bizim olayda ikinci bir sağlık rağorundan haberimizin olmaması ve bilirkişi talebimizin reddedilmesi

Seems key graph

Los operadores booleanos se pueden expresar tanto en símbolos como en palabras: Ejemplos: Otros operadores booleanos Comillas “, Paréntesis (), Asteriscos * trabajan conjuntamente AND, OR, NOT. para poder expresarse entre datos en el sistema de programación.

Segun Erlang (Lenguaje), Gestionar la comunicación mensajería (conexión entre personas). De forma de programación pequeños objetos que se pasan entre si y que pueden ser remplazados fácilmente si falla alguno EJ: Muere una célula y es remplazada por una nueva. es una forma de metáfora para explicar la forma de programación de objetos

Cash (net) realizable value

Allowance method

Direct write-off method

Other receivables

Receivables

Consigned goods

Goods in transit

Raw materials, work in process, finished goods

Multiple-step income statement

Single-step income statement

Periodic inventory system

Perpetual inventory system

Accrued expenses

Accrued revenues

Accruals

Unearned revenues

Prepaid expenses

Deferrals

Expense recognition principle

Revenue recognition principle

Cash basis

Accrual basis

General ledger account

Transposition error

Journalizing

Cash flow statement

Balance sheet

Retained earnings statement

Income statement

Retained earnings

Common stock

Study these definitions to apply to the content of the standard

This is an important section to remember.

For the Bluesky bots we are using, the bots can complete the action of creating a post and sending it to the public in no time and no cooldown, which a normal person definitely could not do. When running this bot, the majority of responsibilities should be on the content creators, which is myself and other students in this class's content; and a smaller portion of the responsibility goes to its creators and maintainers, which is the professor and TAs.

I think that creating bots helps us to work more efficiently in a digital platform. For example, there are good bots that helps us to block out advertisements that usually pops up in website. This allows us to stay focus on the content we are searching for. Some of these bots are created to help us improve our online experience by making it easier for us to navigate digital platforms. On the other hand, bots are sometimes misused where they can be created to spam people. Overall, bots are created by people to serve a specific purpose on how they want the digital platform to be like.

I've watched news featuring similar click farms, but these direct images give me a really direct and close view of a real click farm. The opinions from the click farm workers make me realized that instead of an underdeveloped gray industry, click farms has become a mature line of industry with upper stream parts providers and downstream customers. This means that it's impossible to eradicate this kind of behaviors and nothing we view online is 100% certified authentic, especially for main platform content, which is scary when thinking.

This article highlights the insane operations of click farms and the way that these click farms create artificial engagement on social media to help boost its clients. Personally I believe that this is a negative operation on both sides, on society and on the operators. I personally think that generating artificial engagement is unethical to society despite not causing physical harm to others. Additionally if these operations MUST continue, I believe it's unethical to use human operators rather than bots. Humans should not have to work under these harsh conditions, sitting in front of 100's of screens for hours when a bot can be programmed to do this all.

This article was about how click farms operates which is a corporation that's created to boost popularity in a content and manipulate algorithms in people's social media pages. I thought this article was very insightful as I get to learn that a popular content isn't always generally popular and it could be boosted in different ways. It helped me to understand that I should be mindful of my perceptions to a post where the amount of likes and views shouldn't be the deciding characteristics of the importance of the content. The digital platform reflects on the reality of the world where those with more attention and resources gets a better opportunity to be seen regardless of the quality of their work.

Article talks about how Star Wars: The Last Jedi overall view and opinions of the film were affected by bots outside of the U.S. It tied with political agenda in which social media is being used to push political agendas and for some reason the film had one behind it. Shows that bots can be harmful when gathering data or consensus of a general opinion; or even yet, a political one.

Agreed

Don't cause a problem for HR because you can't hold it together in a workplace. This applies to the higher ups as well but virtually never enforced.

Don't pigeonhole me.

Spectrum

Biases are either considered negative or heavily connotated as such.

I disagree that we should evolve beyond.

I find this statement to be fundamentally wrong and starting from this perspective to be naive and misguided.

Trying to keep our biases out of our decision making is obtuse. Rather we should be better aware of our biases and try harder to understand how they inform our thinking and deciding whether we agree with the inclusion of that bias in our decision making.

Disability usually means something physical or mental that affects how someone moves or experiences the world. But here, they’re lumping in people who are just pushed aside by society people who might be poor, marginalized, or disconnected from mainstream culture and labeling that as a kind of disability. That’s a big problem. It flips the blame onto the person, like they’re the issue, instead of asking what structures or barriers are doing the excluding in the first place.

