SODA Day 1

Priceless: seeing the look on a speaker's face when they're asked for the exact constant in their 'constant factor approximation'

Yes, it's SODA time. And having an iphone means I can leave my laptop in my room, which means blogging waits till the day is over.

It was a good first day. I chaired the session on (as I put it), "geometry, graphs, and the geometry of graphs", and really enjoyed all the papers in the session (David has a summary of the Lee-Sidiropoulos talk). The phrase of the session was 'Poincare inequality'.

Alex Andoni talked about work with T. S. Jayram and Mihai Pătraşcu on lower bounds for computing the edit distance (and related distances like the Ulam distance). The program of attack was via the use of information complexity - a technique first developed for use in streaming lower bounds, but which has general applicability to communication complexity problems. Here's roughly speaking how the argument goes:

The direct-sum family of results says that the communication complexity of a function f expressible as an AND of n functions g is at least n * the information complexity of g. The overall plan is therefore to break down the strings being compared into pieces, and lower bound the information complexity of each piece.

Now let g be a threshold distance function that correctly reports if the distance is "too small" or "too large", but not inbetween. It turns out that the information complexity of g can be lower bounded by a constant relating to the Poincare inequality. So what is this inequality ?

In a sense, it captures the difficulty of distinguishing short distances from long. Specifically, look at the average distance of pairs of points sampled over all "short" pairs, and do the same for "long pairs". If the two resulting numbers are within some constant, then that's the constant used above, and intuitively tells us that we can't tell the pairs apart distributionally speaking.

It's not easy in general to prove Poincare inequalities, although these appear to be at the heart of non-embeddability results. What the authors go on to do is show that if the distance metric being used is "complex" i.e has a reasonably large communication complexity, then this can be used to show a Poincare-type inequality.

Neat stuff.

Author rebuttal for SoCG 2010

There appears to be an author rebuttal phase for socg 2010. This is causing some confusion since many of the reviews are blank. I suspect based on my experience with DB reviewing that this means the reviewers didn't want clarification.

I got one nontrivial "review" back, and am assuming that some response is called for. The text is phrased not so much as a request for clarification but as an actual review. It's a bit easier when there's a specific question to be answered.

I'm glad the committee is doing this though, even with the extra effort involved. It's a good way of clarifying things that could otherwise have consequences beyond their importance.

Guest Post: Question on posting to the arxiv

ed. note: this post is by Jeff Phillips. For another recent post on arxiv publishing issues, see Hal Daume on the arxiv, NLP and ML.


It seems that over the last few months, the number of papers posted to the arXiv has been noticeably increasing, especially in the categories of Computational Geometry and Data Structures and Algorithms.

I have posted several (but not all) of my papers on the arXiv. I still do not have a consistent set of rule under which I post the papers. Here are a couple circumstances under which I have posted paper to the arXiv.

A: Along with Proceedings Version:
When conference version does not have space for full proofs, so in conjunction with proceedings version, post full version to arXiv. This is a placeholder for the full version until the journal version appears. Additionally, the arXiv paper can be updated when the final journal version appears if it has changed.

Sometimes, I link to the arXiv version in the proceedings version. This makes it easy for a reader of the proceedings to find the full proofs.

If more conferences move to the SODA model where proceedings versions can be much longer (~20 pages), then this situation may not often be necessary.


B: Along with Submitted Version:
When you want to advertise a piece of work, but it has only been submitted, post a version to arXiv. This is useful if you are giving talks on the work, and want a documented time stamp so you can't get scooped, or say, you are applying for jobs and want to make your work very available and public.

This is closer to the math philosophy where many (most?) people submit a version of a paper to arXiv as soon as they submit it to a journal. I think it would be great if CS adapted this policy, as it would be a lot easier to track results. I have a friend who as a math graduate student would start every day by perusing the dozen or so new arXiv post in his area and choosing one paper to read. He told me that almost every paper he read as a grad student was on the arXiv. Wouldn't a world like that be extremely convenient?

However, I have had an issue following this rule. Last year I submitted a paper to a conference and concurrently, submitted a longer version to the arXiv. The paper was unfortunately, not accepted to the conference. My coauthor and I extended the results to the point where it made sense to split the paper. Half was then submitted and accepted to another conference, and full proofs were made available through a tech report at my coauthor's institution, as he was required to do. The second half which has also been extended is now under submission.

I might like to post the (full) second half to the arXiv, but do not want to double the part from the previous post. I am not sure if it make sense to merge the papers at this point either. And I would also like to note on the arXiv page that that version has been extended and part appears as a tech report.

What is the proper arXiv etiquette for this situation?

Mixture Models: Classification vs clustering

(this is part of an occasional series of essays on clustering: for all posts in this topic, click here)

We're all family.

k-means is arguably the most popular clustering algorithm (and one of the top 10 data mining algorithms to boot). It has a close relative though (some might say a generalization) in the expectation-maximization algorithm (or EM).

The EM algorithm represents yet another paradigm shift in how we think about clustering, to the point where it's arguable whether we are clustering anything at all. What the EM algorithm really does is density estimation: it assumes the data being clustered is generated by picking one of k distributions according to some probability distribution (the mixture) and then sampling from this distribution.

In other words, the definition of a cluster is not in terms of relationships betweens pairs of data points any more: it's in terms of how a collection of points behave together as a group, as representative samples from a distribution.

