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Professionalism/Thomas le Bonniec and Siri

Siri
Siri is a voice controlled personal assistant developed by Apple originally released in 2011. It is available on all Apple devices such as the iPhone, iPad, and MacOS systems. Siri uses natural learning processing and machine learning to understand and respond to user prompts.

Siri works by converting user voice commands into text using speech recognition. That text is then analyzed through natural language processing methods to understand user intent. That result is used with machine learning algorithms to generate a response from Siri.

In order to improve accuracy and performance, Apple must collect a large amount of user interactions with Siri to analyze. Each interaction must be human reviewed to determine if the correct response was given and what corrections need to be made. This large amount of work requires a large workforce which led to Apple originally outsourcing the work to contractors.

Thomas le Bonniec Background
Thomas le Bonniec completed a master's degree in Sociology from Paris-Sorbonne University in 2018. Post-graduation, Le Bonniec struggled to find a job, going eight months unemployed. Le Bonniec eventually received an offer through a jobs board he had subscribed to for a tech consulting GlobeTech.

During the recruitment process, Le Bonniec received very little information regarding the role. He knew that the salary was decent and the job required proficiency in French and willingness to do repetitive tasks.

Le Bonniec signed an non-disclosure agreement and accepted the offer. Two weeks later, he moved to Cork, Ireland to begin his training. Le Bonniec's first day on the job was May 13, 2019, and it was only then that he found out what his role would entail.

Le Bonniec's role was to review Siri audio recordings and check them against Siri's transcript. Le Bonniec would correct and mistakes and was expected to review 1,300 recordings a day. The job felt wrong to Le Bonniec as he listened in on recordings of "conversations people were having about their cancer treatments, intimate conversations with family and others about sex and relationships."

The Recording
Around three months on the job, Thomas Le Bonniec encountered a recording describing a sexual fantasy involving children. The recording disgusted Le Bonniec, and he flagged it to discuss with his manager. Management thanked him and directed another employee to listen to the audio in order to verify what Le Bonniec had said. Le Bonniec was upset by the recording and appalled that another employee would be subjected to hearing it.

Le Bonniec was directed to a helpline due to the distressing nature of the recording but received no real help. The helpline was primarily concerned with whether he was suicidal and could not offer expert assistance.

Reaction
Once Le Bonniec saw that his superiors were going to do nothing about the pedophilia recording, he started to take multiple screenshots of his work every day. At first, he was scared of taking screenshots since he thought Apple may be monitoring him after his report of the recording. However, he eventually realized how bad the security was at his current company. Additionally, he started to look at the company's Confluence pages to see what the developers thought about Siri and what the developers thought were sensitive topics.

A week after the recording, he decided to quit his job by going back to France permanently. The company tried to reach out about missing work multiple times through emails and calls. However, he never responded to any of these attempts. After four days, the company fired him for not showing up to work.

Reasons
At first, Le Bonniec was not going to come forward with his experience at Apple since his family and friends convinced him that Apply may sue him for breaching the NDA he signed. However, he finally decided to come forward for four different reasons:

 * His disgust with the lack of action from superiors at Apple and his former company from stopping the disturbing content in the recordings
 * His disgust with the invasion of privacy that Apple created with outsourcing the Siri recordings to contractors
 * The possibility that Apple may be selling the data it gets from the Siri recordings to other third-party companies
 * Gaining the courage to come forward due to other contractors coming forward earlier anonymously in the Guardian newspaper about their experience with Apple

Media
He decided to send an email to Mediapart, which is a French investigative online newspaper, and met with a journalist from Mediapart to discuss the information he had on Apple. On August 30, 2019, the journalist would publish the story with the information he provided anonymously.

Around November 2019, the same journalist from Mediapart would introduce Le Bonniec with a documentary filmmaker who wanted to interview him on camera. He was unsure of being interviewed but was reassured when suggested that they would film him in two different ways, one done remaining anonymous and the other showing his face. They gave him the choice to decide on which part would be released.

Around January 2020, he would decide to go public with his face since he decided that this was going to be something that he would dedicate himself to trying to get addressed. On February 2020, the interview would be released with his face publicized.

Government
On May 28 2020, he sent letters to the European National Data Protection Authorities, the European Data Protection Board, and the European Data Protection Supervisor. In addition, he sent multiple letters to European nations that are not a part of the European Union, such as Liechtenstein, Switzerland, Norway, and Iceland. In the letters, he stated that he was concerned with Apple “wiretapping entire populations despite European citizens being told the EU has one of the strongest data protection laws in the world”.

Apple Response
As the story caught the public eye, Apple was quick to suspend their review program to afford them time to consider next steps. Apple decided on the following policy changes before resuming their review program:

 * By default, audio recordings will no longer be retained. However, transcripts will continue to be used in order to improve Siri.
 * Users will be able to opt-in to allow for the use of audio recordings of their exchanges with Siri.
 * Only Apple employees will review the audio recordings. Contractors will no longer be used for this purpose.
 * Inadvertent triggers of Siri will be deleted upon recognition.

Additionally, users were now aware of the review program, whereas the program had been kept undisclosed prior.

As of September, 2022, users are also able to delete recordings previously made of them. This is done through random identifiers associated with individual devices not user accounts.

With the release of iOS 13 in September 2019, Apple introduced on-device processing which allows Apple to do some of the processing, such as speech recognition and natural language processing, on the user’s device, reducing the need for data to be transferred off the device. In iOS 14, updates to Siri allowed it to process certain requests completely on the device, improving response time and user data privacy at the same time.

Cambridge Analytica
In 2018, it was revealed that political consulting firm Cambridge Analytica had obtained the data of nearly 30 million users in 2014. The data was used to create targeted political ads during the United States 2016 presidential election. The scandal resulted in increased criticism of Meta’s data privacy practices and has been a major talking point in the conversation of the need for better data privacy regulations.

Amazon
In 2019, a report by Bloomberg revealed that human review was being done on Amazon Alexa recordings similar to what Apple was doing with Siri. The recordings contained personal and sensitive information. This raised serious concerns regarding privacy as it was not made clear to users that their conversations were being recorded. As a result, Amazon updated its privacy policies to inform users that human reviewer’s may listen to user recordings and added the option to opt out of the program.

Google
In 2019, the National Commission on Informatics and Liberty fined Google 50 million Euros for violating the European Union’s General Data Protection Regulation for not obtaining informed consent from users before collecting their personal data. It was found that Google’s policy did not provide users with sufficient information about what the data would be used for. In response, Google modified their policy to make it more clear how they would use the data and provided users with a clearer way to access and manage their data.

TikTok
Leaked audio from 2022 has shown repeated cases of ByteDance employees in China accessing private US-based TikTok data which was claimed to have been kept separate from the rest of the data. There have been many attempts to restrict the use of TikTok in the United States such as the attempt by the Trump administration in 2020 and in February 2023, the White House declared that all United States federal agencies had 30 days to delete TikTok from all government-issued devices. As a result, TikTok CEO Shou Chew appeared before Congress to discuss United States data privacy and TikTok’s ties to China in 2023.

Relation to GDPR
The EU's GDPR provides a useful reference when considering the ethics of potential privacy violations. GDPR is primarily concerned with data that can identify the source user. Apple emphasizes that the data is anonymized through association with random tokens rather than user accounts. However, Apple does not address the possibility of identifiable information being in the audio. This fact, combined with the lack of public awareness, suggests an ethics problem.


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Professionalism/Port of Beirut Chemical Explosion

Introduction
On August 4th, 2020, Beirut, Lebanon was shaken with a devastating explosion. The blast was caused by a large quantity of the highly explosive ammonium nitrate stored unsafely in a warehouse at the port for years. The explosion resulted in the death of 218 people and over 7,000 injuries. In addition, it caused an approximated $3.8-4.6 billion in material damages. The blast was even felt 150 miles away in the Republic of Cyprus. This event is classified as one of the largest non-nuclear explosions in history and highlighted the need for an increase in safety regulations, and professional conduct when dealing with hazardous materials.

Timeline
The events leading up to the Port of Beirut disaster began in 2013, over 6 years prior to the explosion. In November 2013, the Rhosus, a ship carrying ammonium nitrate destined for Mozambique, stopped in the Port of Beirut to load cargo. There, port officials realized the vessel was not seaworthy and documented the hazardous material. A legal battle regarding what should happen to the Rhosus ensued, and in the confusion, port authorities were ordered to move the ammonium nitrate to land. The explosive material was stored in a hangar with inadequate safety conditions in October 2014. In the years following, customs officials attempted to have the ammonium nitrate moved, but were stopped by legal impediments. Military officials also failed to address the hazard. Documents dating back to November 2015 showed the Lebanese Army was aware of the situation, but did not have a use for the ammonium nitrate. Consequently, they took no action. It was not until early 2020 that State Security officer, Major Joseph Naddaf began an investigation. He found that the explosives posed an immense danger to the port should they detonate. Following Naddaf's report, Prime Minister Hassan Diab, the Cassation Public Prosecutor, Ghassan Oueidat, and President Michel Aoun were notified on June 3th, 2020, June 4rd, 2020, and July 21st respectively. On August 1st, 2020, under Ouiedat's direction, port authorities began maintenance on the hangar. On August 4th, 2020, reportedly, sparks from the welding maintenance ignited the ammonium nitrate causing an explosive chain reaction.

In the months after, Judge Sawan, who was overseeing the legal fallout, initially charged 37 people with responsibility, most of whom were mid and low-ranking port authority officials. When he attempted to charge higher ranking officials including Diab, Sawan was removed from office by the Court of Cassation in February 2021. In January 2023, Ouiedat called for the release of all the detainees associated with the blast.