1. C语言字符串中的转义字符 在C语言中，字符串由双引号括起来，其中一些字符不能直接以字面值表示，需要使用转义字符。转义字符是由反斜杠 \ 开头，后面跟一个或多个字符，用于表示一些特殊含义。常见的转义字符包括：

\n：表示换行（newline）。

\t：表示制表符（tab）。

\：表示反斜杠自身。

\"：表示双引号，用于在字符串中包含双引号。

\0：表示字符串结束符（null character）。

\r：回车（carriage return）。

\b：退格（backspace）。

这些转义字符在编译时会被转换为对应的字符，方便在字符串中表达无法直接键入的特殊字符。

1. 正则表达式中的元字符 正则表达式（Regular Expression）是一种描述字符串匹配模式的语言，其中有一组特殊字符称为元字符（Metacharacters），它们具有特殊的匹配意义。例如：

.：匹配除换行符外的任意单个字符。

^：匹配字符串的开始位置。

$：匹配字符串的结束位置。

*：匹配前面的子表达式零次或多次。

+：匹配前面的子表达式一次或多次。

?：匹配前面的子表达式零次或一次；在某些情况下也表示非贪婪匹配。

[]：表示字符集合，匹配其中的任一字符。

()：用于分组，或者捕获匹配结果。

{}：用于指定前面的子表达式的匹配次数（比如 {n}、{n,}、{n,m}）。

\：转义字符，用来取消元字符的特殊意义，使其作为普通字符处理；同时也用于表示一些预定义的字符类（如 \d 表示数字）。

在正则表达式中，如果希望匹配这些元字符的字面含义，就需要在它们前面加上反斜杠进行转义。

结合在一起的注意事项 当你在C语言的字符串中写正则表达式时，由于两边都有转义规则，你通常需要额外注意。例如，要在正则表达式中匹配加号 +（作为字面字符），你需要写成 "\+"：

C语言层面：字符串中的 "\+" 实际上表示一个反斜杠和一个加号。

正则表达式层面：反斜杠使得加号失去了元字符的意义，从而匹配字面意义上的 +。

for - futuring - Maarten Hajer - youtube - Techniques of futuring: On how imagined futures become socially performative - to - paper - Techniques of futuring: On how imagined futures become socially performative - https://hyp.is/pCJ_iA42EfC_9C-RJoo6wQ/journals.sagepub.com/doi/full/10.1177/1368431020988826

comment - meme - Gien - past - present - future - quote - Gien - past - present - future - When the future becomes the present, - memories will remind us of imaginations in presents past

inner class

genap

kita ga mungkin membuat instance inner class tanpa membuat outer class nya

jatohnya mirip atribut/method dari outer class

ini gabisa karna shapename bukan static

bisa karena classname itu atribut static

1. Inner Class Non-Static:
2. Kelas ini adalah kelas yang didefinisikan di dalam kelas lain (outer class) dan tidak dideklarasikan sebagai static.

3. Kelas ini memiliki akses ke semua metode dan atribut dari outer class, termasuk yang bersifat private. Artinya, inner class dapat menggunakan dan memodifikasi data dari outer class secara langsung.

4. Static Nested Class:

5. Kelas ini juga didefinisikan di dalam kelas lain, tetapi dideklarasikan sebagai static.
6. Kelas ini tidak memiliki akses langsung ke metode atau atribut dari outer class, termasuk yang bersifat private. Untuk mengakses data dari outer class, Anda harus membuat objek dari outer class terlebih dahulu.

Secara ringkas, perbedaan utama antara inner class non-static dan static nested class terletak pada aksesibilitas terhadap anggota dari outer class. Inner class non-static memiliki akses penuh, sedangkan static nested class tidak.

Static variable/method dimiliki oleh class bukan objek

nested class yang berada dalam class hanya dapat dikenali dalam class tersebut tidak bisa dari class luar

kita bisa mendefinisikan kelas dari outer class

DOI: 10.1002/2211-5463.70024

Resource: (CLS Cat# 300168/p708_DU-145, RRID:CVCL_0105)

Curator: @sjvitug

SciCrunch record: RRID:CVCL_0105

What is this?

DOI: 10.1002/2211-5463.70024

Resource: (ECACC Cat# 90112714, RRID:CVCL_0035)

Curator: @scibot

SciCrunch record: RRID:CVCL_0035

What is this?

DOI: 10.1002/2211-5463.70024

Resource: (DSMZ Cat# ACC-256, RRID:CVCL_0395)

Curator: @scibot

SciCrunch record: RRID:CVCL_0395

What is this?

DOI: 10.3390/vaccines13040385

Resource: (IZSLER Cat# BS CL 87, RRID:CVCL_0574)

Curator: @scibot

SciCrunch record: RRID:CVCL_0574

What is this?

DOI: 10.3390/vaccines13040385

Resource: (ATCC Cat# HTB-55, RRID:CVCL_0609)

Curator: @scibot

SciCrunch record: RRID:CVCL_0609

What is this?

DOI: 10.3390/vaccines13040385

Resource: (IZSLER Cat# BS CL 8, RRID:CVCL_1915)

Curator: @scibot

SciCrunch record: RRID:CVCL_1915

What is this?

DOI: 10.3390/vaccines13040385

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.3390/jmmp9040117

Resource: Texas A and M University Materials Characterization Core Facility (RRID:SCR_022202)

Curator: @scibot

SciCrunch record: RRID:SCR_022202

What is this?

DOI: 10.1101/2025.04.03.647023

Resource: Pennsylvania University Perelman School of Medicine Stem Cell and Xenograft Core Facility (RRID:SCR_010035)

Curator: @scibot

SciCrunch record: RRID:SCR_010035

What is this?