It's not particularly hard to see that the k-means algorithm is really a special case of EM on Gaussians(if I might be permitted an indulgence, a "tropical" limiting case of EM). In general though, the EM algorithm is an alternating optimization that finds the maximum likelihood parameters for distributions that capture the data. In the "E" step, it fixes the current parameters (the current hypothesis for the distributions) and computes the expected (log) likelihood of the data by first estimating the "cluster membership probabilities" of assigning a point to a "cluster" or distribution, and then using this to estimate the overall likelihood function. The "M" step then finds the set of parameters that maximizes the likelihood function. In k-means language, the first step figures out which clusters to assign points to, and the second step assigns new centers to each cluster.

The EM algorithm is very powerful. It's generally known that if the distributions under consideration are from an exponential family, then this method is easy to implement, because the E and M steps reduce to simple operations that have closed forms. Via the usual Bregman connection between divergence functions and distributions, it's also possible to give a purely geometric interpretation to the EM method akin to k-means, but with a different distance structure.

What's even more fascinating is the information-geometric perspective. There's a wonderful Riemannian world in which distributions are points on a manifold with coordinate systems given by their (natural) parameters, and where various information distances can be related to affine connections on said manifold. Too much to go into here, but the key insight is this: the E and M steps of the EM algorithm can be seen as projection operations in primal and dual manifolds, so the EM algorithm really looks like a primal-dual procedure. Way cool !

So is this not clustering ? For a detailed argument along these lines, you should read Hal Daume's analysis (with a nifty example). My take on this is that while density estimation is indeed different to the problem of clustering, I think of mixture-model-based clustering as just a much more "volume-based" approach to doing clustering, you expect not that point associate with each other per se, but that the ensemble of points itself satisfies some global properties.

I'll develop this idea further when I talk about information-theoretic clustering: to me, density-based clustering suggests a new abstraction for thinking about what a clustering ought to mean in the absence of even metric structure, and allows us to make pleasant detours into discrepancy, quasi-randomness and kolmogorov complexity. Stay tuned....

p.s apologies for the long hiatus.

Reading papers.

It's been a bad few months for blogging, while I've been doing (shudder) "real work". Whenever people ask me how I make time for blogging, I always say that it takes less time than one thinks. This is generally true, but the last few months have been a blizzard of paper and proposal deadlines, and I just never got the time to sit down and pen coherent thoughts. I've also noticed that my tweeting is bleeding thoughts away from the blog: random throw-away comments that might been assembled into a blog post end up merely getting tweeted.

For the first time in a long time, I took a "true" vacation, where not even a laptop came with me (ok I took my iphone, but that's not the same :)). It was only a week, but I appreciated the complete downtime, especially coming over the frenzy of deadlines. It's been hard to get back into the swing of things, but it was a really good way to recharge.

But you don't want to hear about any of that. What you really want to hear about is..... my lament on how to organize paper reading.

When I was a grad student, life seemed simple. STOC/FOCS/SODA (and then SoCG) would come around, we'd peruse the paper list, chatter on about them, and figure out if someone had scooped us or not. Now, things seem more complicated. Especially since I'm the sole algorithms person at the U, I feel the need to catch up on the latest (and not-so-latest) hot topics in theoryCS, at least to keep abreast of things and share the latest news with my students. I also work in a number of more 'applied' areas, which means that there's a SIGMOD/VLDB/PODS/ICDE timeline to keep track of, and more recently a NIPS/ICML/COLT timeline, not to mention even more applied areas like ICCV/CVPR/MICCAI (more on that later).

There's a large and complicated taxonomy of paper reading: some of the main items are:

• Papers I'm reading for technical material when working on a problem - this is the easiest kind to manage, because you have to read it RIGHT NOW.
• Papers that seem related to the problem I'm working on, and probably need to be cited, but are not in the critical path. This isn't too hard either - things like Mendeley allow me to save (and tag) papers for later use with just a few clicks. It's not a perfect system (what if the paper's on someone's home page), but it mostly works.
  

Then come the more complicated categories:

• Papers related to a problem that I'm not quite working on right now, but seem relevant. I can sock them away, but I have to remember to read them when I return to the other problem
• Papers that everyone's talking about at the latest-greatest conference, but might have nothing to do with my specific suite of problems (like for example the Moser LLL proof).
  
• Papers that might help me catch up on an area that is very hot, but which I wasn't following from the beginning (cough cough AGT cough cough)
• Papers that were just announced in the latest conference/arxiv/eccc issue, that sound worthy of perusal.
  

There are many technological solutions to squirrel stuff away: I even use Dave Bacon's arxiview app for the iPhone (and btw, I've found the only use for 2-column format - reading papers on the iphone). I've also experimented with private wordpress installations to allow me to bookmark interesting papers.

But the real problem, that I have yet to crack, is how to systematically plow through the mountains of reading that I must do to stay abreast of the fields I'm interested in. I've tried "read one paper a day", or "read papers over the weekend" and things like that, but nothing ever seems to stick, and I'm curious about what techniques people have used that actually work. I'm not excluding technological solutions, but I think the problem goes beyond that.

So what say you all ?

