Technology v. Terrorism

Yesterday evening, PBS broadcast an episode of its Nova science program, “Manhunt: The Boston Bombers”, reporting on the role of technology in tracking down those responsible for the Boston Marathon bombings.   I had seen a note about the program in our local paper, and was curious to see what sort of program it would be.

I’m glad to say that, on the whole, I thought the reporting was realistic and level-headed.  It avoided scare-mongering, and took a fairly pragmatic view of what technology can and cannot do, at least at present.  It was organized chronologically, with commentary on forensic technologies interwoven with the narrative.

The first segment dealt with evidence from the explosions themselves. The white smoke that resulted, easily visible in TV accounts, indicated a gunpowder type of explosive, a suggestion reinforced by the relatively small number of shattered windows.   One forensic expert, Dr. Van Romero of the New Mexico Institute of Mining and Technology [NM Tech], quickly suspected a home-made bomb built in a pressure cooker.  Although devices of this type have been rare in the US, they have been relatively common in other parts of the world.  Building a similar bomb, and detonating it on a test range at NM Tech, produced effects very similar to the Boston bombs.  A pressure cooker lid was subsequently found on the roof of a building close to one of the explosion sites.

Because the attacks took place very close to the finish line of the Boston Marathon, and because that location on Bolyston Street has a large number of businesses, the authorities were confident that they would have plenty of still and video images to help identify the bombers.  After examination of this evidence, they came up with images of two primary suspects, who at that point could not be identified.  At first, the police and FBI decided not to release the images to the public; they feared doing so might prompt the suspects to flee, and hoped that facial recognition technology might allow them to be identified.  Alas, as I’ve observed before, these techniques work much better — almost like magic — in TV shows like CSI or NCIS than they do in the real world.  The images, from security videos, were of low quality, and nearly useless with current recognition technology.  Ultimately, the authorities decided to make the images public, hoping that someone would recognize them.

As things turned out, it didn’t matter that much.  The two suspects apparently decided to flee, and car-jacked an SUV.  The owner of the SUV managed to escape, and raised the alarm.  In a subsequent gun battle with police, one suspect died (he was apparently run over by his associate in the SUV); the other was wounded but escaped.  He abandoned the SUV a short distance away, and hid in a boat stored in a backyard in Watertown MA.  He was subsequently discovered because an alert local citizen noticed blood stains on the boat’s cover; the suspect’s location was pinpointed using infrared cameras mounted on a police helicopter.

As I mentioned earlier, I think the program provided a good and reasonably balanced overview of what these technologies can do, and what they can’t.  Magic is still in short supply, but technology can help pull together the relevant evidence.

More work is still being done to improve these techniques.  A group at the CyLab Biometrics Center at Carnegie-Mellon University, headed by Prof. Marios Savvides, is working on a new approach to facial recognition from low-quality images.  They give their system a data base containing a large number of facial images; each individual has associated images ranging from very high to low resolution.  Using information  inferred from this data, and guided by human identification of facial “landmarks” (such as the eyebrows, or nose) in the target image, the system attempts to find the most likely matches.  The technique is still at a very early stage, but does show some promise.  There’s more detail in an article at Ars Technica.

As the NOVA program also pointed out, the growth in and improvement of all this surveillance technology has some potentially troubling implications for personal privacy.  Setting up a portion of the infrastructure for a police state is probably not good civic hygiene; but that’s a subject for a future post.

A Supercomputer ARM Race?

The PC World site has a report of an interesting presentation made at the EDAworkshop13 in Dresden, Germany, this month, on possible future trends in the high-performance computing [HPC] market.  The work, by a team of researchers from the Barcelona Supercomputing Center in Spain, suggests that we may soon see a shift in HPC architecture, away from the commodity x86 chips common today, and toward the simpler processors (e.g., those from ARM) used in smart phones and other mobile devices.

