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"I don't have time," is the worst excuse not to blog.
Yet, I hear it often from fellow academics.
My advisor from grad school recently asked, "How can you write tons of papers and grant proposals, teach your classes, advise students, take care of your family and still have time to blog? Where does that time come from?"
Embedded in his question is an assumption that blogging has to take time.
Were this true, I couldn't recommend it Ph.D. students or pre-tenure profs.
The secret to low-cost academic blogging is to make blogging a natural byproduct of all the things that academics already do.
- Doing an interesting lecture? Put your lecture notes in a blog post.
- Writing a detailed email reply? "Reply to public" with a blog post.
- Answering the same question a second time? Put it in a blog post.
- Writing interesting code? Comment a snippet into a post.
- Doing something geeky at home? Blog about what you learned.
I'll save an argument for the benefits of academic blogging for another post. For now, I'll argue that those benefits need not be high to overcome the cost.
Read below for my efficient blogging strategies.
Tip 1: Lecture as post
A favorite gripe of junior professors is that teaching is a waste of their time.
Excellence in teaching buys no credit for tenure at many universities.
(Of course, putrid teaching can derail a tenure case.)
Teaching is an opportunity to convert lecture notes into blog posts and external evangelism. The conversion usually polishes a lecture too.
It's hard to teach a class without creating lecture notes.
Why not write those lecture notes as a blog post?
Examples
- First-class macros in meta-circular evaluators.
- A lambda-calculus interpreter in C++ templates.
- Compiling Scheme to C.
- Compiling Scheme to Java.
Tip 2: "Reply to public" as post
Many of the academics that "don't have time to blog" seem to have plenty of time to write detailed, well-structured replies and flames over email.
Before pressing send, ask yourself, should this answer be, "Reply," "Reply to all," or "Reply to public"?
If you put effort into the reply, don't waste it on a lucky few. Share it.
Of course, "reply to public" is not limited to email. A few of my recent posts started on Quora. If I still used Usenet, I bet the same would be true there.
Examples
- This post.
- 3 qualities of successful Ph.D. students.
- Greasemonkey scripts for Apply Yourself.
- Why guaranteed file compression is impossible.
Tip 3: Advice as post
I hear some questions with alarming repetition. To name a few:
- What is grad school like?
- How many years does a Ph.D. take?
- How can I get into grad school?
- How should I structure a thesis proposal?
Any question asked more than once is a candidate for a blog post.
Examples
- The illustrated guide to a Ph.D.
- HOWTO: Get into grad school.
- A thesis proposal is a contract.
- Productivity tips for academics.
Tip 4: Vented steam as post
My colleague, Suresh Venkatasubramanian, claims that the need to vent steam is his preferred reason for posting.
Blogs are a way to safely let it out, assuming appropriate diplomacy.
Examples
- The CRAPL: An academic-strength open-source license.
- American Airlines sucks.
- Why peer reviewers should use Tor.
Tip 5: Blog as code repository
I used to be great at starting coding projects, but terrible at finishing them.
That changed when I started posting code on my blog.
Posting my code on my blog forces me to do three things:
- It makes me refactor my code into a clean design.
- It makes me comment my code sufficiently.
- It makes me search for the most concise solution.
I've stopped rewriting code, because I reuse the code I post on my blog.
At the same time, I've picked up months-old projects and continued them.
Now when I write code, I look for ways to turn parts of it into a blog post.
Examples
- Matching regular expressions with derivatives.
- A nonblocking lexing toolkit based on derivatives.
- A pipelined nonblocking webserver in Scala.
- A reference implementation of k-CFA.
Tip 6: Blog as long-term memory
There are lots of things I used to know, but forgot.
When I find myself relearning something for the second time, I write a blog post on it, so that I won't have to relearn it again.
I often write these up as a HOWTO.
Examples
- HOWTO: Create native iPhone apps in JavaScript.
- HOWTO: Make cat 5 network cables.
