NOTES ON CONCEPTS OF LANGUAGES & GRAMMARS, MACHINES & AUTOMATA,
	   COMPUTATION, THEIR QUANTUM ANALOGS AND RELATED BEASTIES

	The definitions here go pretty much in a logical order in that
	any definition depends on what has comes before; the concepts are
	also cross referenced by hyperlinks.  Example and
	further elaboration is relegated to URLS and references, so
	this should be mostly understood as a simple, and possibly even
	a useful crib sheet of definitions and relationships.

  1. An Alphabet
  2. A Language L (spoken or written)
  3. A Syntax or Grammar
  4. A Generative Grammar
  5. A Determinative Grammar
  6. A Complete Generative Grammar
  7. An Incomplete Generative Grammar
  8. Semantics
  9. A Formal Language
  10. Operations On and Between formal languages
  11. Formal Grammar
  12. Lindenmayer Systems (L-Systems) Cf. also Wiki
  13. Chomsky Hierarchy of Formal Grammars
  14. Backus Normal Form, or Backus-Naur Form (BNF)
  15. Extended Backus-Naur Form (EBNF):
  16. A Complete Sequential Machine
  17. Equivalence Classes of States
  18. Transition Preserving Morphisms
  19. Reduced Machine
  20. Submachine
  21. Irreducible Machines
  22. Finite State Machine (FSA)
  23. Automaton
  24. A Nondeterministic Finite Automaton (NFA)
  25. Deterministic Finite Automaton (DFA)
  26. Pushdown Automaton
  27. Turing Machine
  28. Universal Turing Machine
  29. Lambda Calculus of Recursive Functions
  30. Linear Automaton
  31. Stochastic Process
  32. Markov Chain (denumerable)
  33. Markov Process
  34. Complex Markov Process
  35. Stochastic Automaton, Markov Machine
  36. Fuzzy Logic
  37. Fuzzy Automaton
  38. Lattice Automaton
  39. Weighted Automaton
  40. Homomorphisms of Automata
  41. Automorphisms of Automata
  42. Self Reproducing Automaton
  43. Quantum Language
  44. Quantum Grammar
  45. Quantum Logic
  46. Quantum Stochastic Process
  47. Quantum Automaton
  48. Finite Quantum Automaton
  49. Finite Quantum Markov Process
  50. Chu Spaces
  51. Entropy
  52. Information
  53. Entropy-Information Ansatz
  54. Computer
  55. Quantum Computation
  56. The Language of Quantum Field Theory

	An alphabet
	is a finite set of discriminatable and irreducible symbols that
	can be used either as such, or as representations of some other
	such set.  This abstracts and refines the linguistical idea of
	alphabet where the symbols map to sounds in a most highly
	contextual way, most especially in English, which because of its
	multilingual basis is rather unphonetic in a strict sense.

	In spoken lnguage, the alphabetic elements are called phonemes,
	while in written language they are called lexemes.  Both have
	a sense of irreducibility.  Therefore, in English, consider "th"
	voiced and unvoiced as distinct lexemes.  In Polish, consider
	"cz" and "sz" as alphabet elements, similarly in Hungarian,
	"s" and "sz" are distinct.

	This sense of alphabet would also include what a linguist would
	distinguish as a "syllabary" [Wikipedia], of symbols of the forms,
	C, V, CV, VC, and even possily VCV or CVC.  The symbols would
	then most generally be called graphemes.

	A Language L (spoken or written)
	formally given, is a set S(A*) of finite length
	strings A* formed from some primary finite set A usually called
	the alphabet possessed of a syntactical (grammatical) and a
	semantical structure.  S(A*) is a subset of the set A* of all
	possible strings formed from an alphabet A.

	A Syntax or Grammar
	for a language L is a set of rules through which the
	strings of L may either be generated, or through which any element
	of A* can be determined to be an element of S(A*).  These two
	types of grammars can be quite different, and inequivalent, so
	much so that where a grammar of the second type may exist, one
	of the first may not.

	A Generative Grammar
	is of the first type in the definition of grammar for language L.

	A Determinative Grammar
	is of the second type in the definition of grammar for language L.

	A Complete Generative Grammar
	is one that generates all the strings of S(A*) for language L.

	An Incomplete Generative Grammar
	is one that generates some of the strings of S(A*) for language L.
	

