Lenny B. Robinson is Batman

Last week, police pulled a man over on Route 29 in Silver Spring, Maryland because of a problem with his plates. The man was driving a black Lamborghini, had the Batman symbol on his license plate, and was dressed in full head-to-toe Batman regalia. So who was this guy?

The Washington Post has the scoop on Lenny B. Robinson (with B standing for Batman):

The Caped Crusader is a businessman from Baltimore County who visits sick children in hospitals, handing out Batman paraphernalia to up-and-coming superheros who first need to beat cancer and other wretched diseases.

 This is actually an amazing story:

Batman is 48. He is a self-made success and has the bank account to prove it. He recently sold, for a pile of cash, a commercial cleaning business that he started as a teenager. He became interested in Batman through his son Brandon, who was obsessed with the caped crusader when he was little. “I used to call him Batman,” he told me. “His obsession became my obsession.”

Batman began visiting Baltimore area hospitals in 2001, sometimes with his now teenage son Brandon playing Robin. Once other hospitals and charities heard about his car and his cape, Batman was put on superhero speed dial for children’s causes around the region. He visits sick kids at least couple times a month, sometimes more often. He visits schools, too, to talk about bullying

Surely, the world needs more Batman. And as for his sidekick, Robin? He’s staying home, studying for his SATs.

For Factual, The World Is One Big Data Problem

This is a very interesting article about Gil Elbaz, Caltech graduate, and the company he founded, Factual:

Geared to both big companies and smaller software developers, it includes available government data, terabytes of corporate data and information on 60 million places in 50 countries, each described by 17 to 40 attributes. Factual knows more than 800,000 restaurants in 30 different ways, including location, ownership and ratings by diners and health boards. It also contains information on half a billion Web pages, a list of America’s high schools and data on the offices, specialties and insurance preferences of 1.8 million United States health care professionals. There are also listings of 14,000 wine grape varietals, of military aircraft accidents from 1950 to 1974, and of body masses of major celebrities. Odd facts matter too, Mr. Elbaz notes.

He keeps 500 terabytes of storage near Factual’s headquarters. That’s about twice the amount needed to hold the entire Library of Congress. He has more data stored inside Amazon’s giant cloud of computers. His statisticians have cleaned and corrected data to account for things like how different health departments score sanitation, whether the term “middle school” means two years or three in a particular town, and whether there were revisions between an original piece of data and its duplicate.

A quote from Mr. Elbaz: “Having money is overrated when you are brought up not to believe you are entitled to it…You can make enough money to not need things, or you can just not need things.”

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Related: Stephen Wolfram on Personal Data Analytics

Arctic Motion

A little escapism on this lovely Sunday afternoon is this timelapse video by  Tor Even Mathisen. As the title of the film implies, we traverse the Arctic and see mountains, quaint villages, and striking displays of the Aurora Borealis. The accompanying song is “As We Float” by The American Dollar. The timelapse was made with Canon EOS 5D Mark II and Canon 16-35mm f/2.8L II lens.

Also worth watching is Mathisen’s beautiful timelapse captured off the coast of Norway, still one of the best Aurora Borealis compilations I’ve ever seen:

Walking All Five Boroughs of New York City

The New York Times reports on 31-year-old Matt Green, whose goal is to walk every street of all five boroughs of New York City. By some estimates, that’s in excess of 8,000 miles:

Many people have walked every street in Manhattan. The local historian John McNamara, who died in 2004, walked every street in the Bronx. But Mr. Green believes he is the first to try for every block in all five boroughs — a distance he calculates at roughly 8,000 miles, counting parks, paths, cemeteries and occasional overlaps. He estimates that the project will him take more than two years of full-time walking to complete.

Each morning, Mr. Green, who once lived in Bay Ridge, Brooklyn, but now sleeps on friends’ couches throughout the city, scrawls the day’s route into a three-by-five-inch Caliber notebook. He starts walking between 10 a.m. and noon and keeps going until the sun goes down.

Matt Green has already a couple of adventures under his belt, such as having walked from Rockaway Beach (Queens, NY) to Rockaway Beach (Oregon) in five months and having traversed the entire New York City subway system in just over 24 hours. But for his latest project of walking on every street in New York, he is uploading his routes on a Google map and posting pictures from scenes he encounters. Matt Green’s daily photoblog is a must-see, as it’s a wonderful reflection of New York City culture.

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(hat tip: Steve Silberman)

Surviving in Joshua Tree National Park

When promiment real estate broker Ed Rosenthal went missing for six days in Joshua Tree National Park one summer weekend, people assumed that he was a goner. But as he recounts in this amazing story in Los Angeles Magazine, he survived.

