess of 20 percent by using nonlinear techniques, according to his company. Whereas older methods of stock analysis rely on certain assumptions — for instance, that market volatility always reverts to the mean — Mr. Karaali’s model calculates probabilities and generates assumptions on the fly, and might predict that during a panic, investors will sell Microsoft but, for seemingly irrational reasons, hold onto I.B.M.

    “Only an elite group of people are using these ideas, but a lot of people are thinking about them,” said Stacy Williams, director of quantitative strategies at HSBC Global Markets. HSBC is working with Cambridge University in using models based on how viruses spread to forecast foreign currency markets.

    “The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”

    Such qualms, however, have not stopped Wall Street from scouring university doctoral programs or listening to people like Mr. Kurzweil.

    In the pursuit of previously undetectable patterns, hedge funds are racing to quantify things — like newspaper headlines — that were previously immune from number-crunching.

    Both Dow Jones Newswires and Reuters have transformed decades of news archives into numerical data for use in designing and testing algorithmic systems. The companies are beginning to structure news so it can be absorbed by quantitative models within milliseconds of release.

    Moreover, companies like Progress Software are working with news agencies to create computer programs that instantly translate news — for example, a headline regarding Microsoft’s earnings — into data. M.I.T. is examining, among other things, evaluating companies by seeing how many positive versus negative words are used in a newspaper article.

    Software in development could potentially respond automatically to almost anything; changes in weather forecasts on television news, shifting analyst sentiments or what a particular movie critic said about the new blockbuster.

    “Right now, everyone basically has access to the same data,” said John Bates, a Progress Software executive. “To get an edge, we want to give investors the ability to immediately turn news into numbers. We want to automate what before required human analysis.”

    But as these new techniques proliferate, some worry that promotion is outpacing reality. These techniques may be better for marketing than stock picking.

    “Investment firms fall over themselves advertising their latest, most esoteric systems,” said Mr. Lo of M.I.T., who was asked by a $20 billion pension fund to design a neural network. He declined after discovering the investors had no real idea how such networks work.

    “There are some pretty substantial misconceptions about what these things can and cannot do,” he said. “As with any black box, if you don’t know why it works, you won’t realize when it’s stopped working. Even a broken watch is right twice a day.”

    November 04

    陈年旧笑2 - 历史上死的比较窝囊的几个皇帝



    据说,史上死得最窝囊的,是东晋孝武帝司马矅。


      这位老哥跟大多数皇帝一样,沉迷于声色,成天搂着嫔妃喝酒。一次喝醉了,跟宠妃张贵人吵架,注意,是吵架,不是皇帝训斥妃子,而是皇帝和妃子你一句我一句对骂的吵架。最后司马同学给惹急了,甩出一句赌气的话:“俺不理你了!俺那么多妃子,俺找别人去!”说完,倒头呼呼大睡了。


      还在那儿清醒着的张贵人开始琢磨了,老家伙要不理我了?找别人去,那哪儿能行?!现在我这么年轻美貌,你就不理我了,那将来等我老了,还有好日子过啊?!越想越气,越想越不妙,最后,张女士一咬牙,一狠心,招来几个宫女,搬了几床大被子,三下五除二,把还在香甜睡梦中的司马同学给活活捂死了。


      可怜纯真的司马同学,为了小两口拌嘴的这么一句气话,丢了几辈子才修来的一条皇帝命。


      据说,史上死得最离奇的,是春秋时晋国的国君晋景公姬。


      这老哥是真正掌握生杀大权的一代国君,上了年纪,多少有点老年病。晋国的一位算命先生,大概是活腻味了,跟国君说,您老咧,活不过今年吃新麦子的时候了,姬老先生一听当然不痛快了,到了当年新麦子下来的时候,把算命的招来,捧着饭碗说:你看,你说朕活不到吃新麦子,朕这就吃给你看!不过,你得先给朕死,谁叫你算得不准!说罢叫人把算命的推出去砍了。


      姬老头子端起饭碗,刚要吃,突然觉得肚子不舒服,跟左右说,不成,朕得先去上趟茅房,说着放下碗出去了。左右侍从左等右等,饭都凉了,还不见国君回来,咋回事呢?私下分头去找,宫里哪儿都找不到,最后,在茅房发现了姬老先生,原来掉进了粪坑里,已然薨了……