Locating SODA outside the US

David Johnson forwards a note from the SODA Steering Committee about the possibility of having SODA outside the US. Summary: it can be done, with the kind of bid information that accompanies bids to other conferences with independent local arrangement. What follows is the note:

To those who might be interested in having a future SODA conference take place outside North America:

As you may know, local arrangements for SODA have to date always been handled by SIAM. The conference Department at SIAM has wide experience in organizing conferences in North America, but typically relies on help from local organizers when a conference they sponsor takes place elsewhere.

This presented difficulties when, at last January's SODA, the Business Meeting voted a preference for holding the 2011 SODA in Paris. SIAM and the SODA steering committee were unable to find any French organization that was prepared to handle the meeting in Paris. One organization might have been willing to host the meeting in the Parisian suburbs, but we judged that most of the voters for Paris would not have voted for "suburbs of Paris".

(No hotel was available in the second vote-getter, the Virgin Islands, and so SODA will be back at San Francisco, the 3rd-place choice, in 2011, January 22-25.)

In light of the experience in finding a location for the 2011 SODA, we are going to change the ground rules a bit for the 2012 site selection. Proposals of sites outside of North America will still be welcome, but to be considered, they must include the details of what group will handle local arrangements, where precisely the conference will be held, and what the expected hotel costs will be. In other words, the kind of information typically provided by groups proposing to host STOC or FOCS. (Proposals from groups that can collect registrations and take financial responsibility are preferred because of the added costs of SIAM providing these services in a country outside North America.)

If you are interested in making such a proposal at the SODA 2009 Business Meeting, please contact Kirsten Wilden (wilden@siam.org) for more details about what kinds of information SIAM will need about your proposal.

There has also been some thought that costs for SODA in North America could be reduced if local arrangements were placed in the hands of local volunteers, as they are for FOCS and STOC. If anyone wishes to propose to host SODA 2012 in this way, their detailed proposals will also be considered at the Business Meeting. (Again, contact Kirsten Wilden at SIAM in advance.)

And of course, proposals for sites that the SIAM conference staff can handle on their own can be made as usual.

Hope to see you all at SODA 2009 in Austin, January 16-19.

David Johnson, SODA Steering Committee Chair.

Anathem, and mathematical stereotypes

Neal Stephenson is (or should be !) a familiar figure in the sci-fi/speculative fiction landscape: his Cryptonomicon is a retelling of the story of Turing, along with much modern day drama involving advanced crypto and security. His new book, Anathem, came out with much fanfare, and is a vast tale set in a place where mathematics is a pursuit conducted in monastery-like places with strong religious overtones.

I'm reading Anathem right now, and am only at the beginning (so no spoilers please), but there's already a beautifully rendered discourse on stereotypes of the mathematican (or scientist in general). Inside the 'concent' (the monastery), these are termed 'Iconographies', as formal templates by which to understand how the "saecular" world perceives the mathematicians. I was reminded of this when writing I was writing the post on Soviet-style mathematics and realized the stereotypes at the heart of the referenced WSJ article.

So here are some of the iconographies discussed early on (you get one point for each one that you've encountered in real life):

• Tennestrian: seemingly clownish eccentric figures that have a darker side, luring impressionables and innocents into folly (a play on the story of Socrates)
  
• Doxan: brilliant, unemotional and cold, but as a consequence subordinate to passionate leaders with true human feeling. (can you say 'Spock'?)
• Yorran: Criminally insane mad scientist shut up in a lab with plans to conquer the world.
• Rhetorian: Sinister conspiracy plotters, out to take over the world by planting minions out in the secular world to attain positions of power.
• Muncostran: Eccentric, lovable, dishevelled theoreticians, absent-minded and good-natured (the subtext being: ignorable)
• Pendarthan: High-strung nervous meddling know-it-alls, unable to understand the realities of life, always subordinate to more 'masculine' (his words, not mine) secular folk.
• Klevan: Awesomely wise elder statesman who can solve all the problems of the world (apart from Einstein, I don't know of anyone who achieves such stature in our world)
• Baudan: Cynical frauds living in luxury at the expense of the common man (this sounds like the viewpoint of letter-writers to the Salt Lake Tribune)
• Penthabrian: keepers of mystical secrets handed down from above, with 'theory' as a smokescreen used to fool the lay folk (if only...)
• Moshianic: A combination of Klevan and Penthabrian - viewed as the most dangerous iconograph because of the high expectations placed on the theorist shoulders.

It's a neat trick to identify the ways in which the outside world perceives the 'theors', as they are called, and in doing so understand where the outsider is coming from, and what kind of threat they pose. I suspect I'm going to start classifying people in "the real world" the same way when I describe what I do.

Soviet-style mathematics

Via Anand Kulkarni (aka polybot) comes an interesting article in the WSJ by Masha Gessen on Grigori Perelman, Soviet-era mathematics and the question of 'big math'. The premise of the article (Masha Gessen has a book out on Perelman and the Poincare conjecture) is that special environments are needed to prove big results, and the Soviet-era mathematical enclaves fostered this environment both because of, and inspite of the Soviet political system.

It is indeed true that amazing work came out of the isolated confines of Soviet mathematical institutes, often parallel to or well before similar work in the Western world. There's a joke that goes around theoryCS circles that for every theorem proved before the 80s in the west, there's an equivalent result proved 10 years earlier by a Russian mathematician. We need look no further than the Cook-Levin theorem, the Koebe-Andreev-Thurston theorem (on circle packings), Kolmogorov-Chaitin-Solomonoff complexity (and according to some, the Cauchy-BUNYAKOVSKY-Schwarz inequality, though this is disputed).

