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The Super Bowl Scammer: Dion Rich Is the Godfather of Gatecrashing

January 31, 2015 Leave a comment

 

Blogmaster note: This is my 1,550 th blog as a stand-in blogger for

https://fashiontech.wordpress.com/ ….onward and upward! Bud Robinson Jan 30 15

 

 He’s snuck into the Big Game 35 times, hung with presidents and partied at the Playboy Mansion – and never had to wait in line

BY | January 29, 2015

Dion Rich
Dion Rich, left, lifts Cowboys coach Tom Landry at the conclusion of Super Bowl XII. AP

It just kind of happened. At least, that’s how Bill Swank remembers it. He had gotten a call from Dion Rich the night before, to meet him along the San Diego piers. Swank figured they were getting lunch. But Rich shows up and they start walking and talking and soon they’re headed onboard the USS Midway.

Swank thought nothing of it.

There’s Rich, chatting people up, with Swank figuring he’s seen someone he knows or used to know. But Rich waves him over and now they’re walking through the decommissioned ship’s museum, and it’s quiet and empty, except for a small gathering of a few dozen people. There’s a podium and balloons and a lot of military-ish folks milling about, excited about something. There’s a banner on the wall and when Swank started reading it, he realized what was going on:

Dion Rich and Bill Swank were crashing a birthday party for a 99-year-old man.

John Finn, the Pearl Harbor hero, was being honored at a party attended by only Medal of Honor recipients and local dignitaries. And Rich and Swank.

“This was an invitation-only party,” Swank recalls, laughing. “This was something. Finn was, at the time, the oldest living recipient of the Medal of Honor and Dion wanted a picture with him. He actually already had a picture with him, but it was a lousy one, so Dion went down there just to get another one. We had gotten there early enough, that when people started showing up, we didn’t leave. Everyone thought we belonged there.”

Hang around Dion Rich long enough and you’re sure to have a story (or several) like Swank’s. Parties at the Playboy Mansion. The Academy Awards. The Olympics. He is the Godfather of the Gatecrash. The Sultan of the Sneak-In. He has been photographed next to Jack Nicholson and Tiger Woods and Bill Clinton. But his biggest claim to fame?

Crashing Super Bowls.

And not just to sit, like some schlub in Section 318 of the nosebleeds. No, when Dion Rich goes to a Super Bowl, he goes to be seen.

“Never, ever in my life, did I think I would be a famous gatecrasher,” Rich says from his home in the San Diego area. “When I was on the podium after Super Bowl I with Pete Rozelle, Vince Lombardi and Bart Starr, I had no idea I would become famous. I just wanted to get my picture taken.”

Dion Rich
Pete Rozelle (left with trophy), Vince Lombardi (middle with white shirt) and Dion Rich on the right. Courtesy of Dion Rich

“Well, it all begins with a bar I used to own. A lot of good-looking chicks used to hang out there.”

This was the ’60s, back when the Chargers had just moved south from Los Angeles to San Diego. Rich was a jack-of-all-trades: he was a ticket-broker, professional schmoozer, general man-about-town. But he also happened to own a number of bars in the city, including one right near where the Chargers practiced. And as the team gained a foothold, his bar became the unofficial watering hole for players, coaches and team personnel.

When opposing teams came to San Diego, they were directed to Rich’s bar. The booze was free, the company was better and the women were likely topless. He began riding to Chargers games with friends on the team, or coaches – never paying to enter and always watching from the sideline. And he quickly learned how things worked.

“I got to know a lot of the Kansas City players real well,” Rich recalls. “So when they made the first World Championship Game and I found out where they were staying in L.A., I got up early, found where the buses were going to park and got there just ahead of them. When they got off the bus, I brought a jacket a Chiefs player had already given me and walked off the bus with them and into the locker room.”

Just like that, he was in.

He spent the first half on the Chiefs’ sideline, evaluating which side to finish the game on. Since he was already in, Rich’s goal was to get on television. Once the Green Bay Packers put up 21 unanswered points in the second half, Rich ditched the Chiefs jacket and stealthily shifted to the winning locker room to ride the victory wave.

Rozelle, the NFL’s commissioner, was on a small podium in front of TV cameras, waiting to award the championship trophy to Vince Lombardi and quarterback Bart Starr. But Starr stepped off the stage for another interview and Rich saw his opening – he boldly stepped right up behind Lombardi and the legend of the gatecrasher began.

“That was how I got started,” he says. “I was determined to get to every Super Bowl.”

Dion Rich
The “wanted poster” that the NFL put at its Super Bowl venues in the late 70s and 80s. Courtesy of Dion Rich

He did. With the exception of Super Bowl III – when he went on a forgettable ski trip, instead – Rich successfully entered the world’s biggest sporting event 35 times without paying, and made it onto the field at 22 of the games. At first, in the Super Bowl’s infancy, getting in was relatively simple. But as the Super Bowl grew in popularity, security got increasingly more difficult.

Like he’d let that stop him.

He donned wigs, glasses, fake mustaches and beards. He collected old press passes from previous Super Bowls and flashed them at guards. He wore jackets that were the same color as event security and put an earpiece in. Even affixed name tags to a blazer and claimed to be with the NFL. As the years passed and the security increased, Rich raised the stakes.

He had been able to pass by the NFL, undetected, until Super Bowl XII. The league started to notice his face popping up at the most prominent moments – the trophy presentation, usually – and put out an APB for Rich. He made it inside the Louisiana Superdome, thanks to a ride on the Denver Broncos’ team bus and stayed on the side of the field for the game.

Rich made his way to the Dallas Cowboys’ sideline as the second half waned, and as seconds ticked off for the Cowboy victory, he inched closer and closer to head coach Tom Landry.

