Low carb diets

OmegaZero2003 wrote:

"Proton Soup" wrote...

All engineers should know what superposition is. Are you
trying to be condescending?

Who? Me?

Sorry. I was probably a little. It seemed elementary
considering the responses seemed to be intelligent but
missed major aspects of this.

No they didn't. That was a representation in your own wetware.

--
-Wayne

OmegaZero2003 wrote:

Did you bother to see the papers on computing and
representation I referenced?

Nope! 8-)

See, I have a limited capacity for curiosity, and I suspect
we're arguing about subtleties that verge on the meaningless.
At the very least, a lot is being lost in translation.

I do not care what the neurophsyiological response of a
crayfish CNS is; there ain't any instruments yet that can
tell just what processes and properties in *any* part of a
NN exactly *represent* a "piece" of information - even given
what the definition of "information" is (beyond a
difference).

The problem I have with this is that there's a point, with a
very small number of neurons, where this distinction vanishes.
This is the same problem most researchers have with the idea
of "emergence". Beyond a high level of complexity, the
network representation is probably closest to a high-
dimensional hologram. At a low level of complexity, it's
probably pretty much like an ANN. Is the distinction one of
topological significance, or is it really all the same thing?

In terms of computational neuroscience and ANNs(artificial
neural net), remember that the ANN is a couple orders of
magnitude less sophisticated (at least) (using simple I/O
transforms and connection schemes used to build multi-layer
ANNS) than the real thing in situ.

I never claimed otherwise: I brought ANN's into the
discussion because of the clear understanding of the roles of
threshold and saturation on their behavior.

--
-Wayne

OmegaZero2003 wrote:
"Proton Soup" wrote in message
...

On Sun, 21 Dec 2003 06:29:45 GMT, "OmegaZero2003"
wrote:


BTW, as a PS to my other answer post, here are some linear systems.

- those characterizable by linear algebra. there are lots of these!
- Hamiltonian oscillators and like systems. (the direction field
specifically)
- continuous-time systems like electrical networks, many mechanical

systems

Only simple RLC electrical networks fall into this category. And even
then, it's just a theoretical assumption over the useful operating
range. Too much current or voltage or flux will flux up your circuit.
Linear electrical networks only exist on paper.


My original point to the OP on the topic was a retort to the statement that
*all* systems are nonlinear.

That is not true.


- any discrete system with a transfer function whose input, response and
output functions depend on one variable
- any systems preserving homogeneity (output proportional to input) and
superposition (a way of combining linear functions such that the result

is a

linear function)

Even non-deterministic systems can be modeled using statistics for linear
dynamics.

But the main point to not be belabored is that there are linear systems

on

nature and manmade (1)

Most systems *are* non-linear but some of those are characterizable using
linear methods to some degree of accuracy; you did make something like

this

point.

(1) Schwarz, Ralph J. and Friedland, Bernard, 1965, Linear Systems, New
York: McGraw-Hill Book Company, 521 pp.

---
Proton Soup

"If I drink water I will have to go to the bathroom and
how can I use the bathroom when my people are in bondage?"
-Saddam Hussein





As several people have pointed out, linear vs. non--linear, per se,
is meaningless. It all depends on the description. Some things are
exactly linear (in the right description.) Sometimes linear is just
local -- but even if local, this can provide quantitative information.
(The example that comes to mind is the tumbling book. Two
directions are stable, one is not. Linear analysis shows this.)


--
Tom Morley | Same roads
| Same rights
| Same rules
AIM: DocTDM

In article ,
Elzinator no one@com wrote:
Lyle McDonald wrote:

Ok, since the book project this was originally written for is unlikely
to ever get done, I figured I'd post it. It's a long (11 pages)
chapter/piece examining the pros/cons of the major dietary camps

Dude, just upload it to your website! That's what it's for.

I had snagged it, fixed typos and formatting, and put in in the archives at:
http://www.trygve.com/mfwalylediet.html

I haven't put in any links to it, but I can if that's okay.

--
soc.singles FAQ [ Nyx Net, free ISP ] Misc.Fitness.Weights page
www.trygve.com/ssfaq.html [ http://www.nyx.net ] www.trygve.com/mfw.html
today's special featu Santa Claus, Fugitive From Justice
on America's Most Wanton: http://www.trygve.com/mostwanton.html

"Tom Morley" wrote in message
link.net...


