香格里拉

前两天BTChina，昨天是日菁，猪猪的组长辞职，今天轮到VeryCD，其他的各大网站都已被搞掂得七七八八，这次极速的行动应该能令广电得到上头的强烈好评，只是这两天见到BT网站和各大bbs风声鹤唳，场面也是一片悲壮。

我不打算骂广电局，这个部门和很多部门一样，只不过是一个喉舌，况且在版权侵犯上，中国的广大影迷的确是先撩者奸打死无怨。

只是看看今年所谓最强贺岁片档期的名单：

十月围城、三枪拍案惊奇、刺陵、风云II、花木兰、熊猫大侠、扑克王、我的唐朝兄弟、跳出去、火星没事 等；

除了十月围城想看和我的唐朝兄弟勉强可以说想看之外，竟然完全找不到想进电影院的理由。

当国产片只剩下三种类型：大明星大堆头大片、低成本卖雷卖囧、隐蔽电影外国夺奖衣锦还乡；

电影爱好者们除了依靠各种各样的字幕组和下载论坛之外，还剩下什么？

港产片呢？谁不想人往高处走？看着怎么样的小规模烂片在大陆都会有闲闲地千几两千万的票房，谁不心动？从被大剪刀和谐到自我和谐，正如看着《大搜查》里面的青松大佬最后被正义之枪指着头颅的一刻，我舒心地笑了，心想，这片不烂，烂的只是背后说要改的人，看港版吧。

外语片呢？个人觉得的pixar最伟大的电影Wall-E不引进的理由是“观众受众太窄”，号称一刀不剪的电影谁在电影院里面没有发现剪刀的痕迹？除了英文之外的外文片，哪部没有那些令人呕吐的配音？

影院票价呢？这个问题，问问自己的荷包就可以回答。

CCTV6呢？除了有电影院看到的电影的一切问题之外，可能还要忍受有配音却没有字幕的无奈（CCTV6的配音是我从来都听不清楚的普通话）。

广电有张良计，网友有过墙梯，今晚已经发现很多download电影的新方法，有时间可以一试。

facebook等网站已经离开舞台半年，相信都永无翻身之日，每次翻过facebook玩restaurant city或者pet society的时候都觉得很荒谬，荒谬又如何？失败只是在，你不能成为制定社会规则的一个人，这句励志的话，出现在《龙樱》和《女王的教室》，只是令我铭记的这句话的猪猪字幕组，也已经在这场血洗中阵亡。