The final seconds counted down. Rich looked around in the middle of the fray. Then he grabbed Landry’s right leg.

The Associated Press photo of Rich and Cowboys defensive lineman Larry Cole, with Landry on their shoulders, was splashed across newspapers all over the world the next day.

“Really, none of us saw him until right at the end of the game,” Cole recalls from his Colleyville, Texas home. “And then suddenly, he appeared. How he got on the field? I don’t know.”

One problem: His greatest gatecrashing feat immediately started the clock for the NFL to make sure Dion Rich stopped showing up at the biggest moments of the biggest sporting event in the country.

Dion Rich
Dion and Tiger Woods. Courtesy of Dion Rich

An interview.

That’s how the NFL finally got Dion Rich. After crashing 19 straight Super Bowls, the NFL finally had enough. It was tired of seeing Rich lurking behind Lombardi. Holding up Landry. (Following Super Bowl VI, Rich was so bold he danced with one of Landry’s daughters at the after party.) Celebrating with winning quarterbacks. So after years of failed APB’s at stadiums across the country, the NFL tried to use the one thing Rich was after at every crash against him: notoriety.

The NFL knew Rich liked to talk about his crashes, so as Super Bowl XXIII in Miami neared, the league hired a team of private investigators to put together a sting operation. Someone posing under the name of “John Kincaid” contacted Rich in the weeks leading up to the Super Bowl, saying he worked for South Florida Trade Magazine and wanted to do a story on his exploits.

Naturally, Rich agreed, and happily gave him the address and phone number of the condo he would be staying at during Super Bowl week.

“I always joked with Dion,” Swank says, “that if you were going to keep robbing banks, you shouldn’t tell people which bank you’re going to rob!”

When Rich arrived in Miami, Felix Eades – a former Miami cop, now working as a P.I. – met Rich for the interview. Rich told him many (but not all) of the tricks he used to sneak into previous Super Bowls. Eades reported back to the NFL everything he had learned from his meeting: What Rich looked for, how he did things, what he looked like.

“They were no longer amused and were willing to spend some money to get him,” Eades told the Dayton Daily News during an interview about Rich in 1993.

Technically, Rich did sneak into the Super Bowl successfully that year. With the assistance of a friend and a wheelchair, he used the handicap entrance and seating area to get in and watch the game. But, as usual, the main goal was to get down to the field and get in on the trophy presentation after the San Francisco 49ers beat the Cincinnati Bengals.

The NFL had eyes on Rich the whole evening.

“I had run out of disguises,” Rich laughs.

NFL security pulled Rich into a room and read him the riot act, but said that he could avoid jail time if he promised to never to sneak into a Super Bowl again. The reason? The NFL claimed that Rich’s crashing of the Super Bowl was costing the league thousands of dollars each year, money spent trying to keep track of him.

Rolling Stone attempted to contact three of the eight private investigators involved with the sting on Rich. Eades did not respond to multiple attempts to contact him. Edward Du Bois III said that he was unable to comment because he is still under contract with the NFL.

Stu Weinstein, a former independent investigator involved with the Rich case – who is now head of security for the Miami Dolphins – said he would happily recount the details of the sting when reached on his cell phone, provided he was allowed to by his employer.

The Dolphins then declined to make Weinstein available for comment.

Dion Rich
Dion with Bill Clinton Courtesy of Dion Rich

Dion Rich may be old, but he’s not washed up.

He’s 85 now and the crashing has slowed down. Along with Charlie Jones and Swank, both local sportswriters, Rich wrote a book, The Life of Dion Rich: Live Like a Millionaire With No Money Down, detailing his greatest tales of the gatecrashing scene. These days, he spends a lot of time with his assistant, Mariana Aguilar, who he kiddingly refers to as “my girlfriend.” He still lives in San Diego, a place that has always been home to him. He rarely misses Chargers home games or a chance to take friends out on the harbor cruise. (Free of charge, of course.)

“Everywhere we go, he knows somebody,” Aguilar says. “He’s truly an amazing person to be around.”

A local celebrity, Rich uses his fame and connections to do charity work with the city’s underprivileged kids. Not surprisingly, over the years, he’s also gotten to know a number of members of the San Diego Police Department, even taking them out to lunch. (Again, free of charge.) If he sees them while he’s enterting a Chargers or Padres game, he jokes that his disguise these days is of an elderly man looking to catch one final game.

“They tell me, ‘You’ve already tried that gag, Dion – 20 years ago!'” he laughs.

He still gets in through the side door, but it’s at the San Diego Zoo or SeaWorld now. It’s hardly a crash; it’s more through connections he’s made over the years. But crashing another Super Bowl?

“Never again,” he says.

Simply too hard. Too much security to get through. After September 11, the NFL stepped up its security game to safeguard one of the biggest sporting events in the world. Last year, for Super Bowl XLVIII at MetLife Stadium in New Jersey, there were nearly 4,000 police and private security personnel in and around the stadium. (It should be noted, that for a Sports Illustrated story at Super Bowl XXXVI – the first following the terrorist attacks – Rich snuck into the Superdome in six minutes.)

“I miss the thrill of getting away with it,” Rich says. “Doing something that no one is capable of doing. People don’t know how easily you could do it, if you have the expertise. But after the NFL put the sting on me and 9/11 and all the security – and I say this with all sincerity – I could not do today what I did back then.”

Maybe it’s best that way. Maybe it’s best to let Dion Rich’s exploits of showing up next to Vince Lombardi, underneath Tom Landry and beside Don Shula live in a time where things were simpler. Where getting into one of the biggest sporting events in the world was a running gag, as much as it was an adrenaline rush.

Now, it’s a memory. This afternoon, he will board a plane to Phoenix, for another Super Bowl. Rich has a ticket and insists he is not going to crash the game.