- HOWTO: Rip a DVD.
- HOWTO: Create an online book catalog.
- HOWTO: Fix mold and allergy problems.
A few more tips
I have a few miscellaneous tips for busy academic bloggers:
- Don't blog before a deadline.
- Don't post too frequently.
- Don't feel pressure to post with regularity. Twitter and RSS can alert your readers.
- Don't spend too much time on a post. It doesn't have to be as polished as something you submit for peer review. I don't even spell-check.
- Do store up posts if you have free time. Release when you're busy.
- Don't submit your own work to social news sites. If you write well enough, others will do it for you.
- Don't feel the need to have comments. I get plenty of constructive, meaningful interaction with my readers over twitter and email.
Academic blogs I like
- Dave Herman's The Little Calculist. I point this out to my students as a great example of grad student blogging as note-taking. (Dave recently finished his Ph.D., but he's given this blog to himself and to the community forever.)
- John Regehr's Embedded in Academia. John's posts are much more polished than mine, and they're entertaining, educational and thorough as a result. His posts are great outreach and service to the field. He nails the post-tenure associate professor blog perfectly.
- Suresh Venkatasubramanian's geomblog. Suresh's blog is a great mixture of field-specialist and pan-academic writing. There's something worth knowing in every post.
- Daniel Lemire's blog hits topics ranging from his own research interests to broader academic concerns. He thoughtfully compresses many of his posts into small, bite-sized form.
- Dick Lipton's blog does a major service to theory of computation, because he spends time writing engaging, thoughtful and accessible articles. Dick does the esteemed yet friendly full professor blog well.
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Real human motion usually consists footplants,which are periods of time when a foot or part thereof remains in a fixed position on the ground.
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Footskate may be introduced to a motion in several ways銆?/p>
Sometimes the raw data itself is imperfect; for example a sensor may be miscalibrated. In such cases the motion can often still be salvaged, but the footplants may be lost in process.
Footskate can also be added even when the raw data is faithful to the true motion. In standard animation pipelines motion data is usually mapped onto an articulated figure called a skeleton. Since a real human is not rigid,this mapping process can fail to fully perserve footplants.
Skeleton motion data is often edited in order to adapt to the particular needs of an animation.
Motion Editing鐨勬柟娉?/p>
Representative editing operations include warping [Witkin and Popovic 1995] , retargeting [Gleicher 1998], path editing[Gleicher 2001],transition generation[Rose et al.1996],and various signal processing algorithms [Bruderlin and Williams 1995] These editing operations fundamentally involve adding only low-frequency changes to a motion, so high-frequency details like crisp footplants are either lost entirely or only approximately perserved.
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澶х墰(Programmer)鐨刡log
http://sethgodin.typepad.com/seths_blog/
http://www.sethgodin.com/sg/bio.asp
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http://coanor.blog.hexun.com.tw/
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Nonlinear dimensionality reduction methods also can be categorized into two kinds: kernel-based methods and eigenvalue-based methods. Kernel-based methods include : KPCA(kernel principal componet analysis) ,KICA(kernel independent component analysis), KDA(kernel discriminate analysis),and so on. Eigenvalue-based methods include : Isomap( Isometric Feature Mapping) [1], LLE(locally linear embedding) [2] ,Laplacian Eigenmaps[3] ,and so on.
Isomap is an excellent NDR method. Isomap uses approximate geodesic distance instead of Euclidean distance ,and represents a set of images as a set of points in a low-dimensional space which is corresponding to natural parameterizations of the image set. Because there are similarityes within adjacent frames of sequence ,Isomap is very suitabel to analyze moving pictures and videos.
Reference
[1] J.B.Tenebaum, A global geometric framework for nonlinear dimensionality reduction .
[2] Sma T. Roweis, Nonlinear dimensionality reduction by locally linear embedding .
[3] M.Belkin and P.Niyogi Laplacian eigenmaps and spectral techniques for embedding and clustering.
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