	Semantics
	is the structure of a language L that gives it meaning
	relating it to its model referants; it is an interpretation of
	the language in terms of some model M involving interpretive maps 
	from L to M as well as metaphorical maps within L itself.
	Though M is the universe of discourse for L, a unique M for any
	given L is unlikely.  Among human languages, written Chinese and
	a period form of Babylonian are languages that require no sound
	value interpretation to be understood, similarly, mathematics
	and computer languages can be almost completely understood
	through their symbology alone.

	A Formal Language
	is simply the subset S(A*) part (string part) of L
	with it's most general meaning.

	A formal language is a quadruple (N, T, P, S), with

	N - a finite set of nonterminal symbols.
	T - a finite set of terminal symbols that is disjoint from N.
	P - a finite set of production rules where a rule is of the form

	               X  →  Y

		X,Y in (T union N)*
                
		with '*' the Kleene star [Wikipedia] and with the
		restriction that the left-hand side of a rule contains
		at least one nonterminal symbol.
          
	S - a symbol in N that is called the start symbol.
       
	For terminal symbols think "atomic" in the sense that they cannot
	be reduced further.  If we were talking LISP, the terminals
	would include those symbols which evaluate (eval) to themselves:
	't', 'nil', and any real or complex number;
	and the "list delimiter symbols" '(' and ')'.

	Sometimes, languages defined by strings are called
	"algebras of strings", but they do not satisfy the usual
	mathematical requirements of algebras: the only binary operation
	is the generally noncommutative "concatenation", the multiplication
	of the algebra.  A formal language can, however, be extended to a
	genuine algebra over the Galois field G[3] by introducing an
	operation of "reversal", (-1)(abc) = (cba) and allowing sums of
	strings as elements of the algebra with coefficients in G[3],
	with S + (-1)S = (), the null string.  This is a noncommutative
	associative algebra over G[3], without identity.  You can also
	get more sophisticated with the field of the algebra, where it may
	start to look like an algebra that is a "quantum language".

	Some languages are group representations, and their algebras
	by such extensions become group algebras.

	The algebra of transformations of strings is the basis of
	representations of Markov processes,
	and a deep formal connection of these with quantum field theory
	expressed in perturbative form.

	If there are algebras of strings (one of which happens to be
	the metamathematical expression of formal mathematical logic),
	are there also algebras of higher dimensional objects transcending
	linear expressions of language?  There is an algebra of Feynman
	graphs - and one might also say a pseudoalgebra of Chinese
	ideograms, a language in itself, divorced from spoken language
	as was also a certain level of ancient Babylonian cuneiform.
	

	Operations On and Between formal languages
	The only fundamental characteristic of a language generated by
	an alphabet A is that it is a subset of A*, therefore,

	Let L1 and L2 be languages over a common alphabet A, then:

	The set theoretic union of L1 and L2 is a language.
	The set theoretic intersection of L1 and L2 is a language.
	The set theoretic difference L1 - L2 is a language.
	The set theoretic symmetric difference (L1 - L2 union L2 - L1)
	is a language.

	If L2 is a subset of L1, define L2 as a Sublanguage of L1.

	For every language L(A), there is Complement Language Lc(A) with
	respect to A* defined by

		Lc(A)  :=  A* - L(A)


	The string concatenation of all strings xy with x in L1 and y in
	L2 is a language.  This is a direct sum of languages, which happens
	to be equivalent in this case to a direct product of languages,
	as well as a tensor or Cartesian product of languages (L1, L2),
	or written simply as L1 L2.

	As an inverse to concatenation, a right quotient language L1/L2
	exists whose elements are those strings x for which there is a
	y in L2 with xy in L1.  A left quotient language can also be
	defined similarly.

	If L is a language, then L* is also a language; if L is a language
	of sentences, then L* is the language of all its literary works.

	If L is a language, the set of all its reversed strings, Lr 
	is also a language.

	These do not exhaust at all the possibilities, they are simply
	some of the more interesting, fundamental and useful operations.
       
	The category of formal languages with common alphabet A is an
	involutive, commutative and associative ring with identity.

	Now consider languages with different alphabet, A and B.
	If the cardinalities |A| and |B| of A and B respectively are
	equal then A and B are isomorphic and so A* and B* are isomorphic.
	For unequal |A| and |B|, there are three nontrivial possibilities:

		1. A is a subset of B, or B is a subset of A.
		2. A and B have a common nonnull subalphabet.
		3. A and B are disjoint.


	Suppose that B is a subalphabet (subset) of A.  Then, for any
	L(A) there exists a language L(B) by process of deleting the
	elements of (A-B) from the strings of L(A).  Then, call L(A)
	a Superlanguage of L(B).  While the sublanguage so constructed
	is unique, L(B) can have many superlanguages.  A superlanguage is
	a consistent extension of a language.
	
