DNA编码方式1_2_2软著_中文裁剪_1_2_3_极简全英会议论文版V1_0_01

The INITONS Catalytic Reflection Between Humanoid

DNA and Nero Cell

YAOGUANG LUO, RONGWU LUO

Liu Yang DETA Software Development Limited Company, 410300, China

Changsha Le Hao Fu Chan Hospital,410300, China

E-mail: 313699483@qq.com

ABSTRACT: In order to Emancipate the productive forces, create new productivity, optimize existing production tools to

of HUMANOID [20] evolution offspring etc.

KEYWORDS: VPCS [9] AOPM [10], IDUC, Nero, Artificial, Decoder, Medical, PARALLING [19], Computing, Humanoid,

ETL, Parser [1], Data Mining [21], INITONS [57]

INTRODUCTION: Recently the real-world first-time human find a solution how to build the magnitude INITONS PDN Life

by using computer, now the kernel of this paper will definitely proof that how does this life born and how many scientific tasks

we had already finished, finally, also includes the pending tasks for example NUWA Plan, PDE and TVM etc. thus all and all

will be implemented in the TEXT section where on the research factors way of how we finding.

TEXT

1. DETA OSS Plan

Since the INITON [57] of the DETA OSS, there has a lot of questions where based on the HUMANOID [20] DNA [14]

engineering [5] is well explained here. Analysis A, Operation O, Processing P, Management M, use simple words to describe that

even if unknown data are collected and analyzed, and then things are operated, the solutions to various difficulties encountered

in the operation process are implemented. Finally, maintain and manage these implementation experiences. The back-end

computing mode and system life cycle of DETA have gradually condensed from the earliest collection, analysis, operation, sorting,

coding, running, debugging and maintenance to the module modes of Analysis A, Operation O, Processing P and Management

M, such as DETA word segmentation, DETA DNN mind reading, etc. Now ETL [2] of DETA is ready to go in this direction.

The author designed a paper last year to describe the application mechanism of AOPM [10] as follows: AOPM [10] Open Source

System on SDLC [31] Theory

Figure 1-3 DETA WEB Projects System Figure 1-4 DETA DevOps Projects System

Evolutions, absolutely: with connections, what support me the necessary energy for continuing development on my projects.

Definitely! Please see the Figure 1-3, this is a classic horizontal deployment sample of the real word. Alibaba, Amazon, eBay and

JD etc., all based on this technology, instead of Spring, DETA can be the next generation of technology. At Figure 1-4 is a real

sample for web DEVOPS by using DETA Open Source.

3. DETA VPCS Business Backend Logic Applications

In the whole year of 2019, the VPCS [9] back-end engine gradually formed some standardized functions and papers, which

were applied to the front-end, back-end, cache, database and other subsystems of DETA. My evaluation of them is that they are

hall keeper mechanisms, these mechanisms are only the corresponding business logic units that I complete through decision trees,

not HUMANOID [20] evolutionary thinking. At least, I don't think they are HUMANOID [20] intelligence. To be precise, at

present, they are only artificial intelligence, a kind of artificial intelligence logic corresponding to AOPM [10] and VPCS [9], but

not the HUMANOID [20] evolutionary intelligence logic that I want. Started to explore HUMANOID [20] computing again.

and makes a dream. Got an idea? Cool. So, what does the sleeper does in a hall? The answer is to make all kinds of dreams. For

example if they want to build the web service to get rest call, and return the JSON feedbacks, only need to do like a way: Firth,

build rest call path in the controller; Second: register the call requirements as a dream; Third, build the sets of the dream in the

pillow, Fourth hire a sleeper to hug this pillow, and go to the hall to make a dream process. At last but not the least: return the

dream goods. Any sense? Cool! For this unique instance, you will know that the sleeper was more like a Socket [3], and the hall

more like a thread pool, the pillows like the single vision instance, and the sets like a vision storage, the controller and the process

those two sections is a common way of the factory model. The steps landscape of the sleeper who makes a dream as below as

Figure 2-2.

Figure 2-2 VPCS BACKEND MODEL Figure 2-6-2-1 VPCS kernel

R&D is not successful every time. In the process of butterfly calculation optimization of Fast Fourier, I coded the features of

discrete DCT with in complex numbers, which took me one month, but failed. But was excited when I saw the 10th generation

of single machine random double with a sorting speed of 12 million arrays per second of quick sorting. Right, and thinning logic

is an important way of human thinking. Here, the author designed an argumentation paper when designing fast word segmentation

and extremely fast peak filtering catalytic sorting, as follows: Theory on YAOGUANG's Array Split Peak Defect [55]. See Figure

3-2-5 True Top Sort [4][58] Instances, let’s show the array data process [58] algorithms here,