kw: popular culture
I had the chance this Summer to watch all the episodes of Shaq Versus. Great fun! Just more silliness therapy for me. Tonight was more about heartwarming than about serious competition. The "victims" were Jimmy Kimmel and Justin Bieber. With Kimmel, the Diesel traded 5-minute trash-talk monologues. Both were hilarious. Then with Bieber, it was a collaboration rather than a competition. I am not much of a fan of pop, but it is clear why Justin Bieber is so popular. He is a great performer, and Shaq showed he could keep up as well as any man who is pushing fifty. So what if everything is over-hyped? It was plenty of fun, all season, but particularly tonight.
Tuesday, August 31, 2010
Monday, August 30, 2010
Another census mystery
kw: discoveries, genealogy, census records
In this post from about three years ago, I reported on an ancestor of mine who appears twice in the in the 1840 census. This composite image shows portions of two pages from the 1920 census in which my grandmother's brother and his wife were recorded twice.
In the upper section, William F. Nye and his wife were with the wife's mother Mary A. Levy in Carrollton City, MO on 13 Jan 1920 when the census taker visited. In the lower section the same couple (the wife's name is Katherine) were with William's parents, John and Cynthia Nye in Malta Bend, MO, just a week earlier. The two towns are about sixteen miles apart.
I am not sure if they lived at either address, though I think it most likely that they lived with Mrs. Levy, a widow. It may be that there is still a record somewhere of their being visited by a census taker in their own home!
In this post from about three years ago, I reported on an ancestor of mine who appears twice in the in the 1840 census. This composite image shows portions of two pages from the 1920 census in which my grandmother's brother and his wife were recorded twice.In the upper section, William F. Nye and his wife were with the wife's mother Mary A. Levy in Carrollton City, MO on 13 Jan 1920 when the census taker visited. In the lower section the same couple (the wife's name is Katherine) were with William's parents, John and Cynthia Nye in Malta Bend, MO, just a week earlier. The two towns are about sixteen miles apart.
I am not sure if they lived at either address, though I think it most likely that they lived with Mrs. Levy, a widow. It may be that there is still a record somewhere of their being visited by a census taker in their own home!
Sunday, August 29, 2010
Big oops and there goes the day
kw: local events
Early in the day I had a number of possible ideas from which I could write a post. The major one involved helping our son move back to his college digs for his Senior year. However, on the way, he got into an accident, which added about four hours to the whole trip.
Our car and his were packed with his stuff. The tow truck operator took his car to a dispatching location, where we put as much of the stuff from his car into ours as would fit, then stacked the rest next to the station building. He called for a college friend to pick him up (we were about 20 minutes from his destination), and my wife and I drove to his place. His housemates helped us move the stuff to his room while we waited for him to get there. Meanwhile the tow truck driver took the car to a body shop. I'll call them in the morning and tomorrow it'll just go from there.
It is a good thing he has a bicycle with him.
Early in the day I had a number of possible ideas from which I could write a post. The major one involved helping our son move back to his college digs for his Senior year. However, on the way, he got into an accident, which added about four hours to the whole trip.
Our car and his were packed with his stuff. The tow truck operator took his car to a dispatching location, where we put as much of the stuff from his car into ours as would fit, then stacked the rest next to the station building. He called for a college friend to pick him up (we were about 20 minutes from his destination), and my wife and I drove to his place. His housemates helped us move the stuff to his room while we waited for him to get there. Meanwhile the tow truck driver took the car to a body shop. I'll call them in the morning and tomorrow it'll just go from there.
It is a good thing he has a bicycle with him.
Friday, August 27, 2010
Doing something about the weather
kw: book reviews, nonfiction, meteorology, history
There it is, at the lower left of the red blotch, a classic hook echo. That is the radar signature of the May 3, 1999 Oklahoma City tornado-laden supercell. This image is from a descriptive web page here, which includes more images and storm maps of the fifty-tornado outbreak that day. This particular tornado developed into an F5, the strongest class of tornado, with winds in the 250-mph (400 kph) range. Its path of destruction on the ground was a mile wide.
More than 100 years ago, Mark Twain wrote, "Everybody talks about the weather, but nobody does anything about it." Mike Smith is one person who has been doing something about it, for decades now. As he writes in Warnings: The True Story of How Science Tamed the Weather, from a "beginning involving spare World War II leftovers, we have developed an effective and highly cost-effective system that saves lives and dollars, nearly every week." He is writing about the system of weather tracking radar, weather spotting persons and equipment, and meteorology professionals that work together to forecast and warn of severe weather wherever (in the US) it occurs.
While weather modification has so far been pretty much a failure, the watch and warning system that Smith and others developed since the 1960s is a great example of doing what you can do. In this case, if you can't change the weather, at least you can get out of the way.
Starting at age five when he and his family lived through a tornado in their Kansas City, Missouri neighborhood, Mike Smith knew he wanted to study the weather. As time passed, he witnessed firsthand the devastation that results when severe weather hits without warning. For many years, the official stance of the National Weather Service was not to mention words like "tornado", so as to avoid panic. There was a ban against it. Then in 1955, the town of Udall, Kansas was hit by a nighttime tornado that pretty much erased the town, killing 82 and injuring 260 of the 500 residents. It is called the "town that died in its sleep." Not long after that, on-air meteorologists issued a tornado warning, and the ban began to lift. It didn't take long to find out that most people don't panic; they take a warning well and move to rescue themselves in a remarkably calm way. The warning system gradually developed into today's graded messages, where a "watch" means severe weather is forecast to arrive soon, and a "warning" means it has been seen.
Smith recounts story after story of the gradual shift in attitudes, and of the developing technology. Early radar equipment, scavenged from WWII salvage yards, was able to show you the location and size of a storm, as long as the rain wasn't too strong right around the antenna. It worked best from about fifty miles away, but later problems showed that forecasters need to be where the screens are. Co-location of equipment and forecasters has been the norm for more than twenty years. The development of doppler radar in the 1980s was difficult enough as a technical and engineering challenge. But the bureaucratic roadblocks were much more severe! The author's disdain for a government's way of doing things forms a heartbeat of the book.
This thread is strongest in the chapter about Hurricane Andrew and the three chapters devoted to Hurricane Katrina. Andrew showed us just how damaging a Category 5 hurricane could be. Katrina showed us that the "powers that be" had learned nothing from Andrew. Smith is particularly disdainful of the criminally inept performance of the two governors and the New Orleans mayor. Come to think of it, this is the only major piece of writing about the Katrina disaster that puts the blame where it lies, rather than mostly upon President Bush. Though he does get his share of the blame, the President was a lesser player next to those who should have acted and either did nothing or actively hindered those who were willing to help!
The book ends with the tragic but heartening tale of the destruction of Greensburg, Kansas in 2007. This was another F5 tornado like the one that hit Udall. It was 1.7 miles wide; so was the town, and the tornado hit dead center, as the Udall tornado had. Proportionally (Greensburg's population is 1,500), more than 240 people could have died. The death toll was eleven, and injuries were also few. What a difference a few decades of progress have made!
Until somebody invents the Handy Dandy Hurricane and Tornado Stopper and Flood Tamer, the best we can do is to take heed to warnings and get out of the way. Our ability to obtain warnings and other weather data, now at the click of a mouse or flick of a 'phone, is due to the efforts of Mike Smith and his colleagues over the past forty-plus years. I wonder what the next forty will bring?
There it is, at the lower left of the red blotch, a classic hook echo. That is the radar signature of the May 3, 1999 Oklahoma City tornado-laden supercell. This image is from a descriptive web page here, which includes more images and storm maps of the fifty-tornado outbreak that day. This particular tornado developed into an F5, the strongest class of tornado, with winds in the 250-mph (400 kph) range. Its path of destruction on the ground was a mile wide.More than 100 years ago, Mark Twain wrote, "Everybody talks about the weather, but nobody does anything about it." Mike Smith is one person who has been doing something about it, for decades now. As he writes in Warnings: The True Story of How Science Tamed the Weather, from a "beginning involving spare World War II leftovers, we have developed an effective and highly cost-effective system that saves lives and dollars, nearly every week." He is writing about the system of weather tracking radar, weather spotting persons and equipment, and meteorology professionals that work together to forecast and warn of severe weather wherever (in the US) it occurs.
While weather modification has so far been pretty much a failure, the watch and warning system that Smith and others developed since the 1960s is a great example of doing what you can do. In this case, if you can't change the weather, at least you can get out of the way.
Starting at age five when he and his family lived through a tornado in their Kansas City, Missouri neighborhood, Mike Smith knew he wanted to study the weather. As time passed, he witnessed firsthand the devastation that results when severe weather hits without warning. For many years, the official stance of the National Weather Service was not to mention words like "tornado", so as to avoid panic. There was a ban against it. Then in 1955, the town of Udall, Kansas was hit by a nighttime tornado that pretty much erased the town, killing 82 and injuring 260 of the 500 residents. It is called the "town that died in its sleep." Not long after that, on-air meteorologists issued a tornado warning, and the ban began to lift. It didn't take long to find out that most people don't panic; they take a warning well and move to rescue themselves in a remarkably calm way. The warning system gradually developed into today's graded messages, where a "watch" means severe weather is forecast to arrive soon, and a "warning" means it has been seen.
Smith recounts story after story of the gradual shift in attitudes, and of the developing technology. Early radar equipment, scavenged from WWII salvage yards, was able to show you the location and size of a storm, as long as the rain wasn't too strong right around the antenna. It worked best from about fifty miles away, but later problems showed that forecasters need to be where the screens are. Co-location of equipment and forecasters has been the norm for more than twenty years. The development of doppler radar in the 1980s was difficult enough as a technical and engineering challenge. But the bureaucratic roadblocks were much more severe! The author's disdain for a government's way of doing things forms a heartbeat of the book.
This thread is strongest in the chapter about Hurricane Andrew and the three chapters devoted to Hurricane Katrina. Andrew showed us just how damaging a Category 5 hurricane could be. Katrina showed us that the "powers that be" had learned nothing from Andrew. Smith is particularly disdainful of the criminally inept performance of the two governors and the New Orleans mayor. Come to think of it, this is the only major piece of writing about the Katrina disaster that puts the blame where it lies, rather than mostly upon President Bush. Though he does get his share of the blame, the President was a lesser player next to those who should have acted and either did nothing or actively hindered those who were willing to help!
The book ends with the tragic but heartening tale of the destruction of Greensburg, Kansas in 2007. This was another F5 tornado like the one that hit Udall. It was 1.7 miles wide; so was the town, and the tornado hit dead center, as the Udall tornado had. Proportionally (Greensburg's population is 1,500), more than 240 people could have died. The death toll was eleven, and injuries were also few. What a difference a few decades of progress have made!
Until somebody invents the Handy Dandy Hurricane and Tornado Stopper and Flood Tamer, the best we can do is to take heed to warnings and get out of the way. Our ability to obtain warnings and other weather data, now at the click of a mouse or flick of a 'phone, is due to the efforts of Mike Smith and his colleagues over the past forty-plus years. I wonder what the next forty will bring?
Thursday, August 26, 2010
Calculating with Ahnentafels
kw: family history, genealogy
A few days ago I wrote about Ahnentafel numbers, or "ancestry table" numbers. See the explanation there.
When I am confronted with an Ahnentafel number (AN) larger than about 32, I have to do some work to figure out where in the family tree this person fits. Converting the number base makes it simpler. I have a scientific calculator that can convert from base 10 to bases 2, 8 and 16. I use base 2 for smaller numbers, and an intermediate of base 8 for larger ones, for which I can pretty easily convert to base 2 (binary) for whatever next step I have in mind.
If you don't have a calculator that will do it for you, you can convert from base 10 to base 8 with pencil and paper, using successive division and keeping remainders. I'll do an example with the AN 10569:
10569/8 = 1321 with a remainder of 1; that is what the first two lines show. You keep dividing until you get a number smaller than 8, and that is your last remainder. Based on the calculation shown, the base 8 (octal) representation of 10569 is 24511.
