最近感觉到时间利用效率不够高.下面摘抄了一篇关于效率的文章,希望对自己有帮助.
现代人的生活节奏越来越快，压力也越来越大。经常会听到白领人士抱怨一个星期有三到四天的时间在加班，没有时间锻炼身体，身体经常处在一种透支的状态；也有人抱怨，虽然现在的职位已经到了中层管理层，但是没有安全感，因为知识的更新速度太快。其实大家都感觉到时间是个瓶颈，每天列了一大堆的计划，晚上回顾的时候，却发现忙的都是一些琐碎的事情，重要的事情却没有干，这都是因为缺乏时间管理的技能，不能很好的运筹时间。时间是世界上最充分的资源，每个人都拥有24小时的一天，然而时间又是世界上最稀缺的资源。这就要求我们必须懂得充分地利用每一分每一秒的时间。大多数成功者都有一个共同特点，他们都是管理时间的高手，而失败的管理者则无一例外地都不善于管理时间，这是因为，管理时间是有技巧的。
下面介绍几种高效管理时间的方法。
一、优先计划管理(1) 把每天要做的事列一份清单(2) 确定优先顺序，从最重要的事情做起(3) 每天都这么做大家可以看到这三条建议的关键点在于根据事情的重要程度决定优先顺序。为什么要根据事情重要程度来决定优先顺序？举一个例子：有一个非常大的桶具，桶旁边放了一些大石块、小石块、水和沙子，怎么做能最大限度的把这些东西都放进桶里？正确的顺序是先放大石块再放小石块，再放沙子，最后放水，如果按照相反的顺序，先把水倒进去，再放沙子，再放小石块，最后放大石块，这个桶就装不下这么多的东西。其实，人的精力就像这个桶的容量一样是有限的。大石块就相当于那些非常重要的事情，那些小石块、沙子和水其实就相当于琐碎的小事。如果先去处理那些琐碎的小事，到最后大的事情反而会被忽略到一边。所以要按照事情的重要程度来确定优先顺序，这样能够节省你的精力和时间，重点专注于你要做的事情。
二、目标管理很多人都说，定目标就一定有收获，那么制定目标究竟有哪些好处呢？ (1) 通过制定目标，可以节省不少时间。(2) 目标就像一个筛子，可以筛选出要做的事情，过滤掉无关的事情。如果这件事情和目标有关系就去做，和目标没关系就不必做，这样生活会井然有序，并且有目的。(3) 目标就像风向标一样，它能够使你沿着既定方向前进，并唤起你最大的潜能，推动你实现人生价值。成功人士都是那些目标远大并且能够付诸实施的人。有调查显示，100％的人都认为定目标是有好处的，但只有２０％的人清楚自己具体的目标，能清楚地描述出他想要做的事情。这２０％的人中只有３％的人把目标写下来，变成书面的东西。这个调查结果表明，这３％的人无论是从收入还是成就上都要比那97％的人高。大家都知道定目标好，但是在制定目标的时候还是存在很多的误区。那么，到底应该如何制定目标呢？制定目标要遵循一定的原则，否则目标就会很空。制定目标的原则有以下几条：(1)目标必须自己定。只有你自己参与，原动力才会很大。因为只有你自己清楚，你想要做什么，要达到什么样的标准。(2)目标必须切合实际。如果定得太低，没有挑战性，不能激发潜能，就没有任何意义；如果定得太高，往往会中途流产。有句话说得好，目标只要踮点脚尖就能够得着，就是合理的；如果不用踮脚尖就够到，那就没有挑战性；跳起来都够不到，那就不是目标。 (3)目标一定是书面的。要向３％的人学习，目标书面化可以理清思路，时常提醒要实现的事情，就不容易遗忘。(4)目标必须是可衡量的。只有可衡量的目标，才能知道这个目标到底实现了没有，如果不是可衡量的目标，就很难成为行动的指南。(5)目标必须是有时限的。如果定了一个毫无时间限制的目标，那就不称其为目标了。只有有时限的目标，才可以拟制相应的行动计划。(6)目标是平衡和谐的。你制定的目标可能有很多个，包括生活上的、工作上的，这些目标相互间不能冲突，否则就会使你无所适从。
Thursday, May 22, 2008
Wednesday, May 21, 2008
Machine Learning Links
1. Machine Learning Lunch seminar at Carnegie Mellon
http://www.cs.cmu.edu/~learning/
2. Video Lectures
http://videolectures.net/Top/Computer_Science/Machine_Learning/
3. scholarpedia
http://www.scholarpedia.org/
4. Reinforcement Learning
http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html
5. Matrix Cookbook
http://matrixcookbook.com/
6. Hunch
http://hunch.net/
http://www.cs.cmu.edu/~learning/
2. Video Lectures
http://videolectures.net/Top/Computer_Science/Machine_Learning/
3. scholarpedia
http://www.scholarpedia.org/
4. Reinforcement Learning
http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html
5. Matrix Cookbook
http://matrixcookbook.com/
6. Hunch
http://hunch.net/
Wednesday, May 14, 2008
Friday, May 9, 2008
Google VisualRank
Google VisualRank
Google researchers at a web conference in Beijing announced they work on some kind of PageRank specifically aimed at images. Called VisualRank, the technology was so far only applied to a smaller test set of images, as apparently applying it to all images Google indexed would be too computing-intensive (even arguably the world’s largest super-computer can’t do everything imaginable yet). According to the New York Times yesterday, visual rank is an algorithm “for blending image-recognition software methods with techniques for weighting and ranking images that look most similar,” and in Google’s internal scoring tests it achieved far higher quality results.