DOI: 10.1101/2025.04.03.647023

Resource: University of Pennsylvania Perelman School of Medicine Molecular Pathology and Imaging Core (MPIC) (RRID:SCR_022420)

Curator: @scibot

SciCrunch record: RRID:SCR_022420

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_50859

Curator: @scibot

SciCrunch record: RRID:BDSC_50859

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DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_28048

Curator: @scibot

SciCrunch record: RRID:BDSC_28048

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_24651

Curator: @scibot

SciCrunch record: RRID:BDSC_24651

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_1776

Curator: @scibot

SciCrunch record: RRID:BDSC_1776

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_27390

Curator: @scibot

SciCrunch record: RRID:BDSC_27390

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_11044

Curator: @scibot

SciCrunch record: RRID:BDSC_11044

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: RRID:BDSC_9457

Curator: @scibot

SciCrunch record: RRID:BDSC_9457

What is this?

DOI: 10.1101/2025.04.03.646909

Resource: (DSHB Cat# 4F3 anti-discs large, RRID:AB_528203)

Curator: @scibot

SciCrunch record: RRID:AB_528203

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: PerkinElmer Opera Phenix High Content Screening System (RRID:SCR_021100)

Curator: @scibot

SciCrunch record: RRID:SCR_021100

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: (Thermo Fisher Scientific Cat# A-21449, RRID:AB_2535866)

Curator: @scibot

SciCrunch record: RRID:AB_2535866

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: (Thermo Fisher Scientific Cat# A-11008, RRID:AB_143165)

Curator: @scibot

SciCrunch record: RRID:AB_143165

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DOI: 10.1101/2025.04.01.646648

Resource: (Millipore Cat# AB5543, RRID:AB_571049)

Curator: @scibot

SciCrunch record: RRID:AB_571049

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DOI: 10.1101/2025.04.01.646648

Resource: Image Studio Lite (RRID:SCR_013715)

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DOI: 10.1101/2025.04.01.646648

Resource: (Santa Cruz Biotechnology Cat# sc-17764, RRID:AB_628381)

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What is this?

DOI: 10.1101/2025.04.01.646648

Resource: (Thermo Fisher Scientific Cat# A-11004, RRID:AB_2534072)

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What is this?

DOI: 10.1101/2025.04.01.646648

Resource: Life Technologies QuantStudio 7 Real Time PCR System (RRID:SCR_020245)

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DOI: 10.1101/2025.04.01.646648

Resource: Odyssey CLx (RRID:SCR_014579)

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DOI: 10.1101/2025.04.01.646648

Resource: (LI-COR Biosciences Cat# 926-32213, RRID:AB_621848)

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DOI: 10.1101/2025.04.01.646648

Resource: (BioLegend Cat# 801201, RRID:AB_2313773)

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DOI: 10.1101/2025.04.01.646648

Resource: (LI-COR Biosciences Cat# 926-68022, RRID:AB_10715072)

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DOI: 10.1101/2025.04.01.646648

Resource: (Abcam Cat# ab110305, RRID:AB_10861081)

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DOI: 10.1101/2025.04.01.646648

Resource: (BD Biosciences Cat# 611223, RRID:AB_398755)

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SciCrunch record: RRID:AB_398755

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_3661827

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: (Cell Signaling Technology Cat# 62802, RRID:AB_2799632)

Curator: @scibot

SciCrunch record: RRID:AB_2799632

What is this?

DOI: 10.1101/2025.04.01.646648

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_2942251

What is this?

DOI: 10.1101/2025.04.01.646630

Resource: Dartmouth Geisel School of Medicine Molecular Biology Resource Core Facility (RRID:SCR_021293)

Curator: @scibot

SciCrunch record: RRID:SCR_021293

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DOI: 10.1101/2025.03.31.646326

Resource: RRID:Addgene_37004

Curator: @scibot

SciCrunch record: RRID:Addgene_37004

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DOI: 10.1101/2025.03.31.646326

Resource: None

Curator: @scibot

SciCrunch record: RRID:Addgene_131498

What is this?

DOI: 10.1101/2025.03.28.646050

Resource: None

Curator: @scibot

SciCrunch record: RRID:SCR_012359

What is this?

DOI: 10.1101/2025.03.28.646050

Resource: None

Curator: @scibot

SciCrunch record: RRID:SCR_012360

What is this?

DOI: 10.1101/2025.03.28.646050

Resource: University of Rochester Light Microscopy Resource Core Facility (RRID:SCR_023177)

Curator: @scibot

SciCrunch record: RRID:SCR_023177

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)

Curator: @scibot

SciCrunch record: RRID:AB_330924

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Cell Signaling Technology Cat# 12146, RRID:AB_2797829)

Curator: @scibot

SciCrunch record: RRID:AB_2797829

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Cell Signaling Technology Cat# 7413, RRID:AB_10950820)

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SciCrunch record: RRID:AB_10950820

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (StressMarq Biosciences Cat# SPC-403, RRID:AB_10640131)

Curator: @scibot

SciCrunch record: RRID:AB_10640131

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Jackson ImmunoResearch Labs Cat# 715-545-150, RRID:AB_2340846)

Curator: @scibot

SciCrunch record: RRID:AB_2340846

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Cell Signaling Technology Cat# 4967, RRID:AB_330288)

Curator: @scibot

SciCrunch record: RRID:AB_330288

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_10857357

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Jackson ImmunoResearch Labs Cat# 705-175-147, RRID:AB_2340415)