For many needs, it is most convenient to know the binary representation. For example, if you have an AN of 19 for an ancestor, in binary it is 1011. The first 1 is always you, and after that zeroes represent males, and ones represent females, so this is your father's mother's mother. Your father's father's father has a binary representation of 1000, which is 16. Memorize the table that follows if you don't already know these:
These make it simple to turn 24511 (8) into binary: 010 100 101 001 001 or 10100101001001, discarding any leading zeroes. Now, you can read this off as "my father's mother's, etc." but it has a more practical use. I have for years kept family records according to which great-grandparent's line they were in (Great grandparents have AN's of 8-15, or in binary, 1000-1111). The first four binary digits in 10569 are 1010, which equals 10 in decimal. This works out to the line of my father's mother's father.
Also, the charts you can print from Ancestry.com have five generations. The root sheet thus has people with AN's from 1 (you) to 31. If, as I do, you print successive sheets starting with the people in the rightmost column (fifth generation, including you), you can figure out where 10569 is, such as which sheet she is on (odd AN's are for females). Take the number five bits at a time, but overlapping by one bit, and converting a leading zero to a one: 10100, (1)1010, (1)0100, (1)1. These four binary numbers represent 20, 26, 20, and 3.
I number sheets in the corner according to the AN of the root person for that sheet. The figuration here is a bit tricky:
A few days ago I wrote about Ahnentafel numbers, or "ancestry table" numbers. See the explanation there.
When I am confronted with an Ahnentafel number (AN) larger than about 32, I have to do some work to figure out where in the family tree this person fits. Converting the number base makes it simpler. I have a scientific calculator that can convert from base 10 to bases 2, 8 and 16. I use base 2 for smaller numbers, and an intermediate of base 8 for larger ones, for which I can pretty easily convert to base 2 (binary) for whatever next step I have in mind.
If you don't have a calculator that will do it for you, you can convert from base 10 to base 8 with pencil and paper, using successive division and keeping remainders. I'll do an example with the AN 10569:
10569/8 = 1321 with a remainder of 1; that is what the first two lines show. You keep dividing until you get a number smaller than 8, and that is your last remainder. Based on the calculation shown, the base 8 (octal) representation of 10569 is 24511.For many needs, it is most convenient to know the binary representation. For example, if you have an AN of 19 for an ancestor, in binary it is 1011. The first 1 is always you, and after that zeroes represent males, and ones represent females, so this is your father's mother's mother. Your father's father's father has a binary representation of 1000, which is 16. Memorize the table that follows if you don't already know these:
These make it simple to turn 24511 (8) into binary: 010 100 101 001 001 or 10100101001001, discarding any leading zeroes. Now, you can read this off as "my father's mother's, etc." but it has a more practical use. I have for years kept family records according to which great-grandparent's line they were in (Great grandparents have AN's of 8-15, or in binary, 1000-1111). The first four binary digits in 10569 are 1010, which equals 10 in decimal. This works out to the line of my father's mother's father.Also, the charts you can print from Ancestry.com have five generations. The root sheet thus has people with AN's from 1 (you) to 31. If, as I do, you print successive sheets starting with the people in the rightmost column (fifth generation, including you), you can figure out where 10569 is, such as which sheet she is on (odd AN's are for females). Take the number five bits at a time, but overlapping by one bit, and converting a leading zero to a one: 10100, (1)1010, (1)0100, (1)1. These four binary numbers represent 20, 26, 20, and 3.
I number sheets in the corner according to the AN of the root person for that sheet. The figuration here is a bit tricky:
- The root sheet is sheet 1.
- The second sheet with the line to 10569 is sheet 20.
- The third sheet is 20x16+10 = 330 (26 is 16+10).
- The fourth sheet is 330x16+4 = 5284.
- Person 10569 is in position 3 (mother of root person 5284).
Tuesday, August 24, 2010
So bad it is almost beyond belief
kw: book reviews, fiction, advice, anthologies
On three or four occasions, I've read a little of a book and decided it is so bad I would not even dignify it with a review. It didn't take me long to realize that the latest book is in another category entirely. I read the two opening sections, two random ones in the middle, and that was plenty. You're a Horrible Person, But I Like You: The Believer Book of Advice, edited by Eric Spitznagel (I suspect a composite pseudonym here), is in a category that cannot be described by the A through F grade range we all know and love. I grade it an H-, for lower than Hell.
It is clearly intended to be humorous. It ranges instead from banal to hateful. The editor(s) solicited phony advice column screeds from 45 sundry celebrities, and published the result. This isn't a case of offensive material, though plenty of it is sufficiently obscene or obtuse or grotesque. It is, in the end, simply a disappointment. Pity.
On three or four occasions, I've read a little of a book and decided it is so bad I would not even dignify it with a review. It didn't take me long to realize that the latest book is in another category entirely. I read the two opening sections, two random ones in the middle, and that was plenty. You're a Horrible Person, But I Like You: The Believer Book of Advice, edited by Eric Spitznagel (I suspect a composite pseudonym here), is in a category that cannot be described by the A through F grade range we all know and love. I grade it an H-, for lower than Hell.
It is clearly intended to be humorous. It ranges instead from banal to hateful. The editor(s) solicited phony advice column screeds from 45 sundry celebrities, and published the result. This isn't a case of offensive material, though plenty of it is sufficiently obscene or obtuse or grotesque. It is, in the end, simply a disappointment. Pity.
Monday, August 23, 2010
I decide, therefore I err
kw: book reviews, nonfiction, psychology, neuroscience
Once we are "grownups", so much of what we do has become so innate that we have to struggle to think of occasions where we needed to consciously do anything. But do you remember the first time you bought a costly item such as a car, a washing machine, or a house? or chose a college major, or a first job? how about a spouse, did you think about that, or just fall for the first person who seemed interested? No matter what age we are, the first time we do something, it takes a great deal of consideration. And the fact is, we don't like to think. We aren't used to it (except for the occasional physicist or something).
In How We Decide, Jonah Lehrer digs into what lies underneath our decision-making and learning processes. The psychology and neuroscience of making choices reveal why we make so many "pretty good" decisions, and also explain our spectacular blunders.
Almost three weeks ago I reviewed Wrong by David H Freedman, which exposes the worse-than-chance record of most "experts". What makes so many people so wrong so much of the time? Jonah Lehrer has a few answers.
Although he develops his thesis in eight chapters, the most telling is Chapter 7, "The Brain is an Argument". We have the funny archetype of having a devil on one shoulder and an angel on the other, each trying to persuade us. The reality is that the angel and devil have a half dozen companions, each with its own point of view, and, we must understand, each with its own limitations.
At the base of our mental equipment we have a powerful pattern-recognition engine that works very quickly and gives us a "This is/is not familiar" signal almost instantly. The deliberative engine in our frontal lobes goes into high gear when a "not familiar" signal arrives, because we need to evaluate what to do now. But an "it's familiar" signal triggers a lot of other emotional reactions, depending on an accompanying "pleasant/unpleasant" or "reward/loss" memory. The problem is, the pattern recognizer is so powerful it finds patterns that are often imaginary. Trying to figure out a Roulette wheel, or the stock market, keeps it in overdrive, with no useful result. We need to learn to discount random input as just noise.
When we are in a new situation, however, and a decision is needed, we need to figure it out. We are the most thoughtful of animals, with by far the largest amount of deliberative gray matter. Strangely enough, our "thinker" has its limits, and they are more restrictive than we might imagine. Someone can remember a five-to-eight digit number for a minute or two, which is why local telephone numbers were set at seven digits. We can consider three or four factors when making a decision, which makes us really good at choosing the best potato peeler at the kitchen-gadget store: with a little thought, we realize that they all work about as well, so among those that fit the hand best, we can just choose the least expensive. But if we have too much information to weigh, we get analysis paralysis. The author reports on an experiment in which people were given four pieces of information about four kinds of auto, and other people were given twelve bits of information about the same four cars. The ones who had "less to go on" reached a decision quickly, and were more satisfied that their decision was right, when queried a few days later, compared to the others.
In a follow-up experiment, though one group was given lots of information about the cars, they were prodded to decide quickly. In the end, they were happier with their decisions. This is something I have noticed. I have a tendency to analyze things to death. But I have also found that the first answer I reach is usually the best answer in the end. It is like the reptile brain deep inside was able to glance at the problem, send up a feeling about the right choice, and all that the analyzer could do in the end was confirm the decision.
When our life is on the line, and nothing is familiar, what can we do then? It is necessary to think it through. If needed, we might use this or that stopgap to buy time, but the crucial skill is to take the time needed to find a useful remedy. Our emotions are no help when nothing is familiar; they can then be a detriment, perhaps a fatal one if we panic. If we live through the situation, we'll be the one who is able to "see it coming a mile off," should it happen again.
The emotional brain learns from our mistakes. The good or bad feeling we get about any decision (and there is always a feeling) is based on past experience. The key to life is to make our mistakes in non-lethal settings. This makes devices such as flight simulators the tremendous asset that they are. If only we could have enough driving simulators to train our 15-year-olds to drive, by letting them get into virtual accidents where it won't kill them. (I did the best I could by teaching my son basic driving skills starting at age 12. By the time he was licensed, he had almost five years experience in safe settings, and is a safer driver than he might otherwise be.)
I sum up the book's thesis with the proverb, "Good judgment comes from experience. Experience comes from bad judgment."
A centipede was happy quite,I teach music as a side job, guitar and 5-string banjo. When a student gets discouraged at all the mechanics, I say that it takes a few months to learn the instrument, or a year or two for certain challenging techniques. But they can expect a breakthrough at some point, where they find they are not playing the instrument, they are playing the music. Some are comforted, but some give up. When I was learning to play, my father encouraged me to practice in the dark, so my hands would learn where to go without help from my eyes. That is some of the best advice I ever got.
Until a frog in fun
Said, "Pray, which leg comes after which?"
This raised her mind to such a pitch,
She lay distracted in the ditch
Considering how to run.
Once we are "grownups", so much of what we do has become so innate that we have to struggle to think of occasions where we needed to consciously do anything. But do you remember the first time you bought a costly item such as a car, a washing machine, or a house? or chose a college major, or a first job? how about a spouse, did you think about that, or just fall for the first person who seemed interested? No matter what age we are, the first time we do something, it takes a great deal of consideration. And the fact is, we don't like to think. We aren't used to it (except for the occasional physicist or something).
In How We Decide, Jonah Lehrer digs into what lies underneath our decision-making and learning processes. The psychology and neuroscience of making choices reveal why we make so many "pretty good" decisions, and also explain our spectacular blunders.
Almost three weeks ago I reviewed Wrong by David H Freedman, which exposes the worse-than-chance record of most "experts". What makes so many people so wrong so much of the time? Jonah Lehrer has a few answers.
Although he develops his thesis in eight chapters, the most telling is Chapter 7, "The Brain is an Argument". We have the funny archetype of having a devil on one shoulder and an angel on the other, each trying to persuade us. The reality is that the angel and devil have a half dozen companions, each with its own point of view, and, we must understand, each with its own limitations.
At the base of our mental equipment we have a powerful pattern-recognition engine that works very quickly and gives us a "This is/is not familiar" signal almost instantly. The deliberative engine in our frontal lobes goes into high gear when a "not familiar" signal arrives, because we need to evaluate what to do now. But an "it's familiar" signal triggers a lot of other emotional reactions, depending on an accompanying "pleasant/unpleasant" or "reward/loss" memory. The problem is, the pattern recognizer is so powerful it finds patterns that are often imaginary. Trying to figure out a Roulette wheel, or the stock market, keeps it in overdrive, with no useful result. We need to learn to discount random input as just noise.
When we are in a new situation, however, and a decision is needed, we need to figure it out. We are the most thoughtful of animals, with by far the largest amount of deliberative gray matter. Strangely enough, our "thinker" has its limits, and they are more restrictive than we might imagine. Someone can remember a five-to-eight digit number for a minute or two, which is why local telephone numbers were set at seven digits. We can consider three or four factors when making a decision, which makes us really good at choosing the best potato peeler at the kitchen-gadget store: with a little thought, we realize that they all work about as well, so among those that fit the hand best, we can just choose the least expensive. But if we have too much information to weigh, we get analysis paralysis. The author reports on an experiment in which people were given four pieces of information about four kinds of auto, and other people were given twelve bits of information about the same four cars. The ones who had "less to go on" reached a decision quickly, and were more satisfied that their decision was right, when queried a few days later, compared to the others.