“But if I see a side door open with no one around, I might just have to,” he said.

He’s joking. Maybe.

If he is, some folks aren’t laughing. The Glendale Police Department emailed Rolling Stone to say it “would certainly affect an arrest on any individual without a ticket or credential if notified” by building security or the NFL.

So, is Dion Rich still considered Public Enemy No. 1 for the NFL?

An 85-year-old man, who’s slowing down and isn’t as sharp as he once was, couldn’t possibly be a threat to make it in undetected to Super Bowl XLIX in Glendale, Arizona – 349 miles away from his front door – could he? There’s just no way that a multi-billion dollar corporation is still spending money to guard itself against the greatest gatecrasher of them all, right?

When asked if the NFL still considers Dion Rich an active and potential threat to sneak into the Super Bowl, Brian McCarthy – the league’s vice president of communications –responded with a six-word answer:

“We do not have a comment.”

 

Categories: Uncategorized

Safecracking the Brain

January 30, 2015 Leave a comment

What neuroscience is learning from code-breakers and thieves.

 

It’s hard to imagine an encryption machine more sophisticated than the human brain. This three-pound blob of tissue holds an estimated 86 billion neurons, cells that rapidly fire electrical pulses in split-second response to whatever stimuli our bodies encounter in the external environment. Each neuron, in turn, has thousands of spindly branches that reach out to nodes, called synapses, which transmit those electrical messages to other cells. Somehow the brain interprets this impossibly noisy code, allowing us to effectively respond to an ever-changing world.

Given the complexity of the neural code, it’s not surprising that some neuroscientists are borrowing tricks from more experienced hackers: cryptographers, the puzzle-obsessed who draw on math, logic, and computer science to make and break secret codes. That’s precisely the approach of two neuroscience labs at the University of Pennsylvania, whose novel use of cryptography has distinguished them among other labs around the world, which are hard at work deciphering how the brain encodes complex behaviors, abstract thinking, conscious awareness, and all of the other things that make us human.

The Penn scientists have taken their cues from a 73-year-old algorithm that British code-breaker Alan Turing used to read secret German messages during World War II, and a mathematical sequence more famously used to break into digital keypad locks on cars. “Neurons extract information from the world and put it in code,” says Joshua Gold, an associate professor of neuroscience at the University of Pennsylvania. “There’s got to be some kind of code-breaker in the brain to make sense of that.” Employing cryptography in the neuroscience lab, adds Gold, has provided new insights into the “gooshy hardware” that is the brain, exposing its operations as an “information-processing machine.”

Gold didn’t give much thought to cryptography until the early 2000s, when he was working as a postdoctoral fellow in Michael Shadlen’s monkey lab at the University of Washington. The lab focused on how the brain makes simple perceptual decisions. How does it determine, for example, whether an object is moving to the left or to the right? The fundamental problem in making such decisions is the trade-off between speed and accuracy. The brain needs to take in enough information to make the correct decision, but not so much data that by the time it processes it all, the environment has changed and the decision is moot.

In thinking about the computations the brain might use to solve this problem, Shadlen picked up a book called Good Thinking, written by statistician I.J. Good in 1983. Good was a deputy statistician at Bletchley Park, the estate that served as headquarters for the British government’s code-breaking unit during World War II, which was led by Turing, widely regarded as one of best mathematicians of the modern era. When reading Good’s book, Shadlen was struck by a description of one of Turing’s algorithms—a method for deciphering the supposedly undecipherable messages the Germans created using a machine called Enigma.

It’s not surprising that some neuroscientists are borrowing tricks from more experienced hackers: cryptographers.

The Enigma machine looked like an oversized electric typewriter stuffed into a wooden box, with its keyboard connected to several small rotors tucked inside. Every time a letter key was pressed, the rotors turned slightly, causing the re-mapping of the pressed letter into any of the other letters in the alphabet. After the machine scrambled the message, the author could use Morse code to transmit it via radio waves. When the message’s recipients set their own Enigma machine to the corresponding rotor settings (which they’d have to know in advance), they could type in that scrambled message and—presto—the machine would spit out the decoded version.

The Germans thought Enigma encryption was impenetrable; they relied on it to communicate all sorts of juicy information about their military strategy. Enigma operators working for the German navy (whose messages were the focus of Turing’s work) changed the starting positions of their rotors every day.

Beginning in 1939, Turing’s team developed a complex, multi-step process for decoding German messages and their contents. It was only the first step in Turing’s process, however, that intrigued Shadlen and Gold, and which they thought could be adapted to brain research. That was the algorithm that Turing used to determine whether any two intercepted messages—Bletchley Park intercepted hundreds of German messages a day—had been written on Enigma machines in the same rotor state.

Turing’s algorithm hinged on a genius bit of logic. He reasoned that if two Enigma machines had been set in different rotor states, then the probability of the first letter in one message being the same as the first letter in the other message would be random—or, more precisely, 1 in 26, for the 26 letters of the alphabet. The same goes for the second letter in each message, the third letter, and so on.

In contrast, if two messages came from machines set in the same rotor state, then their letters would be more likely to match. Why? Because in the German language (just as in English), some letters are used more frequently than others.

“E,” for example, is the most frequently used letter in German. “That means, if you take any two German texts and just compare them character by character, you know the most likely pair you’re going to find is two E’s,” Gold explains. With Enigma messages, the person trying to break into them wouldn’t necessarily see more matching “E’s,” because all of the letters have been re-mapped to other letters. Still, the probability of getting any matched letter would be higher if the messages originated from machines in the same state.

Turing’s algorithm hinged on a genius bit of logic, which the neuroscientists thought could be adapted to their research.