Looking at historical trends and performance benchmarks, a team of researchers in Spain have concluded that smartphone chips could one day replace the more expensive and power-hungry x86 processors used in most of the world’s top supercomputers.

The presentation material is available here [PDF].  (Although PC World calls it “a paper”, it is a set of presentation slides.)

As the team points out, significant architectural shifts have occurred before in the HPC market.  Originally, most supercomputers employed special purpose vector processors, which could operate on multiple data items simultaneously.  (The machines built by Cray Research are prime examples of this approach.)  The first Top 500 list, published in June 1993, was dominated by vector architectures  — notice how many systems are from Cray, or from Thinking Machines, another vendor of similar systems.  These systems tended to be voracious consumers of electricity; many of them required special facilities, like cooling with chilled water.

Within a few years, though, the approach had begun to change.  A lively market had developed in personal UNIX workstations, using RISC processors, provided by vendors such as Sun Microsystems, IBM, and HP.   (In the early 1990s, our firm, and many others in the financial industry, used these machines extensively.)  The resulting availability of commodity CPUs made building HPC system using those processors economically attractive.  They were not quite as fast as the vector processors, but they were a lot cheaper.  Slightly later on, a similar transition, also motivated by economics, took place away from RISC processors and toward the x86 processors used in the by-then ubiquitous PC.

Top 500 Processor Architectures

The researchers point out that current mobile processors have some limitations for this new role:

• The CPUs are mostly 32-bit designs, limiting the amount of usable memory
• Most lack support for error-correcting memory
• Most use non-standard I/O interfaces
• Their thermal engineering does not necessarily accommodate continuous full-power operation

But, as they also point out, these are implementation decisions made for business reasons, not insurmountable technical problems.  They predict that newer designs will be offered that will remove these limitations.

This seems to me a reasonable prediction. Using more simple components in parallel has often been a sensible alternative to more powerful, complex systems.  Even back in the RISC workstation days, in the early 1990s, we were running large simulation problems at night, using our network of 100+ Sun workstations as a massively parallel computer.  The trend in the Top 500 lists is clear; we have even seen a small supercomputer built using Raspberry Pi computers and Legos.  Nature seems to favor this approach, too; our individual neurons are not particularly powerful, but we have a lot of them.

Lose the Sweet Tooth

One can safely assume that cockroaches are not among the typical urban resident’s favorite animals.  While they are not as dangerous as, say, a malaria-carrying mosquito, or a tse-tse fly, they can potentially transport infectious agents, and have been implicated in some human allergies.  Mostly, though, people just think that they’re gross.  They’re also quite hardy, and able to go for fairly long periods without food or water; getting rid of them can be a chore.

Back in 1976, the Black Flag company introduced a roach trap called the Roach Motel™; it is a small enclosure, which contains a slow-acting poison mixed with a bait.  (Other companies marketed similar products.) The idea is that roaches will be attracted to the bait, and then carry the accompanying poison back to their nests, thereby getting other roaches as well.  The product was quite successful, in part because of aggressive advertising, and its memorable slogan, “Roaches check in, but they don’t check out.”

More recently, though, users have noticed that the traps were becoming less effective. The New Scientist  has a recent article on some research that may explain why.   One of the ingredients in the bait used is the simple sugar, glucose.  It seems that the roaches have evolved a distaste for glucose.

In the race for world domination, cockroaches have scored another point against Homo sapiens. Their weapons? A distaste for sugar and a helping hand from evolution.

In a paper published in the journal Science, researchers at North Carolina State University in Raleigh discovered that some roaches of the species commonly known as “German cockroaches” [Blattella germanica] had a difference in their neurochemistry that caused glucose to “taste” bitter, a trait which they passed on to their offspring.  The use of glucose as a bait in poisoned traps creates selection pressure that favors roaches without a sweet tooth.  Hence, the authors suggest, evolution is at the heart of the traps’ decreased effectiveness.