In a follow-up experiment, though one group was given lots of information about the cars, they were prodded to decide quickly. In the end, they were happier with their decisions. This is something I have noticed. I have a tendency to analyze things to death. But I have also found that the first answer I reach is usually the best answer in the end. It is like the reptile brain deep inside was able to glance at the problem, send up a feeling about the right choice, and all that the analyzer could do in the end was confirm the decision.
When our life is on the line, and nothing is familiar, what can we do then? It is necessary to think it through. If needed, we might use this or that stopgap to buy time, but the crucial skill is to take the time needed to find a useful remedy. Our emotions are no help when nothing is familiar; they can then be a detriment, perhaps a fatal one if we panic. If we live through the situation, we'll be the one who is able to "see it coming a mile off," should it happen again.
The emotional brain learns from our mistakes. The good or bad feeling we get about any decision (and there is always a feeling) is based on past experience. The key to life is to make our mistakes in non-lethal settings. This makes devices such as flight simulators the tremendous asset that they are. If only we could have enough driving simulators to train our 15-year-olds to drive, by letting them get into virtual accidents where it won't kill them. (I did the best I could by teaching my son basic driving skills starting at age 12. By the time he was licensed, he had almost five years experience in safe settings, and is a safer driver than he might otherwise be.)
I sum up the book's thesis with the proverb, "Good judgment comes from experience. Experience comes from bad judgment."
Sunday, August 22, 2010
xMandelbrot on a faster CPU
kw: algorithms, beauty, mathematics
I noted in Mandel Spider my discovery of the xMandelbrot Viewer. It zooms to any level and uses bignum (increased precision) calculations when the zoom level would overwhelm ordinary floating point calculations. I dug into a similar region using the new quad-core CPU computer my son and I recently built. The image below is a deep enough zoom that 38-digit calculations were used, with the limits shown in the Overview pane shown at the bottom of the post. Note that the application needs Java 5 or later, but I found that simple to install.
You find out the extent of calculations by a histogram in the Palette Editor, which was used to set the color palette here. The MaxIterations parameter was set to 500, and the histogram shows that the actual number of iterations ranged from about 300 to about 450.
Such an image requires 15 minutes to produce on the dual-core CPU on my laptop. This took about one minute on the new computer. About a 4x increase is due to the faster processors and that there were four rather than two. The other nearly 4x is due to the faster front-side bus and memory speed.
As you can see in this control panel overview, the X and Y limits only differ after 25 digits. This is a 10-trillion-trillion-X zoom (10 septillion X). There is no real value in such extreme zooms, because the view looks the same after a few thousand X; that is the way fractals are. It simply provides a way to test the efficiency of one's algorithms and hardware.
The main lesson from this is, for more speed, throw more iron at the problem. That is why the weather bureau and military simulation experts keep rushing to produce ever-faster supercomputers. The fastest now are in the petaflop range, approaching an exaflop (1018 math operations per second). My "poor little" home computer just loafs along at about a gigaflop, a billion times slower, but that's ten times as fast as the early Cray supercomputers. Nice to have a pocket supercomputer when you need it.
I noted in Mandel Spider my discovery of the xMandelbrot Viewer. It zooms to any level and uses bignum (increased precision) calculations when the zoom level would overwhelm ordinary floating point calculations. I dug into a similar region using the new quad-core CPU computer my son and I recently built. The image below is a deep enough zoom that 38-digit calculations were used, with the limits shown in the Overview pane shown at the bottom of the post. Note that the application needs Java 5 or later, but I found that simple to install.
You find out the extent of calculations by a histogram in the Palette Editor, which was used to set the color palette here. The MaxIterations parameter was set to 500, and the histogram shows that the actual number of iterations ranged from about 300 to about 450.
Such an image requires 15 minutes to produce on the dual-core CPU on my laptop. This took about one minute on the new computer. About a 4x increase is due to the faster processors and that there were four rather than two. The other nearly 4x is due to the faster front-side bus and memory speed.
As you can see in this control panel overview, the X and Y limits only differ after 25 digits. This is a 10-trillion-trillion-X zoom (10 septillion X). There is no real value in such extreme zooms, because the view looks the same after a few thousand X; that is the way fractals are. It simply provides a way to test the efficiency of one's algorithms and hardware.The main lesson from this is, for more speed, throw more iron at the problem. That is why the weather bureau and military simulation experts keep rushing to produce ever-faster supercomputers. The fastest now are in the petaflop range, approaching an exaflop (1018 math operations per second). My "poor little" home computer just loafs along at about a gigaflop, a billion times slower, but that's ten times as fast as the early Cray supercomputers. Nice to have a pocket supercomputer when you need it.
Saturday, August 21, 2010
The chives bring the bees
kw: wildlife, bees, honey bees
I spent a lot of time, in 15-minute chunks, watching for bees on the sunflower plants I grew for The Great Sunflower Project. I've seen four species of pollen-gathering bees on the sunflowers, including a little striped bee I couldn't identify. I called it DK for "don't know", in my reports.
Today, camera in hand, I took a look at other parts of the garden to see what bees were being attracted elsewhere. The chives are in full bloom right now, and they were a bonanza of bee species. They draw some different bees because they have nectar, which sunflowers don't.
This image shows the largest and smallest species together. Before I tell you, can you find the little one? She is just opposite the bumble bee, in the lower right corner, a little out of focus. This bumble bee is a big one, nearly 30mm long, while the small bee (another species I can't identify, yet) is probably no more than 5mm long. This image is somewhat larger than life size, and the larger image you can see by clicking is about 3x life size.
There were also a number of honey bees on the chive blossoms. This one is working opposite one of the DK bees I've also seen on the sunflowers. This image is much better focused. While the honey bee is the typical 20mm size, the smaller bee is probably half that, smaller than a sweat bee.
Considering the worries over honey bees being decimated by "colony collapse disorder", it was a relief to see several today. There were none on my apple tree this Spring, and I saw very few prior to today.
I had begun cutting off the chive blooms, because I don't want them going to seed. They are perennial, and they've taken over a substantial section of my garden already. But when I saw so many bees, I quit cutting and got my camera instead. I will leave the rest to bloom until they fade, to feed the bees.
I spent a lot of time, in 15-minute chunks, watching for bees on the sunflower plants I grew for The Great Sunflower Project. I've seen four species of pollen-gathering bees on the sunflowers, including a little striped bee I couldn't identify. I called it DK for "don't know", in my reports.Today, camera in hand, I took a look at other parts of the garden to see what bees were being attracted elsewhere. The chives are in full bloom right now, and they were a bonanza of bee species. They draw some different bees because they have nectar, which sunflowers don't.
This image shows the largest and smallest species together. Before I tell you, can you find the little one? She is just opposite the bumble bee, in the lower right corner, a little out of focus. This bumble bee is a big one, nearly 30mm long, while the small bee (another species I can't identify, yet) is probably no more than 5mm long. This image is somewhat larger than life size, and the larger image you can see by clicking is about 3x life size.
There were also a number of honey bees on the chive blossoms. This one is working opposite one of the DK bees I've also seen on the sunflowers. This image is much better focused. While the honey bee is the typical 20mm size, the smaller bee is probably half that, smaller than a sweat bee.Considering the worries over honey bees being decimated by "colony collapse disorder", it was a relief to see several today. There were none on my apple tree this Spring, and I saw very few prior to today.
I had begun cutting off the chive blooms, because I don't want them going to seed. They are perennial, and they've taken over a substantial section of my garden already. But when I saw so many bees, I quit cutting and got my camera instead. I will leave the rest to bloom until they fade, to feed the bees.
Thursday, August 19, 2010
Ahnentafel numbers
kw: family history, genealogy
Some years ago, I came up with a scheme to number ancestors, and thought it was quite original. It didn't take me long to find out it is very old, dating back to 1590, when the first Ahnentafel, or Ancestor Table, was published by Michaël Eytzinger with this numbering system, which is also known as the Eytzinger Method.
The concept of Ahnentafel Numbers is simple, and is based on the fact that each generation you go back, you have twice as many ancestors as in the prior one: two parents, four grandparents, eight great grandparents, and so forth. If you make a pedigree chart in the ordinary way, with the father shown on the line above the mother in each couple, then you number this way:
Before reading further, click on the chart below (clipped from Ancestry.com) to see the full-size image, so you can more easily read the green numbers next to the names.
This is a small Ahnentafel for John D. McCown, an ancestor of mine. Note that two ancestors are not known. The numbers 12 and 13 belong to those two slots, so if some day I find out who they were, they get those numbers, which are reserved for them.
In my own Ahnentafel charts, John D. McKown is numbered 42, so the male succession of this chart continues 84 for James B., 168 for James, and 336 for Gilbert McKown. Priscilla Turner at the bottom right is then numbered 343. (She and her husband are descended from Mayflower families, so information about them has been easy to find.)
If you have a calculator that can convert decimal numbers to binary, you can determine the father-mother sequence that leads to an ancestor when you know the person's Ahnentafel Number. For John D. McKown, 42 is 32+8+2, or 101010. I am the first 1, my father is the second digit, a 0 (because it is even), his mother is next, then her father, then his mother, then the last father, John D. Or, to be short, John D. McKown is my father's mother's father's mother's father.
I have very few quibbles with Ancestry.com, but one of them is that I wish I could print a pedigree that included the Ahnentafel numbers. I've been adding them by hand to the pedigree sheets I print. Since I know one line of ancestors going back 24 generations, some of those numbers have eight digits! Though numbers that big are unwieldy, most of my pedigree has numbers of four digits and fewer. They are a great help for bookkeeping. See this Wikipedia article for a detailed explanation of Ahnentafel Numbers.
Some years ago, I came up with a scheme to number ancestors, and thought it was quite original. It didn't take me long to find out it is very old, dating back to 1590, when the first Ahnentafel, or Ancestor Table, was published by Michaël Eytzinger with this numbering system, which is also known as the Eytzinger Method.
The concept of Ahnentafel Numbers is simple, and is based on the fact that each generation you go back, you have twice as many ancestors as in the prior one: two parents, four grandparents, eight great grandparents, and so forth. If you make a pedigree chart in the ordinary way, with the father shown on the line above the mother in each couple, then you number this way:
- Your number, as the root of the table, is 1.
- Your father's number is 2 and your mother's is 3.
- Your father's parents are numbered 4 and 5, while your mother's parents are numbered 6 and 7.
- The next generation gets numbered from 8 to 15; and so it goes.
Before reading further, click on the chart below (clipped from Ancestry.com) to see the full-size image, so you can more easily read the green numbers next to the names.
This is a small Ahnentafel for John D. McCown, an ancestor of mine. Note that two ancestors are not known. The numbers 12 and 13 belong to those two slots, so if some day I find out who they were, they get those numbers, which are reserved for them.
In my own Ahnentafel charts, John D. McKown is numbered 42, so the male succession of this chart continues 84 for James B., 168 for James, and 336 for Gilbert McKown. Priscilla Turner at the bottom right is then numbered 343. (She and her husband are descended from Mayflower families, so information about them has been easy to find.)
If you have a calculator that can convert decimal numbers to binary, you can determine the father-mother sequence that leads to an ancestor when you know the person's Ahnentafel Number. For John D. McKown, 42 is 32+8+2, or 101010. I am the first 1, my father is the second digit, a 0 (because it is even), his mother is next, then her father, then his mother, then the last father, John D. Or, to be short, John D. McKown is my father's mother's father's mother's father.
I have very few quibbles with Ancestry.com, but one of them is that I wish I could print a pedigree that included the Ahnentafel numbers. I've been adding them by hand to the pedigree sheets I print. Since I know one line of ancestors going back 24 generations, some of those numbers have eight digits! Though numbers that big are unwieldy, most of my pedigree has numbers of four digits and fewer. They are a great help for bookkeeping. See this Wikipedia article for a detailed explanation of Ahnentafel Numbers.