	If A and B have a common subalphabet, L(A) and L(B) have a
	common sublanguage L(A intersection B).

	If A and B are disjoint, L(A) and L(B) each contain a sublanguage,
	which are isomorphic to one another.

	---------------------------

	The category of all formal languages is an involutive, commutative
	and associative ring with identity containing a nested family of
	subrings indexed by alphabets.

	Formal Grammar
	is a way to describe a formal language.  In computer science,
	it is specifically a generative grammar, usually expressed in
	top down Backus Normal Form.  The syntactically
	correct strings of the language are generated by free application
	of grammatical rules of substitution.

	A grammar for a language is not unique whether the language
	be formal or not.

	Assume a start symbol S analogous to an empty set, then the
	substitution or production rules generate new strings from
	known strings.  Cf.  Formal Grammar Wikipedia
	entry for formal definition and examples.

------------

	Lindenmayer Systems (L-Systems) Cf. also Wiki

	The production rules encapsulate the underlying processes 
	that result in potential states.  In their sequences of
	applications to produce and given string, the sequence is not
	necessarily unique; different sequences or paths though
	the string S(A*) may yield the same string.  When different
	paths are possible, the grammar is called ambiguous.

	Chomsky Hierarchy of Formal Grammars
	The definition of formal languages is so general that the
	collection as a whole fails to exhibit what one might call
	"interesting structure".  The very same thing happens in
	topology, where Hausdorff supplies a set of additional
	axioms of increasing stricture (and structure) to create
	topological spaces with more specific interest.  Chomsky
	does likewise with formal languages:

		Type 0: Unrestricted Grammars are all formal grammars.
		They are exactly all those that can be accepted by
		some Turing machine.

		Type 1: Context Sensitive Grammars.  These have rules
		of the form
				aAb → agb

		where to make the substitution, the preexistence of
		prefix 'a' and postfix 'b' is required; that is the
		context to which the rule is sensitive.

		Type 2: Context Free Grammars.  These have production rules
		all of which do not have any of the contextual restrictions
		of type 1 grammars.

		Type 3: Regular Grammars are formal grammar (N, T, P, S)
		where all the production rules in P are of one of the
		forms:

		A → a
		A → aB or A → Ba
		A → e
       
		Regular languages are recognizable by finite state automata.

	Backus Normal Form, or Backus-Naur Form (BNF)
	is a collection of metasyntactical axioms for "top down"
	recursive schemes defining a formal grammar.

	As prototypical example of BNF, BNF is defined in BNF:
	NB: the notation is not the historically prescribed notation.

		grammar
			: grammar rule
			| rule

		rule
			: rule '|' formulation
			| NONTERMINAL ':' formulation
			| NONTERMINAL ':'

		formulation
			: formulation symbol
			| symbol

		symbol
			: NONTERMINAL
			| TERMINAL

	Extended Backus-Naur Form (EBNF):
	with
	[] = optional, {} = multiplicity including 0, () = precedence

		grammar
			: rule { rule }

		rule
			: NONTERMINAL ':' [ formulation ] { '|' formulation }

		formulation
			: symbol { symbol }

		symbol
			: NONTERMINAL
			| TERMINAL
			| '{' formulation '}'
			| '[' formulation ']'
			| '(' formulation ')'


	A Complete Sequential Machine
	is a sextuplet (Q, q0 I, O, i, o), where

		Q  is the set of machine states
		q0 is an initial state, a member of Q
		I  is the set of input symbols (input alphabet)
		O  is the set of output symbols (output alphabet)
		i  is the input function mapping Q x I *into* Q
		         or (transition function)
		o  is the output function mapping Q x I *into* O


		i: (Q x I) -→ Q
		o: (Q x I) -→ O

		i * o : ((Q x I) x I) -→ O

	The completeness is that the domain of i is the complete Cartesian
	product Q x I.  For an incomplete machine, there may be i(q, a),
	for s in Q and a in I that are not defined.

	Equivalence Classes of States

	Transition Preserving Morphisms

	Reduced Machine

	Submachine

	Irreducible Machines
	are those which have no submachines.