Indirect Effects
The aftermath of the explosion had a considerable impact on Lebanon's economy and healthcare system. The economy of the country was already weakened by the decaying value of the currency and the effects of the COVID-19 pandemic, medicine supplies and housing were already expensive. After the explosion, more than 300,000 were left homeless. Housing became unaffordable. The port of Beirut was a vital economic hub for Lebanon, and after the explosion its infrastructure was completely damaged, limiting imports and exports. The healthcare system was already under strain due to COVID-19; the explosion caused damage to hospitals and healthcare facilities. The number of injured, coupled with the weakened healthcare system, made it impossible to attend everyone in need of medical attention. Furthermore, the explosion occurred in a time where there were already political issues in the country. Since October 2019, protests against the Lebanese government had been ongoing. The government was highly criticized for its handling of the aftermath of the explosion, these included many officials accused of incompetence and corruption. The Lebanese citizens called for an independent investigation, and a political reform.

Professionalism
Professionalism, or lack thereof, across all levels of management and execution created the circumstances necessary for the explosion. For one, ammonium nitrate has caused many deadly explosions in the past. Had officials been aware of previous disasters, the danger could have been avoided. Failure to step up and take action was another notable shortcoming of nearly all participants. And lastly, the high ranking officials who were responsible avoided nearly all consequences.

History Repeats
The Tinjing Chemical Storage Explosion in 2015, the West Fertilizer Company explosion in 2013, and the Port Neal fertilizer explosion in 1994, among many others, all had been caused by ammonium nitrate. In the Port of Beirut case, guidelines and regulations were ignored. International groups such as the Ammunition Management Advisory Team (AMAT) have detailed the proper way to store ammonium nitrate. Ventilation, distance to residential zones, and chemical packing density were all disregarded in Beirut. George Santayana's quote, "Those who cannot remember the past are condemned to repeat it" is supported by this event. Adherence to the regulations implemented would have reduced the risk significantly, if not eliminated it all together.

Inaction
As early as 2014 authorities knew of the immense threat the ammonium nitrate posed. Customs official Joseph Skaf was reported to have documented the danger in February 2014. However, he died mysteriously in 2017 leading many to believe he was murdered. Customs official Nehme Brax also sent several letters to legal authorities asking for the chemicals to be disposed of, but his requests were denied by the judge on grounds that they could not authorize such actions. Notably, when the problem reached Prime Minister Diab, he said, “I then forgot about it, and nobody followed up. There are disasters every day.”

In every level of authority from the lowest ranking port official to the prime minister, no one took effective action. For those who had less authority such as the port officials, it would have required a heroic and professional defiance of the legal system to remove the ammonium nitrate. Corruption and potential for extreme consequences including imprisonment and death would make this kind of bravery difficult for the participants. Those with more power, such as Diab, had the authority to fix the problem but chose not to.

Blame
The explosion is attributed to the negligence of Lebanese authorities that were aware of the dangerous chemicals stored in the warehouse, but failed to take the appropriate measures. But in the Lebanese political system, high ranking officials enjoy various immunities from the law. Article 40 of the Lebanese constitution states: "No Chamber member may be prosecuted or arrested, during the session, for committing a crime, unless authorized by the Chamber, except in case he is caught in the act." On December 17 of 2020, the investigation was stopped due to the charges being press against Sawan for targeting these immune officials. Many of the initial 37 low and mid-ranking officials initially charged were merely used as scapegoats. In September 2021, citizens of Lebanon protested against attempted removal of the second lead investigator, Judge Tarek Bitar. The investigation has not been active as of May 2023. Two years after the disaster, United Nations experts called on the Human Rights Council for an international investigation, but no official investigation has started as of May 2023. Laury Haytayan, Middle East and North Africa Director of the Natural Resource Governance Institute, is quoted, "[The Lebanese government] cannot be efficient. They are efficient in corruption. But not in running a country.” Her statement is supported by the fact that it took over 6 years to attempt to secure the ammonium nitrate, but less than 6 months to have Sawan removed from office. By scapegoating lower-ranked officials and attacking those who oppose them, the officials responsible for the explosion have managed to avoid all ramifications for their lack of action.

Conclusion
The Beirut chemical explosion was a tragic event that had significant consequences for Lebanon and its people. The aftermath of the explosion produced anger and discontent among the Lebanese citizens who accused the government of avoiding their responsibilities and failing to provide appropriate protection. The involved participants did not act ethically or professionally because they performed their obligations to the people poorly. They tried to avoid accountability by blaming low-level officials who were present during the storage of the ammonium nitrate, rather than the high level officials that were supposed to be upholding public safety. The Lebanese government also failed to provide safety by ignoring similar chemical explosions that occurred in the past due to unprofessional behavior. The tragedy was entirely preventable if participants had strived for professionalism in their duties.


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Supplementary mathematics/Fourier series

The Fourier series is a periodic expansion for functions such as f(x) in terms of the sum of the infinity of the sine and cosine functions, and the expansion is exponential. is also used in the Fourier series. The study of the Fourier series is one of the calculus of calculus and is known as the analysis of harmonics. Of course, this topic can be an arbitrary tonic function in a trigonometric set. and integral to match.

In particular, since the superposition principle holds for solutions of a linear homogeneous ordinary differential equation, if such an equation can be solved for a single sinusoid, the solution of an arbitrary function is immediately available by expressing the original function in Fourier form. Is. Connect the series and then solve for each sinusoidal component. In some special cases where the Fourier series can be summed in closed form, this technique can even yield analytical solutions.

Any set of functions that form a complete orthogonal system has a generalized Fourier series similar to the Fourier series. For example, using the orthogonality of the roots of a Bessel function of the first kind yields a so-called Fourier-Bessel series.

Fourier series for periodic functions
For example, expressions such as sine, cosine and exponential eikx can be used to define the Fourier series. It provided square waves (1 or 0 or -1) which are good examples, along with delta functions in the derivative. In order to present a spike and a step function, and a sloping surface, etc., Bayez looked at how their expansion should be found theoretically and mathematically. You should start with sin x first. This Fourier series theory has a period of 2π of sin(x + 2π) = sin x. It is an odd function because sin(-x) = - sin x, and vanishes at x = 0 and x = π. Every sin nx function has these three properties, and Fourier looked at infinite combinations of sines

$$S(x)=b_1 \sin x+b_2 \sin 2x+b_3 \sin 3x+...=\sum_{k=1}^{N} b_n \sin nx $$

If we assume that the numbers in the Fourier series decrease quickly enough, the set S(x) will have three characteristics. In this hypothesis, the importance of the decay rate and code is predicted.

Periodic S(x + 2π) = S(x) Odd S(−x) = −S(x) S(0) = S(π) = 0

200 years ago, Joseph Fourier, a French mathematician, expanded the Fourier series with an interesting suggestion. Joseph Fourier realized that the series of the function S(x) with those properties can be written as an infinite series. He expressed sine and cosine. This idea started the huge and important development and development of the Fourier series. The first step for us and you in the Fourier series is to calculate the number bk that multiplies sin kx or cos kx from S(x).

If we assume that the expansion of $$S(x)=\sum b_n \sin(nx)$$, both sides can be multiplied by sin kx. If it is integrated from 0 to π, it is integral:

$$\int_{0}^{\pi}S(x)=\sin(kx)dx=\int_{0}^{\pi} b_1\sin(x) \sin(kx)dx+\int_{0}^{\pi} b_2\sin(2x) \sin(kx)dx+...+\int_{0}^{\pi} b_k \sin(kx) \sin(kx)dx$$

On the right, all integrals are zero except for the highlighted integral with n = k.This "perpendicular" feature will dominate the entire chapter. They make sinuses 90◦ angles in function space, when their inner products are integral from 0 to π:

$$\int_{0}^{\pi} \sin(nx) \sin(kx)dx $$


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Alo, Jonathan!

'''Alo, Jonathan! Numo by the Direct Method'''

Storitel Un
Unme piatluna (1/5)

Pir stes in sitad. Pir sribes in dagkitab. Pir optes sitad.

Pir stes ma in sitad? Ja, pu stes in sitad.

Pir stes ma in… pir? Nai, pu stes nai in pir. Pu stes in sitad.

Pir stes ma in tren? Nai, pu stes nai in tren. Pu stes in sitad. Pir stes in sitad.

Pir sribes ma dagkitab? Ja, pu sribes dagkitab.

Dagkitab sribes ma pir? Nai, dagkitab sribes nai pir. Dagkitab sribes nai. Pir sribes. Pir sribes dagkitab.

Sitad optes ma pir? Nai, sitad optes nai pir. Sitad optes nai. Pir optes. Pir optes ma sitad? Ja, pir optes sitad.

Pir stes in wac? Pir stes in sitad. Pir sribes wat? Pir sribes dagkitab. Pir optes wat? Pir optes sitad. Pu stes in sitad, ai pu sribes in dagkitab, ai pu optes sitad.

Pir stes ma in dagkitab? Nai, pu sribes in dagkitab. Pir optes ma pir? Nai, pu optes nai pir; pu optes sitad.

Pir gros. Pir gud, ai pir intel. Pu soces. Pu soces de sitad. Pu soces: “Sitad es wat? Sitad gud ma? Sitad gros ma?”

Pir sribes ma sitad? Nai, pu sribes nai sitad; sitad gros. Pu sribes dagkitab; dagkitab gros nai.

Dagkitab soces ma de pir? Nai, dagkitab soces nai. Pir soces. Pu soces de dagkitab, ai soces de sitad.

Ai pu sribes in dagkitab. Pu sribes de sitad. Pu sribes: “Sitad gud, ai sitad gros.”

Pu soces: “Sitad gud”; pu soces, das sitad gud. Pu soces: “Sitad gros”; pu soces, das sitad gros. Pu soces de sitad, ai pu soces de dagkitab.

Dagkitab es wat? Dagkitab es, wac pir sribes; pu sribes in dagkitab. In dagkitab, pir sribes de sitad. In dagkitab, pir sribes nai de tren; pir stes in sitad, nai in tren. Pu soces nai de tren, pu soces de sitad, sitad Miuncen.

Wat es sitad? Ku es Miuncen. Miuncen in wac? Ku es, wac pir stes. Pir in wac? Pir in Miuncen.

Ja, Miuncen es sitad gros, ai sitad gud. Pir soces, das Miuncen es sitad gud, ai pu soces, das ku es sitad gros. Pir intel. Pu ses: “Alo, Miuncen!” Pu es pir gud!