Working with linguists, who helped him rank letter frequencies of the German language, Turing determined that two encrypted German messages created from machines in the same state would have a letter-matching probability of about 1 in 13, rather than 1 in 26.

So Turing’s algorithm would essentially compare every letter in one message to every letter in the second and tally up the number of matches. If the messages were long enough, then by comparing the letter-matching frequencies he could determine with statistical certainty whether the messages had come from Enigma machines in the same state. The algorithm could also show whether the messages were too short to bother with this comparison, allowing Turing to quickly move on to the next set.

For messages that were long enough to test, a key feature of Turing’s algorithm was that it summed the evidence as it was being collected—one pair of letters at a time—until enough had accumulated to make a decision about the letter-matching frequencies with a reasonable level of certainty. This method is now known as “statistical sequential analysis.” If the first pair of letters matched, for example, that would be a weak piece of evidence for the hypothesis that the messages came from the same rotor state; after all, that match could have just been due to chance. If, on the other hand, the first 100 pairs of letters included 10 matches, then that would be much stronger evidence. Once the summed probabilities reached a pre-determined level of certainty, Turing’s algorithm could “decide” that the hypothesis—that is, that the two messages came from machines in the same state—was true.

Turing’s methods were hugely successful: Winston Churchill later said that the Enigma decoding was crucial to winning the war. As it turns out, the mathematics behind the algorithm were independently discovered by Abraham Wald, an Austrian-Hungarian Jew who fled to the United States when the Nazis invaded. While Turing was decoding for the British, Wald developed some of the same math tricks to help the U.S. Army determine, for example, whether a cart of munitions were defective or worthy of shipping to the front lines.

Wald’s and Turing’s approach has since influenced many scientific fields, from physics and fluid dynamics to psychology and even economics. “It’s all over the place,” says Roger Ratcliff, distinguished professor of behavioral and social sciences at Ohio State University. Ratcliff has championed these methods for psychological experiments for nearly three decades. He thinks of human behavior as a series of decisions. “Which word to say, whether to go get a cup of coffee or a cup of tea—all of these things are little decisions,” Ratcliff says. “I think this runs through everything we do.”

Hughes_BREAKER

Shadlen and Gold were the first to apply the method to neuroscience. “It turns out to be a great insight for how the brain assembles evidence to make decisions,” Shadlen says. Many neurons in the outer layers of the brain are selective, meaning that they fire in response to specific stimuli. Some neurons in the visual cortex, for example, fire when objects in our visual field are moving toward the left, whereas others fire when objects are moving toward the right.

The neurons aren’t perfect, however; sometimes cells selective for rightward motion will fire at leftward motion, and vice-versa. In that way, Shadlen and Gold reasoned, neurons are akin to the letter pairings of two Enigma messages. A single match of letters does not provide enough evidence to say whether the messages originated from machines in the same rotor state. Similarly, any one neuronal signal is not enough for the brain to accurately determine whether an object is moving to the left or to the right. To figure this out, the brain relies instead on the aggregate activity of thousands or even millions of neurons.

In 2002, Gold and Shadlen published a largely theoretical paper suggesting that the brain uses Turing-like computations—or some close approximation of them—to weigh evidence from neuronal firings and make perceptual decisions, such as determining whether a field of dots are moving to the left or right. “Turing’s work represents a form of probabilistic reasoning that the brain appears to have adopted to solve particular problems that are common to both perception and code-breaking,” Gold says.

Any one neuronal signal is not enough for the brain to accurately determine whether an object is moving to the left or to the right.

Just as the algorithm accumulated evidence in real time, the brain seems to process neuronal inputs as they’re coming in, and adjust its expectations accordingly. If it receives a big batch of signals from neurons that prefer left motion, for instance, and from few that prefer right motion, then it will take that as strong evidence that the object is moving to the left. Once the inputs have crossed some threshold of certainty (scientists are still trying to understand how the brain determines that threshold), the brain makes its decision and moves on to the next.

There’s another important similarity between the tasks of the brain and the code-breakers: Both face an unavoidable tension between speed and accuracy. For the World War II code-breakers, Gold says, speed and accuracy “become visceral when you think about these people realizing that thousands of lives would be saved if they could just decode these messages today.” Turing designed his algorithm to balance speed and accuracy, optimizing both. That suggested to Gold and Shadlen that the brain itself performs a similar optimizing act. In the brain, says Gold, “we always knew the phenomenon existed in a host of perceptual and cognitive tasks—quick decisions save time but tend to be inaccurate, whereas taking time leads to higher accuracy but can be inefficient.” The model of Turing’s algorithm, Gold says, “makes a nice prediction for how the brain might deal with the speed-accuracy tradeoff.” They’re still testing just how that happens.

Geoffrey Aguirre’s best brain hack began late one night, at his home, while trolling Wikipedia. At his University of Pennsylvania lab, Aguirre, a neuroscientist, uses brain scanners to study how perceptions are shaped by the past. The nervous system is a master of adaptation, constantly tuning itself to particular changes in the environment. When you go into a dark basement, for example, your eyes quickly adjust to the lack of light (a change that can be painfully clear when you climb the stairs and hit the light again).

The brain’s nimbleness is great for our everyday lives, but it can be a pain when designing brain-imaging studies, in which researchers take pictures of participants’ brains while they experience different stimuli, such as looking at pictures of faces or hearing a series of sounds. Because of so-called “carry-over effects,” the way a participant’s brain responds to a picture of the color blue, for example, is slightly different if the preceding picture was blue or if it was orange.

Teasing apart these carry-over effects is particularly difficult because the brain scanner—a functional magnetic resonance imaging (fMRI) machine—measures blood flow in the brain, a proxy for neural activity. Blood flow changes relatively slowly, on the order of seconds, whereas neurons fire over milliseconds. So fMRI’s sluggishness often masks neural carry-over, which can be annoying both for scientists who want to correct for carry-over effects and for others, like Aguirre, who want to scrutinize them.