This kind of evolutionary “arms race” is similar to what we have seen in the development of antibiotic-resistant bacteria, DEET-resistant mosquitoes, and herbicide-resistant weeds.  We humans are quite good, in many cases, at devising ways to modify our environment.  But we too often forget that our environment is not just a passive lump of matter — when we push, it frequently pushes back.

Weather Forecasts: Improving

Although there are a lot of different sources from which you can get a weather forecast, those forecasts all come from one of a few sources: national weather services that run large, numerical weather prediction models on their computer systems.  Two of the major suppliers are the US National Weather Service’s [NWS]  Global Forecasting System [GFS] (the source for most US forecasts), and the European Centre for Medium-Range Weather Forecasts [ECMWF], located in Reading, England.  Over the last few years, there has been a growing feeling that the US effort was not keeping up with the progress being made at ECMWF.  The criticism became considerably more pointed in the aftermath of last year’s Hurricane Sandy.  Initial forecasts from the GFS projected that the storm would head away from the US East Coast into the open Atlantic.  The ECMWF models correctly predicted that Sandy would make a left turn, and strike the coast in the New Jersey / New York region.

According to a story in Monday’s Washington Post, and a post on the paper’s “Capital Weather Gang” blog, at least one good thng will come out of this rather embarrassing forecasting error.  It’s anticipated that the NWS will get additional appropriated funds to allow the computers and the models they run to be updated.

Congress has approved large parts of NOAA’s spending plan under the Disaster Relief Appropriations Act of 2013 that will direct $23.7 million (or $25 million before sequestration), a “Sandy supplemental,” to the NWS for forecasting equipment and computer infrastructure.

This should go a long way toward addressing one of the most pressing needs for the GFS: more computing horsepower.

Computer power is vital to modern weather forecasting, most of which is done using mathematical models of the Earth’s climatic systems.  These models various weather features, such as winds, heat transfer, solar radiation, and relative humidity, using a system of partial differential equations.  (A fundamental set of these is called the primitive equations.)  The equations typically describe functions that are very far from linear; also, except for a few special cases, the equations do not have analytic solutions, but must be solved by numerical methods.

The standard techniques for numerical solution of equations of this type involves approximating the differential equations with difference equations on a grid of points.  This is somewhat analogous to approximating a curve by using a number of line segments; as we increase the number of segments and decrease their length, the approximation gets closer to the true value.  Similarly, in weather models, increasing the resolution of the grid (that is, decreasing the distance between points) allows better modeling of smaller-scale phenomena.  But increasing the resolution means that correspondingly more data must be processed and more sets of equations solved, all of which takes computer power.  Numerical weather prediction , although it had been worked on for some years, really only began to be practical in the 1950s, with the advent of digital computers, and the early weather models had to incorporate sizable simplifications to be at all practical.  (It is not too useful to have a forecasting model, no matter how accurate, that requires more than 24 hours to produce a forecast for tomorrow.)

The computation problem is made worse by the problems inherent in data acquisition.  For this type of numerical analysis, the three-dimensional grid would ideally consist of evenly spaced points, covering the surface of the Earth and extending upwards into the atmosphere.  Clearly, this ideal is unlikely to be achieved in practice; getting observations from the center of Antarctica, or the mid-Pacific Ocean, is not terribly convenient.  There are also ordinary measurement errors to deal with, of course.  This means that a good deal of data pre-processing and massaging is requied, in addition to running the model itself, adding even more to the computing resources needed.

Many observers point to the GFS’s limited computer power as one of the chief weaknesses in the US effort.  (For example, see this blog post by Cliff Mass, Professor of Atmospheric Sciences at the University of Washington, or this post by Richard Rood, Professor at the University of Minnesota in the Department of Atmospheric, Oceanic and Space Sciences.)   The processing speed of the current GFS system is rated at 213 teraflops (1 teraflop = 1 × 10¹² floating point operations per second); the current ECMWF system is rated at 754 teraflops (and is listed as number 38 in the most recent Top 500 supercomputer list, released in November 2012 — the GFS system does not make the top 100).