Pir stes ai soces: “Tren in wac?” Pu optes… pu optes tren! Pu soces: “Tren!” Alan pu soces nai de dagkitab, ai pu soces nai de sitad; pu soces de tren!

Storitel Duo
Duome piatluna (2/5)

Alan pir in tren. Pu in Miuncen nai; pu in tren. Pu safares. Pu soces: “Alan anu safares ek Miuncen al Win. Es safar gud. Anu ames safar.”

Pu soces de Miuncen. Pu soces: “Alan anu in tren, ked in fordag anu in Miuncen te. Ai alan anu sribes dagkitab in tren, ked in fordag anu sribeste dagkitab in Miuncen. Ai alan anu soces in tren, ked in fordag anu sribeste in Miuncen. In fordag anu soceste de Miuncen in Miuncen, ai alan anu soces de Win in tren. Alan anu in tren, nai in Win. Ked anu soces ai sribes de Win.”

Pir soces ma alan in Miuncen? Nai, pu soces nai alan in Miuncen. Pu soces in tren. In fordag pu soceste in Miuncen. Pu ses: “Alo, tren!”

Pir in tren, ai pu safares al sitad. Sitad dat es nai Miuncen; Miuncen es sitad de fordag. Sitad dat es Win; Win es sitad de aldag.

Pir soces de Miuncen ai Win. Pu soces: “Miuncen es sitad de fordag, ai Miuncen gud te. Alan es aldag, ai anu in tren; tren gud. Win gud ma?”

Pir soces de Miuncen: Miuncen este sitad de fordag. Pu soces in tren: Pu in tren in aldag. Ai pu soces de Win: Win es sitad de posdag.

Ai pu soces: “Miuncen gros te. Tren gros. Win gros ma?”

Ai pu soces: “In Miuncen anu sribeste in dagkitab. In tren anu sribes in dagkitab. In Win anu sribes ma in dagkitab? Ja, in posdag in Win anu sribes in dagkitab. Anu ames dagkitab.” Pir soces poli (pu soces poli = pu soces ai soces ai soces), ai pu sribes poli. Ja, pu es pir intel. Pir intel sribes poli, ai soces poli. Pu es Jonathan, Jonathan es pir intel.

Pu sribes: “Anu es Jonathan. Anu in tren. In fordag anu steste in Miuncen; in posdag anu stes in Win.”

Pu soces, ai sribes: “Tren… ku gud, ked eski. Ku neo nai; ku eski. In Miuncen tren eski ma? Ja, tren de Miuncen eski. Ked tren de Miuncen gud, ai anu ames tren de Miuncen. In fordag anu ameste tren in Miuncen, ai in aldag anu ames tren alan, ai in posdag anu ames tren in Win. Anu ames tren!

Jonathan sribeste: “Miuncen es sitad gud ai sitad eski, ai Win es sitad gud ai sitad eski. Miuncen ai Win neo nai, ked gud. Miuncen ai Win es sitad eski, ked sitad gud. Anu ames sitad!”

Jonathan soces, das dag duome de safar gud. Pu ses: “Aldag este dag duome de safar gud. Anu ames safaresa!”

Storitel San
Sanme piatluna (3/5)

Jonathan stes in Win: sitad de Win. Jonathan soces, das Win gud, ai das Win sundar. Jonathan soces nai, das Win ansundar; pu soces, das Win sundar. Pu soces: “Win es sitad sundar! Anu sribes de ku!” Ai pu sribes in dagkitab de Win. In dagkitab pu sribes: “Dag sanme anun gud muco! In dag duome anu steste in Miuncen. Ked alan anu stes in Win: Win es nai Miuncen. Miuncen ai Win es duo sitad; Miuncen es nai Win ai Win es nai Miuncen. Anu stes in Win ai sitad sundar muco. Anu ames Win; sitad nai ansundar. Win sundar wam! Ked anu awes problem.”

Wat? Jonathan awes problem? Problem wat? Alan pu optes nai dagkitab; pu optes sitad ai soces. Pu soces poli de problem.

Problem de Jonathan es, das pu ames Win, ked awes nai krono.

Pu soces: “Hmm. Alan es septe (7) sahat. In dek (10) de sahat tren ekires. Dek (10) de sahat minus septe (7) de sahat es san (3). San de sahat es nai krono poli al sitad sundar! Anu awes nai krono. Awesa krono gud, ked anu awes nai! Sribesa dagkitab gud, ked anu awes nai krono al sribesa! Fes wat in Win?

Pu soces: “Anu awes nai krono poli. Fes wat - sribesa in dagkitab, or etesa, or optesa sitad? Anu fes wat?”

Pu soces mos, ai ses: “Anu awes idea gud! Un momen… anu soces. Tren anun ekires in dek (10) de sahat. Res ma tren ankuai mos, in dekun (11) de sahat, or dekduo (12), deksan (13), dekcar (14), or dekpiat (15) de sahat?”

Pu optes… ja! Res tren, das ekires in dekpiat sahat. Alan Jonathan joi.

Jonathan ses: “Dekpiat (15) minus septe (7) es okto (8). Alan anu awes okto de sahat de krono! Anu fes wat?”

Pu ses: “Anu kes! Anu etes stek. Ai anu pijes bir. Un momen… nai, pijes duo bir, or san bir. Idea gud!

Alan Jonathan pijes bir in Win, ai etes stek. Pu joi. Pu ses: “Anu joi wam! Anu ames sitad de Win. Anu ames wam safar!”

Storitel Car
Jonathan in wac? Pu stes ma in Win? Nai, pu stes in sitad hetero. Pu stes in Bistrița. Bistrița es wat? Es sitad in Romani. Alan kaies safar in Romani.

Jonathan sribes in dagkitab pun: “In unme piatluna anu in Miuncen te. In duome piatluna anu in Win te. Dan anu in Budapesto te. Budapesto sundar te. Ked anu aweste nai krono in Budapesto. Anu opteste jol in Budapesto. In Budapesto anu soceste: ‘in iki garbe fines, ai in iki timur kaies. Budapesto hetero.’”

Pu soces mos poko de Budapesto. Pu soces: “Anu es jen, das komeste ek Engli, ek London. Anu tales Englibasa, dar jen in Engli tales Englibasa. Ked in Budapesto anu taleste nai met jen in Englibasa; anu taleste in Doicbasa. Doicbasa anun gud nai, ked jen in Budapesto tales nai Englibasa. Ani tales in Doicbasa. Interesa, das jen in Budapesto tales Doicbasa.”

Alan Jonathan soces de safar pun. Pu soces: “Anu in museum in London te. In museum reste kitab de stan andar. Anu citeste kitab. In museum reste karte de stan andar. Anu opteste karte. Kitab ai karte seste, das stan andar daleko, ked interesa. In stan andar res jen poli, jen hetero poli.”

Alan Jonathan soces mos de safar pun. Pu soces: “In fordag anu in Cluj-Napoca te. Anu eteste in Cluj-Napoca. Nam de et este “Paprika Hendl”. Gud muco, et in Cluj-Napoca. Hotel gud te, ked anu somneste gud nai. Ked stan sundar muco! In tren anu opteste cisitad poli ai kastel poli. Sitad gros mos poli jo cisitad, ked cisitad sundar muco. Kastel gros mos jo hotel, ai sundar mos jo hotel! Cisitad gros anmos jo sitad, ked ki sundar, ai hotel gros anmos jo kastel.”

Jonathan sribes mos in dagkitab pun: “Bistrița es wici interesa. Bistrița es wici eski, eski mos jo wici andar. Aulo hotel anun in Bistrița eski, eski mos jo hotel andar. In hotel anu seste nam anun: “Anu es Jonathan Harker” ai ki seste al anu: “Welkom in hotel.” In hotel anu opteste patra al anu. Alan anu cites patra anun.”

Jonathan cites patra pun. Ku ses:

“Ami anun. - Welkom. Somnes tu gud. Anu in Bukovina; komes tu sai al Bukovina. Safar ek London gud te ma? Stan anun sundar muco. Welkom in stan anun.

Ami tun, DRACULA.”

Jonathan cites patra ai soces de ami pun, Konte Dracula. Pu es wau? Pu kes nai. Ked ami pun interesa. Aulo cisitad Bistrița interesa. Jonathan joi, ai soces de ami pun. Dan pu somnes.

Storitel Piat
Carme Piatluna (4/5)

In aldag Jonathan safares al Konte-Dracula. In hotel pu optes hoteljen, ai optes patra de Konte-Dracula in man de hoteljen. Pu seste al hoteljen in doicbasa: “Alo, ges tu sai patra al anu.” Hoteljen ges patra al pu.

Dan pu kuestiones in doicbasa: “Patra es wat? Dracula sribeste wat in patra?” Ked hoteljen ses: “Anu tales nai doicbasa, anu tales nai doicbasa.” “Annormal! In fordag anu taleste met hoteljen in doicbasa, ked alan pu ses, das pu tales nai doicbasa! War? Pu kuestiones al hoteljen: “Tu kes ma Konte-Dracula?” ked alan hoteljen tales nai.

Jonathan soces: “Annormal! Anu taleste met hoteljen in doicbasa. Pu nes tales doicbasa, ked pu ses, das pu tales nai ku! Anu nes kuestiones mos al pu, ked anu awes nai krono. Anu nes ekires.”

Alan Jonathan in majal pun in hotel. Pos un de sahat, pu nes ekires. Ked optes tu! Mir eski komeste al majal pun.

Mir eski: “War tu ekires? Sai ekires tu nai! Aldag es nai dag gud! Es dag mal, dag de kos mal!”

Jonathan: “Anu gud, anu ires optes ami. Sai tales tu nai de kos mal.”

Mir eski: “Tu kes ma nai, das aldag es dag de Sante-George? In dekduo (12) de sahat kos mal komes! Tu kes ma, tu ires al wac?”

Jonathan: “Nai nai, mir eski, panwat gud. Anu gud, ai in aldag kos mal komes nai.”

Mir eski: “Sai ekires tu nai! Sai ekires tu nai!

Jonathan: “Nai, anu nes ekires. Sai plakes tu nai.”

Mir eski: “Es krusifikse. Krusifikse al tu.”