我只不过是一个影迷，我也不想侵犯我爱的那些导演和演员的版权，我只想我能看的电影可以多一点，完整一点，多简单，只是在这个国度，这件事变得很复杂。

最后，让我对无私奉献很多年的各种语言字幕组成员表达最深的致意，你们是我的偶像，真的。

据称是2009最贫嘴的15句话
        1.漫漫人生路，总会错几步。

　　2.贫僧是自东土大唐而来，专程去往西天拜佛求亲的。

　　3.三分天注定，七分靠打扮。

　　4.只要功夫深，一日夫妻百日恩。——据说是某相声里的词儿

　　5.没有拆不散的夫妻，只有不努力的小三。

　　6.将客户睡服。

　　7.“回床率”，好词儿。

　　8.我先脱了，您随意。

　　9.公司的无耻程度总是超出员工的想象。

　　10.挣的是卖白菜的钱，操的是卖白粉的心。

　　11.早晨在路上见一车，车后贴一标，标上一句话：驾校除名，自学成才。

　　12.琴棋书画不会，洗衣做饭嫌累。拒绝生儿育女，上床按次收费。——新时代女性宣言

　　13.任何一个消息在经过官方否认之前都不能相信。

　　14.贵国有风险，投胎须谨慎。

　　15.你有权保持不沉默，但我们很快会让你沉默

今天查询了一下招行交易记录，意外的发现被骗的300元被退回了，oyeah
估计是淘宝或者招行封锁了骗子的账户，导致款项无法汇入，所以退回。

万幸万幸，感恩~~

昨天遇到第一次被骗的网购，损失RMB300+好心情。忍不住要写出来，作为警惕。

=====================
起因是在魔兽世界中购买金币。
刚练了个70的小牧师，想买点G帮牧师快速积累装备。昨晚在游戏频道有人喊1.1wg=100元，qq联系，淘宝钻石信誉保证（也就是通过淘宝交易）。
这个价格比淘宝上便宜了50%，而且淘宝交易还是比较安全的，于是我就加了那个QQ。那个人非常娴熟的给我发来了“淘宝”交易网址，于是我通过手机银行支付了300元。
支付后那个人还在继续戏弄我，说没收到款项啊，云云。于是我登陆taobao，想看一下交易记录中是否显示已付款，但淘宝的交易记录中根本没上述交易。恍然大悟，被骗了。。。
=====================
骗术分析：
1.用低价诱惑你上当。
100元=1.1万g是一个相当便宜的价格，不由的让人有所心动。
2.伪造淘宝网址
那个人给我发来的网址是http://items.taobao.com.xxx.yyy.zzz.asia/asdfakjhdfakjdhfqieurqieqerqerqerj（反正后面很长），这里实际的域名是zzz.asia，而不是taobao.com。稍微粗心一些，就会蒙混过关。而一旦你相信了开头，相信这是个合法的淘宝交易网址，对后面出现的一些异常情况就不会放在心上，很可能就会被骗。
3.伪造淘宝交易网页
点击进入以上地址，看到的是一个几乎完全一样的淘宝交易页面。后来我细看了一下，还是有一些不同的。但当时虽然心里有些觉得不对，却没放在心上。因为觉得只要是淘宝的交易都是有保障的，心里最重要的一道门已经被攻破。
4.伪造淘宝交易过程
后面的交易过程与正常的淘宝交易模拟的非常相似。首先是选择商品数量（这个他帮你选好了），然后提交交易。这时候会要求你输入淘宝用户名和密码（事后试了一下，输入任何字符都可以通过），然后是交易的具体信息，接着进入付款页面。事后看了一下，这个付款页面倒不是伪造的，确实是支付宝的页面。我选择的是信用卡手机支付，唰~~支付成功，被骗，恭喜。。。
=========================
教训：
1.网购遇到便宜货一定要多长个心眼，检查检查再检查，对骗子的任何蛛丝马迹都要注意，不要着急付款。贪是人心的弱点，也是骗子们使用频繁的心理武器。
2.慎重点击别人发给你的交易地址，对域名要确认再确认。这是网络交易的第一关，也是网络交易时候最重要的一个心理关。一旦你相信了这个开头，对交易网页没有怀疑，后面很难再起怀疑之心；即使骗子的网页有诸多蛛丝马迹你也几乎发现不了。这也是很多电话诈骗可以成功的原因：从接电话开始，骗子就给你留下了一个权威可信的印象，对他后面的所有指示你很难有所怀疑。这是人性的另一个弱点，过于相信第一印象。
3.对没有把握的东西不要一次性投入过多。骗子提供了3种购买方案：100元=1.1万，200元=2.4万，300元=4万，明显300元要便宜许多。其实我也考虑过被骗的可能性，心理还是有点没底。但300元换4万g这个方案太有诱惑力了，忍不住就选了损失最大的这种。所以，对于没把握的交易，宁可先少买点尝试一下，不要一次性全部投入。
4.网络诈骗防不胜防。网上交易要在可信的网站进行。
=======================
这件事发生后我把所有魔兽账号和客户端都删了，从此不再玩这个游戏。这倒不全是自我惩罚的一种方式，而是意识到我现在玩魔兽已经误入了歧途：CWOW陷入遥遥无期的审批门，命运受政府和官僚摆布；公会已经解散，朋友们都去了台服，上线名单一片灰；想玩好一个号只能通过买G参加G团，现在的魔兽世界对我早已失去了温馨感和归属感。现在又多了个骗子。MD，老子不玩了。骗吧，就用这300元为骗子和CWOW送葬。

