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That 112-dimensional cutout is so unrealistic / about 1 year ago
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--------Update : 12 months ago-------
The mathematical content of this post is, as far as I know, original to me, and I posted it shortly after it occurred to me, without thinking through it critically. I later realized that it is entirely false. My next post explains why.
--------End Update---------

I’ve always enjoyed imagining higher dimensional geometry. Not that it’s easy to visualize, but it’s easy to discern what some of the properties would be. Today I thought of an example of such a property.

Consider the different ways two (infinite) lines can be positioned relative to each other in different dimensions:

  1. Lines can’t exist in 0-dimensional space
  2. Any two lines in 1-dimensional space are necessarily the same line
  3. In 2-dimensional space, two distinct lines can relate to each other in two different ways:
    • They can intersect
    • They can be parallel
  4. In 3-dimensional space, there is a third option:
    • They can intersect
    • They can be parallel (which means they are always the same distance apart)
    • They can be skew (if this one isn’t immediately obvious, imagine a bridge, where one line (the road) goes over the other line (the river), and they neither intersect, nor run parallel)

    The first two options imply that the lines are co-planar: there is some flat (2D) plane containing both of them. In the third option the two lines are necessarily not co-planar.

  5. If we add a 4th dimension, we should expect to be able to distinguish between two different kinds of skew lines:
    • Skew lines that can be contained within the same 3-dimensional sub-space, in the same way that parallel lines can be contained within a 2-dimensional subspace. We can refer to them as Skew3
    • Skew lines that cannot be contained within the same 3-dimensional sub-space, in the same way that Skew3 lines cannot be contained within a 2-dimensional subspace. We can refer to them as Skew4

    Intersecting and parallel lines will both still be possible, of course.

  6. With the 5th dimension too, we should be able to have Skew5 lines, which cannot be contained in the same 4-dimensional subspace.

And up and up, as high as you like. Our fellow beings in some 307-dimensional world probably spend most of their high school geometry class memorizing the 307 different ways that two lines can relate to each other (my favorite is Skew108), and developing their ability to quickly recognize them when projected in a 307-dimensional hologram.

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Confuse-a-compiler / about 1 year ago
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Apparently compilers can get confused:

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Rant / about 1 year ago
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Excuse me for a minute while I go on a bit of a rant. I’m tired of all these liberal-wearing hippie-popping gun-toting bigot-eating abortion bankers trying to tell us what color skin we have. If I want the government to stay out of my foreign policy, I won’t get a six-pack of mortgages while I’m at the convenience store! Our president and congress need to spend more money defending our own citizens from external threats, and less money on the military. Take your family-values and go back to the country you illegally came from! The government thinks it can completely ignore the rights of women and force them to endure unwanted marriages that were the result of violent-crime-weddings, all in the name of preserving sanctity or some other hogwash. Well I say, wash those hogs! Get the lipstick off those pigs!

If all those damn front-wing Harvard and Princeton momma’s boys with their fancy schmancy family values and religion think they can run my life, I’ve got just three words for them: The Constitution! This country was founded on the principles of right and wrong, not of black and white. The longer we’ve got a muslim-cowboy fatherless Texan ethnic man in the oval office, the more money this country spends on Medicaid! Do you know why everybody’s so fat? K-mart! If marijuana companies weren’t allowed to advertise to children, drunkenness wouldn’t be so epidemic among all these preschool-dropout droopy-pants-wearing redneck KKK-members blasting their rap music so loud you can’t hear yourself gay-marry!

The Supreme Court (which is behaving more like a fast-food menu item than a judicial body) thinks that it’s ok to judge people by the color of their t-shirts, but I say the Supreme Court can go back to Canada where it came from. If you want to take jobs from honest, hard-working Wall Street con-artists, then you have no place in my America. I wish all these stupid arm-chair bloggers would quit pretending Ronald Reagan was still turning this country into a socialist paradise. When are people going to learn to think for themselves? Do you think the media has your best interests in mind? There are seventy million people in this country who are forced to get their education from Burger King, and if the government can’t fix it, then it’s time for a new government. Get out of my country, NOW!