I finally admitted that I was really and truly lost. I was in a wasteland of ditches near where the park ends. I was too weak to move up the hill to see what was on the other side. No one would ever have found me or my bones. I couldn’t eat. The dates I tried to chew on just stuck to my tongue—I had to spit them out. It was frustrating, but you eventually get over not eating. Afterward I was told I was lucky I didn’t eat, that if you have food while you’re severely dehydrated, your body has to use up resources to help with digestion.

As evening approached I spotted some yuccas nearby. I started to cut away the sheathing at the base of one with my Swiss Army knife—you can suck on the tendrils for water. The stalk was too tough, though: I didn’t want to be away from the tree at night, so I gave up, went back to the tree, and struggled to make myself comfortable. But even under those branches I didn’t feel sheltered on that open hillside. It was freezing. The emergency blanket was falling apart. I tried to wrap pieces around me like a mummy—they just blew off into the night. So I spent my time slathering Mercurochrome and antiseptic from the medical kit onto my cuts. It reminded me of how I’d needed to apply Mercurochrome to my legs after a quadruple bypass ten years earlier: A calamitous real estate deal had triggered the heart attack that led to the surgery; the memory of it helped keep me calm that night. I was determined not to have another heart attack. 

Near the end of his piece, Rosenthal writes:

The moment they gave me water, I threw up all over the helicopter. They brought me to the hospital and started filling me up with fluids. I was in the ICU for two days. I experienced some heart damage, and my left ankle is shot. I don’t know what it is; they can’t do anything about it. But the rest of me is stronger. I’m hiking again. More with the Sierra Club. Now I experience plants as not being a separate species. They’re like cousins. It’s not like, There’s me and there’s plants. There’s us. I’m overwhelmed by how incredible they are.

I’ve hiked in Joshua Tree in October, and even in middle of autumn, the heat takes a lot out of you (even on a moderate three hour hike). Ed Rosenthal’s piece is a must-read story of survival.

Margaret Atwood on Twitter

Margaret Atwood is one of the most popular authors who’s an active user of Twitter. In this fantastic New York Review of Books post, she muses on Twitter’s personality and her evolution as a Twitterer:

[O]n Twitter you find yourself doing all sorts of things you wouldn’t otherwise do. And once you’ve entered the Enchanted E-Forest, lured in there by cute bunnies and playful kittens, you can find yourself wandering around in it for quite some time. You might even find yourself climbing the odd tree—the very odd tree—or taking refuge in the odd hollow log—the very odd hollow log—because cute bunnies and playful kittens are not the only things alive in the mirkwoods of the Web. Or the webs of the mirkwoods. Paths can get tangled there. Plots can get thickened. Games are afoot.

On Margaret Atwood’s early days on Twitter:

When I first started Twittering, back in 2009—you can read about my early adventures in a NYRblog post I wrote two years ago—I was, you might say, merely capering on the flower-bestrewn fringes of the Twitterwoods. All was jollity, with many a pleasantry being exchanged. True, some of those doing the exchanges represented themselves in masks, or as pairs of feet, or as rubber ducks, or as onions, or as dogs—quite a few dogs. But having had an early career in puppetry and a somewhat later phase during which I amused small children by giving voices to the salt and pepper shakers, I was aware of the fact that anything can talk if you want it to. My Twitter friends were not only sportive but helpful, informing me about Twitpic, letting me in on the secrets of acronyms such as “LMAO,” analyzing the etymology and deep symbolic meaning of “squee,” and teaching me to make many an emoticon, such as the vampire face, represented thus: >:>} (Though other vampire-face options are available.) They led me to extra-Twitter adventures: a live chat on DeviantArt, a website where I found the cover for my book, In Other Worlds: SF and the Human Imagination. To this day I rely on my Twitter followers for arcane information, most recently some updates on the vernacular speech of the young. Who knew that “sick” is the new “awesome,” and that “epic” is the rightful substitute for “amazing?” Twitter knew.

As I like to say: Twitter is what you make of it.

Georgia Lottery Players, Also Known as Suckers

The lottery players in the state of Georgia are the biggest suckers in a nation buying more than $50 billion a year in tickets for state-run games, which have the worst odds of any form of legal gambling.

According to Bloomberg:

Georgia residents spent an average $470.73 on the lottery in 2010, or 1 percent of their personal income, while they received the sixth-highest prize payouts, 63 cents for each dollar spent, the Sucker Index shows. Only Massachusetts had higher spending, $860.70 per adult, more than three times the U.S. average.

Georgia had per capita income of $34,800 in 2010, below the national average of $39,945, while Massachusetts’s was higher at $51,302, according to data compiled by Bloomberg.

Massachusetts players were the biggest lottery winners, getting back almost 72 cents on the dollar, according to the data compiled by Bloomberg. That state still places second on the Sucker Index because spending as a percentage of personal income is the most, at 1.3 percent.