Curator: @scibot

SciCrunch record: RRID:AB_2340415

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Abcam Cat# ab89901, RRID:AB_2043201)

Curator: @scibot

SciCrunch record: RRID:AB_2043201

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Abcam Cat# ab62628, RRID:AB_2227510)

Curator: @scibot

SciCrunch record: RRID:AB_2227510

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Novus Cat# NB600-531, RRID:AB_10000930)

Curator: @scibot

SciCrunch record: RRID:AB_10000930

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (Jackson ImmunoResearch Labs Cat# 711-165-152, RRID:AB_2307443)

Curator: @scibot

SciCrunch record: RRID:AB_2307443

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (ATCC Cat# CRL-3250, RRID:CVCL_CZ90)

Curator: @scibot

SciCrunch record: RRID:CVCL_CZ90

What is this?

DOI: 10.1038/s41598-025-95673-9

Resource: (R and D Systems Cat# MAB1817, RRID:AB_2116445)

Curator: @scibot

SciCrunch record: RRID:AB_2116445

What is this?

DOI: 10.1038/s41598-025-94971-6

Resource: Nipype (RRID:SCR_002502)

Curator: @scibot

SciCrunch record: RRID:SCR_002502

What is this?

DOI: 10.1038/s41598-025-94971-6

Resource: FMRIPREP (RRID:SCR_016216)

Curator: @scibot

SciCrunch record: RRID:SCR_016216

What is this?

for - youtube - Ecology or Economics? - David Suzuki - humans and nature - nondual relationship - humans and nature - intertwingled

Summary - David Suzuki gives a great talk on the relationship between ecology ad economy - In particular, the standout for me is the story of intertwingledness and nonseparation he learned from the Haida people. - See the annotations below to find the part of the talk when he has the epiphany that we are not separated from nature, and he learns this from the Haida people's nondual relationship with nature

Using it in a differing way that intended

In the understanding of a modernist; happiness that comes from the (effort-based) reward center, endorphins after pain, and the instinct to help the herd?

I think this can be found true by someone from any class

What I battled with finding inconsiderable in my journal

I can see appeal for people who are like me but feel they lack the will/traits to carry out their ideals

Release from the cycle of death or immortality through some integration with the fabric of the universe? Could be both, or something else of course

Already an emphasis on learning/ignorance rather than good/bad

My initial thoughts on interpretation of this were external practices that mold the foundation for internal acception of the principles, and external teachings (buddhism) that offer the thinking (and possibly environment) to reach internal refuge

I was surprised that antagonistic bots existed, especially since there were so many types. I find it pretty interesting that these bots are placed into so many different types of situations and the different uses for each type. Especially because a lot of these applications will have multiple types of the bots and the different types of functions for each.

Throughout this class, it was interesting to learn that Catholics named the pope as all knowing and as their figure to follow rather than Jesus. I guess it goes to show how little I know about religion because I didn’t think that was allowed? I thought it was taught to worship god and no one else compared to him. It was interesting to think that Martian Luther brought this to light and taught against this, and other questionable teachings by the church.

I found this interesting and felt as though it tied back to the Code of Hammurabi of how there was an offense for each crime. Then, there was rules and curfews that citizens must follow or be punished.

• Repetition: The repetition of "free at last" emphasizes the emotional weight of this freedom.

• Imagery: He uses vivid imagery to paint a picture of unity and harmony, perhaps about the image of children of different races holding hands symbolizes the possibility of a racially integrated and harmonious society. (Core vision of the Civil Rights Movement: integration and equality for all).

• He acknowledges the challenges and hardships that will be faced by them, but he reaffirms his unwavering optimism for the future. We can say that his "dream" *represents hope and the vision of a better and more equal society.

• A proclamation issued by President Abraham Lincoln on January 1, 1863. Refer here for further information: https://www.americanhistorycentral.com/entries/emancipation-proclamation/

• A Baptist minister and prominent leader in the American civil rights movement.
• A federal holiday in the United States.

Symbolism : Standing at the Lincoln memorial. King emphasises the symbolic presence of Lincoln . (Lincoln led the country through its greatest crisis, the Civil War, and was on the verge of victory when he died ) . Hence, 'shadow' here represents both literal location and ideological legacy of justice .

socioeconomic commentary : Even though the country is experiencing post-war economic growth as mentioned from the text, 'material prosperity' but the black Americans remained trapped in systemic poverty.

Metaphor: Lonely island here represents the isolation. The negros were excluded from broader progress and wealth of the nation.

Metaphor : King was comparing the Negro experience of injustice to being 'burned in flames'

RSVP'd for 2025-05-10 with possibly a couple of others (via chat)

I want to find more articles discussing the federalist papers and Hamiltons contributions to them i think that is really interesting as well.

I agree, however I think in the hollywood/mainstream representations of him it is meant to be dramatic.

I don't understand the narrative of serial cheater because he only cheated with one woman even if it was multiple times

I think this sentence summarizes this section really well in addition to the over dramatization of his affair with Ms. Reynolds

I think this is the least interesting thing about his story, I get that it is the most dramatic though

essentially, Jefferson's perspective on Hamilton was that he was an immigrant traitor and did not belong in America yet alone in government.

