Category Archives: Personal

Solution: KDM launches an xterm at login

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I just upgraded my old system and ran into an issue where KDM would launch an xterm rather than executing startkde when logging into a KDE session (and would perform similarly when any other session was selected).

I spent hours troubleshooting this problem and checking that all of the config files were correct. They were.

Finally, I realized that KDM may be reading kdmrc as root, but may be trying to execute the Xsession script as the user that is logging in.

The /usr/share/config directory was currently mode 700. I chmodded it to 705 and the problem was resolved.

Moral: if everything else seems right, check your permissions.

A Counterexample

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If it really takes 10,000 hours to master a skill, then my own mastery of programming is a counterexample. I literally coded for 8 hours a day (pretty much all of the time when I wasn’t asleep or at school, time that most people would allocate to homework included), which would translate into 4 years of coding to attain mastery. I wrote some amazing things at 12, but nothing I would consider a masterwork (Metasquarer included; the first version was riddled with bugs). I don’t think I truly mastered the skill until 17 or so – 9 years and approximately 26,000 hours of work.

And I was always way ahead of my peers in this skill, from the early stages onward, so it’s not as if I had some below-average progression or anything.

System administration, by contrast, is something I’ve spent very little time on. I started at 16ish and spent about 2 hours a day at most on it. By 19, I had attained mastery in the domains I had studied (which is to say that I’m not a master at every aspect of the skill; just the ones I’ve used. But this tends to be enough).

So I’m not sure I’m following the pattern here.

Mundane

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I keep thinking that I should be doing something more exciting, more groundbreaking, more novel. There’s my university project, but I mean something that actually puts the considerable level of skill I’ve acquired in computer science to use. I didn’t go to grad. school to become an entrepreneur, after all; I went there to become a scientist.

But what grand challenges are there in my subfield? The only one I can identify is strong AI, and that is a problem I have no inclination to work on. There’s nothing like the development of MRI to be done here; the best I can hope for is development of some fancy image processing algorithm. Where are the deep, paradigm disrupting innovations? Mostly things that tend to be commercialized, rather than purely scientific innovations. Stuff like Google, which, had it not been commercialized, would have just been an interesting footnote in an algorithms journal somewhere. I’ll get to working on commercial things more at some point (I’m destined to, in a way), but not at the moment. I don’t have the time to split between what I’m currently doing and attempting to run another organization.

I am planning to pursue the biomedical aspect of my research more by enrolling in a medical program in 2010, with the hope of eventually convincing someone to finally train me enough to work on treating diseases, but I feel that there should be something more to show for my CS training than the work I’ve published.

Wow

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I met with the provost of Temple University a few months ago, as part of an interview of Temple’s graduate students to assess their experience with the graduate school and its programs.

At the time, I was fairly convinced that nothing would come out of it. But whether influenced by the interviews or already planned, seeing this gave me a bit of hope. At the least, the interviews were an attempt to gather information before acting, which is generally a sign of competent decision making. Maybe the new graduate school will even start allowing some interdisciplinarity into the mix?

Learning Teaching

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Seems almost like an oxymoron, doesn’t it? Of course, far from being antithetical, teaching and learning go hand in hand. I’ve learned quite a bit from teaching, not all of it related to the subject I was teaching. But I’ve come to a conclusion I’m a bit ashamed to admit: I’m not yet a very good lecturer.

On my good days, I can be excellent. I thought I did a great job on the hash table lecture. Other times I think my wording was very off but the students seemed to enjoy it nevertheless, such as with Huffman coding. And then there are just times when I know I’m doing a bad job of explaining something, yet all I can do is forge ahead and try to do my best. On paper, the presentations all look great. Verbally, however, I can talk too fast, I can select a poor choice of words, I can sound repetitious, I can sound like I’m emphasizing the wrong points, and so on…

I still have a really good excuse for this pattern: it’s my first semester teaching. I still don’t know how best to structure a lecture, how to present that lecture, what pace to set, etc., because I just don’t have enough student feedback to see what is working yet. There’s also the issue of my tight schedule occasionally interfering with my ability to prepare the lectures as long as I would like.

But I’m going to be teaching the course a second time in the Spring, and by then, I’d like to have this down a bit better.

I think the students like me, at least; partially on account of my age – I’m not much older than they are, so I innately understand them a bit better – but also due to my approachability and refusal to put people down. But though that is a necessary condition for student learning (students pay much more attention to you when they respect and admire you), it is far from sufficient. I need to work on my teaching skills more.

Patent

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Apparently the recent work I’ve done for my dissertation is patentable and the team wishes to apply for one. On the one hand, I disagree with the very concept of patenting an algorithm; on the other, this is a huge addition to a CV which would very definitely put me ahead of others when I seek a research position.

Maybe I can get the patent then license it freely?

AnandTech doesn't know me very well.

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Heh… Straight from Anandtech:

http://images.anandtech.com/reviews/video/NVIDIA/Badaboom/cuda2.jpg

“The performance advancements were incredible, NVIDIA was promising upwards of 100x gains over the fastest workstation CPUs. Unfortunately we couldn’t get too terribly excited as most of these applications were far beyond the reach of the typical desktop user. Medical imaging and scientific analysis benefitted tremendously from GPU acceleration, but it’s rare that a gamer with a $400 GPU is going to be searching for oil deposits in his/her spare time on the same machine.”

I don’t know; the gains on matrix multiplication, sequence matching, and the FFT are quite appealing to me… but so is being able to run new games quickly 🙂

Unfortunately, I’m pretty sure these gains are going to require use of a special API to realize. So until Matlab integrates CUDA, they may be a ways off.