This month I sat down to work on my applet pie ala modem and popped open the zipper on my pants. My unzipped state forced me to reflect on my recent research on cookies and chips. I decided to move away from research on food in hopes of trimming down.
In my lifetime I have seen many ways for bigger to become smaller.
When I was a child I loved my mother’s hip trimmer. It consisted of a small motor attached to a wide canvas belt. My mother would loop the belt around her hips, turn on the motor, and jiggle the inches away. At 10 years old, I would loop it under my armpits and shake my entire self into hysterics- my hair has never returned to normal.
At about age 40 my father decided it was time to trim up his mid-section. Every morning at 5 a.m. he would walk out to the road in front of our house and jog back and forth along a 50-foot stretch. He appeared to be running an extremely slow shuffle run. The daily jog and foregoing his nightly wedge of after dinner ice cream did make his tummy shrink.
My grandmother maintained her trim waistline most of her 85 years by limiting her daily caloric intake and exchanging calorie equivalents. She omitted vegetables, meats and biscuits and ate only sweets- divinity, fudge and ambrosia. In her book one square of fudge equaled 10 pounds of broccoli, so she was really cutting back.
In contrast, my grandfather always ate enthusiastically and never had a weight problem. He shaved calories by eating foods that he swore had negative calories, like grapefruit, or by eating foods that expended more calories in the eating than they were worth, like lettuce.
Now, I don’t really mind dieting all that much, but you would think that in this new millennium there would be some better way to go from unzipped to zipped.
It turns out that data files have suffered from being too slow when trying to travel over the Internet. The need to trim up files has led to the use of file compression techniques…data dieting.
One of the most common ways that a file can be compressed or made smaller is by using symbols to represent groups of letters, words or phrases. Here is a simple example of how a “dictionary” can be created to have symbols that replace words. New Bohemian Edie Brickell recorded a song that goes, “What I am is what I am are you what you are or what…” A compression dictionary might assign symbols to the words such as 1=what, 2=I, 3=am, 4=is, 5=are, 6=you, 7=or. Then Edie’s compressed version is 12341235616571, much shorter than the original text.
File compression uses symbols to replace longer text and takes advantage of redundancy.
This seems like a simple task, but efficient file compression is very complex and makes use of mathematical algorithms. An algorithm is a predetermined pathway which looks at alternatives and guides the selection of the most efficient substitution of symbols for patterns of words and letters. File compression/decompression programs usually use some version of the LZ adaptive dictionary-based algorithm.
LZ stands for Lempel and Ziv. Abraham Lempel is an electrical engineer who has spent his research career using discrete mathematics to solve computer science conundrums. He is currently the Director of Hewlett-Packard Labs in Israel. His colleague, Jacob Ziv, is an electrical engineer who has worked for Bell Labs. In 1977 they published the Lempel Ziv algorithm to compress data going out and decompress data coming in- zip and unzip. Some variation of their compression algorithm is a part of your computer’s software today in programs like WinZip or Stuffit.
This type of substitution compression is called “lossless” since the file can be recreated exactly like the original when it is decompressed. Sometimes, particularly with graphics or pictures, the file is compressed by actually leaving out some no critical bits of data. For instance, if an original picture has 10 shades of blue in the sky, then the file might be made smaller by only allowing 3 blue shades in the sky. The detail or resolution might not be as good as in the original, but the information can give the same general idea as the original. This is “lossy” compression since the original is not fully restored.
Folks like Lempel and Ziv are probably too busy discussing the complexity of finite sequences to overeat, but that kind of diet just won’t work for me. I think I’ll opt for your basic compression. I plan to zip up my brownies into Melba toast about the time they reach my stomach and then unzip them back to chocolate dreams at night!
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