Jonathan: “Aa…Englijen ames nai krusifikse…ked abrigan. Sai plakes tu nai.” Jonathan soces: “In Engli ani ames krois, ked nai krusifikse. In Romani jen ames krusifikse, nai krois.”

Alan mir eski ires ai Jonathan in majal solo. Pu soces: “Wici dat annormal! Mir eski, das ges krusifikse! Ked anu nes ekires.”

Storitel Sita
Piatme Piatluna (5/5) - Kastel.

In aldag Jonathan ires al kastel de ami pun, Konte Dracula. Safar al kastel kaies alan. Jonathan stes in fronte de koce ai speres kocejen. "Kocejen in wac?" pu soces. "Aa, iki es kocejen – pu tales met hoteljen. Ki tales de wat? Anu awes nai krono, ai anu nes ekires."

Jonathan optes karib de koce, ai optes jen poli. Jen tales poli, ked pu komprendes nai jen dat. Jonathan soces: "War ki anjoi? Anu anjoi nai. Anu soces, das iki es wici annormal, ked anu anjoi nai." Pu optes, das jen tales, ai mobes man. Pu optes man kin...ki fes wat met man kin? Aa! Alan pu komprendes. Ki mobes man wam krusifikse. Man kin es nai krusifikse, ked wan ki mobes man, man kin es wam krusifikse.

Koce komes. Alan safar kaies. Jonathan sites in koce. Aulo jen andar sites in koce. Jonathan optes stan in karib pun, ai soces, das ku sundar. Res arbor poli. In stan dat res apel poli, ai prun poli, dar res apelarbor ai prunarbor poli. Pu ses: "Mmm! Apelarbor sundar muco, ai anu wes etes apel kin." Pu optes aulo per, dar res perarbor poli in stan dat. Pu ses: "Gud muco! Perarbor sundar muco, ai anu wes etes per kin. Anu ames arbor de senlin." Ked koce mosires, ai mosires...es koce kuai muco! War koce ires kuai? Pu soces jol, wat es nai jol gud; ku eski. Ked koce ires kuai! "Koce nes ma ires kuai, li jol eski ai es jol gud nai?" pu soces. Kocejen ames ires kuai, pu soces.

Alan koce in senlin. In daleko pu optes alpe. Alpe gros muco, ai aulo senlin gros muco. Jonathan soces de alpe in Engli. Pu soces, alpe in Engli ci muco, ci mos jo alpe in Romani.

In karib de koce, Jonathan optes ewan jen andar. Ki es jen de stan dat-iki. Stan dat iki awes jen hetero jo jen in stan dat-dac Engli. Jen in stan dat iki liwes in senlin, ked jen in stan dat dac, Engli, liwes in sitad gros. Ai pu soces de krois ai krusifikse. Ja, jen in stan dat-iki, Romani, ames krusifikse, ked jen in stan dat-dac, Engli, ames krois.

Ku es sahat wat? Es sita-sahat. Wan Jonathan siteste in koce, ku este pagi. Ankuai mos kendag komeste. Kendag es 12-sahat. Dan poskendag komeste; poskendag es krono in pos de 12-sahat. Ked alan es afen. Afen es wat? Afen es sahat, wan sol alto nai, ked analto. Alan sol analto muco.

Wan Jonathan in koce te in kendag, ham te. In kendag Jonathan soceste: "Anu ham! Nes pijes!" Ked alan, wan ku es sita-sahat; anham. Alan pu soces: "Anu anham! Sol in wac?" Wan Jonathan ham, ku ham. Jonathan wes nai ham, ked pu wes nai aulo anham. Pu ames nai ham, ai pu ames nai anham. Jonathan ses neo: "Sol in wac?" ai pu ses: "Wan safar fines? Kastel de ami anun Konte Dracula in wac?"

title: Alo, Jonathan!


text: Category:Book:Alo, Jonathan!

title: Category:Book:Alo, Jonathan!


text: User talk:Uday Kiran

title: User talk:Uday Kiran


text:
Cookbook:Kimchi

Kimchi is a variety of Korean fermented or pickled vegetables. Several varieties exist.

title: Cookbook:Kimchi


text:
Cookbook talk:Nigerian Carrot Stew

Is meat vegan?
The recipe calls for meat but it categorised as vegan. There isn't any mention of what to substitute the meat with to explain how to make this recipe as a vegan version. 77.105.75.228 (discuss) 15:36, 4 May 2023 (UTC)

 * Fixed! Thank you :) —Kittycataclysm (discuss • contribs) 20:17, 4 May 2023 (UTC)

title: Cookbook talk:Nigerian Carrot Stew


text:
Professionalism/Insulin: The Deadly Price

Background: Diabetes and Insulin
Diabetes is a chronic disease that results in an excess of sugar in the blood. Insulin is a hormone that regulates blood glucose levels, and in diabetes, the body’s ability to make or use insulin is impaired. This can lead to serious health problems such as heart disease and kidney disease and when it is not treated it is fatal. There are two types of diabetes; type 1 and type 2. Type 1 diabetes is an autoimmune disease, in which the pancreas makes no insulin. Type 1 diabetes is also known as juvenile diabetes or insulin-dependent diabetes, and can be caused by genetics or by a virus. Type 1 diabetes has no cure, and treatment is directed toward managing sugar levels in the blood using insulin, as well as changes in diet and lifestyle. Glucose is a main source of energy for cells in muscle and tissue, and is enters cells from the bloodstream with the help of insulin. Low blood sugar results in the pancreas putting less insulin into the bloodstream. In type 1 diabetes, there is no insulin to move glucose from the bloodstream into the cells. Sugar then builds up in the bloodstream, causing complications. Type 2 diabetes develops from habit, and is when the pancreas makes less insulin as well as having developed resistance to insulin. It is more common in older adults, and can be caused by obesity. In type 2 diabetes, the role of insulin doesn’t work well, and sugar builds up in the blood. With higher blood sugar, the pancreas releases more insulin, and eventually the pancreas cannot make enough insulin for the body’s needs.

There are two types of insulin; natural insulin and synthetic “human” insulin. Natural insulin is an essential hormone made by the pancreas that helps the body turn food into energy while balancing blood sugar levels, helping glucose from blood move into cells in the body. Synthetic “human” insulin is a prescribed injectable, made to act like naturally produced insulin to help those with diabetes. It helps stop the liver from producing more sugar than the body can handle. Insulin was discovered in 1921 by Frederick Banting and Charles Best, where it was first isolated from the pancreas. After initial success in a dog, insulin was tested in 14 year old Leonard Thompson, whose blood sugar dropped to normal levels. Before this discovery, all children with diabetes died within 1-2 years of diagnosis, with no effective treatments and harsh diets.

Previous production of insulin consisted of extracting insulin from the pancreas of cows and pigs that were slaughtered for food. Now, insulin is produced industrially with recombinant DNA. Recombinant DNA technology is when human genes can be inserted into genetic material of a common bacterium, where the recombinant microorganism is then able to produce the protein encoded by the human gene. The human insulin gene is created in the laboratory. A loop of bacterial DNA known as a plasmid is removed and the human insulin gene is inserted into the plasmid. The plasmid is then returned to the bacteria and the recombinant bacteria is put in large fermentation tanks. The recombinant bacteria uses the gene to produce human insulin, where it is then harvested for medicine.

Timeline of Insulin
Over the last 30 years, the price of insulin has skyrocketed. In 1996, insulin was considered affordable, at $24 per vial. In 2023, the cost of insulin ranges from $65 to over $300, depending on the type of insulin. A timeline of the discoveries and associated prices paints the picture of the dramatic increase in cost over the last century.

1889 : Oskar Minkowski and Joseph von Mering, German researchers, conducted experiments on dogs. They removed the pancreas from the dogs, causing the dogs to develop symptoms of diabetes. The dogs quickly died after the pancreatic removal. Minkowski and von Mering concluded that the pancreas was where "pancreatic substances" were produced.

1910: Sir Edward Albert Sharpey-Shafer coined the term "islets of Langerhans," which were responsible for producing insulin.

1921: Frederick Banting and Charles Best were able to extract insulin from a dog's pancreas. Banting and Best sold the patent to the University of Toronto for only $1. They believed it was unethical for a doctor to profit from a discovery that could save lives.

1922: Mass production of insulin extracted from pigs and cows used to produce vials for diabetic patients. Eli Lilly became the largest producer of insulin. The cost of insulin was approximately $9 per vial for insulin.

1982: Recombinant DNA technology was introduced to E. coli to produce human insulin. Price increased to $14 per vial.

1996: Insulin analogs introduced. Analogs are designed to mimic the body's natural pattern of insulin release. The analogs are completely synthetic. Humalog insulin, produced by Eli Lilly, price listed at $24 per vial.

2019: Humalog insulin cost $332. Price of production ranges from $2-4.

Based on the timeline, insulin price increases correlated with new discoveries related to insulin. Currently, the average price of insulin is $275 per vial. 13 vials of insulin are required per year per diabetic to survive. Solely looking at covering the cost of production, it would cost $71 for a year supply of insulin. However, the current price of insulin comes out to $3575 for a year supply of insulin. The profit margin is 98% on insulin.

Insulin Monopoly
The insulin price increase can be initially understood by analyzing the industry of insulin production. Currently, there are 3 pharmaceutical companies that own 95% of the insulin market: Eli Lilly, Sanofi, and Novo Nordisk. Governments have given 100% of the supply of insulin control to these three companies. These pharmaceutical companies use 3 main arguments to justify the high prices of insulin, but are illogical: high cost of development, free market economy pricing, and high costs are essential for continued innovation. First, the high cost of development is not relevant to insulin because the drug is over 100 years old. Second, there is no free market economy for a product (insulin) where the customer (diabetic) is not in a position to negotiate price, because they need the product to survive. Third, the high costs are not essential for innovation. Inherently there is limited innovation with insulin, and the need for affordability is more pressing.