There’s another important similarity between the tasks of the brain and the code-breakers: both face an unavoidable tension between speed and accuracy.

In 2007, Aguirre published a paper showing that carry-over effects can be managed by placing the experimental stimuli (pictures of different colors) in a particular order. The idea is to arrange the stimuli so that every picture appears both before and after every other picture at some point during the experiment. Then, when analyzing the data, researchers can compare the brain responses for different before-and-after combinations (blue followed by blue, versus orange followed by blue) and easily spot any carry-over effects.

That order of stimuli—in which every picture is “counter-balanced” with the previous one, without any repeat combinations—is called, in the math world, a “type 1, index 1 sequence.” It works well for brain scanning because of its efficiency: It gives the shortest possible counter-balanced sequence, minimizing the participant’s time lying in an uncomfortable scanner.

But for Aguirre, that sequence also had limitations. For example, the algorithm to create a type 1 sequence only works for six or more stimuli. What’s more, it only accounts for the carry-over effects from one previous picture. But what if you want to study the effects of the previous two pictures, or three? This would be important because research has shown that our perceptions can be influenced not only by the last thing we saw, but the last several things. This phenomenon happens at longer time scales, too. The classic example, Aguirre says, relates to facial recognition. “If somebody moves from Chicago to Tokyo, people will describe that for a certain number of months, they find it difficult to tell faces apart,” Aguirre says. “But over time you become familiar with this new range of facial appearances and then you get good at telling faces apart.”

Those problems with type 1 sequences drove Aguirre to “long nights spent poking through Wikipedia,” he says, laughing. He read page after page on the principles of discrete mathematics and graph theory. One night, he stumbled on the page for de Bruijn sequences, a large category that includes the type 1 sequences Aguirre was already familiar with. “De Bruijn sequences are this whole world of sequences that have a special property of counter-balance,” Aguirre says. “I realized that they would be perfect for the kinds of applications we had.”

To understand how de Bruijn sequences work, think of a string of letters, such as Aguirre’s initials, GKA. The de Bruijn sequence arranges those three letters in a long sequence so that every possible three-letter combination is used once and only once. Aguirre made a logo for his lab’s webpage to illustrate what one of these sequences looks like:

AguirreLogo_BREAKER

So why is a de Bruijn sequence so useful? Efficiency. If you were to write out every possible three-letter combination separately, your list might begin with:

GGG
GGA
GGK
GAA
GAG
GAK

… and so on. You’d have to type 81 letters before exhausting every combination. But as the lab logo shows, the de Bruijn sequence allows letters from one triplet to bleed into the next, allowing you to reach all possible combinations by typing just 27 letters.

Thieves can use these sequences to break electronic keypads such as those found on car doors. These generally have buttons representing the numbers 0 through 9, and pressing the right four buttons in a row unlocks the car. “If you know a de Bruijn sequence, you can cut the amount of time it would take you to crack that code substantially,” Aguirre says.

Aguirre immediately saw how these sequences would be useful for brain-imaging experiments. For his purposes, the stimuli of an fMRI experiment (such as pictures of colors) are like the numbers in the car lock. The de Bruijn sequence would give him a way to put them in the right order. Just as a sequence could be assembled for any length of keycode—a three-digit code, say, or four digits, or five—a sequence could be made for any level of counter-balancing of stimuli in the imaging experiment.

The only tricky part is, a given series of stimuli has more than one de Bruijn sequence—many more. For example, if you wanted to design an experiment using 17 different pictures, with each picture counter-balanced so that researchers could later work out the carry-over effects from the previous picture, then you’d have a mind-boggling 8 × 10244 different de Bruijn sequences to choose from.

In 2011, Aguirre and graduate student Marcelo Gomes Mattar published a method to help researchers select from that massive pool the sequences that make the most sense for a particular experiment.

A researcher might have a hypothesis about how the brain responds to seeing a particular string of stimuli—such as red then orange then yellow, to use a fictitious example. Using Aguirre’s methods, the researcher could choose a de Bruijn sequence that not only counter-balanced the stimuli (so that, by the end of the experiment, every picture has appeared both before and after every other picture), but also included that red-orange-yellow sequence that was key to the hypothesis. If the brain then responded in a robust way to the target sequence, the hypothesis would be proven true.

“The basic idea is that, only if your theory is right, and the person you’re studying has a neural code that corresponds to your theory, only then will the neural system ring in a way that you can measure with the neuroimaging technique,” Aguirre says. It’s even possible to test more than one theory in the same sequence, he adds. “If that neural bell rings out at 10 seconds, then maybe I’d know my first theory was right, but if the bell rings out at 15 seconds, I’d know it was the other theory.”

Thieves can use these sequences to break electronic keypads such as those found on car doors.

Aguirre and another of his graduate students, David Kahn, are now using this method to test an intriguing idea about how people with autism see the world. The theory is that individuals with this developmental disorder have more sensitive and discriminating visual perception. This hypersensitivity would allow them to pick up on tiny differences in faces and scenes, but it might also make it difficult for them to adjust to a changing environment. If the theory is true, then a person with autism would show a different brain response to a series of similar-looking faces than a person without autism. And because the researchers want to look specifically at carry-over effects from several consecutive images, the study is perfectly suited for de Bruijn sequences. If the experiment works, then the researchers will have a better understanding of the neural underpinnings of some people with autism, and with it, potentially, a new lead on finding treatments for the bafflingly complex disorder.

Aguirre has made his software freely available through his website, and other laboratories have begun to try it out. Sean MacEvoy, a neuroscientist at Boston College, has used de Bruijn sequences to investigate how the brain’s visual cortex represents an object’s identity and its position in space.