Wednesday, August 18, 2010
Mandel Spider
kw: mathematics, beauty, algorithms
Recent rumination about recursive calculations, rounding errors, high-precision calculations, and strange attractors such as those near the edges of the Mandelbrot Set led me to search for software that I could use to explore some of these ideas. This image is from the best site I've so far encountered:
Just for fun, I call it the Mandel Spider. The overview window, shown below, gives the coordinates. Look carefully, the first difference between Xmin and Xmax occurs in the 18th digit after the decimal. The "window" size is 5.0×10-19 by 3.7×10-19.
The viewer is the xMandelbrotViewer by David Eck. Over "window" sizes much larger than I've shown here (larger than about 10-5) the viewer uses ordinary 32-bit floating point math, which is very fast. As you zoom to smaller and smaller areas, it uses "bignum" math, probably of the BCD variety, to add sufficient precision so that there are five or six guard digits beyond the precision needed to dissect the chosen area into about 750×500 pixels. The image above used 28-digit arithmetic, and ran quite slowly. It took about five minutes on my laptop. I'll have to try it on my newer desktop, which has a fast quad processor. A lot will depend on the smarts of Java 5.
I don't know the limits of the program yet. It gets more time consuming the deeper one goes. It also lets you set the maximum iterations parameter, as high as 50,000, though the default is 100. I used 1,000 for the image above, and of course most of the points (the sky blue ones) required many fewer iterations. But at this depth, we're totally inside an area that would be entirely black with only 250 iterations, the limit imposed by most viewers.
I'm still looking for software that lets me set the precision as I like, to see how it might affect the look of an image. From a bit of exploration I've found that the attractor for most points near the Mandelbrot Set is simple enough to minimize rounding problems, but the closer you get to the edge of the set (the more iterations needed to prove that a point is not in the Set), the more chaotic the attractor is, and the more I would expect rounding errors to tend to overwhelm the calculation. But that is still in the future for me. Maybe I'll find an article by someone who has already done the experiment…
Recent rumination about recursive calculations, rounding errors, high-precision calculations, and strange attractors such as those near the edges of the Mandelbrot Set led me to search for software that I could use to explore some of these ideas. This image is from the best site I've so far encountered:
Just for fun, I call it the Mandel Spider. The overview window, shown below, gives the coordinates. Look carefully, the first difference between Xmin and Xmax occurs in the 18th digit after the decimal. The "window" size is 5.0×10-19 by 3.7×10-19.
The viewer is the xMandelbrotViewer by David Eck. Over "window" sizes much larger than I've shown here (larger than about 10-5) the viewer uses ordinary 32-bit floating point math, which is very fast. As you zoom to smaller and smaller areas, it uses "bignum" math, probably of the BCD variety, to add sufficient precision so that there are five or six guard digits beyond the precision needed to dissect the chosen area into about 750×500 pixels. The image above used 28-digit arithmetic, and ran quite slowly. It took about five minutes on my laptop. I'll have to try it on my newer desktop, which has a fast quad processor. A lot will depend on the smarts of Java 5.I don't know the limits of the program yet. It gets more time consuming the deeper one goes. It also lets you set the maximum iterations parameter, as high as 50,000, though the default is 100. I used 1,000 for the image above, and of course most of the points (the sky blue ones) required many fewer iterations. But at this depth, we're totally inside an area that would be entirely black with only 250 iterations, the limit imposed by most viewers.
I'm still looking for software that lets me set the precision as I like, to see how it might affect the look of an image. From a bit of exploration I've found that the attractor for most points near the Mandelbrot Set is simple enough to minimize rounding problems, but the closer you get to the edge of the set (the more iterations needed to prove that a point is not in the Set), the more chaotic the attractor is, and the more I would expect rounding errors to tend to overwhelm the calculation. But that is still in the future for me. Maybe I'll find an article by someone who has already done the experiment…
Tuesday, August 17, 2010
Fate of two dozen young
kw: wildlife, conservation
I didn't see a single bluebird all Summer, but some of my colleagues did. This year two colleagues and I, as part of a wildlife conservation project at our workplace, monitored six bluebird boxes on a weekly basis beginning in late April. The season was slow getting started, but eventually all six boxes produced young birds, though one clutch all died.
Three nest boxes hosted Tree Swallows, lovely blue-and-white birds. Two nests produced, respectively, four and three young that successfully fledged (the three had a fourth sibling that died), while a third nest produced two chicks that died, almost fully fledged, when temperatures here passed 100°F (nearly 40°C).
Three boxes hosted Carolina Wrens, and we counted, 3, 4, and 5, though the nest with three was set up a month later than the other two. No wren chicks died. On today's run, we found that last nest empty, so all the boxes are empty and have been cleaned out in anticipation of the next season.
That's the tally: 19 live chicks and three dead ones.
I didn't see a single bluebird all Summer, but some of my colleagues did. This year two colleagues and I, as part of a wildlife conservation project at our workplace, monitored six bluebird boxes on a weekly basis beginning in late April. The season was slow getting started, but eventually all six boxes produced young birds, though one clutch all died.
Three nest boxes hosted Tree Swallows, lovely blue-and-white birds. Two nests produced, respectively, four and three young that successfully fledged (the three had a fourth sibling that died), while a third nest produced two chicks that died, almost fully fledged, when temperatures here passed 100°F (nearly 40°C).
Three boxes hosted Carolina Wrens, and we counted, 3, 4, and 5, though the nest with three was set up a month later than the other two. No wren chicks died. On today's run, we found that last nest empty, so all the boxes are empty and have been cleaned out in anticipation of the next season.
That's the tally: 19 live chicks and three dead ones.
Monday, August 16, 2010
A History Channel spinoff
kw: book reviews, nonfiction, history of science
It seems every science history book is obliged to begin with Archimedes in the bathtub, and the Eureka moment when he discovered specific gravity. This book follows the trend: The Greatest Science Stories Never Told: 100 Tales of Invention and Discovery to Astonish, Bewilder & Stupefy, by Rick Beyer, written under the auspices of the History Channel.
The title contains two bits of hyperbole. One is the word "Never". Each of these stories had to be told previously for the author to find it in the first place. And secondly, the subtitle is more worthy of item #53, "The Amazing Doctor Abrams", about a famous charlatan and his inflated prose.
Each two-page spread contains a 200-250 word blurb written in "the rest of the story" style, except for a few that were too obvious to warrant a surprise ending. "Patent President" (# 27), with its picture of a young Abraham Lincoln was rightly begun by identifying Lincoln.
Now, guess who the most famous scientist of all time is? It must be Einstein, and there are three articles about him: "Ticket to Ride", #50, "Einstein's Refrigerator", #62, and "Einstein's Brain", #83, this last about the pathologist who "stole" his brain after the autopsy.
It is amusing to read of a patent issued to Zeppo Marx, and the invention of Liquid Paper by Bette Graham Nesmith, mother of the Monkees' leader Mike Nesmith. However, most of the blurbs are more on unexpected turns in mainline science, such as figuring out who actually invented the laser or radio transmission.
I occasionally take issue with unfelicitous editing, and a case in point is the statement, describing a "Rhino" attachment to military tanks that was a key to the battles in Normandy after D-Day: "The 'rhino' had four prongs…". The photograph clearly shows five prongs. Author and editor both missed that one. One I would not expect them to catch without specialized knowledge, regarding Teflon—which was discovered by accident—is the statement "…no other substance will chemically react with it…". In the library's copy of the book, a previous reader penciled in, "Not true. It reacts explosively with alkali metals such as sodium and potassium giving carbon and metal fluoride." Just so folks will know that Teflon isn't totally impervious.
While I nearly always read books right through (encyclopedias included), this one rewards browsing equally well. It was a fun read, but a bit quick. It is worth keeping around for "Did you know?" story time.
It seems every science history book is obliged to begin with Archimedes in the bathtub, and the Eureka moment when he discovered specific gravity. This book follows the trend: The Greatest Science Stories Never Told: 100 Tales of Invention and Discovery to Astonish, Bewilder & Stupefy, by Rick Beyer, written under the auspices of the History Channel.
The title contains two bits of hyperbole. One is the word "Never". Each of these stories had to be told previously for the author to find it in the first place. And secondly, the subtitle is more worthy of item #53, "The Amazing Doctor Abrams", about a famous charlatan and his inflated prose.
Each two-page spread contains a 200-250 word blurb written in "the rest of the story" style, except for a few that were too obvious to warrant a surprise ending. "Patent President" (# 27), with its picture of a young Abraham Lincoln was rightly begun by identifying Lincoln.
Now, guess who the most famous scientist of all time is? It must be Einstein, and there are three articles about him: "Ticket to Ride", #50, "Einstein's Refrigerator", #62, and "Einstein's Brain", #83, this last about the pathologist who "stole" his brain after the autopsy.
It is amusing to read of a patent issued to Zeppo Marx, and the invention of Liquid Paper by Bette Graham Nesmith, mother of the Monkees' leader Mike Nesmith. However, most of the blurbs are more on unexpected turns in mainline science, such as figuring out who actually invented the laser or radio transmission.
I occasionally take issue with unfelicitous editing, and a case in point is the statement, describing a "Rhino" attachment to military tanks that was a key to the battles in Normandy after D-Day: "The 'rhino' had four prongs…". The photograph clearly shows five prongs. Author and editor both missed that one. One I would not expect them to catch without specialized knowledge, regarding Teflon—which was discovered by accident—is the statement "…no other substance will chemically react with it…". In the library's copy of the book, a previous reader penciled in, "Not true. It reacts explosively with alkali metals such as sodium and potassium giving carbon and metal fluoride." Just so folks will know that Teflon isn't totally impervious.
While I nearly always read books right through (encyclopedias included), this one rewards browsing equally well. It was a fun read, but a bit quick. It is worth keeping around for "Did you know?" story time.
Saturday, August 14, 2010
Enceladus or Bust
kw: book reviews, science fiction, space fiction, space aliens
Just to set the right expectations, the space aliens in Threshold by Eric Flint and Ryk E. Spoor are long extinct, having apparently blown each other up in an interplanetary civil war about the time the dinosaurs went extinct. The book is the sequel to Boundary by the same two authors, and I presume much more is explained there.
In keeping with much modern adventure fiction, there is the obligatory setup of intrigue and power politics, and the expected psychopathic villain, this time a ship's chief of security. While he is the only true villain onstage, there are hints of bigger, more evil villains behind the scenes, even funding his hidden agenda. An unusual feature of this novel is the death of about 90% of the characters, including half of the named cast. It also ends in a cliff-hanger that ensures at least another book (or five) in the series.
Setting all that aside, there are two good ideas of how to power a spaceship, and some really blue-sky notions about nanotechnology and "smart dust" type sensors. The "mass beam" propulsion system is one way to reduce the amount of onboard fuel a ship needs. A railgun type of propulsor sends lots of dust into space ahead of a spacecraft, timed to intercept its expected trajectory. The first half of that trajectory, its velocity will be much greater than the ship, so as it is captured (by a means not explained), it transfers momentum to the ship. It also can act as reaction mass by being heated in an onboard nuclear reactor and spit out the back again. In the latter part of the trip, the dust is traveling slower than the spacecraft, which is now pointed the other way, so the ship is slowed by catching the dust, and slowed again by using it for reaction mass. If the "mass" is smart enough, it can be guided by a ship-mounted laser to more accurately center on the expected trajectory, which reduces waste. I shudder at a technology that can produce thousands or millions of tons of material composed of germ-sized robots.
The nebula drive is more subtle. It uses a huge magnetic field to entrain dust, either dumb or smart, which is pushed upon by both the solar wind and by sunlight. The magnetic field is supported by room-temperature superconducting ribs extending from the ship's midsection (the superconducting technology is courtesy of the extinct aliens). In areas away from a planet's magnetosphere, the field's effective diameter can be a thousand kilometers. That can catch a lot of sunlight. Tacking maneuvers are needed in the second half of a trip, to use the sunlight and solar wind to slow the spacecraft.
