Tunneling

The difference between the euclidean and non relativistic. The euclidean space is a decompressed version of the Markov closed surface. Tunneling measures the error in the Euclidean R from a perfect sphere. Note they introduced the circular function to close the operators.

In Markov terms it is the variation from recoloring, when N is short.  The second derivative in the kinetic energy term just gets you to a linear collapse, it relates N in a volume to N along a radius.

The effect is due to vacuum state, it is being colored.  There is a natural kinetic energy because spin has one uncompressible center.

But actions really leaves the region! Yes it does, the beach ball is carrying a slight leak. No flow, no quantization, Hawkin;'s rule. Measurement cost energy. The fine constant is the kinetic energy of ground state.

In euclidean space you have made an artificial torus. Created a spot where x can go to zero thus obeying Newton's rules of grammar. In Markov units N, the sufficient number of counts is not known, locally. x goes to one and stops at a fuzzy constant.

In Markov, just put the vacuum rates into the best estimate of a 4 Tuple. Then you have an artificial one, and you can tick along in the fake 4D axis.  Count the number of time N, in the 3D would roll over. Then you have a simple unitary expansion, let the round off error go to zero and get Newton, once again.

This line of thinking leads me to believe there is a 4D subtle order in the vacuum, space is toroidal. The theory defers the problem, who or what caused the vacuum to flow?

An excuse to escape the lock downs

US anti-racism protests stretch to distant New Zealand

In the days college kids had a fundamental reason to protest, it was fun. Thus gave birth to the Yippie movement. Like going to parade.

Basketball envy

China considering plans to genetically-modify soldiers to make army of 'Terminator-style troops'...

Fear of being the Asian guy on a basketball court has been a constant anxiety in that culture.

Fun equaion

Looks to me like another generalized Nyquist-Shannon-Markov-Boltzman-Gibbs-Planck in 3D.

I have:

some constant * r^2 = all paths created by q1 and q2 taken two at a time. But q1 an q2 come with an implied spin deviation.

But q1 and q2 have been rounded, they define color operators. r^2 means one axis of symmetry, but it really is x,y,z.  The big constant becomes the scale factor to Euclid. Hidden in that pile up of a constant must be the Boltzman compression, the merged paths. Pi and permitivity make that Boltzman compression reference the unique center.

Butt, take R in this case as being marks along the operator centroid. Shannon, Nyquist, Markov, Boltzman all say that two charge deviations will circle that centroid to create a degree two continued fraction estimate of N, or the steps through the centroid.

This is a workable problem, and should simplify physics quite a bit. q1 and q2 become deviation rates. The centroid we are counting is always vertical to the operator, the system comes with its own tensor. This is time independent,  On a closed surface this will completely color both deviations counts, marked by a spin bit, to within standard 3D light steps. You will get a beach ball.   Paths symmetric to the spin spots have been eliminated, and in the fractional elimination you will get Pi approximated into the transformation that preserves a center. Permitivity is really the total N of all such layers within the closed surface. N being the total number of Plank actions which are a local node exchange in the expansion tree. The expansion tree is simply two integer rates at which the set combinatorials have error.  So in this case, make a Markov 4 Tuple, ignore the magnetic and instead pick a point on the 4 tuple Markov tree that matches you model error along the three axis. Count each q rate independently, this is all the combinatorial version of boundary conditions. 

I see nothing in the equations but the natural limits of combinatorials. When we go from a triple to a quadruple we are just doing an axis decompression, back to Euclid.  That jump from closed to open puts the modeler at the center with counts appropriate to his estimate of an fake Avagado that gives him sufficient accuracy for the experiment. But everything looks like a 3D geodesic to in the decompression.  All thee experiments involve finding the enclosed Euclidian cloud, and working backwards. The fine constant, in Markov deviations, are is the variance between connected opearators. It is non zero, the path of that variance is the kernel of force laws and should appear in Fc. It is Plank's constant, actually, the maximum deviations allowed per three light steps, or one tick up the operator centriod. In finance that fine constant is the pit boss market risk. In Bayesian terms that is round off error in the best match between two independent sets.

Kinetic energy is in this conext, but not internal and it is noticeable when the phycists measures, an Avagadro and works backward from the Markov 4 tuple tree.  N errors show up as motion in Euclidian space.  In Markov model kinetic energy shows up as a recoloring of the closed surface.  So the model has to force a vacuum color operator, there will be combinations where a some operator finds the vacuum is the minimum deviation path.   That is what forces the universe into a toroid.

The quasars must be the derived center, the M+1 dimension. They split N errors, using mass.

Same as last recession, but worse

Coronavirus economic shock to be felt for a long time, CBO says 

By Jonathan Nicholson.
Even over next decade, U.S. economy to be smaller than forecasted in January, analysts say

Trump: absolute ignorance

Trump To Governors: You Should Consider Banning Flag-Burning Because We Have A Conservative Supreme Court Now

I know  a couple of his supporters and they suffer the same fate.. But between Gavin, Trump, Roberts and Kragen, Constitutional ignorance is astounding. The federal courts will be jammed up for years.

Dots are not connected in DC:

One would think this through dirst. He had to clear the protesters to get this shot.  The political cost and benefit were not quite worked out.