MacEvoy’s experiment had 18 different stimuli, and he says the de Bruijn sequence worked like a charm. He plans to use it in future experiments. “With the sequence, you know you don’t have any spurious biases that can cloud the interpretation of your data,” he says. “I don’t see a downside.”

For Aguirre, cryptographic techniques will continue to help neuroscientists penetrate complex functions of the brain. Just as cryptographers have perfected ways to transform a seemingly arbitrary message into a “hash code,” a sequence that can only be interpreted in one valid way, neuroscientists have learned the brain makes hash codes out of seemingly random neural firings. “Our key insight is that, effectively, the brain implements a temporal hash function on the world,” Aguirre says. Providing a way to crack that world-ordering code, he adds, “is the power of this intellectual approach.”

Virginia Hughes is a science journalist specializing in neuroscience, genetics, and medicine

Categories: Uncategorized

Some first impressions of Windows 10

January 30, 2015 Leave a comment

zdnet-logo

Summary:It’s too early for a review of the Windows 10 preview, but there’s enough substance in the latest builds to share some thoughts about the project’s direction.

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There’s a temptation to judge Windows 10 by the same standards we’ve used for earlier Windows versions. By that yardstick, the January Technical Preview, build 9926, would be a major milestone worthy of an in-depth review and a few dozen screenshots. And it would be months before its successor would be ready.But times have changed. Last week’s preview release was an important one, to be sure, but it’s going to be followed within weeks, not months, by another, equally important release. And there will be another and another and another over the next few months, with each update dropping another “feature payload” for preview testers.

What’s most noteworthy about the January Technical Preview is how naggingly incomplete it is. A few of its signature features are practically placeholders. Cortana gets befuddled easily, and the new Start menu is not nearly as customizable as it needs to be. Two key apps, Mail and Music, are missing in action, and the new Photos apps is missing some key features.

But that’s exactly as expected. The whole point of the Windows 10 development process is to be far more transparent than ever before, sharing work in progress and incorporating feedback into revisions quickly. It’s not exactly agile development, but it’s probably as close as Windows will ever get.

In the past week, I’ve installed Windows 10 on a dozen devices: desktops, notebooks, hybrid devices, tablets, and a virtual PC or two. I’ve done upgrades from Windows 7 and 8 as well as clean installs.

And for the most part it’s been a positive experience. Not perfect, by any stretch, but finding rough edges and bugs is the point of putting these unfinished Windows 10 builds into 2 million or so hands.

It’s certainly not time yet for a formal review, but I can share some early impressions.

The visual design is pleasing, in a low-key way.

Just about every digital product we use these days is adopting a flat design, so what was once striking about Windows 8 now seems conventional. Windows 10 has toned down some of the garish excesses of Windows 8, although the new, bright yellow and orange icons in File Explorer might still need some sandblasting.

The decision to shrink the icons on the taskbar from 32 pixels on each side to 24 pixels feels misguided; on high-resolution screens in particular, the icons are too small to be readily identifiable. At a minimum, I’d like an option to restore those too-small buttons to their previous size. An option to scale those icons would be even more welcome.

Navigation is beginning to make sense.

Good riddance to the corner-based navigation that was the hallmark of Windows 8. I am not alone in hating the original navigation paradigm of Windows 8 (move the mouse to a corner and wait for something to appear). The Hot Corners feature in OS X is similarly annoying and one of the first things I disable on a new Mac.

So the gradual emergence of the Windows 10 navigation paradigm feels right. On a conventional PC, with keyboard and mouse, you can use the familiar Alt+Tab shortcut or click the Task View button on the taskbar. Either shortcut produces a view of all running apps, allowing you to click the one you want to switch to.

It works even better on a touchscreen, where a swipe from the left, followed by a tap on a thumbnailed window, makes short work of task-switching.

The Action Center is far more functional than the Charms menu.

Give the designers of the Windows 8 user experience props for thinking outside the lines with the charms menu. Swiping from the right to display a menu is easy to get used to on a touchscreen, although making that menu appear when using a keyboard or mouse is nowhere near as easy or natural.

So Windows 10 replaces the charms with a notifications pane that occupies the same general space but has more value. The customizable buttons at the bottom of the pane actually allow you to do something with a single tap, unlike the charms, which just lead to the place where you get stuff done.

The desktop experience is profoundly better.

The single biggest complaint about Windows 8 is the jarring transition between modern apps and the desktop. Once you get to the desktop, things are familiar, but it’s a constant source of irritation for people who just want to run their familiar desktop programs.

Allowing the option for modern apps to run in a window, as Windows 10 does, makes a huge difference for using them in a desktop setting. The new Start menu helps, too. In fact, the whole experience of using Windows 10 on the desktop finally feels like an evolution of Windows 7 rather than a sharp left turn.

One area that still needs work is how to allow access to settings and other app commands in modern apps. The “hamburger” menu (a stack of three horizontal lines) in the title bar feels incomplete.

Windows 10 on tablets and hybrids? Still a work in progress.

Ironically, given how much work went into building Windows 8 as a touch-first, tablet-oriented experience, Windows 10 feels most incomplete in that environment. Pendulums work that way.

With most of the desktop work out of the way, it’s time for a little more attention to those touchscreen devices over the last few months of this effort.

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Guess who owns JebBushforPresident.com and won’t give it back?

January 30, 2015 Leave a comment
Former Florida governor Jeb Bush (R-FL) addresses the Wall Street Journal CEO Council in Washington December 1, 2014. REUTERS/Jonathan Ernst    (UNITED STATES - Tags: POLITICS BUSINESS HEADSHOT PROFILE) - RTR4GBP1

The answer is CJ Phillips and Charlie Rainwater! They are a self-described “bear couple.”