Then there is the smart dust itself. The technical protagonist, named A.J., deploys two kinds of the stuff. One kind, called Locusts, is larger, the size of a pencil eraser or so, and a flotilla of these can carry out complex jobs and those requiring strength. Faerie Dust, on the other hand, is almost invisible but can interact with electronic systems. Both communicate with A.J. by cooperatively generating a radio signal and acting as an extended antenna.
The capabilities of such micro- and nanosensors (and -effectors) are gradually unfolded as the novel proceeds. At one point, a dose of Faerie Dust is made into an antipersonnel weapon by A.J. to immobilize a few bad guys. Somehow they get inside their bodies and interrupt key nerve signals. At another, A.J. is able to gain partial control of another ship's propulsion and communications systems. The guy must have an IQ of 700. In real life, just writing the virus code one wants the dust to carry could take months, and the software that allows the dust motes to insert code fragments into a computer system could take decades to perfect. He does the first job in a matter of hours, and the other over a week or two.
The novel is fast-paced, which necessitates leaving out a lot: its time frame is just about ten years. Even with the super-fast drives described above, it takes a few months to get a ship to Mars or Ceres, and even longer to get to "outer system" space, Jupiter and beyond.
The rub in interplanetary voyaging is the time required. The ballistic orbit between Earth and Mars, the Hohmann orbit, takes about a year, each way. The NASA Mars project is considering a somewhat more costly alternative that cuts the one-way time to about eight months. To get three men to the Moon and back required a little over a week, and the supplies, mainly water and oxygen, were a huge inventory item. Multiply that to between seventy and ninety weeks for a Mars mission. The more water, oxygen and food you take along, the more fuel you need to push it all into the transfer orbit, slow the craft down at Mars, get back into Mars orbit, and return to Earth.
But put all that aside and enjoy the ride. It is likely at some point that people will go to Mars and even farther. Some of the ideas in Threshold may indeed be used to help them get there.
Just to set the right expectations, the space aliens in Threshold by Eric Flint and Ryk E. Spoor are long extinct, having apparently blown each other up in an interplanetary civil war about the time the dinosaurs went extinct. The book is the sequel to Boundary by the same two authors, and I presume much more is explained there.
In keeping with much modern adventure fiction, there is the obligatory setup of intrigue and power politics, and the expected psychopathic villain, this time a ship's chief of security. While he is the only true villain onstage, there are hints of bigger, more evil villains behind the scenes, even funding his hidden agenda. An unusual feature of this novel is the death of about 90% of the characters, including half of the named cast. It also ends in a cliff-hanger that ensures at least another book (or five) in the series.
Setting all that aside, there are two good ideas of how to power a spaceship, and some really blue-sky notions about nanotechnology and "smart dust" type sensors. The "mass beam" propulsion system is one way to reduce the amount of onboard fuel a ship needs. A railgun type of propulsor sends lots of dust into space ahead of a spacecraft, timed to intercept its expected trajectory. The first half of that trajectory, its velocity will be much greater than the ship, so as it is captured (by a means not explained), it transfers momentum to the ship. It also can act as reaction mass by being heated in an onboard nuclear reactor and spit out the back again. In the latter part of the trip, the dust is traveling slower than the spacecraft, which is now pointed the other way, so the ship is slowed by catching the dust, and slowed again by using it for reaction mass. If the "mass" is smart enough, it can be guided by a ship-mounted laser to more accurately center on the expected trajectory, which reduces waste. I shudder at a technology that can produce thousands or millions of tons of material composed of germ-sized robots.
The nebula drive is more subtle. It uses a huge magnetic field to entrain dust, either dumb or smart, which is pushed upon by both the solar wind and by sunlight. The magnetic field is supported by room-temperature superconducting ribs extending from the ship's midsection (the superconducting technology is courtesy of the extinct aliens). In areas away from a planet's magnetosphere, the field's effective diameter can be a thousand kilometers. That can catch a lot of sunlight. Tacking maneuvers are needed in the second half of a trip, to use the sunlight and solar wind to slow the spacecraft.
Then there is the smart dust itself. The technical protagonist, named A.J., deploys two kinds of the stuff. One kind, called Locusts, is larger, the size of a pencil eraser or so, and a flotilla of these can carry out complex jobs and those requiring strength. Faerie Dust, on the other hand, is almost invisible but can interact with electronic systems. Both communicate with A.J. by cooperatively generating a radio signal and acting as an extended antenna.
The capabilities of such micro- and nanosensors (and -effectors) are gradually unfolded as the novel proceeds. At one point, a dose of Faerie Dust is made into an antipersonnel weapon by A.J. to immobilize a few bad guys. Somehow they get inside their bodies and interrupt key nerve signals. At another, A.J. is able to gain partial control of another ship's propulsion and communications systems. The guy must have an IQ of 700. In real life, just writing the virus code one wants the dust to carry could take months, and the software that allows the dust motes to insert code fragments into a computer system could take decades to perfect. He does the first job in a matter of hours, and the other over a week or two.
The novel is fast-paced, which necessitates leaving out a lot: its time frame is just about ten years. Even with the super-fast drives described above, it takes a few months to get a ship to Mars or Ceres, and even longer to get to "outer system" space, Jupiter and beyond.
The rub in interplanetary voyaging is the time required. The ballistic orbit between Earth and Mars, the Hohmann orbit, takes about a year, each way. The NASA Mars project is considering a somewhat more costly alternative that cuts the one-way time to about eight months. To get three men to the Moon and back required a little over a week, and the supplies, mainly water and oxygen, were a huge inventory item. Multiply that to between seventy and ninety weeks for a Mars mission. The more water, oxygen and food you take along, the more fuel you need to push it all into the transfer orbit, slow the craft down at Mars, get back into Mars orbit, and return to Earth.
But put all that aside and enjoy the ride. It is likely at some point that people will go to Mars and even farther. Some of the ideas in Threshold may indeed be used to help them get there.
Thursday, August 12, 2010
This will be the biggest encyclopedia
kw: blogging, biology
To the right of the posting area you will find a list of links to blogs I like. I have just added a new one, the blog of the Encyclopedia of Life. Go there and bookmark this one!
The founder, E.O. Wilson, to my mind the greatest naturalist ever, dreamed of having an encyclopedia of every known species, containing everything known about each one. The World Wide Web provides the perfect vehicle for this project, for it could never be accomplished by paper publishing.
There are at present about 1.8 million known species of living things. A single-page blurb about each one would take about 3,600 books the size of a Harry Potter novel. And we know much more than a single page's worth about hundreds of thousands of species. I thought of having a few sample photographs in this post, but the very idea is appalling in its effrontery. Just go there and see!
To the right of the posting area you will find a list of links to blogs I like. I have just added a new one, the blog of the Encyclopedia of Life. Go there and bookmark this one!
The founder, E.O. Wilson, to my mind the greatest naturalist ever, dreamed of having an encyclopedia of every known species, containing everything known about each one. The World Wide Web provides the perfect vehicle for this project, for it could never be accomplished by paper publishing.
There are at present about 1.8 million known species of living things. A single-page blurb about each one would take about 3,600 books the size of a Harry Potter novel. And we know much more than a single page's worth about hundreds of thousands of species. I thought of having a few sample photographs in this post, but the very idea is appalling in its effrontery. Just go there and see!
Wednesday, August 11, 2010
As it is so it does
kw: book reviews, nonfiction, philosophy, evil
"Evil is as evil does." Isn't that the proverb? Not according to Terry Eagleton in On Evil. In this small, but wide-ranging book, he draws upon literature, theology, philosophy and contemporary history and culture to examine a phenomenon so many would think outmoded, even passé, and certainly not a fit subject for polite discourse.
Fully half the book draws upon fiction, for it is in fiction that our understanding as a human people is tested and either confirmed or found wanting. William Golding has examined evil in all its viciousness and banality over a long career, most notably with Lord of the Flies. The discussion begins with the anti-character Pincher Martin by Golding, and Milton's Mephistopheles in Paradise Lost is a necessary stop along the way, of course, but the author returns most often to the tragedies of William Shakespeare, particularly Macbeth and Othello.
Taking Iago of Othello as prototypical in the English oeuvre, Eagleton concludes that true evil is purposeless, destructive for the sake of destruction, and as causeless as God. This is ironic, as his own Marxism is the most atheistic of ideologies. But he's just getting warmed up. His second section examines philosophical and theological treatments, from theodicy to the nihilism of Schopenhauer.
Freud is brought in as having identified the evil in any of us with the death drive (most of us know it by the term death wish), that longing for nothingness, that existential angst that threatens to overcome us when we realize we can't have it all. Turned inward, it leads to suicide; turned outward, it leads to mass murder, or at least petty mayhem of all sorts. In most of us, subjugated and denied, it results in the dragging, enervating depression that dogs our days in "the rat race."
He starts to go seriously awry when he restricts his discussion of religious philosophy to two viewpoints: Roman Catholicism and "Moral Majority" puritanism. Both are rooted in asceticism, which is fundamentally not Biblical; Jesus said, "I came that they may have Life, and have it abundantly." But he cannot see beyond the crucifix, claiming that "Christian belief is that God achieves supreme self-expression in a tortured human body." (p 67) Now that is evil! Yet it is entirely expectable in a Marxist.
Strangely, he gets around to the "abundant life" statement, using it to support his contention of the purposelessness of evil, writing that the evil "are those who are deficient in the art of living." (p 128) Coming in a chapter titled "Obscene Enjoyment", that is so trite it beggars belief. The elephant strove and delivered a mouse. By the time the final chapter comes around, he has almost defined evil out of existence. When Jesus said, "Father forgive them, for they don't know what they are doing," it was not a general statement, but Eagleton takes it to be. Stephen echoed it while being stoned, "Let not this sin be held to their charge," accurately naming the sin while praying for its forgiveness. It was still a sin. What made these murders evil was that they were thought righteous by those doing them.
Here we come to the crux of the matter for me. The author uses Jesus' statement to say that, because the purpose was thought to be noble, the crucifiers of Jesus (and the stoners of Stephen by extension) "are perhaps not beyond the pale." But now he is painted into a corner, stating immediately afterward that "Stalin and Mao murdered for what they was as an honorable end, and if they are not beyond the moral pale then it is hard to know who is." (p 145) I agree with half of this statement; those two monsters are most certainly evil, even the epitome of it. But the first half? Ha! They claimed their purposes were noble, but clearly did not believe it, as even a cursory understanding of their more private utterances will attest. They did not care; good or evil were alike to them.
Thus, by the example of Mao and Stalin, and indeed Hitler, and in contradistinction to the author's thesis, we find that the evil are purposeful, often very purposeful. The banality of evil so often remarked upon is not in them, it is in the armies of lesser people "doing their job" at the behest of purposeful, powerful but uncaring leaders. It is in the clerks and laborers, the fillers of forms and diggers of mass graves, the makers of nerve gas or Uzis who go home to play with their children. But there is no banality about a Josef Stalin, none whatever. He knows what he is about, and it is all about him: his power, his image, his comfort, his country, his political system.
That archetypical Evil One, Lucifer, with his seven "I will" statements in Isaiah 14, says to God in effect, "I don't like the way you are running the Universe, so I will take over." This, the basis of Nietzsche's will to power, is the root of evil (and to calm a quibble, the "love of money" is "a root of all kinds of evils", not as the KJV translates it "the root of all evil"). To take over God's work is evil; to cooperate with God in His work is good. The rest doesn't matter.
"Evil is as evil does." Isn't that the proverb? Not according to Terry Eagleton in On Evil. In this small, but wide-ranging book, he draws upon literature, theology, philosophy and contemporary history and culture to examine a phenomenon so many would think outmoded, even passé, and certainly not a fit subject for polite discourse.
Fully half the book draws upon fiction, for it is in fiction that our understanding as a human people is tested and either confirmed or found wanting. William Golding has examined evil in all its viciousness and banality over a long career, most notably with Lord of the Flies. The discussion begins with the anti-character Pincher Martin by Golding, and Milton's Mephistopheles in Paradise Lost is a necessary stop along the way, of course, but the author returns most often to the tragedies of William Shakespeare, particularly Macbeth and Othello.