The projected improvements to the GFS system will raise its capacity to approximately 2600 teraflops; in terms of the most recent Top 500 list, that would put it between 8th and 9th places.  (Over the same period, the ECMWF system is projected to speed up to about 2200 teraflops.)   This will enable the resolution of the GFS to be increased.

The NWS projects the Sandy supplemental funds will help enhance the horizontal resolution of the GFS model by around a factor of 3 by FY2015, enough to rival the ECMWF.

There are also plans to make other improvements in the model’s physics, and in its associated data acquisition and processing systems.

These improvements are worth having.  The projected $25 million cost is a very small percentage of the total Federal budget (about $3.6 trillion for fiscal 2012).  As we are reminded all too often, extreme weather events can come with a very large price tag, especially when they are unexpected.  Better forecasts have the potential to save money and lives.

Chrome for Windows Updated

Google has released a new version, 27.0.1453.94, of its Chrome browser; this update is only for Windows, and fixes a Graphics processing (GPU) bug that can cause a crash.  (I am not aware of any security consequences of this bug.)  Further details are available via the Release Announcement.

Windows users should get the new version via the built-in update mechanism.

Statistical Twisters

During yesterday evening’s ABC World News program, which was largely taken up with coverage of the tornado disaster in and around Moore OK, there was a segment on a 90+ year old resident who had lost her house to a tornado for the second time.   (The first time was in May 1999, when a similar strong twister hit Moore.)  There was then a statement, which caught my attention, that the odds against this happening were “100 trillion to 1”.

Now, those are pretty long odds.  One hundred trillion is 100 × 10¹²; by way of comparison, it is about twenty times the estimated age of the universe, since the Big Bang, measured in days.  If the odds are true, we are talking about a really rare phenomenon.

Thinking about the question this morning, I decided to double-check the report — perhaps I had just misunderstood the number that was being quoted.  I found a report on the ABC News site, which actually made the whole odds business more questionable:

A recent tornado probability study, published by Weather Decision Technologies, predicted the odds of an E-F4 or stronger tornado hitting a house at one in 10,000.

That same study put the odds of that same house getting hit twice at one in 100 trillion.

It is almost impossible to imagine how both these probability assessments could be correct, or even reasonable guesses.  If the odds against the house being hit once are one in 10,000 (probability 0.0001) , then, if tornado hits are independent, the probability of a house being hit twice is (0.0001)², or odds of 1 in 100 million.  That would make the quoted odds (1 in 100 trillion) off by a a factor of one million.  Of course, if tornado hits are not independent, then my calculations are inappropriate.  But for the numbers to work as quoted, the first hit would have to, in effect, provide truly enormous protection against a second hit.  (If the odds against the first are one in 10,000, then the odds against the second must be truly astronomical to produce cumulative odds of one in 100 trillion.)

Now, I don’t actually believe that tornado hits are independent.  Tornadoes certainly do not occur uniformly across the world, or even across the United States.  The NOAA Storm Prediction Center’s Tornado FAQ Site has a map highlighting “tornado alley”, the area where most significant tornadoes occur.  Although a tornado may, in principle, occur almost anywhere, you are considerably more likely to encounter one in Kansas or Oklahoma than you are in northern Maine or the upper peninsula of Michigan.

This question of independence is directly relevant to the news segment I mentioned at the beginning; it turns out that the unfortunate lady who has lost two houses built the second one on the same site as the first one, destroyed in 1999.  If the odds are affected at all by location (as they seem to be, at least “in the large”), then this was not, perhaps, the best possible choice.

I’ve griped before about the widespread ignorance of journalists and others when it comes to statistical information.  I have tried to find a copy of the  “Tornado Probability Study” mentioned in the quote above, so far without success.  I’ll keep trying, and report on anything I discover.  If I’m missing something, I’d like to know; if the probabilities are just made up, I’d like to know that, too.