宾得貌似6月份出了一款新机器：K7。细看了一下参数，与Canon 500D非常接近，技术上属于相同级别。但K7只能拍摄720p的视频，不如500D的1080p。

K7大卖点估计是它的金属机身，比canon入门级机器的塑料外壳强不少。机身防抖也不错，日后购镜头能省不少钱。

最大的噱头是所谓的机内特效，HDR 以及滤镜。移动版的photoshop么？真搞笑。测评在此：“机内滤镜以及模式有点点奇怪。常见的有用的东西有之（类似黑白模式），还有些其他的模式，类似鱼眼效果，说实话，不是那么有意思（译者：俺倒是觉得挺好玩儿），是不是搞笑了一点点啊。。。当然，搞笑的功能也不是完全没有用处，机内HDR就更奇怪了。K7可以设定为获取strong（强烈）或者 standard（标准）HDR照片，这需要拍摄三张连续的照片，然后花上几秒钟生成HDR图像。等待过程削微感觉有点慢，但是因为缺乏控制所以常常出现令人郁闷的结果。除此之外，米有三脚架，那基本不用指望有好结果；而HDR模式没法用自拍定时器，所以机震的概率就更大了。”

加上这些东西，K7越来越像傻瓜机了，而且是单反级的。会有人买它的单么？技术参数与500D接近，价钱却贵了3K。一个机身防抖值3k么？8k买一个500D+防抖+傻瓜机功能，值么？

I don't think so。按照与500D对比来看，这个套机在6k以下才会有市场。

当桑普拉斯也来到场边时，你就知道，这将是一场伟大的比赛。

5-7，7-6，7-6，3-6，16-14.前无古人。

15个大满贯+全满贯，6年时间轻易将桑神甩在了后面。而这个人看起来刚刚来到第二个巅峰期。

罗迪克已经超水平发挥了，几乎已经接近了神。可惜对手是个真正的神。全场只破发一次，就终结了所有的悬念。

唯一可以阻止神的纳达尔深受膝伤困扰。也许只有凭借远远超出身体负荷的付出，才能阻止神；年初澳网惊鸿一现，极佳的状态让人恍然觉得新神已经登基。可惜，这一切难以长久。

2009.7.5，温布尔顿，费德勒，见证伟大。

测光表为什么要定义18%反射率为测光依据呢?

    以反射式测光表为例,测光表测得的物体亮度有二个方面的因素所决定:光源照度和物体的反射率.这二个都是变量的话,那测光值是没有实用意义的!可以设想,黑卡纸在强光灯照射下测到的亮度,远比白纸在弱光下的亮度要高!那不要得出黑纸比白纸更亮的结论了吗?!所以测光表必须要将上面二个变量中的一个规定为常量(不变量),才能有"测光"的意义,所以物体的反射率必须得以规定.

    那么规定怎样的反射率好呢,广义上来说都是可以的,但是这里必须要说明的是,规定了哪种反射率,以后测光应用时,就必须对这种反射率的物体测光才有意义!现在的测光表为什么要定义18%反射率而不是别的呢?这是个景物反射率统计平均值的问题,或者称为反射率"几何中点"(不过许多人误以为是几何反射率的平均中点,误以为应该是50%了.其实是平均反射率的几何中点),就是收集尽可能多的测试样本,测出各自的反射率,再取平均值的意思,有时也称为"积分灰".所以18%其实不是随意规定,而是由统计平均值所决定的.

    最后一点,我们在测光应用时,就要以反射率为18%的对象为测光对象了,所以"灰卡"的结果是最准确的(对专业摄影师的要求来说)!当然,业余条件下也可以采用替代测光法,就是用反射率接近18%的容易获得的物体为测光替代物,比如亚洲人的黄皮肤,草坪,柏油路面等等.但是文章看到这里也可以明白了,这样的测光结果也只是接近正确!

    好在99.99%的照片并不需要那么准确或精确的曝光.

新华网6月26日报道 美国著名流行歌星迈克尔·杰克逊２５日因心脏病发作在洛杉矶的一家医院去世。享年50岁。

美国媒体对此冠以“迈克尔·杰克逊的死标志着一个悲剧的结束”这样的字眼。悲剧么？从他私生活来看，确实是悲剧：“人们不断看到他越来越白的皮肤、塌陷的鼻子、以及总是带着口罩的脸，无休止的整容、破产传闻、05年的娈童案也让他形象一落千丈”。

但他的歌从来没有烂品，他的演唱会也从来都充满激情；他的唱片销量世界第一；2006年，吉尼斯世界纪录颁发了一个最新认证：世界历史上最成功的艺术家！他也一直是我中学时候崇拜的偶像。

Michael走了，他的音乐一直在。BLESS！

越来越多的人对现在的经济学研究方式产生不满了。。。

The Crash-Test Solution

by Mark Buchanan April 2009 Issue

We now know that few people saw the downturn coming. Scientists are working to make sure that never happens again.

