Physical linguistics

Physical linguistics consists of two indispensable components: computational verbs
and computational nouns.
Theory of the Unicogse
In the first graduate-level textbook of physical linguisitics
the theoretical basis of physical linguisitics is set as the theory of the Unicogse. The most foundamental question in physical linguisitics is how to measure cognition; namely, how to make cognition measurable. The theory of Unicogse claims that it is possible to measure quantities in cognition via the measurements of their duals in the Universe. The theory of the Unicogse also claims that the door to a computable cognition is a measurable cognition. Physical lingusitics is the first paradigm in the theory of the Unicogse.
Differences in truth manipilation
The computational verb theory addresses issues of computational verbs while probability theory, Boolean logics, multi-valued logics and fuzzy logics address different issues of computational nouns. While any a logic system is truth-conserved, it doesn't manipulate truth directly. Instead, any logic system manipulates only information. The truth is manipulated by the computational verb theory. Therefore, in any logic system, the truth doesn't need a system of metrics to associate with. For example, in a logic system if we say: "atrue is as twice as larger as btrue", it sounds very funny and is unnecessary because all "true" is the same.
Therefore "true+true"(e.g., "atrue OR btrue") is not equal to "2true", but simply "true". However, in computational verb theory where the truth is directly manipulated and information is not directly manipulated, the "true" can have different sizes. In computational verb theory, we can have a "true1" of size 1.5UNITs and a "true2" of size 3.4UNITs, then when the statement "true1+true2" appears, we get a "true3" of size 4.9UNITs. After a Truth in a computational verb collapses into a piece of information in logics, we get a "truth value" of the information. However, at this point, the Truth itself already returned to the Cognition.
The main goal of physical linguistics is to translate all sentences in natural languages into mathematical formulas. For example, the following sentence
The apple turns red from green.
can be expressed by a linguistic differential equation as
d(color of apple)/dt = F (color of apple, t)
with the following boundary conditions
color of apple(beginning) green, and color of apple(end) red.
Computational verb

The purpose of computational verb is to make all verbs in any natural language computable. Each computational verb consists of an inner system and an outer system. The inner system of a computational verb is invisible to the outer observers. For example, for the computational verb “feel”, the inner system is the statues of a brain that are invisible even to the brain itself. For a human being, the inner system is the body together with the brain. In a computer, the inner system is called machinself (from machine + itself). The outer system of a computational verb is the visible part of the computational verb. Therefore, the outer system can be measured and modeled by using mathematical functions that are called outer functions of computational verb.
The concept of computational verb is closely related to the concept of fuzzy set. Fuzzy set is mainly to make adjectives computable while computational verb is to make verbs and adverbs computable. While a membership function is used to model an adjective like “red”, “tall” and “fast”, an evolving function is used to model a computational verb like “go”, “increase” and “feel”.
Computational verb logic
Computational verb logic is an extension of Boolean logic and fuzzy logic dealing with the concept of truth of irrationals. Whereas binary logic and fuzzy logic hold that everything can be expressed in a truth value at any moment, computational logic replaces static truth values with truth implied from dynamics. In a word, classical logic and fuzzy logic deal with “BE true” while computational verb logic deals with “BECOME true”.
Computational verb theory
Computational verb theory is the theory of how to implement verbs and relative verbal phenomena in any natural languages into computers. The building block of the computational verb theory is computational verbs. The basic mathematical concept in the computational verb theory is the computational verb set(verb set, for short), which corresponds to set in classical mathematics and fuzzy set in fuzzy theory. The following statement constitutes a verb set: “All people will go to the States”. While a fuzzy set or a classical set is most likely to state as follows: “All people (BE) in the States”. A computational verb set is more “irrational” than its classical or fuzzy counterpart.
Another important mathematical concept in computational verb theory is computational verb number(verb number, for short). While a real number, an interval number and a fuzzy number can respectively represented as “3”, “”, and “close to 3”, a computational verb number has the form such as “increase to 3”, “become old” and “remain high”. Computational verb number give numbers dynamic lives. Many operations between dynamic processes associated with numbers can be computed by applying computational verb numbers.
In the computational verb theory, the relation between adverbs (adverbials) and verbs are mathematically modeled by using a mathematical concept called operators. In this theory, each computational verb is modeled by a dynamical process of which the dynamics can be modified by either adverbs or operating verb such as “must”, “will” and “be”.
Applications
Physical linguistics was successfully applied to many image understanding tasks
based on its powerful, reliable and robust representation to the visual signals samples from devices such as webcams. Although the design of image processing systems takes enormous amount of efforts, the mechanism of developing an image understanding application based on physical linguistics makes it pretty easy to accumulate dynamic and static experiences and make the learning curve of the design of image understanding systems fast. Yet, physical linguistics also make the threshold for becoming an expert in the design of image understanding systems much lower comparing with conventional methods.
Computational verb PID controllers(verb PID controllers, for short) were used to control different kinds of industrial instruments, including fuel annealer
and industrial motors. Computational verb controllers were used to control auto-focusing system for microscope
,
chaotic systems
, and so on.
The known applications of physical linguistics are:
*Flame detection using a CCD camera or a CMOS camera. In this application the spectrum characteristics of flames are modelled by computational nouns and the dyanmical behaviors of flames are modelled by using computational verbs. By using reasoning engine built upon physical linguisitics, engineers made a much more robust visual flame detector than previous ones.
*Traffic control. In this applications, the shapes and colors of different cars are modelled by using computational nouns while the moving patterns of cars are modelled by using computational verbs. Many high level cognitive image processing tasks are them performed within a physical linguistic reasoning software.

Main Menu

See Also

Main Menu