      后来有人赞扬说,姬老先生是第一个殉难于厕所的帝王……


      一向以文笔简洁有力著称的《左传》,仅用了一句话描写这一事件:“将食,涨,如厕,陷而卒”。


      不记得谁问过我了,什么叫傻缺?我说,好像就是傻冒加缺心眼儿吧。我觉着,以下两位可以竞争死得最傻缺的皇帝……


      一位是秦国的君王泰武王赢荡。其实这位国君多好的前途啊,17岁即位,年轻有为,秦国也国势鼎盛,诸侯皆惧。哪儿哪儿都好,就是有点傻缺,喜欢跟人家比力气,见什么都不服不吝,尤其看不得大玩意。23岁那年外出,看见人家洛阳的大鼎,较上劲了,听说姓孟的大力士能举起来,非说自己也能举起来,结果还真举起来了,可是没抗住,掉下来砸断了大腿,搁着当时医疗条件也差点,没过两天就死在洛阳了。


      另一位最傻缺奖的候选人,不是帝王,但是怎么说也是金枝玉叶呀,汉武帝的儿子,名头够响的吧,广陵王刘胥。这位刘同学也是好胚子,天生的身强体壮,勇力过人。可是您再能耐,也不能喜欢这么变态的活动吧。刘同学不爱金银美女,就喜欢跟狗熊打架。据说他在自己的封地里有一个很大的熊苑,里面豢养着棕熊,灰熊,黑熊,马来熊,白熊……总之,刘同学成天啥也不干,就琢磨着怎么跟熊掐架,还为此请了老师。隔三差五的,进熊苑去揪出一只熊来一顿胖揍,英雄啊!不过呢,英雄也有失手的时候,终于有一天,刘同学遇着厉害熊了,打着打着,被狗熊给挠死了……


      你说堂堂汉武帝,多英明天纵啊,怎么生这么个宝贝儿子……


      大家认为,赢同学和刘同学,谁更配得上最傻缺奖呢,俺比较心仪刘同学哦……


      嗯,故事讲到这里,我们也该总结总结了,学习历史嘛,就是要以史为鉴,我们学到了什么呢?我以为,有以下几点是我们今后一定要借鉴的:


      1.小两口吵架,不要说气话;


      2.算命的说点什么,还是要当真的;


      3.举重是危险运动,没受过专业训练,千万别尝试;


      4.人是掐不过野兽的,尤其是狗熊,不到生死关头,千万别逞能;


      5.最后,也是最重要的,上厕所,尤其是去便便,可千万不能着急啊……

    陈年旧笑

    1. 某次考试考语文,我的同桌在默词的时候突然灵感来了~前句:问君能有几多愁,要求补后句,他补了句:恰似一道红叉卷上留(原句:恰似一江春水向东流) 老师毫不客气得在卷上打了个X 他还沾沾自喜说:“原来我的灵感好灵的!”

    2.也是语文考试默词,题目是:玉不琢,不成器,结果我们班一强人答:朋友妻, 不客气,第二天家长就被叫到学校了

    3.又是语文考试,题目:长江后浪推前浪,某人答:一代更比一代浪.结果自然是家长,又见家长

    4.高中的时候考试有道题是这样的:请写出鲁迅先生的作品《藤野先生》中藤野先生的全名。其答案如下:藤野菜菜子,藤野英二狼,(当时正好有放棒球英豪这个动画片)藤野武大郎,藤野花道,藤野五十六,藤野内丰,藤野隆史等等等等,比较绝的有:藤野小绵羊,气的老师在广播里骂我们无知

    5. 改卷,一题曰“清水出芙蓉”,或答“乱世出英雄”,或答“山村出美女”,或答“深海出蛟龙”……,叫人哭笑不得。

    6.还有一次,题目是:良药苦口利于病,偶一同学答曰:吸烟喝酒伤身体,末了还在后面加个感叹词---啊!!!

    7.考试有一题材为:葡萄美酒夜光杯,接下句。有一同学这样写道:“金钱美女一大堆。:”结果不言而喻。

    8.上句:穷则独善其身 一同学接下句:富则妻妾成群

    9.一次老师提问:“烈士暮年,下句什么?” 偶没听过这句,听成“烈士墓前”了于是张口就说“黄泉路上”全班晕倒

    10.语文老师提供的材料名句默写:后宫佳丽三千人,某生接:铁棒磨成锈花针

    11.初中的时候有一回考语文,之后我和另外几个同学被老师叫去帮忙改其他班的试卷。有一个名言题,吾生也有涯,有个学生接了一句对仗特工整的:尔死也无边。

    12.忧劳可以兴国,对曰:闭目可以养神

    13.蝉噪林愈静, 对曰:狗叫人更欢。(巨寒)

    14.初中的时候考历史,问“刘邦的休养生息政策是什么?” 我一同学答到:笑一笑,十年少,少娶妃子多睡觉。

    15. 有次考语文,问:春风吹又生的前面是什么? 答:斩草不除根。(那几天看武侠小说看多了)