Even in death he could NOT catch a break

Jefferson along with Burr and Madison frequently refer to Hamilton in the musical as a immigrant bastard so historically and musical this isn't far off

This reminds me of whats going on right now in the government and i could make a good podcast connection to current time with musk/trump rn

描述性函数论（Descriptive Function Theory）详细介绍

1. 概念与背景

“描述性函数论”并非一个标准的数学或工程学术语，但根据知识库信息，可能涉及以下两方面的内容： - （1）描述函数法（Describing Function Method）：<br /> 非线性系统分析的工程近似方法，用于研究系统稳定性、自激振荡等问题（见知识库条目[1][2][3]）。<br /> - （2）函数的描述性定义：<br /> 数学中早期对函数的直观定义，强调变量间的依赖关系（见知识库条目[4][5]）。

下文将分别介绍这两部分内容。

2. 描述函数法（Describing Function Method）

（1）定义与目的

描述函数法是一种非线性系统分析工具，通过将非线性元件近似为线性频率响应（称为“描述函数”），利用频率域方法（如奈奎斯特判据）分析系统稳定性及自激振荡特性。其核心思想是：<br /> - 将非线性系统近似为线性系统：通过输入正弦信号，分析输出的基波分量，得到非线性环节的等效线性增益与相位。<br /> - 适用场景：研究具有单一非线性环节的闭环系统（如饱和、死区、继电器等特性）。

（2）基本原理

1. 非线性环节的描述函数：
2. 假设非线性环节的输入为正弦信号 \(x(t) = X \sin(\omega t)\)，输出 \(y(t)\) 为周期函数。
3. 将输出展开为傅里叶级数：<br /> [ y(t) = Y_0 + Y_1 \sin(\omega t + \phi_1) + Y_2 \sin(2\omega t + \phi_2) + \cdots ]

4. 描述函数（\(N(X)\)）定义为输出基波分量与输入信号的复数比：<br /> [ N(X) = \frac{Y_1}{X} e^{j\phi_1} ]<br /> 其中，\(Y_1\) 是基波幅值，\(\phi_1\) 是相位差。

5. 系统稳定性分析：

6. 系统结构为：非线性环节 \(N(X)\) 与线性环节 \(G(s)\) 串联的闭环系统。
7. 稳定性判据：
   • 在复平面上绘制 \(G(j\omega)\) 曲线（线性部分的频率响应）和 \(-1/N(X)\) 曲线（非线性部分的等效响应）。
   • 稳定条件：若 \(G(j\omega)\) 不包围 \(-1/N(X)\)，系统稳定；若包围，则不稳定。
   • 自激振荡条件：若两曲线相交，可能产生周期振荡，需进一步判断交点的稳定性。

（3）应用条件

描述函数法的准确性依赖以下条件（知识库条目[2][3]）：<br /> 1. 系统结构：非线性环节与线性环节串联，且非线性环节唯一。<br /> 2. 非线性特性：<br /> - 奇对称性：\(y(-x) = -y(x)\)，确保输出无恒定分量（\(Y_0 = 0\)）。<br /> - 基波占优：输出中基波分量 \(Y_1\) 远大于高次谐波分量。<br /> 3. 线性部分：具有良好的低通滤波特性，能衰减高次谐波（如高阶系统）。

（4）典型应用示例

• 自激振荡分析：
• 当 \(G(j\omega)\) 与 \(-1/N(X)\) 曲线相交时，交点对应的幅值 \(X\) 和频率 \(\omega\) 即为自激振荡的幅值和频率。
• 通过判断交点两侧区域（稳定/不稳定），确定振荡是否稳定。

3. 函数的描述性定义（Descriptive Definition of Functions）

（1）数学中的描述性定义

在数学教育中，函数的传统定义强调变量间的依赖关系，而非严格的集合论形式。例如：<br /> - 经典描述：<br /> “一个变量 \(y\) 随另一个变量 \(x\) 变化，且每个 \(x\) 值对应唯一 \(y\) 值。”（知识库条目[4]）<br /> - 局限性：<br /> - 未明确自变量与因变量的集合定义（如定义域、值域）。<br /> - 无法处理复杂函数（如多值函数、抽象映射）。

（2）从描述性到形式化的发展

1. 形式化定义：
2. 集合论视角：函数是集合 \(A\) 到集合 \(B\) 的映射 \(f: A \to B\)，满足每个 \(x \in A\) 对应唯一 \(y \in B\)（知识库条目[4]）。
3. 三要素：定义域、值域、对应法则。
4. 教育中的演变：
5. 基础阶段：通过变量关系（如 \(y = f(x)\)）直观引入函数概念。
6. 深化阶段：引入集合论定义，强调严谨性（如高中到大学的过渡，知识库条目[5]）。

4. 两者的区别与联系

| 方面 | 描述函数法 | 函数的描述性定义 | |-------------------------|----------------------------------------|--------------------------------------| | 领域 | 控制理论与非线性系统分析 | 数学基础与教育理论 | | 核心思想 | 非线性近似为线性频率响应 | 变量间的依赖关系直观描述 | | 数学工具 | 傅里叶级数、奈奎斯特判据 | 集合论、映射理论 | | 目标 | 分析系统稳定性与振荡特性 | 建立函数的直观理解与严谨定义 |