The insulin monopoly has been the status quo and is the contributor to insulin’s high cost. Governments have historically been hesitant to impose price caps because insulin companies have halted production of insulin and shut down entire factories in protest to government sanctions. In the 1980s, Argentina was going through an inflation crisis of up to 800%. To combat this, the government placed price controls on all products, including insulin. Argentina’s only insulin-producing company at the time was Eli Lilly, who closed their insulin-producing plants, creating a sudden insulin shortage for 73,500 men, women, and children. The company eventually returned to Argentina in the 1990s, due in large part to a new convertibility exchange policy, which pegged the Argentine peso to the US dollar.

Insulin is still out of reach for many Americans, but there are possible solutions at the state and federal policy level :

 * Protection against monopoly.
 * Reforms to the regulatory and legal processes that prevent generics and biosimilars from entering the market.
 * Reforms to the patent system to prevent abuse.
 * Creation of an agency to oversee pricing.
 * Nonprofit generic manufacturing.
 * Creation of laws that provide access to affordable insulin in cases of emergency.
 * Greater advocacy from professional and patient organizations.

Insulin Price Capping
There are several strides being made to cap the price of insulin, both by companies and the government. Several members of Congress have made proposals to address insulin pricing - such as creating a generic insulin manufacturing plant owned by the federal government, or by proposing legislation to create an insulin pricing model. In January 2019, Congress held hearings on insulin prices with insulin company executives and launched a bipartisan probe in the next month. State governments have also been contributing to the fight against insulin prices. In 2019, Colorado became the first state to legislate a price cap on insulin, with several other states following suit. In 2017, Nevada passed legislation to mandate insulin pricing transparency. In 2022, the passing of the Inflation Reduction Act capped the price of insulin at $35 per month for diabetics covered by Medicare. A proposal to do the same for diabetics with private insurance was killed in the senate, with certain members of Congress claiming that capping insulin will hurt competition in business and innovation in research. In President Biden’s 2023 State of the Union Address, he called for this cap to be extended to those with private insurance.

Insulin companies initially took smaller strides to insulin price capping, but have started to take bigger leaps. In a 2019 response to mounting public pressure, Eli Lilly announced it would provide a generic version of its insulin analog Humalog. This generic version is identical to Humalog, but would cost half the price. Since then, Novo Nordisk announced it would also sell a generic version of their insulin analog Novolog at half price, and Sanofi announced it would cap insulin at $99 a month for those without insurance. Following the passing of the Inflation Reduction Act and industry competition, Eli Lilly announced that they would cap insulin prices at $35 a month. The cap went into effect immediately, and has already encouraged other companies to do the same.


text:
Among Us/Running Games

If you plan to play Among Us with your friends, you need to know how to run games of Among Us. If you wish to host an online game, click the 'Online' button on the homescreen, then click the 'Create Game' button. After that, you can configure what region server you want (choose the one closest to you), and configure information like the map, number of Imposters, and maximum players. To host a local game, the process is similar; instead of clicking the 'Online' button, click the 'Local' button.

Once you create a game, you will join a lobby, where, if you use the laptop in the lobby, you can customize more things, like whether visual effects for tasks is enabled. Once enough people join the game, you can start it by pressing the triangle shaped Start button. After you do all this, you should have a game of Among Us!

Kicking and Banning Players
What happens if someone is misbehaving in your game, perhaps saying or doing obscene things? Well, Among Us has tools to deal with those things. While in the lobby, you can kick a player by going into the chat and clicking on the boot. From there it should be obvious what to do, but if you do not know, you should click on their name, then click 'KICK.' This player can still rejoin, though. To kick them out permanently, click 'ban.'

title: Among Us/Running Games


text:
Among Us/Imposter Gameplay and Strategy

An Imposter is a role in Among Us. The Imposter is the most fun role. As an Imposter, your goal is to get rid of Crewmates, one way or another, without getting caught. There are two methods of doing this:
 * Get rid of Crewmates manually, until there are equal numbers of Imposters and Crewmates. Manipulating Crewmates to vote eachother out and not you is very important.
 * You can do this by killing Crewmates directly and getting them voted out by their fellow Crewmates.
 * Causing a critical sabotage, and having the time run out. Critical sabotages will be covered later. Just know that sabotages are problems that Crewmates have to solve, or face some consequence.

If you successfully do one of these things, you and your fellow Imposters will win the game.

Kill
Killing is an iconic part of Among Us. When you are near a Crewmate, you can click the skull button. Once you do this, you will see that the upper half of the Crewmate's body will be gone, and the body will fall to the ground. The crewmate will become a ghost, unable to vote or communicate to Crewmates in any way. There is a cooldown, so you can't kill too many crewmates in a short time. Obviously, you should not do this in view of a Crewmate (other than the one you intend to kill, of course). Preferably, you should try to kill near a vent or other escape route. Killing in an area where many Crewmates are extremely close together also works. Alternatively, you can kill and then report your own kill, called a self report in Among Us parlance. This is an easy way to get out of a situation where you are likely to get caught as the Imposter, but be prepared to answer questions about where the body was, and if you saw anything.

Sabotage
An Imposter can sabotage to hamper the Crewmates, or even take them out completely. Even if you have been voted out, you can still sabotage as a Ghost. Sabotages can be used strategically to help with killing, by, for example, closing the doors on an area to give you more time to escape. Sabotages can also be used to lure Crewmates away from the room where a killing happened, allowing you to escape. A list of sabotages is available for each map's individual chapter. To sabotage, click the button with a crewmate on fire. This will pull of a map, with individual systems and door you can sabotage. Click on the system or door you want to sabotage to sabotage it. If there are a small number of crewmates left, sabotages can be used to lure one near you to be killed. Remember, a report will stop the sabotage, so if you are sabotaging with the aim of killing the Crewmates via the sabotage, don't self report.

Vent
Vents are a quick transportation system for your map. You can use vents to easily escape after killing people, look for lone Crewmates to kill, and simply get around the map quickly. (Admin can also be used to look for lone Crewmates.) Make sure when you're venting, nobody sees, because venting is an ability exclusive to Imposters and Engineers. If there are no engineers in the game, seeing someone vent tells you they are the Imposter, and even if there are Engineers in the game, it's not very good to be seen venting.

Strategy
Imposter strategy is much more complex than Crewmate strategy, so be prepared. Ignore any and all Crewmate strategy. Remember, if you are playing with other Imposters, use them! Rat them out when you need to, use them to create an alibi, etc.

During Emergency Meetings

 * Always vote with the crowd, even if it means voting out your fellow Imposter. Standing out isn't a good idea.
 * Be calm when accused. Begging for people to trust you is a bad idea. It makes you look like a desperate Imposter, which is exactly what you don't want to be seen as.
 * When a Crewmate is about to be voted out, try to defend them weakly. If role reveals on vote out are enabled, the Crewmates will see that they weren't an Imposter and trust you more.
 * Asking someone to join up with you and do tasks can help establish your alibi. Just sabotage the lights and go away from the group when you want to kill.
 * Talk during meetings; silence is suspiscious.
 * Attempt to create a state of confusion; accusations should be thrown around randomly.
 * Whatever you do, never accuse your accuser. They will be simply voted out, and after that, you.
 * Never accuse a verified Crewmate, who has done a visual task. In general, accusing people is something you should not do (especially if the game is set to reveal roles after vote out.)
 * Support other player's accusations.
 * In the event you are sure that you are going to be ejected, claim that a Crewmate is the other Imposter, helping them avoid getting voted out.

During Normal Play

 * Kill off dangerous Crewmates, like those that claim you are the Imposter, or are well trusted in the crew.
 * Keep players the Crewmates are suspicious of in the game. This will lead to them being voted out, hopefully instead of you.
 * If a Crewmate sees you vent, they will likely attempt to call an emergency meeting. To prevent this, simply vent to Cafeteria (in the Skeld map, of course) and kill them.
 * Kill Crewmates who are doing tasks that cover their field of view. Be wary of this strategy, though. Some Crewmates will pretend to do tasks without activating them to bait Imposters.
 * Don't underestimate Comms Sabotaged. It can disable Admin and Security, allowing you to vent and kill with impunity.

title: Among Us/Imposter Gameplay and Strategy


text:
User:The Capitalist Bookworm

I love books so much that I spend hours every day reading. I like history,sports,and exciting novels. Happy to be here! :)

title: User:The Capitalist Bookworm


text:
User:QuintinDogNET/sandbox

NWL: New Wrestling League.

The New Wrestling League is a online wrestling fictional company that has 3 brands Warzone,Bloodfest and Afterfight every monday a new Warzone comes out and every Wednesday a new Bloodfest comes out and every Friday a new Afterfight the last saturday of every month a event comes out with title matches and rivals both men and women has a NWL World heavyweight title and Mens/Womens title plus theres a NWL European title for mid card men and a NWL Mens Tag Team championship for the teams.

title: User:QuintinDogNET/sandbox


text:
User:0631132010 hlela

Userediting

title: User:0631132010 hlela


text:
Talk:AI Art Generation Handbook/Training

Possible Dreambooth Training Parameter - In Progress
d8ahazard / sd_dreambooth_extension: 926ae204

Vladmandic Auto Fork from Auto1111 Version:  22090d7b

--Hardware-- 16GB VRAM NVidia RTX3080 Usage: 14.5GB VR ---

Dataset:

Images : 44 different pictures (512X512px)

---

If not mentioned, it is default values.