Taking Iago of Othello as prototypical in the English oeuvre, Eagleton concludes that true evil is purposeless, destructive for the sake of destruction, and as causeless as God. This is ironic, as his own Marxism is the most atheistic of ideologies. But he's just getting warmed up. His second section examines philosophical and theological treatments, from theodicy to the nihilism of Schopenhauer.
Freud is brought in as having identified the evil in any of us with the death drive (most of us know it by the term death wish), that longing for nothingness, that existential angst that threatens to overcome us when we realize we can't have it all. Turned inward, it leads to suicide; turned outward, it leads to mass murder, or at least petty mayhem of all sorts. In most of us, subjugated and denied, it results in the dragging, enervating depression that dogs our days in "the rat race."
He starts to go seriously awry when he restricts his discussion of religious philosophy to two viewpoints: Roman Catholicism and "Moral Majority" puritanism. Both are rooted in asceticism, which is fundamentally not Biblical; Jesus said, "I came that they may have Life, and have it abundantly." But he cannot see beyond the crucifix, claiming that "Christian belief is that God achieves supreme self-expression in a tortured human body." (p 67) Now that is evil! Yet it is entirely expectable in a Marxist.
Strangely, he gets around to the "abundant life" statement, using it to support his contention of the purposelessness of evil, writing that the evil "are those who are deficient in the art of living." (p 128) Coming in a chapter titled "Obscene Enjoyment", that is so trite it beggars belief. The elephant strove and delivered a mouse. By the time the final chapter comes around, he has almost defined evil out of existence. When Jesus said, "Father forgive them, for they don't know what they are doing," it was not a general statement, but Eagleton takes it to be. Stephen echoed it while being stoned, "Let not this sin be held to their charge," accurately naming the sin while praying for its forgiveness. It was still a sin. What made these murders evil was that they were thought righteous by those doing them.
Here we come to the crux of the matter for me. The author uses Jesus' statement to say that, because the purpose was thought to be noble, the crucifiers of Jesus (and the stoners of Stephen by extension) "are perhaps not beyond the pale." But now he is painted into a corner, stating immediately afterward that "Stalin and Mao murdered for what they was as an honorable end, and if they are not beyond the moral pale then it is hard to know who is." (p 145) I agree with half of this statement; those two monsters are most certainly evil, even the epitome of it. But the first half? Ha! They claimed their purposes were noble, but clearly did not believe it, as even a cursory understanding of their more private utterances will attest. They did not care; good or evil were alike to them.
Thus, by the example of Mao and Stalin, and indeed Hitler, and in contradistinction to the author's thesis, we find that the evil are purposeful, often very purposeful. The banality of evil so often remarked upon is not in them, it is in the armies of lesser people "doing their job" at the behest of purposeful, powerful but uncaring leaders. It is in the clerks and laborers, the fillers of forms and diggers of mass graves, the makers of nerve gas or Uzis who go home to play with their children. But there is no banality about a Josef Stalin, none whatever. He knows what he is about, and it is all about him: his power, his image, his comfort, his country, his political system.
That archetypical Evil One, Lucifer, with his seven "I will" statements in Isaiah 14, says to God in effect, "I don't like the way you are running the Universe, so I will take over." This, the basis of Nietzsche's will to power, is the root of evil (and to calm a quibble, the "love of money" is "a root of all kinds of evils", not as the KJV translates it "the root of all evil"). To take over God's work is evil; to cooperate with God in His work is good. The rest doesn't matter.
Tuesday, August 10, 2010
A nut among the fruitcakes
kw: opinion, eating, weight loss
In a radio ad I heard Steve Bostic, of "Right Size Smoothies", claim "You can gain weight eating 1,000 calories a day if you eat the wrong foods" (the quote may not be exact, but it is close).
Calories are calories. There are several calculators online that will tell you how many calories are needed to maintain your body weight, depending on age, sex and activity level. This is a very well known area of nutrition science. The one I used is at Shapefit.
I figured initially that a tiny, sedentary person might need less than 1,000 calories a day. I put in figures for a variety of undersized adults. Here is one result, at a ridiculous extreme: Male, age 65, sedentary, 5 feet tall, weight 80 pounds. Calories needed: 1,106 per day. A female the same size would need a little more.
For myself (6 feet, age 63, 200 pounds) the daily caloric need is 2,337.
Clearly, the claim made in the ad is bogus.
In a radio ad I heard Steve Bostic, of "Right Size Smoothies", claim "You can gain weight eating 1,000 calories a day if you eat the wrong foods" (the quote may not be exact, but it is close).
Calories are calories. There are several calculators online that will tell you how many calories are needed to maintain your body weight, depending on age, sex and activity level. This is a very well known area of nutrition science. The one I used is at Shapefit.
I figured initially that a tiny, sedentary person might need less than 1,000 calories a day. I put in figures for a variety of undersized adults. Here is one result, at a ridiculous extreme: Male, age 65, sedentary, 5 feet tall, weight 80 pounds. Calories needed: 1,106 per day. A female the same size would need a little more.
For myself (6 feet, age 63, 200 pounds) the daily caloric need is 2,337.
Clearly, the claim made in the ad is bogus.
Monday, August 09, 2010
The music endures
kw: book reviews, fiction
I read very little mainstream fiction. Having read four of Alexander McCall Smith's Ladies' Detective Agency series, when I saw a volume outside that series, I decided to try it. McCall Smith has also three other series, but La's Orchestra Saves the World is in none of them. It is, instead, a tale of growth and transformation in rural wartime England.
Throughout the novel, until the very end, La, or Lavender, is drawn along by events. She is married then widowed (during divorce proceedings) at a young age, installed in a country villa by her ex-husband's parents, employed as a farm worker to help the war effort, prodded into starting a village orchestra by a friend in the military, finds herself falling in love with another soul who has "dropped through the cracks", conducts a "victory concert" shortly after V-E Day, and finds herself quite adrift as she moves back to London to a house she has inherited.
Even visiting her old tutor at Cambridge simply returns her to the uncertainty and timidity of her undergraduate days. It is only at the very end that her own character, which we've seen only hints of throughout, prevails. With a single sentence she changes her own life as profoundly as the war had changed everything and everyone else.
The author is a master of dialog and inner soliloquy. With these he builds sympathetic characters for us to enjoy. In the Ladies' Detective Agency novels, the rhythms of language and thought, which usher us into the minds of the principal characters, convey the mental habits of native Setswana speakers. In this novel the linguistic habits seem much more familiar, at least to an Anglo-American like me. Yet La is a very different person from myself, and it takes all of McCall Smith's skill to get me into her skull.
Having sampled this very different side of the author, I have gained an interest in seeing how he handles the characters in the three series I haven't touched.
I read very little mainstream fiction. Having read four of Alexander McCall Smith's Ladies' Detective Agency series, when I saw a volume outside that series, I decided to try it. McCall Smith has also three other series, but La's Orchestra Saves the World is in none of them. It is, instead, a tale of growth and transformation in rural wartime England.
Throughout the novel, until the very end, La, or Lavender, is drawn along by events. She is married then widowed (during divorce proceedings) at a young age, installed in a country villa by her ex-husband's parents, employed as a farm worker to help the war effort, prodded into starting a village orchestra by a friend in the military, finds herself falling in love with another soul who has "dropped through the cracks", conducts a "victory concert" shortly after V-E Day, and finds herself quite adrift as she moves back to London to a house she has inherited.
Even visiting her old tutor at Cambridge simply returns her to the uncertainty and timidity of her undergraduate days. It is only at the very end that her own character, which we've seen only hints of throughout, prevails. With a single sentence she changes her own life as profoundly as the war had changed everything and everyone else.
The author is a master of dialog and inner soliloquy. With these he builds sympathetic characters for us to enjoy. In the Ladies' Detective Agency novels, the rhythms of language and thought, which usher us into the minds of the principal characters, convey the mental habits of native Setswana speakers. In this novel the linguistic habits seem much more familiar, at least to an Anglo-American like me. Yet La is a very different person from myself, and it takes all of McCall Smith's skill to get me into her skull.
Having sampled this very different side of the author, I have gained an interest in seeing how he handles the characters in the three series I haven't touched.
Saturday, August 07, 2010
There is a king back there
kw: musings, genealogy
I just spent a long evening tuning up some of the Hints in my family tree at Ancestry.com. I also numbered all the people in the primary reference I used to begin the online tree, titled "Genealogy of the Lindsey Family", compiled in 1901 by my great-grandfather's sister, and updated by an aunt in 1962. It really gave us a leg up on the family tree my mother and I worked on through the 1960s and 1970s, and which she continued to compile through the 1990s.
One thing about genealogy as a hobby: it is a huge consumer of time. The online experience just makes it more intense. I "discovered" several new ancestors tonight, back in the mid 1600s. At the genealogy club my mother belonged to, they would hold a party every time someone found a new ancestor. When we found in the library a book with a complete family history of the Nantucketers from the founding in 1659 to about 1950, we "discovered" forty or more ancestors of one Joseph Macy, the one who first moved from Nantucket to the Carolinas. He is six generations back in my tree, while the founders are nine generations back, and many of them have three or four more generations that are known.
Then a few years later, someone who gave a talk at the club asked my mother, "Nantucketers? Are you descended from Dorcas Gayer? Then you're descended from Charlemagne!" There is still debate whether one of the links in the Gayer family tree is genuine, but if so, then Charlemagne is back there, 39 generations. I have my tree traced back to Edward I Plantagenet, who is 23 generations back, and I figure that is about far enough. The connection is almost meaningless.
Almost anyone of European ancestry is descended from the kings somewhere (though tracing it is another matter). Considering that the early "royalty" were just the biggest bullies around, it is a dubious distinction. Anyway, there is a numerical point that interests me here.
The human genome contains about 25,000 genes. If you divide 25,000 by two fifteen times, you get 0.76, which means you have a 76% chance that a single gene that was present in a particular ancestor fifteen generations ago is present in you. Of course, in that generation, you have about 32,000 ancestors, so there are plenty of people for every single gene to come from. But what about 23 generations? 223 is 8,388,608. Divide that by 25,000 and you get 336. There is one chance in 336 that any single gene from Edward I would be found in my genome.
Of course, as kings do, he had lots of children, most of them illegitimate, and in 23 generations, it is likely that some distant and not-so-distant cousins married one another, so there are more chances than exactly 1/336. But it is still not too likely.
I find the stories closer to this time of more interest. I am lucky to know who all of my great-great grandparents are, and well over half of the generation before that. Several of these were immigrants to America, and one was a Cherokee, so the immigrant there is about 500 generations back. The line I was researching today all lived and died in Salem, New Jersey. Any living descendants are about eighth cousins.
By the way, here is how I figure "Nth Cousin" and how many times removed. It is the rule of G's. First cousins share a grandparent. That is the first "G". Count the G's in the shortest line. For example, if two people share a great-great grandparent, that is three G's so they are third cousins. But if they are of different generations, the extra G's in the longer line are the "times removed". So, my third cousin and I are descended from a particular great-great grandmother. We are of the same generation. But his daughter is once removed, and her daughter is twice removed, from me. I have a son, and the daughter is his fourth cousin, while the daughter's daughter is his fourth cousin once removed.
For those of us who know lots of our ancestors, and perhaps lots of cousins, at various removes, we know that, when people say, "Family matters", it means, "Close family matters." An eighth cousin relationship is possibly interesting, but hardly significant.
I just spent a long evening tuning up some of the Hints in my family tree at Ancestry.com. I also numbered all the people in the primary reference I used to begin the online tree, titled "Genealogy of the Lindsey Family", compiled in 1901 by my great-grandfather's sister, and updated by an aunt in 1962. It really gave us a leg up on the family tree my mother and I worked on through the 1960s and 1970s, and which she continued to compile through the 1990s.