5. 总结

• 描述函数法是工程领域分析非线性系统的实用工具，其核心是通过描述函数将非线性近似为线性响应，适用于满足特定条件的系统。
• 函数的描述性定义是数学教育中的基础概念，强调变量关系的直观理解，后期发展为严格的集合论形式。

若需进一步探讨具体应用（如描述函数法的计算步骤）或数学定义的演变细节，请随时提问！

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These questions can make or break the report. They help the audience understand the topic and help drive the hypothesis

Research should be specific and help prove the hypothesis or answer questions

Good definition to keep in mind while working on this assignment

Personally I believe that this paragraph here is very realistic but also a bit frightening at the same time to think that humans can create bots to cause trouble either through simple trolling or as harsh as impersonation. I am wondering though if we would consider accounts that automatically impersonate and contact accounts as bots? If a human messages through the bot but the account and impersonation is created through code, is it still considered a bot?

贝叶斯滤波核心目标详解

1. 核心目标分解

贝叶斯滤波的核心目标是通过观测数据和系统模型，递归地计算状态的后验概率分布（即置信度分布），从而实现状态估计。这一过程可以拆解为以下关键要素：

2. 关键要素详解

（1）观测数据（Observations）

• 定义：从系统外部获取的测量值，例如传感器数据（如GPS、摄像头、雷达等）。
• 作用：提供关于系统状态的间接信息，但通常包含噪声或不确定性。
• 数学表示：用 \( z_t \) 表示第 \( t \) 时刻的观测数据。
• 知识库支持：
• 知识库[3]指出，观测数据（\( z_{1:t} \)）与控制输入（\( u_{1:t} \)）共同用于估计状态 \( x_t \)。
• 知识库[6]提到，观测数据是滤波算法的基础，用于更新状态的后验分布。

（2）系统模型（System Model）

系统模型由两部分组成： 1. 状态转移模型（State Transition Model） - 定义：描述系统状态随时间变化的规律，通常为概率分布。 - 数学表示：\( p(x_t | x_{t-1}, u_t) \)，其中 \( u_t \) 是控制输入。 - 作用：预测下一时刻的状态分布。 - 知识库支持： - 知识库[1]和[3]指出，状态转移模型是系统动态的数学描述，例如机器人运动模型。 - 知识库[4]提到，状态转移方程可能包含噪声（如高斯噪声）。

1. 观测模型（Observation Model）
2. 定义：描述状态与观测数据之间的关系，通常为条件概率。
3. 数学表示：\( p(z_t | x_t) \)。
4. 作用：将状态映射到观测空间，量化观测的不确定性。
5. 知识库支持：
   • 知识库[3]和[5]强调，观测模型（如传感器模型）是连接状态与观测的关键。
   • 知识库[6]指出，观测模型需描述状态到传感器读数的统计关系。

（3）递归计算（Recursive Computation）

• 定义：通过迭代步骤（预测和更新）逐步更新状态估计，而非从头开始计算。
• 步骤：

• 预测步骤（Prediction Step）：

  • 根据前一时刻的后验分布 \( p(x_{t-1} | z_{1:t-1}, u_{1:t-1}) \) 和状态转移模型，预测当前时刻的先验分布： [ p(x_t | z_{1:t-1}, u_{1:t}) = \int p(x_t | x_{t-1}, u_t) \cdot p(x_{t-1} | z_{1:t-1}, u_{1:t-1}) \, dx_{t-1} ]
  • 知识库支持：知识库[2]和[3]详细描述了预测步骤的数学推导。

• 更新步骤（Update Step）：

  • 利用贝叶斯定理结合当前观测 \( z_t \)，更新后验分布： [ p(x_t | z_{1:t}, u_{1:t}) \propto p(z_t | x_t) \cdot p(x_t | z_{1:t-1}, u_{1:t}) ]
  • 归一化：通过计算归一化因子 \( Z_t = \int p(z_t | x_t) \cdot p(x_t | z_{1:t-1}, u_{1:t}) \, dx_t \)，确保概率分布的总和为1。
  • 知识库支持：知识库[2]和[3]强调了贝叶斯定理在更新步骤中的核心作用。

（4）后验概率分布（Posterior Probability Distribution）

• 定义：在给定观测数据和系统模型后，对状态 \( x_t \) 的概率分布估计，即 \( p(x_t | z_{1:t}, u_{1:t}) \)。
• 意义：
• 不确定性量化：通过概率分布而非单一值，反映对状态的置信程度。
• 递归更新：当前后验分布成为下一时刻预测的先验分布。
• 知识库支持：
• 知识库[1]和[3]指出，后验分布是贝叶斯滤波的核心输出，用于状态估计。
• 知识库[4]提到，后验分布 \( p(x_k | y_{1:k}) \) 是贝叶斯滤波的最终目标。

（5）状态估计（State Estimation）

• 定义：从后验概率分布中提取对状态的最佳估计，通常为期望值或众数。
• 方法：
• 期望估计：\( \hat{x}t = \mathbb{E}[x_t | z{1:t}, u_{1:t}] \)。
• 最大后验估计：\( \hat{x}t = \arg\max{x_t} p(x_t | z_{1:t}, u_{1:t}) \)。
• 知识库支持：
• 知识库[5]指出，卡尔曼滤波通过均值和协方差估计状态。
• 知识库[6]提到，滤波的目标是计算 \( p(x_k | y_{1:k}) \)，并从中提取状态估计。