But in the article is a more thought-provoking claim:

The flow is probably unstoppable by now: A promising graduate student in Moscow or St. Petersburg, unable to find a suitable academic adviser at home, is most likely to follow the trail to the U.S.

But the math culture they find in America, while less back-stabbing than that of the Soviet math establishment, is far from the meritocratic ideal that Russia's unofficial math world had taught them to expect. American math culture has intellectual rigor but also suffers from allegations of favoritism, small-time competitiveness, occasional plagiarism scandals, as well as the usual tenure battles, funding pressures and administrative chores that characterize American academic life. This culture offers the kinds of opportunities for professional communication that a Soviet mathematician could hardly have dreamed of, but it doesn't foster the sort of luxurious, timeless creative work that was typical of the Soviet math counterculture.

For example, the American model may not be able to produce a breakthrough like the proof of the Poincaré Conjecture, carried out by the St. Petersburg mathematician Grigory Perelman.

This is a reflection of one of the enduring myths of mathematical research, "a mathematician would be happy in jail if they had paper and pen", with a bit of the 'a mathematician is a solitary (and slightly crazy) genius'. I can see the allure in the idea: mathematics requires great concentration, and removal of distractions would surely make it easier to focus on a big problem.

But is this really impossible to achieve in the Western model of research ? After all, even Perelman's work built heavily on a program first outlined by Richard Hamilton from Columbia. Andrew Wiles proved Fermat's theorem while at Princeton. Ketan Mulmuley has been banging away at P vs NP while shuttling between Chicago and IIT Bombay (yes, I know it's not a perfect comparison because it hasn't been resolved yet). Stephen Cook proved that SAT is NP-Complete while at Toronto. And so on and so forth.

Possibly one argument in favor of the 'isolation: good' theory is that Perelman didn't need to prove himself for 6-7 years, maintain a steady stream of funding, and teach lots of classes in order to "earn" the right to study such a hard problem. It's hard to imagine a researcher in the US being able to do this before they get some kind of job security (tenure, or otherwise).

Innovation in Computer Science

As the polylogblogdogslogglog blog points out, the ICS results are out. 39 papers were accepted in all - at some point I knew the number of submissions, but I've forgotten since.

The ICS folks didn't make life easy for themselves by explicitly stating that they wanted "conceptual contributions". But looking over the list of papers, a few things come to mind:

• It's a great list of papers. Nothing to complain about really, and any of these could have been a credible paper at FOCS/STOC
• The Arora et al paper on designing derivatives using NP-hard problems has already received so much chatter, one might argue that the conference mandate has already been satisfied. Similarly for the quantum money followup.
  
• Even if the whole 'conceptual contributions' thing doesn't pan out, I see no harm in having a third conference inserted between FOCS and STOC - the more the merrier.
  
• I guess innovation = "game theory + crypto + quantum + misc" :)

A side note. 8 of the 12 PC members have papers at the conference. This is a departure from the theory "norm". While I don't necessarily think it's a problem (everyone except theoryCS does this, and this is also a new conference), it's worth discussing. Is this something we'd like to have happen in other theory conferences as well ? Recall that the first year of the SODA 2-page experiment also had PC-submitted papers (and that was mainly to ensure submission volume, from what I recall).

Update: Shiva Kintali has PDFs for the accepted papers.

What is computational topology ?

Jeff's post on his computational topology class (and the wonderful class outline), prompted me to write about something that I've tried to explain to people before.

Computational topology is arguably the hottest thing in SoCG-land right now, and has been so for a number of years (for curious folk, the "other" hot topic is high dimensional approximate geometry). But if you ask different people, you'll get different answers to the question "what is computational topology ?". I've even had to explain to local students why my computational geometry class is different from the computational topology class being offered. So here goes:

CT I: Using topological ideas to understand the 'shape' of data
This is the version of CT that has taken up the largest fraction of CT mindshare in the SoCG land. Dating back to work by Edelsbrunner and Mucke on alpha-shapes, the field now encompasses a range of ideas for extracting topological structure from data. New topological constructs that have come from this area include various notions of persistence, as well as related work on reconstruction, data smoothing, and visualization.

Afra Zomorodian's thesis (now a book) is a nice introduction to these concepts for a CS audience. Herbert Edelsbrunner is coming out with a book on this topic very soon (Jan 16, 2010! Mark your amazons!), and Valerio Pascucci teaches a class on computational topology at the U.

CT II: Asking computational questions about fundamental topological objects and concepts.
This is closest to the 'Computational X' flavor of work, where a computational perspective is brought to the study of X. There are many interesting problems here, and they have a nice discrete flavor that makes them 'friendly' for theoryCS folk. For example, computing the Betti numbers of a simplicial complex efficiently, or finding homotopy equivalent paths on a surface, or lots of work on graphs on surfaces.

I don't think there's a single book on this topic. JeffE has put together a fantastic course outline (with reading material), and there's also a great book on graphs on surfaces by Mohar and Thomasson. It's worth noting that some of the deeper tools in the Robertson-Seymour graph minor results take us into graphs-on-surfaces-of-large-genus land.

