I find it frustrating why statisticians can't just give us something we could indeed use with confidence that it actually tells us what we think it tells us...
The physicists I talked with were really bothered by our 250 year old Bayesian-frequentist argument. Basically there’s only one way of doing physics but there seems to be at least two ways to do statistics, and they don’t always give the same answers.
This says something about the special nature of our field. Most scientists study some aspect of nature, rocks, stars, particles; we study scientists, or at least scientific data. Statistics is an information science, the first and most fully developed information science. Maybe it’s not surprising then that there is more than one way to think about an abstract subject like “information”.
"Terrible" is a little harsh. I found the benchmark to be interesting because it's similar to many NLP-style analysis tasks I have myself. Of course reproducible ones would be great too.
This is a hard problem -- there's a bunch of research in NLP on it, where it's sometimes called temporal tagging. HeidelTime is a system that does this; some examples on their webpage, https://code.google.com/p/heideltime/
Yeah, that's the main PTB-style tagset resource for tweets (that I know of). Our tool can also be run to produce tags in their form (we just retrained our software on their annotated data). See further down the webpage for information on how to get this.
Part of it is genuine differences between online conversational language versus standard written English, like emoticons, Twitter-specific discourse markers, and hard-to-segment compounds or clitic constructions (see the Gimpel and Owoputi papers (2011, 2013) linked on the page, and/or the annotation guidelines document too). Part of it is just that it's easier for humans to annotate the coarse-grained POS tagset, and we didn't have many resources for annotation when we did it.
These things also intersect ... for example, you'd have to figure out how dialectical English verbal auxiliaries like "finna", or the second or so word in "imma", map to PTB tags. It's possible but just takes more work and thinking through the descriptive linguistics and what you want to use it for. Someday I'd like to update the whole thing for a more PTB-like POS tagset, if it can be done well. I feel like Chris Manning's whitepaper on issues in PTB POS data convinced us (well, it convinced me, at least) that it might be a good idea to focus on making high quality tag annotations. (http://nlp.stanford.edu/pubs/CICLing2011-manning-tagging.pdf )
Also, see Cosma Shalizi's excellent review of "Red Plenty", which includes a mini-introduction on the application of linear programming to economic policymaking.
Here's a positive report on teaching "back to basics" first-semester programming with Java. They only teach procedural concepts and no OO at all. The point is, while Java itself is non-ideal for this and slants towards OO, you can just ignore that part and teach with it anyway.
Lots of people here have raised concerns that the way Java forces class and static declarations is confusing for beginners. Interestingly, the instructors were concerned about that too, but found that students don't seem to mind.
The second major concern being discussed here, that functional programming isn't possible in Java, I think is a non-issue for a true introductory first-semester class, which needs to focus on more basic issues of procedural programming and abstraction. The SICP approach is beautiful and great for already advanced and highly motivated students, but that's honestly a small minority.
(You can find many more examples of reports like this if you search for "CS1", which is the shorthand for intro-to-CS that's used in the CS education literature.)
You can get some of this from Google Scholar's metrics pages, which list, for each venue, the most-cited papers since 2008. These indeed seem to be different than the best-paper selections, though I haven't examined this systematically. (I guess you could take the best-paper and look at its citation rank out of papers that year. But this raises other questions; for example, is the point of best paper award to select the paper that will be most influential?)
From this page, http://scholar.google.com/citations?view_op=top_venues&hl=en... , click "Subcategories" under "Engineering & Computer Science": for example, "Artificial Intelligence," "Computational Linguistics," or "Human Computer Interaction." It lists the most-cited-venues for each area, but if you click on a particular venue you get its list of most-cited-papers.
Here are a few that are also on Jeff Huang's list:
Ah, wonderful! So if I understand this correctly, this doesn't give C/Fortran access to Java-native primitive arrays; but instead, it's specific to NIO byte buffers (and then the matrix libraries have to build on top of that). But that should be fine for doing R replacements, at least in theory.
(Personally, when programming Java I find it more convenient to use primitive arrays as opposed to matrix libraries, but that might be dependent on the operations I tend to do: lots of increment/decrements and only occasional linear algebra. I guess this isn't exactly relevant to the R replacement question.)
The programming language analysis is pretty interesting, but you have to ask, what's the point of a brand-new Java implementation? R isn't just a programming language, but it's a software framework/ecosystem. They mentioned this in the slides, but it's problematic because R crucially relies on C and Fortran interaction (which I thought the JVM can't do efficiently, since it doesn't like giving C/Fortran raw memory access to its internals). Decades of work has gone into highly optimized Fortran linear algebra libraries, for example -- which R and all the other high-level numerical languages (NumPy/SciPy, Matlab, Julia) use. And many of the CRAN packages (the availability of which are a major reason anyone uses R in the first place) are partly or mostly C/Fortran code.
The presentation focuses on the R programming language, which they nicely show has all sorts of misfeatures that impede rapid execution. If you're going to not try to have compatibility with R and CRAN, you might as well start from scratch with design and performance in mind, as in Julia: http://julialang.org/
This is on a slightly different statistical methodology issue, but quoting from Brad Efron (http://statweb.stanford.edu/~ckirby/brad/papers/2005BayesFre...):
The physicists I talked with were really bothered by our 250 year old Bayesian-frequentist argument. Basically there’s only one way of doing physics but there seems to be at least two ways to do statistics, and they don’t always give the same answers.
This says something about the special nature of our field. Most scientists study some aspect of nature, rocks, stars, particles; we study scientists, or at least scientific data. Statistics is an information science, the first and most fully developed information science. Maybe it’s not surprising then that there is more than one way to think about an abstract subject like “information”.