3. 核心流程示意图

初始化：p(x₀) → 预测 → 更新 → 预测 → 更新 → ... → 最终估计 - 初始化：设定初始状态分布 \( p(x_0) \)。 - 递归循环： 1. 预测：利用状态转移模型将 \( p(x_{t-1} | z_{1:t-1}) \) 转为 \( p(x_t | z_{1:t-1}) \)。 2. 更新：结合观测模型 \( p(z_t | x_t) \)，更新为 \( p(x_t | z_{1:t}) \)。

4. 应用示例：机器人定位

假设一个机器人在迷宫中移动，其状态包括位置和速度： - 观测数据：GPS（含噪声）和里程计数据。 - 系统模型： - 状态转移：基于运动模型（如 \( x_t = Ax_{t-1} + Bu_t + w \)，\( w \sim \mathcal{N}(0, Q) \)）。 - 观测模型：GPS噪声模型（\( z_t = Hx_t + v \)，\( v \sim \mathcal{N}(0, R) \)）。 - 递归过程： 1. 预测：根据前一时刻的位置和控制指令，预测当前可能位置。 2. 更新：结合GPS观测修正预测，得到更精确的位置估计。 - 输出：后验分布 \( p(x_t | z_{1:t}) \) 表明机器人最可能的位置及不确定性。

5. 优势与局限性

优势：

1. 不确定性量化：提供完整概率分布，而非单一估计值。
2. 递归高效性：计算复杂度与时间无关，适合实时应用。
3. 灵活性：可处理非线性、非高斯系统（如粒子滤波）。

局限性：

1. 计算复杂度：高维系统需近似方法（如卡尔曼滤波假设线性高斯）。
2. 假设依赖性：
3. 状态转移和观测模型需正确建模。
4. 马尔可夫假设（仅依赖前一时刻状态）可能不成立。

6. 总结

贝叶斯滤波通过递归结合观测数据和系统模型，在每一步迭代中更新状态的后验概率分布，从而实现对动态系统的最优估计。其核心在于： - 观测数据提供实时信息，修正状态估计。 - 系统模型描述状态演化规律，支持预测。 - 递归机制确保高效计算，适用于实时场景。 - 后验分布量化不确定性，支持鲁棒决策。

这一框架在机器人导航、目标跟踪、金融预测等领域具有广泛适用性，是处理动态系统状态估计的基石方法。

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THIS IS WHAT THAT GIRL DID GIES.

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URBAN PLANNING, CONCRET, IMPOSITION-SUPIRIORITY

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ASSES THE VLAUE TO HUMAN USE .....

This can definitely be seen as problematic.

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Important!

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As AI is continue to grow, this is a prime example of mechanisms and policies that need to be put into place in order to prevent bots from gaining a negatively biased language model. Testing on racist comments wasn't part of the development process which without it can further the already existent cyberbullying space.

cuando existe sobrerepresentación de minorias y infrarepresentación de mayorias.

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The link between individualized learning and arts integration struck me as particularly potent. Giving students the freedom to express themselves creatively and contribute their own viewpoint to academic material not only increases student engagement but also boosts their self-esteem and sense of ownership over their education. For pupils who might not benefit from traditional, text-heavy training, this seems especially crucial.

The article highlights how arts integration offers emotional safety, especially when it comes to taking risks in a safe setting. How can educational institutions make sure that all teachers, particularly those who are not trained in the arts, are equipped to use arts integration to create this kind of emotionally secure environment?

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political activity or practices that seek support by appealing to the desires and prejudices of ordinary people rather than by using rational argument.

GAN（生成对抗网络）的训练过程与朗之万方程（Langevin Equation）的微分形式之间的关联，可以通过以下方式理解：GAN的训练可以视为通过求解随机微分方程（SDE）来寻找生成器分布的平稳解。以下是详细解释：

1. 朗之万方程的数学形式

朗之万方程是描述粒子在流体中受热噪声影响的随机微分方程： [ dX_t = -\nabla U(X_t) dt + \sqrt{2 \beta^{-1}} dB_t, ] 其中： - \( U(X_t) \) 是势能函数（Potential Energy），其梯度驱动确定性部分； - \( \beta \) 是逆温度参数（与热噪声强度相关）； - \( B_t \) 是布朗运动（Wiener过程），代表随机噪声。

该方程的平稳分布是势能函数 \( U \) 的玻尔兹曼分布： [ \pi(X) \propto \exp(-\beta U(X)). ]

2. GAN的训练目标与朗之万方程的联系

GAN的核心目标是让生成器 \( G \) 的分布 \( P_G \) 接近真实数据分布 \( P_{\text{data}} \)。从优化动力学的角度看，生成器的参数更新可以建模为一个随机微分方程，其解的平稳分布即为目标分布。

（1）生成器的优化动力学

生成器 \( G \) 的参数 \( \theta_G \) 通过梯度下降更新： [ \frac{d\theta_G}{dt} = -\nabla_{\theta_G} \mathcal{L}(G, D), ] 其中 \( \mathcal{L} \) 是GAN的损失函数（如最小最大损失）。