OmegaZero2003 wrote:
"Proton Soup" wrote in message
...

On Sun, 21 Dec 2003 06:29:45 GMT, "OmegaZero2003"
wrote:


BTW, as a PS to my other answer post, here are some linear systems.

- those characterizable by linear algebra. there are lots of these!
- Hamiltonian oscillators and like systems. (the direction field
specifically)
- continuous-time systems like electrical networks, many mechanical

systems

Only simple RLC electrical networks fall into this category. And even
then, it's just a theoretical assumption over the useful operating
range. Too much current or voltage or flux will flux up your circuit.
Linear electrical networks only exist on paper.


My original point to the OP on the topic was a retort to the statement
that
*all* systems are nonlinear.

That is not true.


- any discrete system with a transfer function whose input, response
and
output functions depend on one variable
- any systems preserving homogeneity (output proportional to input) and
superposition (a way of combining linear functions such that the result

is a

linear function)

Even non-deterministic systems can be modeled using statistics for
linear
dynamics.

But the main point to not be belabored is that there are linear systems

on

nature and manmade (1)

Most systems *are* non-linear but some of those are characterizable
using
linear methods to some degree of accuracy; you did make something like

this

point.

(1) Schwarz, Ralph J. and Friedland, Bernard, 1965, Linear Systems, New
York: McGraw-Hill Book Company, 521 pp.

---
Proton Soup

"If I drink water I will have to go to the bathroom and
how can I use the bathroom when my people are in bondage?"
-Saddam Hussein





As several people have pointed out, linear vs. non--linear, per se,
is meaningless. It all depends on the description. Some things are
exactly linear (in the right description.).

Sometimes linear is just
local -- but even if local, this can provide quantitative information.

Sure - but that is known as a subclassed system. You are only examining it
within a range of I/O/Xfer_function(s). Which is fine as far as it goes,

I was referring to a system that exhibits true linear behavior throughout
all known or extraploated ranges of input.

(The example that comes to mind is the tumbling book. Two
directions are stable, one is not. Linear analysis shows this.)

Yes; although the point that was made , as I now understand, was about
biological system, which certainly have more than the bulk of examples of
non-lineear systems.




--
Tom Morley | Same roads
| Same rights
| Same rules
AIM: DocTDM

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Proton Soup" wrote...

All engineers should know what superposition is. Are you
trying to be condescending?

Who? Me?

Sorry. I was probably a little. It seemed elementary
considering the responses seemed to be intelligent but
missed major aspects of this.

No they didn't. That was a representation in your own wetware.

How do you know I am not an ANN!

You still have not shown how robustness is a function of saturation and/or
thresholding.

Although after thinking about it, in an oblique way, one can make up a story
about it - a mind game ala Einstein. I.e., theoretically, I can imagine
that a system can be thought of as robust if it escapes
deterioration/degradation and/or elimination from the context/environment if
it exhibits saturation/thresholding and that prevents state spaces leading
to elimination.

Coming up with a *real* example of such a charaterization in nature is left
to the reader.


--
-Wayne

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

Did you bother to see the papers on computing and
representation I referenced?

Nope! 8-)

See, I have a limited capacity for curiosity, and I suspect
we're arguing about subtleties that verge on the meaningless.
At the very least, a lot is being lost in translation.
OK.


I do not care what the neurophsyiological response of a
crayfish CNS is; there ain't any instruments yet that can
tell just what processes and properties in *any* part of a
NN exactly *represent* a "piece" of information - even given
what the definition of "information" is (beyond a
difference).

The problem I have with this is that there's a point, with a
very small number of neurons, where this distinction vanishes.

First, I suspect that that type of system is uninteresting.

Second, it probably does not exist as a real system in situ' one can take
away only so much of a system auntil it ceases *being* anything like what
you were trying to show in the first place.

Such is the case with a system whose function is representation (and
transformation/translation/signalling).

Third, that distinction is not a quatitative one - it is qualitative. take
away the neurochemical soup for example, and what you show about information
representation is apt to be misleading at best. *Analysis* (in the form of
reductionism)is not always a good approach when dealing with complexity .