	Finite State Machine (FSA)
	is a sequential machine with a Q of finite cardinality.
		Acceptors - answer yes or no to their input.
		Recognizers - classify their input.
		Transducers - translate their input, as one language
			to another.

	Automaton
	An automaton is the heart of a sequential machine, that which
	deals only with inputs and states.  Unless otherwise noted,
	the automata considered here will be finite.  They can profitably
	be classified as either nondeterministic or deterministic.

	NB: It is important to notice that a nondeterministic machine
	can exist in several states simultaneously while a deterministic
	machine exists only in one state at a time.  The transitions for
	a nondeterministic machine are not a matter of competing
	alternatives, as they are for a quantum automaton.  It is in this
	sense that a nondeterministic automaton can be represented by a
	simple statisticalized collection of deterministic automata.

	A Nondeterministic Finite Automaton (NFA)
		There can be several *simultaneous* transitions for each
		possible input.  These can be implemented with larger
		deterministic automata, at worst of exponentially
		larger size.  It is defined as a sextuplet

	(Q, q0 I, O, d, f), where

		Q  is the set of machine states
		q0 is an initial state, a member of Q
		I  is the set of input symbols (input alphabet)
		V  is the valuation set {0, 1}
		d  is the transition function governming whether there is
		   an exisiting transition between q and q', elements of
		   Q, and so is a mapping

			d: Q x I x Q  →  V

		f  is the final state determination function

		        f: Q  →  V

	Again, if Q is finite, the automaton is finite.

	Deterministic Finite Automaton (DFA)
		As usually defined these are a special kind of NFA.
		There is only one possible transition for each possible
		input.  It is defined as a sextuplet as for a
		nondeterministic automaton with the condition that for
		any q in Q and a in I, there is a unique q' such that

			d(q, a, q')  =  1

	Pushdown Automaton
	is an automaton equipped with a potentially infinite amount of
	memory in a (LIFO) stack.  Every pushdown automaton accepts a formal
	language.  The languages accepted by nondeterministic pushdown
	automata are precisely the formal languages with context free
	grammars.  Compilers for computer languages are approximations
	to an ideal finite state pushdown automaton, a foible being that
	they can "blow their stacks".

	Turing Machine

	Universal Turing Machine

	Lambda Calculus of Recursive Functions

	Linear Automaton

	Stochastic Process
	is a random process that takes place over time which can be
	considered as given by X(t), a family of random variables
	parameterized by t.  While t is often taken as a continuous real
	parameter, for purposes here it will be taken as uniformly
	discrete, or it may take on values in some discrete set.

	The idea of stochasticity, breezily stated, is that the random
	variables X(t) and X(t') are not correlated.  Prototypically,
	think of infinite sequences of coin tosses where the random
	variables all have values in the set {H, T} and are governed by
	a binomial distribution; the result of any one toss is not dependent
	on, or correlated to any other toss.

	Markov Chain (denumerable)
	A sequence of random variables (Xn : n = 0, 1, 2, ...} is called
	a Markov chain if for every finite collection of integers
	n1 < n2 < n3 < ... < nr < n, the conditional probability
	distributions,

	        Pr{ Xn | Xn1, Xn2, Xn3, ... Xnr }  =  Pr{ Xn | Xnr }

	The chain is called a Denumerable Markov Chain or a
	Finite Markov Chain according on the cardinality of the set of
	possible values of the random variables Xn.

	Markov Process
	A Markov process is a stochastic process exhibited by a Markov
	chain that lends itself to the description of a stochastically
	sequential machine or an automaton because of its sequential
	assumption:
	the state of the system s(k) depends only on its predecessor
	s(k-1).  When this condition is moved via some limiting process
	from a discrete environment to a continuous environment, it
	becomes the condition that the "law of motion". i.e., the law
	which predicts future states from a current state or a past state
	is a differential equation.  Not all physical processes are
	of this nature.  E.g., in rheology, the theory of deformations
	of continuous,
	plastically deformable substances, the fundamental equation
	is an integrodifferential equation because the current state
	depends on the entire history of the deformations which is
	summed up in an integral with a variable limit of integration.
	This turns up in a realistic treatment of a spring which takes
	into account the limit of stretching noticed by Young beyond
	which stretching becomes irreversible.