Training Steps Per Image : 175

Batch Size : 4

Learning rate : 0.00000125

Use EMA : True

Optimizer: 8bit AdamW

Mixed Precision: bf16

Memory Attention: flash_attention

Weight Decay: 0.01

Time taken: ~ 1hour

MrAlanKoh (discuss • contribs) 04:47, 5 May 2023 (UTC)

Captioning Link
https://github.com/baaivision/CapsFusion MrAlanKoh (discuss • contribs) 10:49, 15 June 2024 (UTC)


text:
User:Ratmcboo

Ratmcboo

title: User:Ratmcboo


text:
User:Ratmcboo/sandbox

fagmaster82

title: User:Ratmcboo/sandbox


text:
Cookbook:Papaya Lassi

Ingredients

 * ½ cup water
 * 4 cups plain yogurt
 * ⅔ cup honey
 * ½ teaspoon salt
 * 1 cup papaya pulp
 * ½ teaspoon ground cinnamon

Procedure

 * 1) Combine everything in a blender.
 * 2) Blend until smooth.

title: Cookbook:Papaya Lassi


text:
Cookbook:Sweet Mango Lassi

Ingredients

 * 1 L (4 cups) mango nectar (strained puréed mango)
 * 500 ml (2 cups) full-fat yoghurt
 * 500 ml (2 cups) chilled milk
 * 250 ml (1 cup) sugar

Procedure

 * 1) Mix sugar with the mango nectar until dissolved.
 * 2) Gradually add the milk to the yoghurt.
 * 3) Mix together the mango and yoghurt mixtures.

title: Cookbook:Sweet Mango Lassi


text:
Cookbook:Sweet Lassi

Ingredients

 * 4 cups (1 L) yogurt
 * 3 cups (750 ml) cold water
 * 6 tbsp sugar or honey
 * ¼ tsp ground cardamom
 * A few drops rosewater
 * 1 cup (250 ml) crushed ice

Procedure

 * 1) Combine all ingredients except ice in blender, and blend until smooth.
 * 2) Add ice and continue blending until frothy.

title: Cookbook:Sweet Lassi


text:
Cookbook:Mild Salty Lassi

Ingredients

 * 1 teaspoon (4 g) cumin seeds
 * 250 ml (1 cup) full-fat yogurt
 * 250 ml (1 cup) chilled milk
 * 2 teaspoons lime or lemon juice
 * ½–1 teaspoon (2–4 g) salt
 * 100 ml (½ cup) ice cubes
 * ¼ teaspoon mint (finely crumbled, dried; optional)
 * 1 small pinch of cayenne pepper (optional)

Procedure

 * 1) Dry-roast cumin seeds by cooking them over low heat in a small pan until you can smell the seasoning (about 2–3 minutes). Cool and grind.
 * 2) In blender, blend cumin seed powder with yoghurt, milk, lemon juice, salt, and ice cubes.
 * 3) Serve chilled, optionally garnished with crumbled mint and a small pinch of cayenne.

title: Cookbook:Mild Salty Lassi


text: Category:Flower water recipes

title: Category:Flower water recipes


text:
User:JSory

See Wikipedia page

title: User:JSory


text:
User:GiantStar34028

Hello! I'm GiantStar34028!

This is GiantStar34028's user page!

= GiantStar34028 =

GiantStar34028
GiantStar34028's userbox

= Sandbox =

Sandbox
11790184577738583171520872861412518665678211592275841109096961

Sandbox End
= Links =

GiantStar34028 links

 * User:GiantStar34028
 * User:GiantStar34028/sandbox
 * User:GiantStar34028/sandbox2
 * User:GiantStar34028/test
 * User:GiantStar34028/GiantStar34028
 * User:GiantStar34028/11790184577738583171520872861412518665678211592275841109096961
 * User:GiantStar34028/328
 * User:GiantStar34028/e
 * User:GiantStar34028/pi
 * User talk:GiantStar34028
 * Special:CentralAuth/GiantStar34028

Wikibooks links

 * Main Page

title: User:GiantStar34028


text:
User:GiantStar34028/sandbox

GiantStar34028

title: User:GiantStar34028/sandbox


text:
User talk:GiantStar34028

Hello! I'm GiantStar34028!

This is GiantStar34028's talk page!

= GiantStar34028 =

GiantStar34028
GiantStar34028 GiantStar34028 (discuss • contribs) 03:13, 6 May 2023 (UTC)

= GiantStar34028's Talk Page =

11790184577738583171520872861412518665678211592275841109096961
11790184577738583171520872861412518665678211592275841109096961 GiantStar34028 (discuss • contribs) 03:14, 6 May 2023 (UTC)

title: User talk:GiantStar34028


text:
User:GiantStar34028/GiantStar34028

GiantStar34028

title: User:GiantStar34028/GiantStar34028


text:
User:Tripler06/sandbox

Splitting Fields
Let $$F$$ be a field and $$p(x)$$ be a nonconstant polynomial in $$F[x]$$. We already know that we can find a field extension of $$F$$ that contains a root of $$p(x)$$. However, we would like to know whether an extension $$E$$ of $$F$$ containing all of the roots of $$p(x)$$ exists. In other words, can we find a field extension of $$F$$ such that $$p(x)$$ factors into a product of linear polynomials? What is the "smallest" extension containing all the roots?

Existence of Splitting Fields
We will see that rather than looking at arbitrary field extension, splitting fields will be the things to consider. First we need to know that they always exist.Proof. This is a largely uninteresting case of proof by induction. We will induct on the degree of $$f(x)$$. If $$f(x)$$ is linear, then clearly its roots (in fact just the one root) is contained in $$F$$ so $$F$$ itself is a splitting field. Suppose $$\deg(f(x)) > 1$$. If $$f(x)$$ splits into the product of linear terms, then again all the roots are contained in $$F$$, so we already have a splitting field. So suppose $$f(x)$$ has an irreducible factor of degree at least 2. Then there exists a field extension $$E_1$$ containing a root $$\alpha$$ of $$f(x)$$. Then in $$E_1$$, we can factorise the polynomial into $$f(x) = (x - \alpha)f_1(x)$$ where $$f_1(x)$$ is a polynomial of degree $$\deg(f(x)) - 1$$. Then by induction there exists $$E_2$$ a field extension of $$E_1$$ that is a splitting field of $$f_1(x)$$. Therefore $$E_2$$ is a field extension of $$F$$ that contains all the roots of $$f(x)$$. Taking the intersection of all subfields of $$E_2$$ containing $$F$$ and the roots of $$f(x)$$ gives us $$E$$, a splitting field of $$f(x)$$. $$\blacksquare$$

Uniqueness of Splitting Fields
Above we were careful to say a splitting field of $$f(x)$$. In fact, this was an unnecessary precaution since the splitting field of a polynomial is unique up to isomorphism. This follows from a generalisation of Theorem 4.1.4, where we claim the theorem holds even if we adjoin all the roots of the polynomial, instead of just one.Proof. This is once again a proof by induction on the degree of $$f(x)$$. If $$f(x)$$ is of degree 1 or indeed splits into factors of degree 1 then the splitting field of $$f(x)$$ is $$F$$ so we can take $$\sigma = \varphi$$. Thus suppose $$f(x)$$ has an irreducible factor $$p(x)$$ of degree at least 2 so $$\widetilde{p}(x) = \varphi_*(p(x))$$is an irreducible factor of $$\widetilde{f}(x)$$. Then by the previous theorem we know $$\varphi$$ extends to an isomorphism $$\psi: F(\alpha) \to \widetilde{F}(\beta)$$ where $$\alpha$$ is a root of $$p(x)$$ and $$\beta$$ is a root of $$\widetilde{p}(x)$$. Therefore over $$F(\alpha)$$ and $$\widetilde{F}(\beta)$$ respectively we can write $$f(x) = (x - \alpha) f_1(x)$$ and $$\widetilde{f}(x) = (x - \beta)\widetilde{f}_1(x)$$. Notice that $$E$$ is a splitting field of $$f_1(x)$$ over $$F(\alpha)$$. Indeed if a splitting field was strictly contained within $$E$$, then it would contain all the roots of $$f_1(x)$$ and $$\alpha$$ and hence would contain all the roots of $$f(x)$$. But this would contradict $$E$$ being a splitting field of $$f(x)$$. Of course the same holds true for $$\widetilde{f}_1(x)$$ over $$\widetilde{F}(\beta)$$. Since $$f_1(x)$$ and $$\widetilde{f}_1(x)$$ have degree strictly less than $$\deg(f(x))$$, by induction we can assume that the statement of theorem holds for them. In particular, $$\psi$$ extends to an isomorphism $$\sigma: E \to \widetilde{E}$$. But since $$\psi$$ was an extension of $$\varphi$$, $$\sigma$$ must also be an extension of $$\varphi$$ concluding the proof. $$\blacksquare$$Proof. Apply Theorem 4.1.7 to the case with $$F = \widetilde{F}$$ and $$\varphi$$ as the identity map. $$\blacksquare$$

---

Example 1: Let $$p(x) = x^4+2x^2-8$$ be in $$\mathbb{Q}[x]$$. Then p(x) has irreducible factors $$x^2-2$$ and $$x^2+4$$. Therefore, the field $$\mathbb{Q}(\sqrt{2},i)$$ is a splitting field for p(x).

Example 2: Let $$p(x) = x^3-3$$ be in $$\mathbb{Q}[x]$$. Then p(x) has a root in the field $$\mathbb{Q}(\sqrt[3]{3})$$. However, this field is not a splitting field for p(x) since the complex cube roots of 3, $$\frac{-\sqrt[3]{3}\pm(\sqrt[6]{3})^5i}{2}$$ are not in $$\mathbb{Q}(\sqrt[3]{3})$$.

Algebraic Closures
Given a field F, the question arises as to whether or not we can find a field E such that every polynomial p(x) has a root in E. This leads us to the following theorem.

Theorem 21.11 Let E be an extension field of F. The set of elements in E that are algebraic over F form a field.

Proof. Let $$\alpha,\beta\in E$$ be algebraic over F. Then $$F(\alpha,\beta)$$ is a finite extension of F. Since every element of $$F(\alpha,\beta)$$ is algebraic over $$F,\alpha\pm\beta,\alpha\beta$$, and $$\alpha/\beta\text{ }(\beta\ne 0)$$ are all algebraic over F. Consequently, the set of elements in E that are algebraic over F forms a field.

Corollary 21.12 The set of all algebraic numbers forms a field; that is, the set of all complex numbers that are algebraic over $$\mathbb{Q}$$ makes up a field.

Let E be a field extension of a field F. We define the algebraic closure of a field F in E to be the field consisting of all elements in E that are algebraic over F. A field F is algebraically closed if every nonconstant polynomial in F[x] has a root in F.

Theorem 21.13 A field F is algebraically closed if and only if every nonconstant polynomial in F[x] factors into linear factors over F[x].