CT III: Using topological ideas in the heart of complexity theory.
Almost no one I know uses 'computational topology' in this sense, and there isn't a coherent and connected body of work to talk about as such. But there are some fascinating results at the core of complexity theory that rely on topological constructions. There's the algebraic complexity work of Yao/Steele/Ben-Or and others, showing that lower bounds for algebraic complexity of certain problems can be related to the sum of Betti numbers of associated surfaces. There's the Kahn-Saks-Sturtevant (and Chakrabarti-Khot-Shi) work on evasiveness of graph properties, and then the work (that I don't quite understand) on topology in distributed computing (that got Herlihy, Saks, Shavit and Zahoroglou the Godel Prize)

This is one of the reasons I think a course in topology (of some flavor, whether it be combinatorial, algebraic or point-set) should be required mathematical background for all theoryCS students.

Clustering interlude: Time series

In the absence of an actual post on clustering, I encourage all of you to go and read Sorelle's (or should I say ***SORELLE***'s) post on time series clustering.

"Wave"ing...

(while I wait for actual inspiration to strike)

I just acquired an invite to Google Wave (thanks to John Moeller), and have been finding it quite intriguing (note: you can't "get" Google Wave unless you're invited, and no, I don't have any invites to give out).

Google Wave is a mysterious combination of email, chat, wikis and browser extensions that's hard to describe - you can read descriptions of it all over the web, but unless you are able to get in and start playing, it's like trying to buy wine based on text descriptions of the taste.

So far I've used it to:

• discuss an outline for next semester's seminar with my students (Topics in Graph Algorithms, for those curious)
• Start working with a collaborator on an outline for a tutorial we want to do
• set up administrative struts for our research group svn server (I've started using svn for writing papers - it's an amazing experience - more on that some other time)

By far the coolest feature of Wave for me is that you can include a LaTeX extension into any "wave"or conversation, and it automatically converts things between $$...$$ marks into latex, which makes me hopeful that it will be useful for more substantive discussions (since I often wish I had such capabilities in Skype/Google chat)

Although the preview is painfully slow, and is crippled in various ways, the potential is clearly there, and as more people get on it, it will only start getting more effective. I'm looking forward to it.

On the virtue of NOT working on a problem

Semester has hit with a vengeance, and while I've been busy (among other things) with this and this, my clustering series has gone on temporary hiatus, hopefully to return shortly.

In all the pages and pages of advice given to grad students, postdocs, and starting faculty, I think one item tends to get left by the wayside, or at least is not explicitly stated.

You always underestimate the time spent managing a project from start to finish.

What I mean is this: problems (at least in theoryCS) are easy to state, and fun to work on. Sometimes they take a while to crack, and sometimes they give up their secrets easily. But the time you spend on any given project is much more than the actual time spent thinking about it. There's

• Writing up the first few drafts
• Iterating to get a polished submission version
• (...this step repeats until the paper is accepted)
• Preparing the final (often very cramped) version
• Making slides for the talk/talks you'll be giving
• Preparing a full/journal/arxiv version, which often involves simplifying, rewriting, reproving, adding new references, etc etc.
• Submitting to a journal, and waiting endlessly for updates on its status.
  
• Addressing reviewer concerns, and resubmitting
• And finally, getting it into print.
  

The few-months thrill of actually thinking about the problem and solving it ends up being a multi-year odyssey filled with many fallow periods punctuated by bursts of activity.

It's not so much the time involved - papers tend to time-multiplex quite well so you're usually in different phases of the above sequence for different papers.

It's more a matter of motivation. I don't think I'm the only person who feels this, but once I have some nice results, and especially if there isn't follow-on work to be done, I get bored with a paper. Having to deal with it for months and months afterwards is then as excruciating as killing off zombies that keep coming back (not to mention what happens if it keeps getting rejected).

So be careful when you choose a project: make sure it can last through at least a few papers, or you'll be spending a lot of time cursing yourself for the time you spend.

Rajeev Motwani Memorial

I just returned from the technical workshop and memorial in honor of Rajeev Motwani. The workshop was excellently run by Ashish Goel, and designed (extremely well, I thought) as a mix of retrospective and technical content, with one speaker presenting a brief retrospective and introduction, and a technical speaker laying out a body of work.

The topics were varied: they started with Sanjeev Khanna discussing matchings in random graphs (Rajeev's thesis work), and going onto brand new results in this area: for example, an expected time O(n log n) bound for matchings in d-regular bipartite graphs. Piotr Indyk talked about locality-sensitive hashing, David Karger talked about randomized min cuts, Aneesh Sharma discussed monetization problems in social networks, and Sudipto Guha concluded with a overview of data streams.

The talks were surprisingly technical: if I closed my eyes, I could have easily imagined being in a SODA conference room. The only difference was that people were actually paying attention, as opposed to clicking away on laptops (or tweeting!). It was a large crowd: over 100 people, by my casual count.

There were many retrospectives, given by Dick Karp, Jeff Ullman, Chandra Chekuri, and Ron Conway. Chandra spoke in particular about the experience of being Rajeev's student, and as a former student myself, his words touched me the most. He talked with feeling, compassion and honesty, and drew a compelling picture of a man that we began to know all over again.

There was a beautiful memorial service in the Stanford Church, with words in English and Sanskrit, a hauntingly beautiful hymn from the Vedas sung by Rajeev's elder daughter, and testimonials from colleagues and friends old and new. Don Knuth was the organist for the entire ceremoney, and played pieces you didn't think could be played on a church organ. After the service, and a reception, there was a concert by one of Rajeev's favorite bands, Indian Ocean. They played amazing music, and I'm downloading their songs as we speak, but that's a tale for another time.