Unless there are some dots I am missing.  Maybe Gavin is deliberately provoking the Supremes by singling out churches in a covid order. Maybe Trump deliberately shows missing facts because he has connected some dots I don't know about.

But I doubt it.  All these boneheads simply restart some long standing political cycle repeatedly which comes back to haunt. My other evidence is Jerry Brown, whatever you think of him, he connected dots, better political dot connector as I have seen. Dan Walters, another political dot connector. The Gavins and Trumps demean their voters, create voter regrets and their supports skip the ballot box. Voters in California voted for Brown, from both sides, because we were close enough to see the dots connect, and he would point out gaps that do not close. His tensor picked a good verticals, mostly.

That is a bit if deflation

COVID pandemic will shrink economy by $8 trillion in next decade: CBO

What is unique about this crisis?

It is a covid panic, no one wants to get the nasty disease. So that put the economy onto a single axis, the covid risk axis, has been pointed pointed out. The other thing is it is black swan, no one is really to blame.  As the crisis drags on the solution is a restructuring of the medical industry an covid safety.

Obama pens essay on using George Floyd protests as 'turning point for real change'

Yea, we always say this stuff after every uprising of the natives.

Dirty Old White Men dunnit:

Seattle Mayor: You Know, White Men Are Behind The Violence At These Riots

A government Markov model

We have a bicameral, so we want money to states to match money to districts  Our minimization function is we want a citizen to be equally indifferent to state of federal subsidies. 
Count states up to 50, I think, and count districts by 435.    Let the small counter be the number of pariitons you think in groups of states, it is a test variable, you want to see if all the paritions are in equipartition.  P,S,D and M.  M are payments.  Count payment unit at some precision.  Allocate bandwidth by -iLog(i) as in last time.

This is time independent, it gives you the number of payments you need to make to get the best match.  Then you can scale the unitary payment and do it all at once.

Comes from a Markov triple and beyond

Thermodynamic derivation of the energy density[edit]

The fact that the energy density of the box containing radiation is proportional to ${\displaystyle T^{4}}$ can be derived using thermodynamics.^[17][18] This derivation uses the relation between the radiation pressure p and the internal energy density ${\displaystyle u}$, a relation that can be shown using the form of the electromagnetic stress–energy tensor. This relation is:

${\displaystyle p={\frac {u}{3}}.}$

(x^2+y^2+z^2)/3= xyz

Which can be generalized to the general Shannon equations in N dimensions.   The equipartition theorem is simply the -iLog(i) must all partitions be within an integer. 

But then they go on, which Markov N-tuple do not. They separate kinetic along the transformed axis and provide the connection to N, total energy, which is what the stress-energy matrix does, it is a 4D. Total energy is a Hamitonian, and the 3 becomes a 3+1 after clever cancellations.  

That makes 4T be the log of something because xyz it total number of combinations, Temperature, no? But they have boundary conditions.  Then they need to scale by light steps as a wavelength, and essentially quantize x,y,z; that is, determine how many deviation counts they are allowed per coloring.

Use a Markov 4-Tuple, find a z deviation count that matches the need for euclidean precision, a z rate that divides up a complete sequence adequately to count integers. Then on your lower three counters,  Then the w,x,y counters  on the Markov 4-Tuple need to be -iLog(i)., that gives you the precision of the three axis is,w,x,y in Euclidean.   Then encode your w,x,y,z as you and sum along the z count. w,x,y,z will be a string of continued fractions. z is essentially taking half the deviation counts.

Remember, you are working in deviations in w,,x,y in the 3-tuple, continued fractions. z should count integers because you made it unconstrained from path merging.  But the Markov Nth integer, time,  still needs t be -iLog)i)I will keep repeating this to my self. The model is setting a fake N and suffering the consequences in kinetic energy on the paths in euclidean space.

When you are done, decode back to approximate original scale. Then set some standard ;like feet/sec.  You will find the general shape of the curve a result of the generalized Shannon equation for M dimensions, and M will mostly be three.

Now the three body in gravitation problem.

Start with a 4-tuple. m,x,y,z, where m,x,y,z are deviations in a surface covering the three point masses in Newton's gravity.  The problem is unresolvable, we know that. So our finite element strategy will be one of slightly being off count, relatively prime,  on the current deviation count. I gave mass the lower axis as it goes linear.  the gravity equations.    Now select your precision and find a matching 5-Tuple where the granularity of time are suitable..

Repeat the process, find the -iLog(i) for your m,x,y,z,t; then for each step of z, count the lower counters appropriately, convert all four counter back (only one or two changed).. 

Note, I am not computing the equations of motions, the transformed m,x,y,z,m are small differentials, the reals M,X,Y,Z,T  are integers with continued fractions.. The user has to move  differentials on an axis along with its finite unit of mass, and do the (X-Z)^2 and ( X-Y)^2  and M linear outside the Markov.  It is the model that takes us from Markov deviations to fractional real in another transformation. That is not a Markov thing, but the N-tuple concept is quite flexible, it can require a lot of extra counting in t, but you boil it down to a summation over a closed line.

It is simply the optimum frame reference for computing a model to fractional approximation. It works when model frame has the same equipartition as the Markov frame, the appropriate axis resolution.  Where works ,mean you gain the most precision with the fewest transactions.