Anyone who read Nassim Nicholas Taleb’s bestseller The Black Swan will probably regard the global financial meltdown as proof of Taleb’s point. He argued that it’s not the normal events—the mundane and expected “white swans”—that drive socioeconomic history but the magnificent outliers, the completely unexpected “black swans.” Think September 11, 2001, or the invention of the internet. Human history pivots on the rare seismic shifts that no one predicts or even has a chance of predicting.

In recent months, we’ve seen the real estate bust, the implosion of hedge funds, and the demise of investment-banking giants like Lehman Brothers, Merrill Lynch, and Bear Stearns. Surely this economic collapse was a black swan, as unpredictable as it was rare?
Maybe not.
A small but growing cadre of scientists are arguing that our current crisis was in fact predictable and that the technology exists to make sure that it won’t happen again. The problem may be that we’ve used only economists to try to solve our economic predicaments. Instead, the solution may be found by physicists and other scientists accustomed to studying complex systems.
To anticipate the next crisis and find our way out of this one, we may have to cast off economic and financial dogma and adopt ideas inspired by physics and other natural sciences, disciplines in which the notion of unstable and unpredictable systems is nothing new. For instance, the technology now exists to go beyond economics to build a massive, complete computer model of the modern economy, from the corner store to the city bank and the Federal Reserve.
With such a model, physicists would be able to track changes in the economy dynamically. There have even been calls for an ambitious effort akin to the Manhattan Project, which built the atomic bomb, to bring the most sophisticated mathematics and computer modeling to bear on managing the world’s economies more aggressively than has ever been attempted.
Some of the work has already begun. When the state of Illinois decided to deregulate its electricity market, it wanted to avoid the disastrous outcome California suffered after Enron Corp. manipulated prices, created shortages, and spurred rolling blackouts. So the state hired scientists at Argonne National Laboratories to build a sophisticated model of Illinois’ power market, incorporating suppliers, consumers, regulators, and the like. The model showed that Illinois’ initial market design was vulnerable to Enron-like manipulation. Having learned this lesson in virtual reality, the state was able to change its approach before embarking on deregulation in the real world. The state made its changes and so far has avoided even a hint of California-style problems.
Generally, economists have been loath to use such techniques. “We’re not currently using the best capabilities of science,” says physicist Dirk Helbing, who leads a new division devoted to social modeling at the Swiss Federal Institute of Technology in Zurich. “We need to bring together scientists from different fields and put together tools that can be used as a kind of wind tunnel for testing out social and economic policies.”
The idea is that by studying so-called complex systems—traffic flow, ecosystems, organisms, weather—we can begin to make sense of an increasingly unpredictable economic world. Didier Sornette, for instance, is a world expert on earthquakes. Now he’s heading up a lab in Zurich called the Financial Crisis Observatory, examining how frothy markets show the same signs of stress that the earth shows before an earthquake. Sornette’s group is trying to develop the ability to provide economic warnings, in part by monitoring the stocks of the 500 largest U.S. companies.
In addition, the group has studied the real estate market in hopes of finding signs of coming collapse. By looking at the prices of new homes sold in the U.S. in 2005, the group’s models predicted the bubble that eventually formed, particularly in the Northeast and the West.

At the Santa Fe Institute, Yale economist John Geanakoplos has teamed up with two physicists to look at the natural competition that emerged among hedge funds as they competed to attract investors. The group is examining how hedge funds took on additional leverage during that process—and how the state of the market changed fundamentally as a result of the added debt.
This way of thinking is foreign to mainstream economic theory, which assumes that people, firms, and other economic agents act rationally. Markets are presumed to exist in economic equilibrium, a more or less stable balance achieved through the players’ varying aims. “It’s completely unrealistic,” says finance professor Andrew Lo of the Massachusetts Institute of Technology. “Economists like the model because it can be solved, but people would have to be superhuman in their intelligence to fit the assumptions.”