So how does the Sucker Index work? Bloomberg took the total spent on ticket sales in each state and subtracted the amount of lottery prizes awarded. The difference was divided by the total personal income of each state’s residents. Georgia was at the top (or bottom, depending on how you view it) of the list.

Conclusion: if the saying “There’s a sucker born every minute” holds any merit, there’s a very good chance he is living in Georgia.

Finding Waldo with Mathematica

For those of you who are fans of Finding Waldo and have a bit of a nerdy side to you, you’ll appreciate that someone figured out how to find Waldo using Mathematica:

Finding Waldo with the help of Mathematica.

The author describes his technique and provides the relevant code:

First, I’m filtering out all colours that aren’t red

waldo = Import["http://www.findwaldo.com/fankit/graphics/IntlManOfLiterature/Scenes/DepartmentStore.jpg"]; 
red = Fold[ImageSubtract, #[[1]], Rest[#]] &@ColorSeparate[waldo]; 

Next, I’m calculating the correlation of this image with a simple black and white pattern to find the red and white transitions in the shirt.

corr = ImageCorrelate[red,    Image@Join[ConstantArray[1, {2, 4}], 
ConstantArray[0, {2, 4}]],    NormalizedSquaredEuclideanDistance]; 

I use Binarize to pick out the pixels in the image with a sufficiently high correlation

and draw white circle around them to emphasize them using Dilation

pos = Dilation[ColorNegate[Binarize[corr, .12]], DiskMatrix[30]]; 

I had to play around a little with the level. If the level is too high, too many false positives are picked out.

Finally I’m combining this result with the original image to get the result above

found = ImageMultiply[waldo, ImageAdd[ColorConvert[pos, "GrayLevel"], .5]]

Amazing.

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(via Kottke)

World’s Longest (And Most Expensive) Taxi Ride

Over the past 13 months, Paul Archer Archer and two college buddies, Leigh Purnell, 24, and Johno Ellison, 28, have traveled more than 32,000 miles around the globe from London to New York in a hired London Black Cab—which they’ve christened Hannah. They want to set a new world record for world’s longest and most expensive taxi ride.

The previous record was a 21,691-mile, four-month taxi ride from London to Cape Town, South Africa, and back, set in 1994 by Jeremy Levine and Mark Aylett, of the U.K., and Carlos Arrese of Spain. That trip ran the meter up to $64,645.

The Wall Street Journal summarizes the trip so far:

Since leaving the U.K. in February last year, the team has plowed into a snow bank inside the Arctic Circle in Finland, dinged a fender on a lamppost in Dunhuang, China, blown the radiator at an Iraqi border crossing, dodged the Taliban, and ran afoul of police officers, military personnel and armed mercenaries from Moscow to Tehran to Texas.

They were also forced to take a thousand-mile detour around much of the Middle East during the height of the Arab Spring—avoiding Libya, Egypt and Syria for a “safer route” through Iraq, Iran and Pakistan…

By the completion of their trip, Paul Archer and his team will have traveled nearly 50,000 miles through 39 countries. They’ve already eclipsed the $100,000 barrier on their fare.

What an adventure they’re having!

How to Apologize

This is a very thoughtful post in which the author enumerates the elements of a good apology:

  1. I hear you.
  2. I am truly sorry.
  3. (semi-optional, depending on what happened) This is what went wrong.
  4. I am doing x to make sure this doesn’t happen again and y to make it right with you.
  5. Thank you. I appreciate the feedback.
#1 is crucial. The person or group you’re addressing has to know that you’ve heard their complaint and understand it. Apologies that lack this element sound cold and disconnected. And this is the main problem with Sqoot’s “others were offended.”  They aren’t speaking to the people they offended. This is just guaranteed to further piss people off.
 
#2 should be unconditional. Not “I’m sorry if you were offended.” Indeed, if you find yourself pushing the focus onto the people whom you pissed off at all, you may be sliding into non-apology territory. This isn’t about them—they’re mad because you made them mad. Note that a good apology is not defensive, and does not attempt to shift the blame, even if that blame belongs to an employee whom you’ve just fired.  If you did that, it’s part of #4, the “how I’m fixing it” part, not the “I’m sorry” part. Don’t try to save face in a genuine apology. Indicating that you meant no harm is fine, but if you’re apologizing, it means you caused harm regardless of your intent.
 
#3 is a bit more tricky. People want to know how this could have happened, but it doesn’t do to dwell on it too much, and this is another mistake Sqoot makes. They probably shouldn’t quote the line that made everyone mad (it will make the readers mad all over again). It would have been enough to say they put something stupid and sexist into an event page which they now regret. On the other hand, you do have to acknowledge what happened and not look like you’re trying to dodge it. So don’t go into excruciating detail about what went wrong with a customer’s order, for example. “I’m afraid you found a bug in our shopping cart” is probably enough detail. 
I could do a better job at offering unconditional apologies (element number 2 above).