	In a Markov process for a sequential machine, the machine output
	becomes in essence the current state which as the next input
	results in the successor state.  All that is required to make
	the machine run is an initial input.

	Complex Markov Process


	Stochastic Automaton
	has the same definitions as for a nondeterministic automaton,
	NFA except that for d(q, a, q'), we say instead that any given q
	and a, that

			Sum d(q, a, q')  =  1
			 q'

	the sum being taken for all q in Q.  Now, however, a determination
	of a future state becomes more problematic since the history of
	the transitions comes into play, and the waiting time them matters.
	Consider an extension of d(), d'() defined for a sequence of n
	inputs (a) := (a1, a2, ... an), with (q) = (q1, q2, ..., qn) a
	vector of "dummy variables of summation", each qk ranging over Q,

		d'(q, (a), q')  :=

	Sum d(q, a1, q1) d(q1, a2, q2) d(q2, a3, q3) ... d(qn, an, q')
        (q)

	Defining a matrix D(a1) with elements d(q, a1, q1), q indexing
	rows and q1 indexing columns, the above sum of products is seen
	to be a matrix product, so that for the similarly define d'(),
	a function of the (time ordered) sequence of inputs (a),

		D'((a))  :=  D(a1) D(a2) ... D(an)

	The elements D'(q, (a), q') of D'((a)) now give the probabilities
	after the input sequence that the state is q' if the automaton
	began in state q.

	Notice that the essential condition on d() says that the sums
	of the rows of the matricies D equals 1, which is the condition
	for D to be a stochastic matrix.  One might also call a stochastic
	automaton a Markov Machine since it models a Markov process.

	As the transition matrix D(a) of a stochastic process can be
	Doubly Stochastic, i.e., in addition, the columns sum
	to one, there are also Doubly Stochastic Automata with the
	additional condition that

			Sum d(q, a, q')  =  1
			 q

	Fuzzy Logic
	There is an intimate relationship between formal set theory and
	formal logic, an isomorphism in fact.  While in formal set theory,
	the membership relation x is a member of X is a logical proposition
	with truth values in the diploid set {0, 1}, in fuzzy set theory,
	the truth values lie in the real interval [0, 1] which is then
	associated with a probability measure.  From this assumption on
	"truth valuation" all Fuzzy logic flows.  E.g., if A and B are
	two disjoint sets,

	x in A with prob. p1 AND x in B with prob. p2 implies that

		x in A union B with prob. p1 + p2.

	Since formal logic/formal set theory is the foundation of all
	mathematics, this "fuzzification" is extendible to the entire
	to the entire body of mathematics.  "Fuzzification" is simply
	a "statisticalization"; quantization is more sophisticated.
	The word "quantization" is a fuzzy thing in itself that depends
	for its proper understanding, a context that can vary quite
	widely in the realms of both mathematics and physics.

	Fuzzy Automaton
	is similar to a stochastic automaton.  Where stochastic automata
	generalize the DFA, fuzzy automata generalize the NFA.  Formally,
	a fuzzy automaton is a sextuple (Q, q0 I, O, d, f), where

		Q  is the set of machine states
		q0 is an initial state, a member of Q
		I  is the set of input symbols (input alphabet)
		V  is the valuation set {0, 1}
		d  is the transition function governing whether there is
		   an exisiting transition between q and q', elements of
		   Q, and so is a mapping

			d: Q x I x Q  →  V

		f  is the final state determination function

		        f: Q  →  V

	Again, if Q is finite, the automaton is finite.

	------------------ Sup and Inf stuff --------------------------

	Lattice Automaton

	Weighted Automaton

	Homomorphisms of Automata

	Automorphisms of Automata

	Self Reproducing Automaton

	Quantum Automaton

	Quantum Language

	Quantum Grammar

	Quantum Logic
		orthomodular lattice

	Quantum Stochastic Process

	Quantum Automata

	Finite Quantum Automata

	Finite Quantum Markov Process

	Chu Spaces

	Chu Guide

	Entropy
	First arises in the context of classical thermodynamics and the
	Carnot engine (an ideal heat pump) where it is defined as an
	integral over a cycle of the Carnot engine,

	                S  :=  I dQ/T

	where dQ is an infinitesimal amount of heat transfered at
	temperature T.  Its fundamental relation, derived from the
	first law of thermodynamics (an expression of the conservation
	of energy) is,

	               ++++++++++++++++++++++++++++++++

	The original concept of entropy was a heat that was unavailable
	to do work, an idea that was apparently first understood by Carnot,
	who also gave the second law of thermodynamics as a "principle"
	of the subject.