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\__________ Joel -- 11 months ago __________/
I couldn't agree more, Gary.
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\__________ chaosmotic -- 3 months ago __________/
This is a bit of poetry. I must say.
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Computer Science / about 1 year ago
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A list of the research interests of all the Computer Science professors at MIT:

  • Computational complexity, quantum computing.
  • Artificial intelligence, scientific computation, educational computing, societal and legal frameworks for information technology.
  • Program analysis and optimization, computer architecture.
  • Architecture synthesis and verification, digital design, term rewriting systems and lambda calculus. Parallel architectures and programming languages.
  • Computer networks, mobile and sensor computing systems, distributed systems.
  • Natural language processing.
  • Natural language processing: computer models of language acquisition and parsing. Computational biology and evolutionary theory including evolution of language. Artificial intelligence: formal models of learning, including inductive inference and computational complexity analysis of language. Cognitive science: word learning, semantics of natural languages.
  • Natural language processing and machine learning.
  • Theory of computation. The interdisciplinary fields of algorithmic game theory, computational biology, social networks and applied probability.
  • Artificial intelligence, intelligent multimodal interfaces and natural interaction; intellectual property issues in software.
  • Algorithms and data structures. Discrete and computational geometry. Combinatorial games.
  • Parallel computer system design to support functional languages and advanced environments for modular programming. Study of architecture, performance and reliability issues.
  • Image generation and creation; realistic rendering, real-time graphics, perceptually-based algorithms, non-photorealistic rendering, image-based rendering and editing, digital photography.
  • Machine learning applied to computer vision and computer graphics. Bayesian belief propagation and its generalizations. Bayesian models of visual perception.
  • Computational and systems biology, computational functional genomics. Expression of scientific models in computational form. Machine learning.
  • Cryptography, pseudo randomness, property testing, computational number theory, multi-party computations.
  • Computer vision, medical image analysis, medical image processing, image guided surgery, activity recognition.
  • Computational imaging, machine vision. Representation of objects and space. Photogrammetry.
  • Computational geometry, especially in high-dimensional spaces; databases and information retrieval; learning theory; design and analysis of algorithms; streaming and sketching algorithms.
  • Statistical inference and machine learning. Applications to computational biology and information retrieval. Artificial intelligence.
  • Software design and specification; design methods, tools and analysis; dependability; safety-critical systems; reverse engineering; static analysis, model checking, programming languages.
  • Computer systems: operating systems, networking, programming languages, compilers, and computer architecture for distributed systems, mobile systems and parallel systems.
  • Behavior learning, visually-guided map learning for mobile robots, planning in very large stochastic domains, learning relational models.
  • Information retrieval and digital libraries; analysis of algorithms, especially for graphs and optimization problems; applications of randomization; parallel algorithms.
  • Computer networks and data communication. Congestion control, network measurements, scalability and robustness of communications systems. Differentiated services, internet pricing, routing, content distribution, self-configurable and wireless networks and network security.
  • Computational biology. Genome interpretation, comparative genomics, regulatory networks, cellular signals, developmental biology, evolutionary theory. Algorithms and machine learning applications in genomics.
  • Computer science. Hardware design and machine architecture through distributed systems and programming languages to user interfaces and office automation.
  • Theory of computing machinery, parallel computation, graph theory, algorithms, computer architecture, supercomputing, multithreading, internet computing, scalable systems, chip multiprocessing, multicare systems.
  • Programming methodology, programming languages, distributed systems, object-oriented databases.
  • Artificial intelligence; robotics and computer vision.
  • Theory of distributed and real-time computing: mathematical models, specification, algorithm and system design, performance and fault-tolerance analysis. Distributed data management, communication, synchronization. Languages and tools for abstract distributed programming. Hybrid (continuous/discrete) systems. Mobile wireless networks.
  • Databases and computer systems; query processing, distributed systems, management of streaming data, adaptive data processing, sensor networking.
  • Software education environments. Semantics of programming languages, logic of programs, concurrent programs, lambda calculus.
  • Cryptography, secure protocols, pseudo-random generation, proof systems, zero knowledge, mechanical design.
  • Human-computer interfaces, intelligent interfaces, programming by demonstration, end-user programming languages, usability, software engineering, usability and security.
  • Artificial intelligence. Robotics and machine vision. Representation of knowledge and structure of personality. Common sense reasoning, theories of emotion and consciousness.
  • The design of an easy-to-control data networking infrastructure designed to bring about a new level of flexibility to network configuration. The Resilient Overlay Networks Project. Grid routing protocols.
  • Organization of large complex systems, artificial intelligence.
  • Program analysis, compilers, distributed computing, software engineering.
  • Cryptography. Computer/network security. Algorithms. Voting technology.
  • Sublinear time algorithms, randomized algorithms, computational complexity theory.
  • Robotics, mobile computing and information access.
  • Programming systems with a focus on software synthesis. Programming tools for parallel and high performance computing.
  • Database systems, query processing, data warehouses, federated databases, data visualization.
  • Complexity of finding ‘approximate’ solutions to combinatorial optimization problems; interplay of algebra with computer science and coding theory.
  • Artificial intelligence: learning, problem solving and programming. Computational performance models for intelligent behavior, especially modeling the behavior of engineers. Numerical models of physical systems.
  • Application of artificial intelligence techniques to medical decision making. Effective representation of knowledge. Personal health information systems, medical confidentiality.
  • Machine learning and robotics, including reinforcement learning, optimal control, legged robots, flapping-wing flight, nonlinear control theory, biological motor control, and computational neuroscience. Particular emphasis on solving difficult robotic control problems through a close coupling of mechanical design and learning control.
  • Autonomous robotics for mobility and manipulation; situational awareness through sensing and inference; location-based infrastructure and applications; assistive technology for health care.
  • Computer vision, machine learning and human perception; development of computer vision systems and solving real world recognition tasks; modeling human perceptual and cognitive capabilities; object recognition, classification of whole scenes; visual recognition and classification of places and objects.
  • Computer architecture and operating systems.
  • Artificial intelligence and computational theories of human intelligence, with special emphasis on the roles of language, vision and analogical reasoning.
  • Building practical secure systems. Operating systems, hardware design, networking, and distributing systems. Programming languages and tools, security analysis and verification.
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Doubt / about 1 year ago
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The Lady and I watched Doubt last night, and I thought it was excellent. I don’t remember seeing a movie with Philip Seymour Hoffman that I didn’t think was excellent. If he starred in Spy Kids 4, I’d probably think it was great. That’s an exaggeration.