It was good to go back and meet people who I knew so well for a brief period of time, and then lost touch with. Many (if not all) of Rajeev's former students were there, and there were many others who cohabited the Gates Building along with me. All of us older, a little grayer, but still recognizable :). Spread-apart families often only get together at weddings or at funerals, and this was one of those occasions where it was great to see everyone, but as we all kept murmuring "unfortunately it had to happen like this".

If I had to describe the feeling that dominated my thinking that day, it was a sense of being robbed. Upon hearing testimonial after testimonial, anecdote after anecdote, listening to this divine rock group that Rajeev listened to and loved, I could only wonder at the many sides of this person whom I knew so little of. I wished I had known more about him: that our interactions had been more multidimensional than that of advisor and student, and that I (and my fellow students at the time) had seen more of the ebullience and vivacity that others spoke so vividly of.

By the end, a new picture began to emerge, of a 'hub', a 'connector' and a 'facilitator', someone who had the clarity to know what people really needed to succeed, and the self-effacement to stand back and make it happen, by connecting people together. He helped legions, and legions came to bid him farewell.

It therefore seems oddly fitting that his career in research started with studying random matchings, and ended with new explorations of social networks. His life, one might think, has always been about creating, analyzing and enriching connections.

Recent items

Deadlines are keeping me busy, and away from blogging.

Speaking of which, the Fall Workshop on Computational Geometry has a submission deadline this Friday. The Fall workshop is a "true" workshop, in that you go, give a talk on whatever you're working on, and there's no messing around with proceedings, publications and things like that. It's a pure meet-and-chat kind of venue, which means the pressure is low, and the visibility is quite high (many of the east-coast geometry folks show up).

This year it's in Tufts, so the location is even better. So get those 2-page abstracts in !

In other news, FOCS registration deadline is looming. Bill mentions a workshop to celebrate 50 years of FOCS (which dates back to when it was the conference on switching and circuits (!)) and 20 years of the GATech ACO program.

The workshop is NOT like the Fall workshop :). It's a series of invited talks by a star-studded cast: KARP !! YANNAKAKIS !! ALON !! BLUM !! All together, for one brief engagement !!

Sounds like a great idea: the kind of thing we probably need to do more of to get more folks to the conference.

Memorial Workshop for Rajeev Motwani: Update

I had mentioned a memorial workshop for Rajeev Motwani to be held at Stanford Friday Sep 25. Registration is now open for the workshop and the memorial service to follow.

Registration is free, but mandatory. So if you plan on attending either the workshop or the service or both, make sure you register.

Beamer + Ipe + views...

At this point, mostly everyone is aware of how to use beamer to make presentations in LaTeX. However, many fewer people (mostly only geometry folk) are aware of Ipe, a kind of next-generation xfig.

You may stop reading right now if

• you always use powerpoint for slides OR
• you rarely have to use LaTeX in slides OR
• you really hate non-WYSIWYG presentation software

Still here ? ok...

I wanted to share a workflow tip that I found quite useful when making slides with step-through animations. Suppose you have a sequence of slides in a presentation that unfold a figure step by step, or animate some algorithm execution etc. Ipe, coupled with a few LaTeX commands, provides a really nifty way of rendering the animation without jumps, misalignments, or misdrawings.

Ipe (and many other drawing programs) has the notion of a layer. More powerfully, Ipe also has the notion of a 'view', which you can think of as a (sub)set of layers. For example, if you have a drawing with layers 1,2,3,4,5, then view 1 might consist of {1,2,3}, and view 2 might consist of {1,2,5}, and so on.

What this means is that when you want to do step-animation, it's really easy. Each time you move to a new step, you create a new view in Ipe (which also usually creates a new layer), and you can select whichever subset of the current set of layers you want to render, as well as drawing new ones.

Ipe stores all the views as separate pages in a PDF file, so your final animation consists of a multi-page PDF file. And now comes the cool part.

Suppose you want to include this sequence of views in a beamer slide, with each view appearing after the next in response to a mouse click. You need two things:

• pdftk (which comes standard in most linux installations), which allows you to split a PDF file into multiple files (one per page), with any format for the filename that you specify. For example, I have a command called 'splitpdf' that does this:

  pdftk $1.pdf burst output $1-%d.pdf

  
  which takes a file name.pdf and splits it into name-1.pdf, name-2.pdf and so on.
  


• Next, you need the (standard) LaTeX package 'xmpmulti' which gives you the command \multiinclude. It allows you to include multiple figures that share a common prefix. So for example, to include all the figures created by the previous pdftk command, you merely write

  \multiinclude[start=1,format=pdf]{name}

  .The 'start=1' starts counting from 1 instead of 0, and you can also specify an end-counter.
  

But the best part is when you instead use

\multiinclude[<+>][start=1,format=pdf]{name}

Now, the files are included with an automatic 'replace each by the next' mode (<+> is standard beamer notation for this). At this point, you have a sequence of animations ready to go. In fact, when I give lectures, I have a number of slides that just look like this:

\begin{frame}
\frametitle{Title}
\mi{name}
\end{frame}

where \mi is a macro I defined for the above multiinclude. Ipe does all the layering and viewing work for me, and multiinclude takes care of the rest. This has made making complicated animations really simple and fast.

p.s if you're still wondering why one should use Ipe instead of xfig, the LaTeX integration in Ipe is superb. No nonsense with special flags, and pstex_t and craziness like that. You get WYSIWYG LaTeX inside Ipe, you can use whatever macros you have in your text, and the various nifty alignment tools make an Ipe drawing look really clean.