The Oregon techies bought the domain name in 2008, when Jeb Bush was rumored to be seeking the Republican presidential nomination, reports Business Insider. Phillips and Rainwater are no supporters of Bush, though — in fact, they wanted to use the website to blog about LGBT rights and draw attention to the former Florida governor’s dismal equality record.

The site is not up and running just yet but they promise that there is more to come.

Work in Progress

Positive dialogue to drive positive change

Hi there, and welcome to the page. CJ and Charlie are two guys in a great relationship who are looking to inform our friends and family about some of the challenges we face being part of the LGBT community.

This page is intended to prompt understanding, insight, and healthy dialogue in the ever changing landscape of civil rights and social justice. Now we just need to find some time!

More to come…

Can’t wait.

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VISUALIZING BMI’S “BLIND SPOTS”

January 30, 2015 Leave a comment

LAB NOTES BLOG

BY BODY × LABS

Body Mass Index, commonly known as BMI, is something you’ve probably calculated for yourself at some point. It’s a metric that places you on a health scale that’s purely based on your height and weight. For example, I’m 6’1” and 197 lbs, which places me in the “overweight” category with a BMI of 26.0. I’d say that I’m relatively muscular and somewhat big-boned, but could probably stand to lose a few pounds depending on who you asked.

You can calculate your own BMI using our BMI Visualizer:

BODY MASS INDEX
BMI is a very controversial metric, often scrutinized for not taking enough information into account to produce meaningful results. It fails to consider important factors like age, gender, body mass composition (muscle vs. fat), or body mass distribution (body shape). This means that people can be placed incorrectly on the BMI scale, depending on their actual body type. And even though BMI was not originally intended to be used as an individual health metric[1], it is now often used as such. This can become more of a problem when it is employed beyond the individual level as a quick and easy metric for group and population statistics. This is concerning, as its shortcomings can become even more apparent and misleading when it is used to evaluate health in the aggregate. However, looking beyond these potential issues, there is also evidence[2] that has shown it does act as a good measure of obesity, and thus can be used as a general health metric to base medical treatment on.

The point of this post is not to reinforce or tarnish the validity of BMI. I am not qualified to do either. Rather, I’d like to bring some of its shortcomings to light through data visualization, and look beyond it to newer and more thoughtful alternatives. These shortcomings must be considered when using BMI, and when thinking about how to improve upon it.

HOW IS BMI CALCULATED?

Your BMI is calculated using your height and weight, as determined by the following equation:

 

 

With this calculated BMI value, you are placed into one of four weight categories:

 

BMI chart_1

 

BMI’S SHORTCOMINGS

Since BMI doesn’t take body mass composition or distribution into account, it is basically blind to fitness level and body shape. You probably won’t be found to be obese when you’re actually underweight, but for people who are borderline, being misplaced is easy. For example, you can be an athlete in great shape and be deemed overweight or even obese. Or, you could have an average BMI but carry a significantly higher amount of mass in your torso, which as some studies show[3] can be a predictor of health risks such as cardiovascular disease or diabetes. Shouldn’t our go-to health metric account for things like this?

For fun, and to give you a better idea of what BMI “looks” like, here’s a list of some well-known people and their heights, weights, and BMIs (heights and weights may not be accurate or up to date):

 

BMI table

 

Here at Body Labs, we have lots and lots of data surrounding the human body. Mainly, we have thousands of 3D body scans and metadata, which we’ve quantified and analyzed by converting them into body models — if a 3D body scan is like a scanned sheet of text, a body model is like a Word doc. As an attempt to show BMI’s blind spot, we reached into our data and pulled out some examples of people with the same height and weight, and thus identical BMI values.

 

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An enlarged view of person 1 and 2. Their torsos were separated out from their bodies so that the volumes could be measured independently. The six people had an average Vtorso/V of 46%.

 

A heatmap comparison of person 2 to person 1. The heatmap coloring represents the point-to-point distances between the same points on both of their bodies (i.e. navel to navel). Blue means their bodies were the same, and hot pink means they differed greatly. You can see their bodies varied most in their stomachs, backs, back of legs, and leg lengths (as represented by the crotch area coloring).

 

As you can clearly see, especially from the heatmap comparison, bodies can have quite different physiques even if they have the same height and weight. You may have skinny arms and legs, but a big beer belly. Or you can have a built chest from working out, with little overall body fat.

The bottom line is that height and weight alone cannot accurately represent body composition, shape, or overall health.

LOOKING BEYOND BMI

Moving forward, there are many promising new methods for general weight and health measurement. Some of these take advantage of the growing proliferation of 3D scanners, such as the new metric named Body Volume Index, or BVI. It’s still in the early stages, but generally, it measures your total body volume and the volume of individual body segments (i.e. torso, arms, legs, etc.). By more directly measuring body shape through body volume, BVI would potentially catch the differences between person 1 and 2 that BMI did not. And even looking beyond BVI, with technology like ours, much deeper analysis is possible by doing direct body-to-body comparisons, and using methods such as machine learning to look for non-obvious correlations between body shape and health.

Because 3D scanning and technology like ours have not been accessible until recently, this sort of measurement and analysis was never possible or economical. However, all of this is possible today and will only become cheaper, easier, and more accessible. Here at Body Labs, it’s one of our goals to enable the creation of new and innovative health metrics. With a little time and research, you might just find yourself calculating something other than your BMI in the near future…

 

 

NOTES

[1] BMI was originally devised by Belgian mathematician Adolphe Quetelet to describe the relationship between normal human proportions, of height and weight, in adults.