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Hints / about 1 year ago
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I was doing my online Physics homework, and when I got a problem wrong, the little gray box that sometimes pops up with a hint asked me to supply my own:

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\__________ Iain -- 2 months ago __________/
Yeah. Stupid templates...
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Lists / about 1 year ago
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I’ve added a “lists” feature to the blog (linked at the top). It will allow me to have running lists of anything that I feel ought to be enumerated. Starting with topics I’m interested in learning about. There will be a list of good names for a rock band just as soon as I think of the first one.

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Exploring the Prime Reciprocals / about 1 year ago
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One of my favorite mathematical theorems that I've been exposed to so far is the divergence of the sum of the prime reciprocals (as referenced here and proven here). It says that if you take all the prime numbers (which are infinite) and turn them each upside down so that they're in the denominator of a fraction, and add up all those fractions, the resulting sum will be infinite. If this doesn't sound remarkable, let's try a few.

The first ten primes:

	1/2     0.50000000000000000000
	1/3     0.33333333333333333333
	1/5     0.20000000000000000000
	1/7     0.14285714285714285714
	1/11    0.09090909090909090909
	1/13    0.07692307692307692307
	1/17    0.05882352941176470588
	1/19    0.05263157894736842105
	1/23    0.04347826086956521739
	1/29    0.03448275862068965517
	Total=  1.53343877187203202214

Not very big, but of course ten primes is not that many either. Let's try some more

The first 500 primes, 2 --> 3571

	...     .......
	1/3541  0.00028240609997175939
	1/3547  0.00028192839018889202
	1/3557  0.00028113578858588698
	1/3559  0.00028097780275358246
	1/3571  0.00028003360403248389
	Total=  2.36532946588456968519

By adding 490 more primes, we increased our total sum from 1.533 to 2.365. Finally, if we add the reciprocals of the first million primes, the result is only 3.06821904805 (that's as far as my computer is interested in calculating). I doubt it would ever count high enough to get to 4. And yet, the theorem says that if the process continues indefinitely, the sum will get as high as you like.

I like it.

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\__________ Rachelle -- about 1 year ago __________/
It makes sense that if they're infinite, the sum of even a small part of them would be infinite too.
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\__________ Me -- about 1 year ago __________/
Well the idea is that the pieces you're adding get proportionally tiny as you go. It makes a big enough difference when adding the reciprocals of another sequence of numbers, the squares (1/1 + 1/4 + 1/9 + 1/16 + 1/25 ...). I know that sequence is finite, and I doubt it ever reaches 2. A more intuitive example involves powers of two: 1/1 + 1/2 + 1/4 + 1/8 + 1/16 + 1/32 + 1/64... This sequence converges to 2, and is very much related to the well-known Zeno's paradox.
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Goin' Swimmin' / about 1 year ago
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Somebody stopped me downtown today to ask me for directions to the beach. I asked if they were referring to the beach at the ocean, and they said yes.

But the ocean is 115 miles from here.