Memorial Workshop for Rajeev Motwani

Plans have been in the offing for a memorial workshop for Rajeev Motwani, and now the details are available. Below is the announcement (sent by Ashish Goel):

Dear friends,

As you might know, our dear friend and colleague, Rajeev Motwani, passed away in a tragic accident on June 5, 2009. We are holding a technical workshop titled

Randomized Algorithms: Theory and Applications

at Stanford University on Sep 25th, 2009, from 10am - 2:30pm to honor Rajeev's research in the area of algorithms and their applications. If you did not know Rajeev's research, please see http://www.stanford.edu/~ashishg/cgi-bin/RememberingRajeev/ for a brief introduction.

The workshop will take place at the Bechtel Conference Center in Encina Hall. Workshop attendees are also invited to a memorial celebration starting at 4:00pm at the Stanford Memorial church, followed by a performance by one of Rajeev Motwani's favorite bands, Indian Ocean. The registration URL, web-page information, parking directions, and talk titles will follow in a later email.

Registration will be free, but mandatory. Please feel free to bring this to the attention of any colleagues/students who you think might wish to attend, and send me an email if you have any questions.

Workshop program:

10 - 10:45 am
Welcome remarks
Retrospective by Richard Karp, UC Berkeley
Technical talk by Sanjeev Khanna, University of Pennsylvania

10:45 - 11:30 am
Retrospective by Jeff Ullman, Stanford University
Technical talk by Piotr Indyk, Massachusetts Institute of Technology

11:30 am - 12:15 pm
Retrospective by Chandra Chekuri, University of Urbana-Champaign
Technical talk by David Karger, Massachusetts Institute of Technology

12:15 - 1:30 pm
Lunch for registered attendees

1:30 - 2:30 pm
Retrospective by Ron Conway, Venture Capitalist
Technical talk by Sudipto Guha, University of Pennsylvania
Technical talk by Aleksandra Korolova, Stanford University

The Scientific committee for the workshop consisted of:
Moses Charikar
Ashish Goel
Richard Karp
Prabhakar Raghavan
Tim Roughgarden

I'll update this post with the registration URL when it becomes available.

Geometry at ESA (guest post)

(ed. note: Jeff Phillips is a researcher in computational geometry, data analysis and statistics. He's also one of the newly minted CIFellows, and will be working with me starting in a week. He kindly agreed to file this report from ESA.)

ESA (along with ICALP) is one of the top two European conferences for Theory A. And probably since the ICALP deadline is typically between the SoCG submission and notification, ESA is the home of many of the best computational geometry talks. This year was no exception, with two "geometry" sessions, as well as many other geometry talks mixed in other sessions.

Another interesting pattern at ESA which makes it different to other algorithms conferences is the juxtaposition of theoretical and experimental work. The two tracks (A: Design and Analysis, B: Engineering and Applications) have separate submissions, but are not treated separately in the program. This leads to the fun game of trying to determine whether a talk was from track A or track B. Sometimes you are surprised when a talk starts with a nice theoretical results, and then the speaker presents several experiments to show the algorithm works well in practice, or mentions that it has already been added to CGAL.

I think this a great practice and should be considered by other conferences such as SODA or SoCG. For instance ALENEX talks could be mixed in with SODA talks. This would encourage more algorithms people to actually implement their algorithms, because it would allow them to apply to a separate track that would give more value to practical algorithms. How would this not be a positive for our community and its perception from other parts of computer science!

A couple of related data points: There were 56 track A papers and 10 track B papers, and both were accepted at a rate of about 25%.

The invited talks followed the theme of theory and practice as well. Michael Mitzenmacher began with a very clear talk explaining many open problems in Cuckoo hashing. He has been working on small variations in the algorithm that lead to large differences in performance, and then has been going to great lengths to explain why these variations make such a large difference.

Erik Demaine gave a very entertaining talk describing how a certain line of his work has alternated between art and the theory behind the art. He also performed a couple magic tricks, reminding us that most of the best magic requires lots of research, and sometimes algorithms!

On the third day, Noam Nisan presented a great high level view of how Google's TV ad auctions work. Much of his talk served to point out all of the important details often ignored in theoretical analysis, but critical in implementing such a system effectively.

I'd like to note a few papers I found interesting. This is not by any means an exclusive list, but just enough to give a taste of the (geometric) results.

Manuel Caroli and Monique Teillaud. Computing 3D Periodic Triangulations.

They presented a set of rules and an algorithm for computing triangulations in 3D which are periodic, that is they tesselate to fill an infinite space, but can be defined as a complex within a unit cube. These triangulations are needed for many simulations where it is difficult to deal with boundary conditions, so these can be avoided, but effectively having no boundary. Despite, the usefulness of these triangulations, they had not really been properly formally defined until this paper. Furthermore, their algorithm has been implemented and will soon me in CGAL, if not already.

Mohammad Ali Abam, Mark de Berg, Mohammad Farshi, Joachim Gudmundsson and Michiel Smid. Geometric Spanners for Weighted Point Sets.