The evident failure of these assumptions put former Federal Reserve chairman Alan Greenspan into an intellectual tailspin, as his recent testimony before Congress shows. “Those of us,” Greenspan admitted, “who have looked to the self-interest of lending institutions to protect shareholders’ equity (myself especially) are in a state of shocked disbelief.”
Other economists are finally beginning to acknowledge that basic notions such as equilibrium—an idea originally imported into economics from 19th-century physics—aren’t adequate to understanding complex markets.
Consider financial derivatives, for example. It’s taken for granted by economists that derivatives make markets more stable. They are designed to give market participants more flexibility by allowing them to take highly specific market positions. But some economists—notably William Brock of the University of ­Wisconsin and colleagues—have suggested that this view may be backward. They are exploring the consequences of adding one rather obvious fact to standard economic models: that people learn as they participate in markets and may quickly copy other investment strategies if they seem to be working. The result is a pile-on that makes the initial strategy ineffective. Brock’s results show that such adaptive learning leads to derivatives actually destabilizing markets.
A similar conclusion emerges from mathematical physics too. Over the past 30 years, physicists have developed methods for calculating the properties of what they call disordered systems, which have a range of linked components. Adapting these techniques to markets, statistical physicist Matteo Marsili of the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy, showed recently that the proliferation of derivatives inevitably produces an unstable market—a finding that traditional economists have acknowledged only in hindsight.
Marsili emphasizes that we still know next to nothing about the overall consequences for markets from the use of derivatives. Both Brock and Marsili point out that derivatives can be used beneficially to hedge risk, even if they’re often used in practice to leverage positions and thereby increase risk. To know when and how derivatives can be used safely, investors must, at the very least, take the issue of systemic risk seriously.
Mauro Gallegati, of the University of Ancona, in Italy, says that even without the subprime problem, instability in the global credit markets would have eventually prompted a meltdown of some sort. The credit network had become so highly interlinked that all participants were, as he put it, “very ­fragile with respect to the possible collapse of their partners.” Traditional central-bank controls and banking regulations just weren’t up to controlling such instability.
Gallegati believes that governments will need to take a much more ambitious approach in the future. Rather than just looking at individual banks’ lending practices to see if their risks are at acceptable levels, regulators will need to take a more holistic view, monitoring the nature of the links between institutions and the overall stability of the credit network.
“These networks have to be checked,” Gallegati says.

Anyone who read Nassim Nicholas Taleb’s bestseller The Black Swan will probably regard the global financial meltdown as proof of Taleb’s point. He argued that it’s not the normal events—the mundane and expected “white swans”—that drive socioeconomic history but the magnificent outliers, the completely unexpected “black swans.” Think September 11, 2001, or the invention of the internet. Human history pivots on the rare seismic shifts that no one predicts or even has a chance of predicting.

In recent months, we’ve seen the real estate bust, the implosion of hedge funds, and the demise of investment-banking giants like Lehman Brothers, Merrill Lynch, and Bear Stearns. Surely this economic collapse was a black swan, as unpredictable as it was rare?
Maybe not.
A small but growing cadre of scientists are arguing that our current crisis was in fact predictable and that the technology exists to make sure that it won’t happen again. The problem may be that we’ve used only economists to try to solve our economic predicaments. Instead, the solution may be found by physicists and other scientists accustomed to studying complex systems.
To anticipate the next crisis and find our way out of this one, we may have to cast off economic and financial dogma and adopt ideas inspired by physics and other natural sciences, disciplines in which the notion of unstable and unpredictable systems is nothing new. For instance, the technology now exists to go beyond economics to build a massive, complete computer model of the modern economy, from the corner store to the city bank and the Federal Reserve.
With such a model, physicists would be able to track changes in the economy dynamically. There have even been calls for an ambitious effort akin to the Manhattan Project, which built the atomic bomb, to bring the most sophisticated mathematics and computer modeling to bear on managing the world’s economies more aggressively than has ever been attempted.
Some of the work has already begun. When the state of Illinois decided to deregulate its electricity market, it wanted to avoid the disastrous outcome California suffered after Enron Corp. manipulated prices, created shortages, and spurred rolling blackouts. So the state hired scientists at Argonne National Laboratories to build a sophisticated model of Illinois’ power market, incorporating suppliers, consumers, regulators, and the like. The model showed that Illinois’ initial market design was vulnerable to Enron-like manipulation. Having learned this lesson in virtual reality, the state was able to change its approach before embarking on deregulation in the real world. The state made its changes and so far has avoided even a hint of California-style problems.
Generally, economists have been loath to use such techniques. “We’re not currently using the best capabilities of science,” says physicist Dirk Helbing, who leads a new division devoted to social modeling at the Swiss Federal Institute of Technology in Zurich. “We need to bring together scientists from different fields and put together tools that can be used as a kind of wind tunnel for testing out social and economic policies.”
The idea is that by studying so-called complex systems—traffic flow, ecosystems, organisms, weather—we can begin to make sense of an increasingly unpredictable economic world. Didier Sornette, for instance, is a world expert on earthquakes. Now he’s heading up a lab in Zurich called the Financial Crisis Observatory, examining how frothy markets show the same signs of stress that the earth shows before an earthquake. Sornette’s group is trying to develop the ability to provide economic warnings, in part by monitoring the stocks of the 500 largest U.S. companies.
In addition, the group has studied the real estate market in hopes of finding signs of coming collapse. By looking at the prices of new homes sold in the U.S. in 2005, the group’s models predicted the bubble that eventually formed, particularly in the Northeast and the West.