	Reinterpreting the idea and the thermodynamic variable in terms
	of the statistics of atomic theory, and equating the old variable
	with the statistically derived quantity by Ludwig Boltzmann
	was quite a transcendental leap that both clarified the concept
	and complicated the second law, and its legitimate applications.
	In preatomic thermodynamics, it was understood that the second
	law applied to closed systems.  This remains from the statistical
	mechanical point of view, but the added understandings are that
	it applies also only over long times; furthermore, entropy now
	allows statistical fluctuations that are unknown in classical
	thermodynamics.

	With the advent of statistical mechanics based on atomic theory,
	the statistics of the classical mechanics of atoms is to "explain"
	thermodynamics, and in this case, the functional of a probability
	distribution {pk} usually taken to express the entropy is

	               S({pk})  :=  - Sum  pk ln( pk )
	                                k

	This is not the only expression for entropy, just the first.

	Information
	First defined by Shannon, independently of Boltzmann's work,

	               S({pk})  :=  + Sum  pk log( pk )
	                                k

	This is not the only expression for information, just the first.
	The logrithm base often used in 2.  It is defined as a functional
	of a proability distribution, with obeisance to the abstract
	fundamental bit encoding of abstract computers.


	The mathematical sense of "information" is not about
	*information* in the ill defined common sense.  The Shannon
	type functional is an intrinsic definition that transcends
	language and semantics, and has to do with "information to
	a possible decoding machine - and I do mean machine like a
	Turing machine" reading explicitly from a string of symbols.
	Our garden variety concept of information is more complicated
	since it depends on semantics and extended metaphors.  The
	mathematical definition avoids all that and concentrates on
	the statistics of the symbolic stream: an 'e' is the most
	prevalent letter in English text (and also probably French
	text, I never saw the numbers for that and don't remember if
	I did), therefore, it carries the least information:
	distinguish all English words that have 'e' in them.  A rare
	letter like 'z' carries more determining weight (and
	information): distinguish all English words that have 'z'
	in them.  Letter 'z', however is more plentiful in Hungarian.
	No, I haven't done or looked up the statistics, I go on what
	I learned of Hungarian, and partially in that "az" is the
	Hungarian "the".  The flatter the probability distribution,
	the less info any alphabetic symbol has, the less information
	that symbol can be said to carry, and the more entropy
	(disorder) associated with the probability distribution.  

	Entropy-Information Ansatz
	Structure and order of energy is a measure of information, while
	entropy is a measure of energetic disorder.  Historically, the
	thermodynamic concept of entropy came first, but almost since
	Shannon's first papers, the idea that the two concepts are
	intimately related arose.  Strangely, the semantics of their
	relationship as negatives of each other has only recently been
	proposed, particularly in the understanding of the entropy of
	black holes in general relativity, but before that in analyzing
	why the Maxwell Demon of statistical mechanics ultimately
	fails.

	Increasing the entropy of any closed physical system
	decreases its information content by an equivalent amount.
	In a sense, looking at entropy and information in the context
	of physical systems can be seen as different interpretations
	of the same thing.  Remember though that Shannon's original
	idea has to do with strings of alphabetic symbols sent and
	received over a "channel" that as a process had probabilities
	attached to the symbols.  Out of this comes coding theory,
	and selfcorrecting codes suitable for transmission through
	noisey channels.

	Computer
		Neumann Models

		Turing Models

		Alonzo Church Models

	Quantum Computation
	(from Feynman's suggestion in 1982,
	that the real world actually operates as a vast computer)
	A situation where it is possible that the physical theory
	which may be useful to understand how things happen, may
	refuse to extend itself to useful engineering applications
	for the purposes of control.  In the history of science
	and engineering, this may be the first instance of where
	government cannot use scientific developments for their
	all too usual purposes of control and genocide.  This does
	not mean that governments will not create nonscience to
	to excuse their own rather insane and irrational lusts;
	acting on their own lusts is, after all, what governments
	do, and so long as they exist, there is no stopping them
	from their attempted mendacious and genocidal intents.
	Realities never disuade them from their primitive obsessions.

	The Language of Quantum Field Theory
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Footnotes


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The URL for this document is:
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Created: June 3, 2004
Last Updated: October 24, 2005
Last Updated: January 25, 2006