若考虑训练中的噪声（如小批量梯度估计的方差或判别器的波动），可以引入随机项： [ d\theta_G = -\nabla_{\theta_G} \mathcal{L}(G, D) dt + \eta dB_t, ] 这与朗之万方程的形式一致，其中： - \( \nabla_{\theta_G} \mathcal{L} \) 对应势能函数的梯度 \( \nabla U \)； - \( \eta \) 是噪声强度参数。

（2）平稳分布的对应

朗之万方程的平稳分布由势能 \( U \) 决定，而GAN的训练目标是让 \( P_G \) 接近 \( P_{\text{data}} \)。因此，可以将损失函数 \( \mathcal{L} \) 设计为与势能 \( U \) 相关： - 当 \( P_G \) 趋近于 \( P_{\text{data}} \)，损失函数 \( \mathcal{L} \) 的梯度趋于零，此时参数 \( \theta_G \) 达到平稳状态； - 这种平稳状态对应朗之万方程的解，其分布即为 \( P_{\text{data}} \)。

3. GAN训练的SDE建模

通过将生成器的参数更新视为随机微分方程，可以建立以下模型：

（1）连续时间下的参数动力学

假设生成器的参数 \( \theta_G(t) \) 满足以下SDE： [ d\theta_G = -\nabla_{\theta_G} \mathbb{E}{x \sim P{\text{data}}}[\log D(x)] dt - \nabla_{\theta_G} \mathbb{E}{z \sim P_z}[\log(1 - D(G(z)))] dt + \eta dB_t. ] 这可以简化为： [ d\theta_G = -\nabla{\theta_G} \mathcal{L}(G, D) dt + \eta dB_t, ] 其中 \( \mathcal{L} \) 是GAN的损失函数。

（2）解的收敛性

当训练过程收敛时，参数 \( \theta_G \) 的分布应满足朗之万方程的平稳分布： [ \pi(\theta_G) \propto \exp\left(-\beta \mathcal{L}(G, D)\right). ] 若损失函数 \( \mathcal{L} \) 的最小值对应 \( P_G = P_{\text{data}} \)，则平稳分布即为生成器的目标分布。

4. 与知识库的关联

（1）SDE的求解方法（知识库条目[2][4]）

• 线性SDE的解法（如伊藤公式）可用于分析生成器参数的演化路径；
• 通过求解SDE的期望和方差，可以研究生成器分布的统计特性。

（2）GAN的训练动力学（知识库条目[1][3][5][6][7][8]）

• GAN的对抗训练本质是生成器与判别器的博弈，其参数更新的梯度方向与朗之万方程的确定性项一致；
• 噪声项（如小批量梯度的方差）对应朗之万方程的随机项，有助于避免局部最优。

5. 实际意义与挑战

（1）优势

• 理论解释：将GAN的训练过程建模为SDE，提供了数学上的严谨性，便于分析收敛性；
• 稳定性提升：通过控制噪声强度 \( \eta \)，可改善GAN训练的稳定性（如WGAN的改进）。

（2）挑战

• 非线性复杂性：GAN的损失函数 \( \mathcal{L} \) 通常是高度非线性的，导致SDE的解析解难以求得；
• 高维参数空间：真实场景中参数维度极高，数值求解SDE的计算成本巨大。

6. 总结

通过将GAN的训练过程视为求解朗之万方程的随机微分方程，可以： 1. 理论层面：利用SDE的数学工具（如伊藤公式、平稳分布分析）解释GAN的收敛性； 2. 实践层面：借鉴SDE的数值方法（如欧拉-马尤拉方法）设计更稳定的训练算法。

这一视角为GAN的优化动力学提供了新的分析框架，但实际应用中仍需结合深度学习的工程实践进行调整。

Compare this definition to 1-1 tensor Outer product

The uniqueness of adjoint operator lies in that inner product is non-degenerate

From what I can tell, this article mainly focuses on white and black students but as someone who grew up near Berkeley, I wonder how being of Asian origin would affect this, especially because of the high Asian student population in Berkeley. I think Asian culture definitely affects the way students perform and I wonder how much of it is affected by income levels. I really like the analysis these articles are doing, but I wonder how these education gaps affect students who aren't black or white.

I am confused... I thought that the Joaquín we were talking about is the bandit but no one can live for five hundred years. I understand the sorrow, hurt, and longing of the text but I don't understand the perspective I should be reading it in. Is it a single person or are they speaking in the sense of many?

The word "machismo" has always been used in an unpleasant way as society develops but in this instance, it is used as something good. He is a man with pride for his country, family, and ancestors. Not machismo in a toxic way like it is used today.

Killing to stay alive versus the Anglos killing to conquer...

Joaquín Murrieta: A Mexican bandit known for his freedom fighting ethnic discrimination.

As I read this, I think it is coming from a place of encouragement of themselves not as a downplay on anyone else.

Zapotec: Indigenous peoples of Mexico who live in Oaxaca. The Zapotec people developed advancements to Mesoamerica like a writing system and calendar which was before the Aztecs and Mayans.

This sentence doesn't make grammatical sense.

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