One thing about genealogy as a hobby: it is a huge consumer of time. The online experience just makes it more intense. I "discovered" several new ancestors tonight, back in the mid 1600s. At the genealogy club my mother belonged to, they would hold a party every time someone found a new ancestor. When we found in the library a book with a complete family history of the Nantucketers from the founding in 1659 to about 1950, we "discovered" forty or more ancestors of one Joseph Macy, the one who first moved from Nantucket to the Carolinas. He is six generations back in my tree, while the founders are nine generations back, and many of them have three or four more generations that are known.
Then a few years later, someone who gave a talk at the club asked my mother, "Nantucketers? Are you descended from Dorcas Gayer? Then you're descended from Charlemagne!" There is still debate whether one of the links in the Gayer family tree is genuine, but if so, then Charlemagne is back there, 39 generations. I have my tree traced back to Edward I Plantagenet, who is 23 generations back, and I figure that is about far enough. The connection is almost meaningless.
Almost anyone of European ancestry is descended from the kings somewhere (though tracing it is another matter). Considering that the early "royalty" were just the biggest bullies around, it is a dubious distinction. Anyway, there is a numerical point that interests me here.
The human genome contains about 25,000 genes. If you divide 25,000 by two fifteen times, you get 0.76, which means you have a 76% chance that a single gene that was present in a particular ancestor fifteen generations ago is present in you. Of course, in that generation, you have about 32,000 ancestors, so there are plenty of people for every single gene to come from. But what about 23 generations? 223 is 8,388,608. Divide that by 25,000 and you get 336. There is one chance in 336 that any single gene from Edward I would be found in my genome.
Of course, as kings do, he had lots of children, most of them illegitimate, and in 23 generations, it is likely that some distant and not-so-distant cousins married one another, so there are more chances than exactly 1/336. But it is still not too likely.
I find the stories closer to this time of more interest. I am lucky to know who all of my great-great grandparents are, and well over half of the generation before that. Several of these were immigrants to America, and one was a Cherokee, so the immigrant there is about 500 generations back. The line I was researching today all lived and died in Salem, New Jersey. Any living descendants are about eighth cousins.
By the way, here is how I figure "Nth Cousin" and how many times removed. It is the rule of G's. First cousins share a grandparent. That is the first "G". Count the G's in the shortest line. For example, if two people share a great-great grandparent, that is three G's so they are third cousins. But if they are of different generations, the extra G's in the longer line are the "times removed". So, my third cousin and I are descended from a particular great-great grandmother. We are of the same generation. But his daughter is once removed, and her daughter is twice removed, from me. I have a son, and the daughter is his fourth cousin, while the daughter's daughter is his fourth cousin once removed.
For those of us who know lots of our ancestors, and perhaps lots of cousins, at various removes, we know that, when people say, "Family matters", it means, "Close family matters." An eighth cousin relationship is possibly interesting, but hardly significant.
Thursday, August 05, 2010
Where coal is king - like it or not
kw: musings, mining, coal
Mining and farming are major industries in South Dakota, where we lived for eight years. During our time there, a nationwide political push got underway to add restrictions to mining, particularly coal mining, and to reduce agricultural subsidies. The farmers and miners both protested, and a popular slogan was seen on bumper stickers and tee shirts: "Let 'em starve in the dark." We are totally dependent on these much-maligned professions.
After two well-publicized coal mining disasters this year, the coal mines of West Virginia have received lots of unfavorable publicity. But this has focused mainly on underground mining. A bigger, and growing, phenomenon has been "mountaintop" mining, in which the overburden, sometimes a hundred feet or more, is removed, the coal stripped out, and the broken rock put back. If it has any virtue, it is that fewer miners will die underground.
I snagged this view of about 400 square miles of Boone and Raleigh counties in southwestern West Virginia on Google Earth. To see this area in GE, "fly to" Whitesville. The image is about a year old (Summer 2009), except for the brown rectangle, from an older image taken in Winter.
The gray areas tell the story. More than five percent of the landscape, which comes to at least 20 or 30 square miles, has been blasted, set aside, and stripped of coal. This kind of mining is now a major source of coal for making electricity. This is not just a WV situation, it is seen all up and down the eastern US. It is where we are getting the power for our air conditioning this summer, folks.
I looked at the historical imagery for some of these "removed mountains". The oldest images are about fifteen years old, and show much smaller and fewer areas of disturbance. Mountaintop mining has become a big thing in the opening years of the Twenty-First Century. In the US as a whole, about half the energy use is from coal. In the Northeast, it is closer to 75%. Although projected reserves are expected to last between 50 and 100 more years, there's a lot of mountainous country around here that'll be flat by the time that's done!
Like many people, I'm fairly addicted to air conditioning. It is likely to come to an end in my lifetime, mainly because, having used up half of the coal already, the cheap half, we will find its price continue to climb until nearly nobody can afford to cool their homes in summer, and I expect winter heating will gradually change in style also.
The working miners have another generation ahead of them, maybe two. It'll end someday, and by then, to see a mountain, you'll have to go somewhere that there never was any coal.
Mining and farming are major industries in South Dakota, where we lived for eight years. During our time there, a nationwide political push got underway to add restrictions to mining, particularly coal mining, and to reduce agricultural subsidies. The farmers and miners both protested, and a popular slogan was seen on bumper stickers and tee shirts: "Let 'em starve in the dark." We are totally dependent on these much-maligned professions.
After two well-publicized coal mining disasters this year, the coal mines of West Virginia have received lots of unfavorable publicity. But this has focused mainly on underground mining. A bigger, and growing, phenomenon has been "mountaintop" mining, in which the overburden, sometimes a hundred feet or more, is removed, the coal stripped out, and the broken rock put back. If it has any virtue, it is that fewer miners will die underground.
I snagged this view of about 400 square miles of Boone and Raleigh counties in southwestern West Virginia on Google Earth. To see this area in GE, "fly to" Whitesville. The image is about a year old (Summer 2009), except for the brown rectangle, from an older image taken in Winter.The gray areas tell the story. More than five percent of the landscape, which comes to at least 20 or 30 square miles, has been blasted, set aside, and stripped of coal. This kind of mining is now a major source of coal for making electricity. This is not just a WV situation, it is seen all up and down the eastern US. It is where we are getting the power for our air conditioning this summer, folks.
I looked at the historical imagery for some of these "removed mountains". The oldest images are about fifteen years old, and show much smaller and fewer areas of disturbance. Mountaintop mining has become a big thing in the opening years of the Twenty-First Century. In the US as a whole, about half the energy use is from coal. In the Northeast, it is closer to 75%. Although projected reserves are expected to last between 50 and 100 more years, there's a lot of mountainous country around here that'll be flat by the time that's done!
Like many people, I'm fairly addicted to air conditioning. It is likely to come to an end in my lifetime, mainly because, having used up half of the coal already, the cheap half, we will find its price continue to climb until nearly nobody can afford to cool their homes in summer, and I expect winter heating will gradually change in style also.
The working miners have another generation ahead of them, maybe two. It'll end someday, and by then, to see a mountain, you'll have to go somewhere that there never was any coal.
Wednesday, August 04, 2010
and every man a liar
kw: book reviews, nonfiction, experts, polemics
On occasion my grandmother said, "Everybody's pixilated except the two of us; but sometimes I worry about you." For the X, Y and Z generations: "pixilated" is related to "pixie", not "pixel", and means "somewhat removed from reality". Even the Bible tells us, "Let God be true, and every man a liar," hence my title. Why? I've just read a book that presents a thesis very much in accord with my beliefs, Wrong: Why Experts Keep Failing Us—And How to Know When Not to Trust Them by David H Freedman. Since I agree with the author so much, is it safe for me to trust him? We'll get to that.
Here's a good example of a phenomenon that is part of the problem. Mark Van Stone, a Mayan archaeologist, has written 2012: Science and Prophecy of the Ancient Maya. In the face of literally millions of predictions of disaster, he demonstrates that December 21, 2012 is about as important as January 1, 2000 was to those of us in the Western world: A significant New Year to celebrate, but not a whole lot different from December 31, 1999. He's having a hard time finding bookstores to carry the book, and venues to lecture about it. People may seek out experts' help when they don't know what to think, but they are pretty careful to select experts whose pronouncements confirm what they already believe or wish to be true. Disaster sells. "Tomorrow will be like today" doesn't sell. But Dr. Van Stone is much more likely to be right than the millions of doom-and-gloomers.
In Wrong, the author first establishes that the "expertise" of informal experts (such as some actor telling you what brand of mouthwash he prefers) is quite suspect, and most people understand this. The standard of established scientists is higher, or so we hope, but how much higher? This is the theme of most of the book. There are several kinds of studies, which are accorded different levels of trust:
In fact, this and many, many other well-understood phenomena are known only from case studies. The athletes and coaches of the world have done their own informal epidemiological study of steroids and concluded they have a huge effect. We all know what has followed: laws, scandals, asterisks in the record books, and a number of untimely deaths.
Since the RCT enjoys ultimate trust, is it worthy of it? It can be. But we always have to look beneath the covers. Consider this: the results of most RCT's are reported statistically, with a statement that the conclusion "is significant at the 95% confidence level." The dark side of 95% confidence is that other 5%. At the very least, it means that statistical flukes could invalidate one-twentieth of the RCT's that have been published. But there's more.
Publication Bias is the tendency of "positive" results to be published, and the tendency of "negative" results to be either trashed by the researcher (or at least filed away) or rejected by the journal to which they are submitted. Freedman quotes researchers who estimate that at most one in ten "negative" experiments gets published. To be clear, a "negative" result means that a researcher started out with an idea, but the statistical analysis of the experiment indicates that the idea is false. Thus, the result means, "Nothing new, folks. As you were." This is not exciting, so why publish it? The researcher will only try to publish it if it contradicts something already published, particularly if she and the other author aren't friends.
So, let's consider an ideal case: twenty research teams gather data and wind up with 100 numbers. Suppose the numbers are the duration of an episode of common cold. A medication is being tested, to see if the duration is different from the "normal" seven days. They apply standard statistical tests, and 19 teams' results can be stated, "The mean [average] is not significantly different from seven days." The twentieth team finds that, not only is the average value six days, but the "95% confidence interval" for cold duration in the test subjects is between 5.3 and 6.8 days. Thus they conclude, incorrectly, that the medication is effective. They publish.
Of the other 19 teams, two manage to get their findings published (It is much more likely, in my experience, that none of the 19 negative findings will see print). The result is that the published record contains one positive finding and two negative findings: one-third of the published record is in error!
I spent fourteen years in academia. The pressure to publish is terrific, and you'd better publish something "interesting" and "significant". Tenure is typically based on this. Before attending graduate school, I worked as a machinist at Cal Tech. I happened upon a cabinet full of research documents, recording work done on a synchrotron during the 1960s. Nearly everything was negative results, like they tried to find "particle X" and failed. Fortunately for graduate students everywhere, proving yourself wrong is OK at this level, and numerous PhD's were conferred anyway. But very little of those research results was ever submitted for publication.
As a fun exercise, I generated 10,000 "random normal" numbers and grouped them by hundreds. Each number was the sum of twelve RAND() values. This is a very good proxy for a more rigorous standard normal variable. I applied statistical tests, and the one we'll focus on here is the t-test to determine if the mean (numerical average) is significantly different from the expected value of 6.00. I used a t-test with a z-factor of 1.96 for the 95% confidence level.
95 of the 100 passed the test, and five failed it. I was suspicious, and ran the "recalculate" a few times to check. Mostly, 95 passed, but in one case, 98 did so, and in another 93 did so. This is not unusual. I picked one of the sets for which 95 sets passed the t-test to analyze a little further. I grouped them by twenties. Two sets of 20 had 19 passes and one fail. One had 17, and for the other two sets, all 20 passed. I selected four of the sets of 100 values to plot on probability coordinates, as shown here:
Var028 has the highest mean value, and is one of the five "failures". Var064 has the lowest mean value, and is also a "failure". In the world of "significantly different is good", they would be "positive" results. Var021 has a mean value (5.9971) closest to the mean for the total population of 10,000 (6.0015). Var078 has the greatest scatter (SD = 1.165; 1.0 is expected). Note the apparent outlier in Var078, down and to the left. Many researchers would throw this out, not including it in the statistical tests. Yet this is a valid member of the original data set from which all these variables were drawn. It happens to be the lowest of the 10,000.