If I understand the gist of the research paper [PDF] right, then it seems the core of Google’s VisualRank algo consists of not only looking at textual cues in regards to images, but also image content itself. After identifying the most authoritative set of picture candidates for a given query, Google then improves the ranking of images found to be sharing the most visual characteristics with the group at large, by creating a similarity network (which also would understand e.g. imagery shown from different perspectives, to a certain extent). Center node images or those images containing large resolution versions would then determined to be the most relevant. In 1000 sample queries – taken from the top Google Product search queries – 762 VisualRank results were tested to be more relevant than Google’s old approach, with 202 equal quality results and only 70 results that were worse.
(On a side-note, I wonder what motivates Google to publicize this information, as it could tip of their competition? Are they only being nice, potentially attracting more good researchers, or is there more to it?)
As far as I can tell, the paper does not yet indicate that Google is any further in specific image recognition, e.g. figuring out that the image found on the web contains, say, a vase of flowers without looking at textual descriptions in the vicinity of the embedded pic. 4 years ago, Google co-founder Sergey Brin said, “I don’t think that in the near future we’re going to have a service that takes a picture, and the computer decides, oh, that’s an elephant, so we search for an elephant. That seems funny to us. We should be able to do it.” Google does have face recognition features for Google Images, though; it’s found in the advanced image search dialog and works very well, and you can also use it on your own site, if you have one with indexed images, by searching Google Images for site:yourdomain.com with the face search activated.
Google researchers at a web conference in Beijing announced they work on some kind of PageRank specifically aimed at images. Called VisualRank, the technology was so far only applied to a smaller test set of images, as apparently applying it to all images Google indexed would be too computing-intensive (even arguably the world’s largest super-computer can’t do everything imaginable yet). According to the New York Times yesterday, visual rank is an algorithm “for blending image-recognition software methods with techniques for weighting and ranking images that look most similar,” and in Google’s internal scoring tests it achieved far higher quality results.
If I understand the gist of the research paper [PDF] right, then it seems the core of Google’s VisualRank algo consists of not only looking at textual cues in regards to images, but also image content itself. After identifying the most authoritative set of picture candidates for a given query, Google then improves the ranking of images found to be sharing the most visual characteristics with the group at large, by creating a similarity network (which also would understand e.g. imagery shown from different perspectives, to a certain extent). Center node images or those images containing large resolution versions would then determined to be the most relevant. In 1000 sample queries – taken from the top Google Product search queries – 762 VisualRank results were tested to be more relevant than Google’s old approach, with 202 equal quality results and only 70 results that were worse.
(On a side-note, I wonder what motivates Google to publicize this information, as it could tip of their competition? Are they only being nice, potentially attracting more good researchers, or is there more to it?)
As far as I can tell, the paper does not yet indicate that Google is any further in specific image recognition, e.g. figuring out that the image found on the web contains, say, a vase of flowers without looking at textual descriptions in the vicinity of the embedded pic. 4 years ago, Google co-founder Sergey Brin said, “I don’t think that in the near future we’re going to have a service that takes a picture, and the computer decides, oh, that’s an elephant, so we search for an elephant. That seems funny to us. We should be able to do it.” Google does have face recognition features for Google Images, though; it’s found in the advanced image search dialog and works very well, and you can also use it on your own site, if you have one with indexed images, by searching Google Images for site:yourdomain.com with the face search activated.
Image-search startup Riya calls Google's plans "largely impossible"
Image-search startup Riya calls Google's plans "largely impossible"
Google-backed researchers Shumeet Baluja (pictured) and Yushi Jing presented the Mountain View company's latest image search and recognition efforts to an audience in Beijing, China on Thursday. VisualRank attempts to do for images what PageRank has done for typical Web pages — rank them in search results according to "authority," which will presumably increase the relevance of results. Problem is, their limited success came at a cost Google is typically loathe to pay: 150 units of homo sapiens who helped sort and rank the images by hand. Munjal Shah, CEO of image-search startup Riya, remarked to the Times: "I think what they're trying to accomplish is largely impossible." Funny, because large-scale, advanced image recognition is what Marissa Mayer says will solve Street View's privacy conundrum.
Google-backed researchers Shumeet Baluja (pictured) and Yushi Jing presented the Mountain View company's latest image search and recognition efforts to an audience in Beijing, China on Thursday. VisualRank attempts to do for images what PageRank has done for typical Web pages — rank them in search results according to "authority," which will presumably increase the relevance of results. Problem is, their limited success came at a cost Google is typically loathe to pay: 150 units of homo sapiens who helped sort and rank the images by hand. Munjal Shah, CEO of image-search startup Riya, remarked to the Times: "I think what they're trying to accomplish is largely impossible." Funny, because large-scale, advanced image recognition is what Marissa Mayer says will solve Street View's privacy conundrum.
Thursday, May 8, 2008
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