Quantum interactions should have the beauty of coupling like electromagnetism, and there should be mutual transformations inside. There are boundaries www.biquge.info the pen

The model of the network is based on probability, and the probabilistic model serves as an axiom

The iteration is carried out using the Bayesian formula, and convolution is a type of ergodic collapse like a probability collapse

The basic unit of a network is a loop, a circuit is an analog, and the mathematical form is a differential equation. The equivalence of the different levels of the network (Norton's theorem, Thevenin's theorem), the macroscopic level of interaction, i.e., the conversion and storage of energy

Algorithms, the structure of data structures, based on great mathematical ideas (fuzzy eigenguesses, adjusted based on a priori experience, increased probability: consideration of limits, ignoring variables; dimensional analysis; continuous approximation operations, discretization; generalization, visualization)

Omics builds networks, networks derive variation, and the possibility of variation collapses to natural selection

Machine learning is the continuous experimentation and screening of hierarchical coupling forms

Omics: sequence analysis, hidden Markov models (not only for pairs but also for combinations), gene search, structure folding, sequence pairing, hierarchical coupling due to similarity (correlation).

Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-Free Networks, Network Motifs (Pathways), Aggregation, Power-Law Distribution, Convergence Radius, Network Evolution

Evolution: Comparison of omics, combinations, rapid derivation and screening of possibilities

The matrix suggests an energy-optimized path (fraction = likelihood of matching pairs), perhaps introducing multi-level matching pairs, and then making certain selections at this level (gene sequence AAG and amino acids corresponding to different codons).

The expert system is a simulation of artificial intelligence, which is full of games and choices at all levels to maintain sufficient openness and integrity. It requires the skills of a quantum computer to solve the eigenvalues in a sufficiently short time (central law, fixed point principle (yf) = (f(yf)))

Programs and recursion are computational needs, but this is a large-scale hard-scale equivalent to small-scale high-dimensional operations using simple superposition. We might add an iteration of the Bayesian network, multithreading

Applications are king

Language is an intelligent system: the logic of syntax, semantics, (network structure, collapse of probabilities, various distributions, classifications).

A circuit, an analog of a network, can perform infinite computations (idealization), but it conflicts with time, i.e. we need to choose an equilibrium point that meets our real-world needs.

The fundamental interaction is the electromagnetic force, which is based on Maxwell's equations: all are dynamic, electrodynamic generates magnetism, and kinetic magnetism generates electricity. Its own electricity and magnetism are also dynamic. Next is KCL, where the current of KVL is conserved at one node and the voltage of the loop in the region is coupled to 0

Differential equations can describe interactions and are eigensolutions to networks. According to the Taylor series decomposition and the Fourier decomposition of the network (time domain and frequency domain), different matrices are combined to obtain different eigens according to the different coupling cases of the hierarchy, and the combination of eigens is a matrix, and its eigens are the eigenvalues of the level, i.e., properties or expectations.

Turing's first-order operations can be folded into multi-dimensional networks to perform connection operations with the determination of eigenvalue comparisons. Such as logic circuits. The final pattern emerges so that our computation can not only synthesize the multi-dimensional data of the network, but also make it possible to computation at a relatively fast speed. Of course, there are errors, but this is a first-order simulation

The equivalence of the network, the concatenation of Thevenin and the paralleling of Norton, makes the output equivalent, as Turing judged

Software, a highly integrated model, itself has new combinations and network intrinsic based on the hierarchical increment of the language hierarchy (machine-assembly-c/c++ language, etc.), using different coupling modes of the hierarchy to make the hardware express a certain ordered structure.

Use structured language such as ints to determine the direction so that the network ultimately expresses the result we want

Semantics is an eigenvalue of a language, but different ways of expression and understanding make the semantics different, that is, the eigenvalues of matrices are different. This is related to the coupling of levels, which are related by similarity and at the same time act on a probabilistic network

Keywords: language, semantics, specifications, sublayers, code, variables, functions, coupling, interfaces (hierarchical connections), patterns

As with a linkage structure, the degree of freedom of each node is different, and the rotation of the individual is probabilistic. When combined, due to the movement of the whole, the different levels of coupling make the output distributed, and the period that can be regarded as the whole is the periodic coupling of the individual, which requires Fourier analysis to decompose: the period of the individual can be decomposed (relatively independent)

Frequency and feedback, the formation of loops allows various actions to encounter resistance changes that may initiate new changes (using marginal utility, i.e., directionality). This forms a network in the movement of the whole

The basic elements, i.e. their combinations, their combinations...... This is a decreasing distribution. Because of the selection of natural selection, meaningful patterns can emerge

The transmission of information and the control of the whole level, the continuous optimization of the mode makes the highest efficiency (increase the granularity e=2). 718）

Information is the reduction of uncertainty transmission, i.e. randomness, probability

Networks are sufficiently multi-layered coupling, and intelligence may be coupling of enough networks

Refers to and maps the ports of connection, which are high-dimensional hierarchical connections. For example, visualization, language, and symbols are actually trended through various data to the level where we can understand them

The layering and coupling of music, such as the first-order notes in a particular situation, have the status of group intrinsicism

Knowledge is a trusted pattern of network connections, a collapse of probabilities

Similarity determines affinity and determines the direction of the network

Data structure: determine the boundaries and constraints, divide the modules, system structures, and function invocation relationships

The purpose of using natural language is to avoid getting bogged down in details, and the hierarchical high-dimensional structure is intrinsic

Depth of continuous nesting: language, command, function

The data structure is the basis of the algorithm implementation, and the algorithm always depends on a certain data structure to implement it. Linear tables are basic, stacks and queues, strings, arrays and generalized tables, trees and binary trees, and graphs are two-dimensional structures, dynamic structures

g = (d,r) where d is the set of data elements and r is the finite set of relationships between all data elements on d. This is the mathematical form of probability

Diversity of structures, combined structures, organic composition

The big truth, do the contrast, multi-level. , make assumptions, explain 123, sequence, intersection, importance, vision

Community networking

Brooks' Law, subroutines, a certain direction to find and define

Use the tools, use the experience

Photoelectric effect, quantum level

The computational function of the automata is the multi-dimensional coupling of the Turing machine, with logic as the basic unit, constantly improving the level, and finally looking at the results of the game between the levels as the essence of the output, the emphasis is that the function of the distribution can be statistical

Precise positional correspondence and operations are required, which are the basis of the language, and a common understanding is required: the pointer

Any law is based on a certain network framework, but the direction and degree of emphasis are different

The signal is the essence of a variable in the network system, i.e., the amplitude of change and the frequency dv/dt

Convex optimization, the optimal solution of a series of functions coupled (based on the assumption that the curve in which the eigenis is located is tangent to the function), is the result of statistics, such as linear regression

Scoring matrix, optimization means the path with the greatest probability, but in fact the path is scattered, that is, the eigen-centered normal distribution

Duality, different degrees of expression and different standards of observation

Approximation is to take a finite number of decomposition terms, such as the first two terms of the Taylor series, which is sufficient for the accuracy of the simulation

The hierarchical coupling of probabilistic networks is not only the structure of the construction, but also the structure itself

The contradiction of the level is its convergence radius, and only by breaking through under the action of probability can it jump to a new level (such as narcissism to love)

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