[2] “Comparison of anthropometric and body composition measures as predictors of components of the metabolic syndrome in a clinical setting

[3] “Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men“, “Body Fat Distribution and Risk of Non-Insulin-dependent Diabetes Mellitus in Women“, “Body Fat Distribution and Risk of Cardiovascular Disease

The “Body mass index chart” was created by User:InvictaHOG. Licensed under Public Domain via Wikimedia Commons

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KATE SPADE SATURDAY AND JACK SPADE TO SHUT DOWN ALL STORES

January 30, 2015 Leave a comment
The move comes as a big surprise.
Kate Spade Saturday's L.A. store. Photo: Kate Spade
Kate Spade Saturday’s L.A. store. Photo: Kate Spade

After just two years on the market, Kate Spade SaturdayKate Spade‘s lower-priced, more casual offshoot — is closing all 19 of its stores. Jack Spade, Kate Spade & Company’s 22-year-old menswear brand, is also shuttering all 12 of its stores. Stores will close gradually over the first half of 2015, and Kate Spade Saturday’s e-commerce site will remain active during that time.

In a statement, the company said that “key elements of Kate Spade Saturday’s success” will be incorporated into the main Kate Spade line, and that Jack Spade will pursue “a new business model… to leverage the distribution network of its retail partners and expand its e-commerce platform.”

The Kate Spade Saturday news is a surprise, given that the label was opening stores as late as the fourth quarter of last year. But in recent quarters, the company noted that Kate Spade Saturday’s weak sales and heavy promotionswere hurting the company’s margins — and its stock price, which investors couldn’t have been happy about.

Still, we’re sad to see Kate Spade Saturday go — it’s a brand that stands for fun, and certainly one of the innovators in the retail marketing landscape. A spokesperson for the brand tells us that the Saturday label won’t disappear entirely however, and will pop up at Kate Spade New York stores (and its e-commerce site) in the future.

Note: This story was updated to reflect that Kate Spade Saturday will still have its own label and merchandise, to be sold at Kate Spade New York stores.

Categories: Uncategorized

Mobile Web beats apps for detail-oriented shoppers: report

January 30, 2015 Leave a comment
By


January 30, 2015

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As shoppers become increasingly comfortable on mobile, they are demanding more detailed experiences, leading to a growing preference for retailers’ mobile Web sites over applications, according to a new report from Siteworx.

Retailers that do not offer detail-oriented mobile Web sites are at risk of losing commerce, as 63 percent of consumers are more likely to shop using a site rather than a mobile application. The findings suggest investing in an all-encompassing mobile site is imperative for retailers seeking to retain significant market share, as more shoppers are using mobile to research potential purchases and read reviews.

“Data reveals complex buyer journeys that demand retailers constantly monitor buyer behaviors in order to adjust experiences and processes that better engage with consumers in the moment,” said Patricia Mejia, chief marketing officer of Siteworx, Reston, VA.

“Waterfall approaches are not sufficient when it comes to anticipating and predicting how consumers will change the way they use mobile to make purchases.”

Changing trends

While showrooming and mobile applications were two top strategies of choice for retailers in previous years, Siteworx has discovered that mobile shopping behaviors are changing rapidly, prompting brands to constantly deliver new mobile functions so that all shoppers’ needs can be met.

Consumer interest in mobile apps is on the decline, with the rate of survey participants claiming they prefer to shop via an app over a mobile site falling to 37 percent.

However, if a brand is adamant about continuing to invest in its mobile app, speed is a priority.

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Users appreciate the details found on mobile sites

When consumers were asked what feature would prompt them to download a retailer’s mobile app this past holiday season, 32 percent tapped speediness over the Web site as a top priority. Twenty percent said special in-app deals were the main draw for downloading, while 24 percent could be swayed by a streamlined checkout process.

“Speed is king,” Ms. Mejia said. “Web site and application response times are paramount, and while they are what happens in the background, they have a tremendous impact on experience and conversions.

“This is something that retailers can’t take their eye off even as they focus on the customer-facing aspects of the experience. It’s complex but equally as important.”

Mobile device users’ tendency to browse on 3G networks is partially to blame for the decline of Web site speed.

Piggybacking on the rise
The survey discovered that device “piggybacking” during customers’ paths to purchase is on the rise, as results suggested buyers prefer to compare prices on desktop devices, due to the easy browsing process, and then purchase items in bricks-and-mortar locations or on smartphones.

This past holiday season, 41 percent of shoppers bought their gifts in-store, while 47 percent and 26 percent relied on laptops and smartphones for purchasing, respectively.

Siteworx identified several possible shopper journeys for marketers to keep in consideration as they develop mobile strategies for 2015. One set of buyers may prefer searching for items on desktop, but be swayed to purchase in-store with a mobile coupon.

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Reading product views and viewing style options are two perks of mobile sites

Another set might become interested in a product in a bricks-and-mortar location but not purchase due to high price, leading the consumer to set up a text alert for sales and buy the item on a tablet or smartphone when the price lowers.

Shopping is now truly an omnichannel experience, so brands must work to optimize all of their purchasing options to fit customers’ expectations. More importantly, all channels must work cohesively with each other.

The most frequently performed mobile activity in stores was deemed to be checking product reviews, proving that brands need to offer this feature on their mobile sites or apps. Conversely, consumers were not as interested in using their devices to locate sale prices for retailers to match, with a 50 percent decline in this activity from 2013 to 2014.

The 2014 holiday season was strong on mobile, and was especially instrumental in driving customer traffic to brands’ Web sites.

Ultimately, brands must look at mobile in the context of the digital shopping ecosystem. Each branch of the ecosystem can function independently, but works best in conjunction with the others.

“Retailers need to optimize across the end-to-end digital experience because consumers are using virtually all channels in tandem today,” Ms. Mejia said.

Final Take
Alex Samuely is an editorial assistant on Mobile Commerce Daily, New York

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