Proof. Let F be an algebraically closed field. If $$p(x)\in F[x]$$ is a nonconstant polynomial, then p(x) has a zero in F, say α. Therefore, $$x-\alpha$$ must be a factor of p(x) and so $$p(x)=(x-\alpha)q_1(x)$$, where $$deg(q_1(x)) = deg(p(x))-1$$. Continue this process with $$q_1(x)$$ to find a factorization

$$p(x)=(x-\alpha)(x-\beta)q_2(x)$$, where $$deg(q_2(x))= deg(p(x))-2$$. The process must eventually stop since the degree of p(x) is finite.

Conversely, suppose that every nonconstant polynomial p(x) in F[x] factors into linear factors. Let $$ax-b$$ be such a factor. Then $$p(b/a)=0$$. Consequently, F is algebraically closed.

Corollary 21.14 An algebraically closed field F has no proper algebraic extension E.

Proof. Let E be an algebraic extension of F; then $$F\subset E$$. For $$\alpha\in E$$, the minimal polynomial of α is $$x-\alpha$$. Therefore, $$\alpha\in F$$ and $$F=E$$.

Theorem 21.15 Every field F has a unique algebraic closure.

It is a nontrivial fact that every field has a unique algebraic closure. The proof is not extremely difficult, but requires some rather sophisticated set theory. We refer the reader to [3], [4], or [8] for a proof of this result.

We now state the Fundamental Theorem of Algebra, first proven by Gauss at the age of 22 in his doctoral thesis. This theorem states that every polynomial with coefficients in the complex numbers has a root in the complex numbers. The proof of this theorem will be given in Abstract Algebra/Galois Theory.

Theorem 21.16 (Fundamental Theorem of Algebra) The field of complex numbers is algebraically closed.

title: User:Tripler06/sandbox


text:
User:GiantStar34028/11790184577738583171520872861412518665678211592275841109096961

= 11790184577738583171520872861412518665678211592275841109096961 =

11790184577738583171520872861412518665678211592275841109096961
11790184577738583171520872861412518665678211592275841109096961


text:
User:GiantStar34028/328

328

title: User:GiantStar34028/328


text:
User:GiantStar34028/pi

= pi = 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659 33446128475648233786783165271201909145648566923460348610454326648213393607260249141273724587006606315588174881520920962829254091715364367892590360011330530548820466521384146951941511609433057270365759591953092186117  38193261179310511854807446237996274956735188575272489122793818301194912983367336244065664308602139494639522473719070217986094370277053921717629317675238467481846766940513200056812714526356082778577134275778960917363  71787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031  37838752886587533208381420617177669147303598253490428755468731159562863882353787593751957781857780532171226806613001927876611195909216420198938095257201065485863278865936153381827968230301952035301852968995773622599  41389124972177528347913151557485724245415069595082953311686172785588907509838175463746493931925506040092770167113900984882401285836160356370766010471018194295559619894676783744944825537977472684710404753464620804668  42590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211  65344987202755960236480665499119881834797753566369807426542527862551818417574672890977772793800081647060016145249192173217214772350141441973568548161361157352552133475741849468438523323907394143334547762416862518983  56948556209921922218427255025425688767179049460165346680498862723279178608578438382796797668145410095388378636095068006422512520511739298489608412848862694560424196528502221066118630674427862203919494504712371378696  09563643719172874677646575739624138908658326459958133904780275900994657640789512694683983525957098258226205224894077267194782684826014769909026401363944374553050682034962524517493996514314298091906592509372216964615  15709858387410597885959772975498930161753928468138268683868942774155991855925245953959431049972524680845987273644695848653836736222626099124608051243884390451244136549762780797715691435997700129616089441694868555848  40635342207222582848864815845602850601684273945226746767889525213852254995466672782398645659611635488623057745649803559363456817432411251507606947945109659609402522887971089314566913686722874894056010150330861792868  09208747609178249385890097149096759852613655497818931297848216829989487226588048575640142704775551323796414515237462343645428584447952658678210511413547357395231134271661021359695362314429524849371871101457654035902  79934403742007310578539062198387447808478489683321445713868751943506430218453191048481005370614680674919278191197939952061419663428754440643745123718192179998391015919561814675142691239748940907186494231961567945208  09514655022523160388193014209376213785595663893778708303906979207734672218256259966150142150306803844773454920260541466592520149744285073251866600213243408819071048633173464965145390579626856100550810665879699816357  47363840525714591028970641401109712062804390397595156771577004203378699360072305587631763594218731251471205329281918261861258673215791984148488291644706095752706957220917567116722910981690915280173506712748583222871  835209353965725121083579151369882091444210067510334671103141267111369908658516398315019701651511685171437657618351556508849099898599823873455283316355076479185358932261854896321329330898570642046752590709154814165498  594616371802709819943099244889575712828905923233260972997120844335732654893823911932597463667305836041428138830320382490375898524374417029132765618093773444030707469211201913020330380197621101100449293215160842444859  637669838952286847831235526582131449576857262433441893039686426243410773226978028073189154411010446823252716201052652272111660396665573092547110557853763466820653109896526918620564769312570586356620185581007293606598  76486117...

title: User:GiantStar34028/pi


text:
User:GiantStar34028/e

= e = 2.7182818284590452353602874713526624977572470936999595749669676277240766303535475945713821785251664274274663919320030599218174135966290435729003342952605956307381323286279434907632338298807531952510190115738341879307 021540891499348841675092447614606680822648001684774118537423454424371075390777449920695517027618386062613313845830007520449338265602976067371132007093287091274437470472306969772093101416928368190255151086574637721112  52389784425056953696770785449969967946864454905987931636889230098793127736178215424999229576351482208269895193668033182528869398496465105820939239829488793320362509443117301238197068416140397019837679320683282376464  80429531180232878250981945581530175671736133206981125099618188159304169035159888851934580727386673858942287922849989208680582574927961048419844436346324496848756023362482704197862320900216099023530436994184914631409  34317381436405462531520961836908887070167683964243781405927145635490613031072085103837505101157477041718986106873969655212671546889570350354021234078498193343210681701210056278802351930332247450158539047304199577770  93503660416997329725088687696640355570716226844716256079882651787134195124665201030592123667719432527867539855894489697096409754591856956380236370162112047742722836489613422516445078182442352948636372141740238893441  24796357437026375529444833799801612549227850925778256209262264832627793338656648162772516401910590049164499828931505660472580277863186415519565324425869829469593080191529872117255634754639644791014590409058629849679  12874068705048958586717479854667757573205681288459205413340539220001137863009455606881667400169842055804033637953764520304024322566135278369511778838638744396625322498506549958862342818997077332761717839280349465014  345588970719425863987727547109629537415211151368350627526023264847287039207643100595841166120545297030236472549296669381151373227536450988890313602057248176585118063036442812314965507047510254465011727211555194866850  80036853228183152196003735625279449515828418829478761085263981395599006737648292244375287184624578036192981971399147564488262603903381441823262515097482798777996437308997038886778227138360577297882412561190717663946  50706330452795466185509666618566470971134447401607046262156807174818778443714369882185596709591025968620023537185887485696522000503117343920732113908032936344797273559552773490717837934216370120500545132638354400018  63239914907054797780566978533580489669062951194324730995876552368128590413832411607226029983305353708761389396391779574540161372236187893652605381558415871869255386061647798340254351284396129460352913325942794904337  29908573158029095863138268329147711639633709240031689458636060645845925126994655724839186564209752685082307544254599376917041977780085362730941710163434907696423722294352366125572508814779223151974778060569672538017  18077636034624592787784658506560507808442115296975218908740196609066518035165017925046195013665854366327125496399085491442000145747608193022120660243300964127048943903971771951806990869986066365832322787093765022601  492910115171776359446020232493002804018677239102880978666056511832600436885088171572386698422422010249505518816948032210025154264946398128736776589276881635983124778865201411741109136011649950766290779436460058519419  98560162647907615321038727557126992518275687989302761761146162549356495903798045838182323368612016243736569846703785853305275833337939907521660692380533698879565137285593883499894707416181550125397064648171946708348  19721448889879067650379590366967249499254527903372963616265897603949857674139735944102374432970935547798262961459144293645142861715858733974679189757121195618738578364475844842355558105002561149239151889309946342841  39360803830916628188115037152849670597416256282360921680751501777253874025642534708790891372917228286115159156837252416307722544063378759310598267609442032619242853170187817729602354130606721360460003896610936470951  41417185777014180606443636815464440053316087783143174440811949422975599314011888683314832802706553833004693290115744147563139997221703804617092894579096271662260740718749975359212756084414737823303270330168237193648  00217328...

title: User:GiantStar34028/e


text:
User:GiantStar34028/sandbox2

GiantStar34028

title: User:GiantStar34028/sandbox2


text:
AI Art Generation Handbook/Training/DreamBooth

What is Dreambooth ?
We will be focusing exclusively training for Dreambooth. So let go on with it

DreamBooth is a method for customizing and personalizing TextToImage diffusion models. It can achieve excellent results with only a small amount of training data. DreamBooth is developed based on Imagen and the model can be exported as ckpt and loaded into various UIs. However, the Imagen model and pre-trained weights are not available. Therefore, DreamBooth was initially not suitable for stable diffusion.

Later, the diffusers implemented the DreamBooth function and fully adapted it to Stable Diffusion. DreamBooth is easy to overfit quickly. To obtain high-quality images, we must find a "sweet spot" between the training steps and learning rate. It is recommended to use a low learning rate and gradually increase the number of steps until reaching a satisfactory state.

Installing Dreambooth
To download Dreambooth extension is a very easy. First, you need go to the tab "Extension", click on "Available" tab which is below it.

Later, search for extension named: sd_dreambooth_extension and click "Install"  button to install it.

Click on "Installed" tab and click "Apply & Restart UI". For better installation, close the webui.bat and re-open again to ensure proper installation.

Note: The instructions here are based on last update by dev on rev 926ae20

Training In Dreambooth / Model tab
(1) Click on the Dreambooth tab.

(2) For creating new models from scratch, type the Name of the model and select the list of model you already installed in Source Checkpoint. Ensure 512x Model is ticked.