They study Euclidean a size n point sets with weights so that a weighted distance between two points p and q is described
d_w(p,q) = w(p) + d(p,q) + w(q)
where d(.,.) is the Euclidean distance and w(.) is the weight of a point. This may, for instance, represent a potential road network where it takes w(p) time to get to the center of a city from its outskirts. This paper shows that typical spanner-type results can be found for a weighted point set using this weighted distance. I.e. in R^d a (5+eps)-spanner can be found with O(n) edges, and (2+eps)-spanner can be found with O(n log n) edges.

Sumit Ganguly and Christian Sohler. d-dimensional Knapsack in the Streaming Model.

This paper studies the d-dimensional knapsack problem, that is given a set of n items with a value v_i and a size in d-dimensions s_{i,1}...s_{i,d} find a set of items I with maximum total value such that for all j \in [1,d] that sum_{i in I} s_{i,j} <= 1. This problem is studied in the streaming model, which I found interesting, because they were unable to store the actual solution, because it might have linear size. Instead, they approximate the optimal cost and present a "template" solution where they give an approximate size of each element in their approximate solution I'.

Bernhard Haeupler, Siddhartha Sen, and Robert E. Tarjan. Rank-Pairing Heaps.

A data structure paper, but for one very useful in geometry, among other places. This paper does not present any new results from a classical theoretical perspective. They describe a heap data structure which has the same running time for all operations as Fibonacci heaps. The contribution is that their data structure is considerably simpler than any variant of Fibonacci heaps, in particular, the structure of the heap never needs to be restructured. As an added benefit, their data structure easily outperforms Fibonacci heaps.

Other notes (mainly from business meeting): See also Michael Mitzenmacher's post.

• There were 37 computational geometry submissions, 10 were accepted.
• Mark deBerg (next years track A chair), declared that next year the page size will be STRICTLY ENFORCED and he is not opposed to automatically rejecting papers if they play too many games to squeeze more text in the alloted number of pages. Be warned.
• next year, ESA 2010 (and ALGO) will be in Liverpool, England, and it was voted for ESA 2011 to be in Saarbrucken. Which won over a bid from Greece. (ed: HOW ON EARTH ? !!!!!!)
  
• Proceedings were not included in registration, but could be bought. On the second day, after politely asking Springer, participants were able to download a single pdf of the proceedings from a couple USB keys. This was very useful!, but it would have been better earlier in the conference.

Things I missed (I had to leave partway through the third day).

• - best student paper: Heidi Gebauer. Disproof of the Neighborhood Conjecture with Implications to SAT.
• - best paper: Christoph Dürr, Flavio Guiñez and Martín Matamala. Reconstructing 3-colored grids from horizontal and vertical projections is NP-hard.

Attached workshops which are part of ALGO on Thursday and Friday:

• IWPEC: International Workshop on Parameterized and Exact Computation
• WAOA: Workshop on Approximation and Online Algorithms
• ATMOS: Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems

SODA 2010

Ironically (I'm on the PC), I'm probably the last one to post about the SODA acceptances (although I did tweet it a while ago)

In case you don't already know, one major change this year is the 20 page limit on final versions, brought about by the all-electronic proceedings. It's worth pointing out here that this is a major change that I don't think ANY conference (theoretical or otherwise) has put into place (I can say this because I had nothing to do with it :)). 20 pages is huge: there's no way you can wiggle out of writing full proofs at this point, and I hope that this trend will continue as more of our conferences go all-electronic.

One caveat: I don't know what the final version format is. It seems unnecessary to go with the butt-ugly Sheridan 2-column format, but we'll see.

On the papers:
I'm not going to comment on the papers, since I reviewed many of them. Suffice it to say that (even though it sounds like a cliche), I was impressed by the quality of the submissions, and there were many good papers that unfortunately couldn't make it. We're in the process of writing decision summaries for the authors: these are intended to capture more of the discussion surrounding a paper. Hopefully, they will give a better sense of what the reviewers liked and disliked about the paper than just the actual reviews.

On the process:
I felt a bit more disconnected from the overall deliberations this time, and maybe others felt this way too. I spent a lot of time on "my pile", but I didn't have a lot of time to look over papers that I wasn't in some way responsible for. Given the number of submissions, this is unavoidable I guess.

I actually think the reason I felt this way was because of the software interface. Instead of the (clunky) SIGACT server interface used in years gone by, we used the snazzy HotCRP interface. In almost every way, this is a vastly superior interface (and I've used EasyChair, the Microsoft CMT, and other packages as well). It feels lightweight, it has great searching and tagging capabilities, and most of the interfaces are one or two clicks from the home page. It cleanly combines email notifications and uploads/downloads with web-based editing, and I'd recommend it very strongly for anyone organizing a conference in the future.

The only feature the SIGACT server had which this doesn't, was a landing page where you got a stream of all comments on all papers. It was a serendipitious way of picking up on discussions not related to papers you were in charge of, and I remember in the past getting interested in a discussion and a paper and actually reading and submitting a review myself. In HotCRP, you land at a page containing your reviews, and it doesn't give you a direct view into the global picture (except maybe for the PC chair).

One amazing feat of social engineering that HotCRP also does: at this landing page, it tells you how many reviews you've put in compared to the average. There was a time during the review process where I'd reload the page every few hours and see myself falling behind the PC average, increasing the pressure on me to submit reviews :).