At the Santa Fe Institute, Yale economist John Geanakoplos has teamed up with two physicists to look at the natural competition that emerged among hedge funds as they competed to attract investors. The group is examining how hedge funds took on additional leverage during that process—and how the state of the market changed fundamentally as a result of the added debt.
This way of thinking is foreign to mainstream economic theory, which assumes that people, firms, and other economic agents act rationally. Markets are presumed to exist in economic equilibrium, a more or less stable balance achieved through the players’ varying aims. “It’s completely unrealistic,” says finance professor Andrew Lo of the Massachusetts Institute of Technology. “Economists like the model because it can be solved, but people would have to be superhuman in their intelligence to fit the assumptions.”

The evident failure of these assumptions put former Federal Reserve chairman Alan Greenspan into an intellectual tailspin, as his recent testimony before Congress shows. “Those of us,” Greenspan admitted, “who have looked to the self-interest of lending institutions to protect shareholders’ equity (myself especially) are in a state of shocked disbelief.”
Other economists are finally beginning to acknowledge that basic notions such as equilibrium—an idea originally imported into economics from 19th-century physics—aren’t adequate to understanding complex markets.
Consider financial derivatives, for example. It’s taken for granted by economists that derivatives make markets more stable. They are designed to give market participants more flexibility by allowing them to take highly specific market positions. But some economists—notably William Brock of the University of ­Wisconsin and colleagues—have suggested that this view may be backward. They are exploring the consequences of adding one rather obvious fact to standard economic models: that people learn as they participate in markets and may quickly copy other investment strategies if they seem to be working. The result is a pile-on that makes the initial strategy ineffective. Brock’s results show that such adaptive learning leads to derivatives actually destabilizing markets.
A similar conclusion emerges from mathematical physics too. Over the past 30 years, physicists have developed methods for calculating the properties of what they call disordered systems, which have a range of linked components. Adapting these techniques to markets, statistical physicist Matteo Marsili of the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy, showed recently that the proliferation of derivatives inevitably produces an unstable market—a finding that traditional economists have acknowledged only in hindsight.
Marsili emphasizes that we still know next to nothing about the overall consequences for markets from the use of derivatives. Both Brock and Marsili point out that derivatives can be used beneficially to hedge risk, even if they’re often used in practice to leverage positions and thereby increase risk. To know when and how derivatives can be used safely, investors must, at the very least, take the issue of systemic risk seriously.
Mauro Gallegati, of the University of Ancona, in Italy, says that even without the subprime problem, instability in the global credit markets would have eventually prompted a meltdown of some sort. The credit network had become so highly interlinked that all participants were, as he put it, “very ­fragile with respect to the possible collapse of their partners.” Traditional central-bank controls and banking regulations just weren’t up to controlling such instability.
Gallegati believes that governments will need to take a much more ambitious approach in the future. Rather than just looking at individual banks’ lending practices to see if their risks are at acceptable levels, regulators will need to take a more holistic view, monitoring the nature of the links between institutions and the overall stability of the credit network.
“These networks have to be checked,” Gallegati says.

近似公式：

其中

H 代表超焦距

F 代表镜头焦距.

N 是光圈 f

C 代表模糊圈

超焦距还有另一个重要的特点:如果镜头对焦在超焦距，则景深是从超焦距之半到无穷远。

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google到的某些网页内容（公式）是错的。更正一下。