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\__________ Rachelle -- about 1 year ago __________/
What did you tell them?
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\__________ Me -- about 1 year ago __________/
Well I tried to say that there wasn't any ocean nearby, so they asked if there was a lake, and I mentioned Lake Murray but didn't know offhand if it had any beaches. so I kinda told them which highway to get on, and didn't know much beyond that. I wondered how those people get anywhere. They were initially blocking traffic just to ask me the question until I advised them to pull over.
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Digging Holes / about 1 year ago
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If we can agree that

when the surface of a perfect sphere is divided into two halves (A and B), the point directly opposite any point on half A will necessarily be on half B and vice versa
and
the earth is nearly spherical, and the equator divides it nearly perfectly in half
and
The United States of America is located entirely and indisputably within one of the two hemispheres marked out by the equator
and finally that
The People's Republic of China is located entirely within the very same hemisphere as The United States of America
then I think we can forcefully conclude that
there cannot exist any point in The United States of America that is directly opposite any point in the People's Republic of China.

Let us consider this subject completely laid to rest.

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\__________ Rachelle -- about 1 year ago __________/
Not really, because you wouldn't have to dig in any way tangentially to the surface, or parallel or perpendicular to any diameter of the sphere, or even in a straight line!
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\__________ Me -- about 1 year ago __________/
I never said anything about digging (outside the title of course), I was just saying that the two countries are not opposite each other. If you want to bring digging into it, then the complaint would be that you may as well use any south-Asian, east-African, or Australian country as the target of your deep-deep hole, rather than exclusively China (if you're on some sort of Columbian expedition to the far East, I think India was his goal). Or just say the Indian Ocean and be the rightest of them all.
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\__________ Rachelleagain -- about 1 year ago __________/
By the way, I just wanted to point out that I am very smart and had not even noticed the title of this post before commenting and rather had figured out on my own what you were talking about.
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\__________ Me -- about 1 year ago __________/
That's the smartest thing I've ever seen!
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\__________ Rachelle -- about 1 year ago __________/
Thanks.
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Waa Waa Waa! / about 1 year ago
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Opening sentence about mildly aggravating thing that happened! Assertion that I’d prefer it hadn’t happened that way! Interrogative regarding these things always happening to me! Further developing various contours of negative feelings! Conveniently omitting positive aspects of situation! Stomp! Jump! Stomp! AAAAAHH!! Gripe! Complain! Moan! Threats not directed at anybody in particular! Lamentation about the state of things! EXCLAMATION POINT!!!

This post is a satircal look at political corruption.

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Instantiating Non-static Nested Classes in Clojure / about 1 year ago
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In the interest of furthering Google’s ability to solve minor development issues, I should show what I recently learned by trial and error about Clojure’s syntax for instantiating Java’s inner classes. Java distinguishes between static nested classes and non-static nested classes, and the only information I could find on Clojure syntax only applied to the static nested classes, which incidentally are instantiated like this:

(new OuterClass$InnerClass)

Not too hard. Non-static classes on the other hand must be constructed on a member of the outer class. And so

(OuterClass$InnerClass. my-outer-class)

I assume appropriate arguments could be appended to either of those statements if the constructor required it.

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Physics Too / about 1 year ago
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I just had my first Physics 2 lecture. According the schedule on the registration server, the class consists of a 2:15 hour period in the morning, a 2 hour lab in the afternoon with another 1 hour period immediately following, four days a week. I went to the first lecture this morning, and the professor spoke for 2 hours and fifteen minutes purely about physics, with almost no information about the class, and no syllabus. I had my hunches about the structure of the class from taking Physics 1 last fall, but I was hoping for some indication that those suspicions were correct. Instead all I got was a class decision that exams should be on Thursdays rather than Mondays, and a casual reference to the existence of online homework problems. He didn’t say anything about labs, or about the 1 hour period at the end of the day (which I concluded wasn’t a lecture because at the end of the class he said “see you tomorrow”, but at the very least was the time period during which the exams would be held, which I luckily figured out from another casual reference). When I took Physics 1 there was an extra hour session on the schedule for help with homework, but I was able to neglect attending it altogether with no major issues. All the exams were during the regular lecture time period.

Naturally I went up to the professor after class to ask for a syllabus, or at least some more information about how the class was going to be run. He told me that he doesn’t have a syllabus and doesn’t normally make them for summer classes, and when I asked for specific information in place of the syllabus, he refused to answer any of my questions, and told me to simply ask a classmate who had been in his Physics 1 class last month. I generally don’t socialize with the other students as much as I can help it, due to the fact that I’m several years older than most of them, and have an irrationally age-centered view of social status, so I didn’t like his suggestion all that much. However, he seemed pretty firm on that solution, so I decided to just forget it and make my best guess about everything. Evidently though, it upset me enough to motivate me to write two decently-sized paragraphs about it.

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Reproducibility / about 1 year ago
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Trying to fix a software bug that you can’t reliably reproduce is like chasing a ghost.

That’s a good analogy because it’s something that everybody has experienced.

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