This is the same problem most researchers have with the idea
of "emergence". Beyond a high level of complexity, the
network representation is probably closest to a high-

But that is only the network representation; one of several maps, none of
which is the territory.

And most neuroscience researchers or AI researchers for that matter, so not
have a problem with emergence. It is quite well described and accepted.
Again, a really good book is Alwyn Scott's! I recommend it to any scientist
I speak with (just as I recommend Wolfram's work, and Bucky Fuller's
Synergetics).

dimensional hologram. At a low level of complexity, it's
probably pretty much like an ANN. Is the distinction one of
topological significance, or is it really all the same thing?

I don't follow; I don;t think even a highly-connected network like the brain
has properties at the network level (nodes, connections, vertices etc.) that
are appropriate in a discussion about holographical metaphors.

Now, quantum effects, or other field effects -now we're talking.


In terms of computational neuroscience and ANNs(artificial
neural net), remember that the ANN is a couple orders of
magnitude less sophisticated (at least) (using simple I/O
transforms and connection schemes used to build multi-layer
ANNS) than the real thing in situ.

I never claimed otherwise: I brought ANN's into the
discussion because of the clear understanding of the roles of
threshold and saturation on their behavior.

YEs - OK - I understand. But those are man-made systems that, llike I said
above, are so abstracted, or rahter, simplified from the actual NN in the
CNS -in situ with all the attendent functions provided by messenger
molecules, densities , field effects etc., that the analysis has analysed
away any chance of getting a parsimonious, satisfying sanswer to things like
representation in a real brain.



--
-Wayne

OmegaZero2003 wrote:

"Wayne S. Hill" wrote...

The problem I have with this is that there's a point, with
a very small number of neurons, where this distinction
vanishes.

First, I suspect that that type of system is uninteresting.

Perhaps to you, but I've found that ANNs have unique properties
for state space analysis. The chief one is this: given a set
of inputs, each considered a different dimension for the state
space of the problem, ANNs have the property of being able to
scale each neighborhood of the state space differently. This is
exceedingly difficult to achieve with explicit state space
analysis techniques.

--
-Wayne

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Wayne S. Hill" wrote...

The problem I have with this is that there's a point, with
a very small number of neurons, where this distinction
vanishes.

First, I suspect that that type of system is uninteresting.

Perhaps to you, but I've found that ANNs have unique properties
for state space analysis. The chief one is this: given a set
of inputs, each considered a different dimension for the state
space of the problem, ANNs have the property of being able to
scale each neighborhood of the state space differently. This is
exceedingly difficult to achieve with explicit state space
analysis techniques.

Well - yeah - classifiers is a classic use for such.

I meant from a research_into_brains standpoint, not man-made *A* NNs applied
to other problems.


--
-Wayne

In article ,
Lyle McDonald wrote:

I can also report empirically the feedback I've gotten. This was from a
few years ago (on the lowcarb-l list) from folks comparing things like
Isocaloric (33/33/33) diets to Bodyopus (CKD) types of diets. These
were motivated bodybuilder/athletic types who are known for being anal
compulsive about their diet and who were using similar protein inakes
and caloric deficits (and weight training and the rest). At *most*, the
variance in fat loss/LBM loss was ~3 lbs over 12 weeks. That is, they
might report 3 lbs more fat lost and 3 lbs more LBM maintained over that period.

Adding:
a. Even then, the effects weren't consistent. Some folks did better on
CKD's, some folks better on Isocaloric (and lost more muscle on the
CKD). Meaning there was no consistent pattern with one diet being
absolutely superior.
.. . .
b. 3 lbs is within measurement error (sorry, this is the cynic in me
speaking). Hell, it's within the error of glycogen and water balance.

c. 3 lbs of fat vs LBM is hardly relevant for the majority of dieters.
For an athlete or bodybuilder, yeah, it matters. But without a
consistently superior diet or a way to know who will be ideally suited
for one or the other, the above is kind of meaningless (at this point,
there's no good way to apply it).

A consistent difference of 3 lbs would be worthwhile.

If the maximum difference measured is 3 lbs, and some measurements had
the opposite sign, then I'd guess the average is unde 1 lb. That's
well within the measurement error noise.

Seth
--
"There is no such thing as an essential carbohydrate" -- Will Brink
Except sushi rice, seaweed, and wasabi.