Removing that seeming outlier changes the mean by 0.0372 and the SD by 0.055. This would be enough to make a "positive" into a "negative", except that Var078 was negative already, just barely.
OK, so even RCT's need to be taken with a grain of salt. Add to this that not everyone is totally honest. In fact, the system seems to be primed to reward dishonestly, so like in politics, at least some of the scum rises to the top.
What is a fellow to do? The last chapter is titled "Eleven Simple Never-Fail Rules for Not Being Misled by Experts". As if it were that simple. The take-away message is, be suspicious of simple answers to complex questions, look for experts who have nothing to gain (this can be devilish hard), and give yourself time to think before making decisions.
Although I have four major instances in my life where a doctor was wrong, and in one case I had to save my own life, I still go to doctors. I go not as a "patient" but as a customer paying a consultant to render advice, which I weigh carefully, and sometimes to do what I can't (self-surgery is not advised). When I buy shares of a mutual fund, I am trusting an expert, the fund manager, to make better stock purchase decisions than I would (or have the time for). Even, when we select a grocery store to patronize, we're trusting the store's purchasing agents to get the quality goods at fair prices from producers (though we do go to Farmers' markets when we can).
Experts are like parents. Some are good, some bad. We learn as we grow up that our parents are not perfect, though most of us (sadly, not all) learn that they are at least well-intentioned. The author of Wrong has added four Appendices, and the fourth is an essay on the factors that might make the book worthwhile. Considering that "…and every man a liar" is an exaggeration, one can hope that David Freedman has at least made a valiant attempt to present the truth about expertise. He had to rely on experts for much of his own material! So this book is a meta-analysis, and is likely to be slightly more reliable than the individual analyses. Finally, consider these four pronouncements (page 39) from medical studies, answering the question, "Can vitamin D help fend off cancer?":
On occasion my grandmother said, "Everybody's pixilated except the two of us; but sometimes I worry about you." For the X, Y and Z generations: "pixilated" is related to "pixie", not "pixel", and means "somewhat removed from reality". Even the Bible tells us, "Let God be true, and every man a liar," hence my title. Why? I've just read a book that presents a thesis very much in accord with my beliefs, Wrong: Why Experts Keep Failing Us—And How to Know When Not to Trust Them by David H Freedman. Since I agree with the author so much, is it safe for me to trust him? We'll get to that.
Here's a good example of a phenomenon that is part of the problem. Mark Van Stone, a Mayan archaeologist, has written 2012: Science and Prophecy of the Ancient Maya. In the face of literally millions of predictions of disaster, he demonstrates that December 21, 2012 is about as important as January 1, 2000 was to those of us in the Western world: A significant New Year to celebrate, but not a whole lot different from December 31, 1999. He's having a hard time finding bookstores to carry the book, and venues to lecture about it. People may seek out experts' help when they don't know what to think, but they are pretty careful to select experts whose pronouncements confirm what they already believe or wish to be true. Disaster sells. "Tomorrow will be like today" doesn't sell. But Dr. Van Stone is much more likely to be right than the millions of doom-and-gloomers.
In Wrong, the author first establishes that the "expertise" of informal experts (such as some actor telling you what brand of mouthwash he prefers) is quite suspect, and most people understand this. The standard of established scientists is higher, or so we hope, but how much higher? This is the theme of most of the book. There are several kinds of studies, which are accorded different levels of trust:
- Observational: data-gathering. Interesting, not trusted much.
- Epidemiological: case studies, hopefully of many similar cases. More trustworthy, and the larger the better.
- Meta-analysis: review of many studies. Considered quite trustworthy.
- Randomized controlled trial (RCT): The gold standard, particularly if large.
In fact, this and many, many other well-understood phenomena are known only from case studies. The athletes and coaches of the world have done their own informal epidemiological study of steroids and concluded they have a huge effect. We all know what has followed: laws, scandals, asterisks in the record books, and a number of untimely deaths.
Since the RCT enjoys ultimate trust, is it worthy of it? It can be. But we always have to look beneath the covers. Consider this: the results of most RCT's are reported statistically, with a statement that the conclusion "is significant at the 95% confidence level." The dark side of 95% confidence is that other 5%. At the very least, it means that statistical flukes could invalidate one-twentieth of the RCT's that have been published. But there's more.
Publication Bias is the tendency of "positive" results to be published, and the tendency of "negative" results to be either trashed by the researcher (or at least filed away) or rejected by the journal to which they are submitted. Freedman quotes researchers who estimate that at most one in ten "negative" experiments gets published. To be clear, a "negative" result means that a researcher started out with an idea, but the statistical analysis of the experiment indicates that the idea is false. Thus, the result means, "Nothing new, folks. As you were." This is not exciting, so why publish it? The researcher will only try to publish it if it contradicts something already published, particularly if she and the other author aren't friends.
So, let's consider an ideal case: twenty research teams gather data and wind up with 100 numbers. Suppose the numbers are the duration of an episode of common cold. A medication is being tested, to see if the duration is different from the "normal" seven days. They apply standard statistical tests, and 19 teams' results can be stated, "The mean [average] is not significantly different from seven days." The twentieth team finds that, not only is the average value six days, but the "95% confidence interval" for cold duration in the test subjects is between 5.3 and 6.8 days. Thus they conclude, incorrectly, that the medication is effective. They publish.
Of the other 19 teams, two manage to get their findings published (It is much more likely, in my experience, that none of the 19 negative findings will see print). The result is that the published record contains one positive finding and two negative findings: one-third of the published record is in error!
I spent fourteen years in academia. The pressure to publish is terrific, and you'd better publish something "interesting" and "significant". Tenure is typically based on this. Before attending graduate school, I worked as a machinist at Cal Tech. I happened upon a cabinet full of research documents, recording work done on a synchrotron during the 1960s. Nearly everything was negative results, like they tried to find "particle X" and failed. Fortunately for graduate students everywhere, proving yourself wrong is OK at this level, and numerous PhD's were conferred anyway. But very little of those research results was ever submitted for publication.
As a fun exercise, I generated 10,000 "random normal" numbers and grouped them by hundreds. Each number was the sum of twelve RAND() values. This is a very good proxy for a more rigorous standard normal variable. I applied statistical tests, and the one we'll focus on here is the t-test to determine if the mean (numerical average) is significantly different from the expected value of 6.00. I used a t-test with a z-factor of 1.96 for the 95% confidence level.
95 of the 100 passed the test, and five failed it. I was suspicious, and ran the "recalculate" a few times to check. Mostly, 95 passed, but in one case, 98 did so, and in another 93 did so. This is not unusual. I picked one of the sets for which 95 sets passed the t-test to analyze a little further. I grouped them by twenties. Two sets of 20 had 19 passes and one fail. One had 17, and for the other two sets, all 20 passed. I selected four of the sets of 100 values to plot on probability coordinates, as shown here:
Var028 has the highest mean value, and is one of the five "failures". Var064 has the lowest mean value, and is also a "failure". In the world of "significantly different is good", they would be "positive" results. Var021 has a mean value (5.9971) closest to the mean for the total population of 10,000 (6.0015). Var078 has the greatest scatter (SD = 1.165; 1.0 is expected). Note the apparent outlier in Var078, down and to the left. Many researchers would throw this out, not including it in the statistical tests. Yet this is a valid member of the original data set from which all these variables were drawn. It happens to be the lowest of the 10,000.
Removing that seeming outlier changes the mean by 0.0372 and the SD by 0.055. This would be enough to make a "positive" into a "negative", except that Var078 was negative already, just barely.
OK, so even RCT's need to be taken with a grain of salt. Add to this that not everyone is totally honest. In fact, the system seems to be primed to reward dishonestly, so like in politics, at least some of the scum rises to the top.
What is a fellow to do? The last chapter is titled "Eleven Simple Never-Fail Rules for Not Being Misled by Experts". As if it were that simple. The take-away message is, be suspicious of simple answers to complex questions, look for experts who have nothing to gain (this can be devilish hard), and give yourself time to think before making decisions.
Although I have four major instances in my life where a doctor was wrong, and in one case I had to save my own life, I still go to doctors. I go not as a "patient" but as a customer paying a consultant to render advice, which I weigh carefully, and sometimes to do what I can't (self-surgery is not advised). When I buy shares of a mutual fund, I am trusting an expert, the fund manager, to make better stock purchase decisions than I would (or have the time for). Even, when we select a grocery store to patronize, we're trusting the store's purchasing agents to get the quality goods at fair prices from producers (though we do go to Farmers' markets when we can).
Experts are like parents. Some are good, some bad. We learn as we grow up that our parents are not perfect, though most of us (sadly, not all) learn that they are at least well-intentioned. The author of Wrong has added four Appendices, and the fourth is an essay on the factors that might make the book worthwhile. Considering that "…and every man a liar" is an exaggeration, one can hope that David Freedman has at least made a valiant attempt to present the truth about expertise. He had to rely on experts for much of his own material! So this book is a meta-analysis, and is likely to be slightly more reliable than the individual analyses. Finally, consider these four pronouncements (page 39) from medical studies, answering the question, "Can vitamin D help fend off cancer?":
- No, said a 1999 study.
- Yes, from 2006, it cuts risk by 50%.
- Yes, from 2007, it cuts risk by 77%.
- No, said a 2008 study.
Tuesday, August 03, 2010
I didnt get to go
kw: photographs, travel
My father invited the extended family on a cruise to Juneau and Skagway, Alaska. My wife and I had schedule conflicts and couldn't go, but our son went, along with several of his cousins and their parents. This is kind of like, "My son went to Alaska, and I got this ball cap"; he brought us a few small souvenirs. He also brought back nearly a thousand pictures. For the moment, I'll just share a couple that caught my eye as I was transferring the camera cards to the computer last night.
This totem pole assembly is from south of Juneau, and in the "doorway" you can just see on of the cousins. A couple of the stops were places that are famous for totem poles, and the number of genuine poles (not those made just for the tourists to view) is impressive. A postcard he brought me shows around a dozen totem poles.
The little orange thing next to the iceberg is a Zodiac raft with about a dozen people on it. The freight for taking that Zodiac ride was rather steep, so none of "our folks" went on it. But it really is true that glacier ice gets deep blue! They did get a closeup visit to the Mendenhall Glacier later on, and when I locate a picture of that I'll post more.
My father invited the extended family on a cruise to Juneau and Skagway, Alaska. My wife and I had schedule conflicts and couldn't go, but our son went, along with several of his cousins and their parents. This is kind of like, "My son went to Alaska, and I got this ball cap"; he brought us a few small souvenirs. He also brought back nearly a thousand pictures. For the moment, I'll just share a couple that caught my eye as I was transferring the camera cards to the computer last night.This totem pole assembly is from south of Juneau, and in the "doorway" you can just see on of the cousins. A couple of the stops were places that are famous for totem poles, and the number of genuine poles (not those made just for the tourists to view) is impressive. A postcard he brought me shows around a dozen totem poles.
The little orange thing next to the iceberg is a Zodiac raft with about a dozen people on it. The freight for taking that Zodiac ride was rather steep, so none of "our folks" went on it. But it really is true that glacier ice gets deep blue! They did get a closeup visit to the Mendenhall Glacier later on, and when I locate a picture of that I'll post more.
Monday, August 02, 2010
Cat strike
kw: pets
I had a couple ideas that rattled around in my head all day, but none jelled into a usable post. Then at home this evening, we gave up trying to get the cat to use the scratching post. She seems to prefer the carpet. We bought one of those cardboard thingies from the pet store. They come with some catnip in them.
So far, not one scratch. She likes the smell, and will roll on it, but nothing we've done so far has induced her to scratch it. Some days are like that.
I had a couple ideas that rattled around in my head all day, but none jelled into a usable post. Then at home this evening, we gave up trying to get the cat to use the scratching post. She seems to prefer the carpet. We bought one of those cardboard thingies from the pet store. They come with some catnip in them.
So far, not one scratch. She likes the smell, and will roll on it, but nothing we've done so far has induced her to scratch it. Some days are like that.