(3) Click Create Model

(4) Wait for ~1minute for WebUI generate template folder

Training In Dreambooth / Settings tab
You can click on Performance (WIP) to make the recommended settings for Dreambooth training on the PC

There are checkboxes in General, namely

(i) Use LORA - Recommended for training of PC with 8GB ~12GB VRAM

(ii) Train imagic Only - Training with single images only

Intervals
"Training Steps Per image(Epoch)"

In "Intervals" section, "Training Steps Per image(Epoch)" specifies the number of steps to train per image.

For the training steps, try training for 175 steps (for Train Object/Style).

Note: The "Train Person" and "Train Object/Style" sections on the "Concepts" page have different optimal values. The higher it goes, the longer training will takes.

For example, if there are three images uploaded and you want to train for a total of 1500 steps, then each image would be trained for 500 steps.

"Pause After N Epochs" specifies how many intervals between each epochs and is set to 0.

"Amount of time to pause between Epochs (s)" is of the same type as the previous setting and is also set to 0.

"Save model Frequency" indicates how often to save a checkpoint. Each save is approximately 4-5 GB, so if disk space is limited, a higher value can be set.

"Save Preview(s) Frequency (Epochs)" indicates how often to preview images during each epoch. It is generally set to 5 and may affect training speed.

Batching
"Batch Size" is used to speed up training time, but it increases GPU memory usage.

"Gradient Accumulation Steps" is the number of steps before the gradient is calculated and backpropagated. Make sure the number of images used in training is divisible by the product of these two values. For example, if both values are set to 2, the overall speed is increased by 4 times, and it is important to ensure that the number of images used in training is divisible by 4.

 "Setting Gradients to None When Zeroing"  uses more GPU memory.

 "Gradient Checkpointing"  reduces GPU memory usage.

Learning Rate
The learning rate is a value typically between 0.0 and 1.0 used during the neural network training process. The goal of the model is to minimize loss, which represents the difference between the predicted output for an input and the true label for that input. Recommended to use any values between 0.000001 to 0.00000175 for good results. Depending on the complexity of models, learning rate for simpler object might requires only 0.001

However, the higher number may produce models that not quite looked what is envisioned with your expectations (wild card) meanwhile too low number meaning model output may produce very similar to what your model looked like (overfitting).

 "Learning rate Scheduler"

These scheduler are tested and confirmed to work with "Train Object/Style" and explained as these

Constant with Warmup: Imagine you're trying to climb a mountain, but your legs are stiff. You need to warm up first by doing some stretches and light exercises to get your blood flowing before you can start hiking.

Similarly, with this learning rate scheduler, we start with a low learning rate and gradually increase it over a certain number of warmup steps, so that the model can gradually adjust to the training data before we start increasing the learning rate.

Linear with Warmup: Imagine same case as constant warmup, you need to warmup before hiking up the mountain. Once you've warmed up, you start with a slow pace and gradually increase your speed as you get closer to the summit.

This is similar to how the linear with warmup learning rate scheduler works. We start with a low learning rate and gradually increase it linearly over a certain number of warmup steps, and then we keep it constant for the remaining training steps.

Polynomial Learning Rate: Imagine you are climbing mountain on an area with very unpredictable weather. You want to adjust your speed so that you can safely navigate the hike and avoid fall off the mountain. If you go too fast during rains/strong winds, you risk losing control and fall off, and if you go too slow, you won't make it to your destination on time.

This is similar to how the polynomial learning rate scheduler works. We start with a high learning rate and gradually decrease it over time using a polynomial function. The degree of the polynomial determines how quickly the learning rate decreases, and we can adjust this to find the optimal speed for training the model.

Tuning
Use EMA: This option is generally not selected. EMA (Exponential Moving Average) is used for gradient descent and is not important for fine-tuning. It uses exponential moving average weights to avoid overfitting in the final iteration. It can improve the quality of the model, but uses more VRAM during training.

Use 8-bit Adam: Select this option to use 8-bit Adam from Bitsandbytes. It reduces memory usage. Choose fp16 or bf16 in mixed precision, but it is recommended to choose bf16 for better performance.

Memory Attention: Choose Xformers (if use Torch 1.x) to speed up the training process. There is not much difference in memory usage between Xformers and Flash_attention (a type of attention mechanism that can reduce memory usage). Default is the fastest but uses more VRAM, Xformers has average speed and VRAM usage, and Flash_attention is the slowest but uses the least VRAM.|

P.S: Note that there are unconfirmed rumours that Torch 2.0 removed the needs for Memory Attention

Cache Latents : This options is like having a storage space where the model can save the intermediate results it gets when processing data during training. This way, when it needs to process the same data again, it can use the cached results instead of recalculating everything from scratch. This can speed up the training process, but it also requires more VRAM to store the cached results.

Train U-NET : This option is enabled to let the U-NET network is trained concurrently with the diffusion model to improve the quality of the image generation. The U-NET network is trained to take the output of the diffusion model and further refine it to produce a higher quality image. This option is *may* improve the overall quality of the generated images (depending on case by case basis), but it may require additional GPU memory and increase the training time.

Set Ratio of Text Encoder Training: The best value for face images is 0.7, and for style images it is 0.2. If there is not enough GPU memory, set it to 0. Generally, training U-Net may yield better results.

Offset Noise: If the value is set to 0, the effect is disabled and the model will not learn to adjust the brightness and contrast. If enabled, Dreambooth will add some random noise to the brightness and contrast of the input images, which allows the model to learn to adjust these parameters in order to create more realistic and varied output images. A positive value will increase the brightness/contrast of the generated images, while a negative value will decrease the brightness / contrast. However, it is highly recommended to use the input images taken at different lighting condition to simulate realistic looking images.

Freeze CLIP Normalization Layers: When enabled, this options may help to prevent overfitting, which is when the model becomes too specialized to the training data and does not generalize well to new data. By freezing these layers, the model is forced to learn more meaningful and robust representations of the input data, which can improve its ability to generalize to new data. It does not increase VRAM usage but it may increase training time since the model is not able to learn from the normalization layers during training.

Clip Skip: If Clip Skip is set to a high value (> 1), the model training is essentially skipping amount of training layers thus generating images based on a more limited understanding of the input text, which can lead to less coherent and less relevant outputs.

Training in Dreambooth / Generate tab
(1) For Image Generation Library select Diffuser

(2) For Image Generation Scheduler, try with PNDM or LMSDISCRETE scheduler first for better result. If the training are overfitting very quickly, DDIM is usually much better than PNDM and LMSDISCRETE when the model is overfitting.

Settings known to use more VRAM

 * High Batch Size
 * Set Gradients to None When Zeroing
 * Use EMA
 * Full Precision
 * Default Memory attention
 * Cache Latents
 * Text Encoder

Settings that lowers VRAM

 * Low Batch Size
 * Gradient Checkpointing
 * fp16/bf16 precision
 * xformers/flash_attention
 * Step Ratio of Text Encoder Training 0 (no text encoder)


text:
User:GiantStar34028/340282366920938463463374607431768211456

2^128


text:
AI Art Generation Handbook/Training/Dataset

Type of training
Before start on training, consider first the concept you want to use

As far as per limited studies goes, it seems like Dreambooth able to perform four types of training

(i) Introduce totally new concepts to models

As per current existing version of Stable Diffusion ,although SD model able to generate a variety types of images but however there are quite few things unable to be generated by SD model such as eyeballs.

(ii) Adding dataset to existing concepts but create a separate "token"

This is more usual route of existing concept of "male" / "woman" but you want to add dataset of faces of yourself to create a a look that more resembled of yourself into dataset images.

(iii) Finetuning the existing concept

A concept that already existed but due to limitations of CLIP / limited dataset of images, it may not be able to generate properly.

Such as new concepts of centaur (which is basically a half men and half centaur)

(iv) Force the existing concepts to learn different concepts

For examples, you may force the existing keyword "bank" that is strongly related to the bank as in depositing your money in to the "bank" but refers to the river bank. This practices is strongly discouraged as many concepts that is related to that keyword is related and may cause "model collapse"

Source of Images
There are wide range of free photos to choose from when you want to train a model of images you found.

Here is the list of free stock photo site that you can use

Wikimedia Commons ( https://commons.wikimedia.org/wiki/Main_Page )*

Unsplash ( https://unsplash.com/ )

Pexels ( https://www.pexels.com/ )

Pixabay ( https://pixabay.com/ )

Flickr Creative Commons ( https://www.flickr.com/creativecommons/ )

FreeImages ( https://www.freeimages.com/ )

Public Domain Picture (https://www.publicdomainpictures.net/)

Game Art for Glitch ( https://www.glitchthegame.com/public-domain-game-art/)

Josh Game Asset ( https://gameassets.joshmoody.org/)

Quality of Images
You may heard a lot from lots of Dreambooth tutorial mentioning dataset must have quality. The quality of the output image produced by the AI Art generative model is directly related to the quality of the input image used to train the model. If the input images used to train the model are of low quality, contain noise or artifacts, or are poorly composed, the resulting output images will also have similar issues.

Image should have following attributes:

(a) Diverse but consistent -

This is an example to make a diverse dataset to train for object/style but make sure the subject (in this case rhino) is always the center of the training subject.

Framing:

Note: Whenever possible, try not to include images for training if they have following characteristic:

(i) Have more than 1 subject (although same subjects) in same picture

(ii) Have distinct but common features (If you trained with 1 horns, ensure all images dataset ideally have 1 horn)

(b) Noisy or compression artefacts and such

(c) Blurred or not enough resolution


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User:ToluT15/sandbox

ToluT15

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Supplementary mathematics/Laplace's equation

In mathematical calculations and analysis, Laplace's equation or Laplace's finite differential equation is a second-order partial differential equation that is denoted by the divergence symbol ▽ and is used in mathematics, physics, vantage point calculus, geometry, engineering, etc. This is a useful approach and function to determine an energy or electrical potential in free space or area, which is used in energy and high degrees compared to its structure, and for mathematics for sine and cosine signal waves, etc. in spherical coordinates. and cylindrical and Cartesian are used. Laplace's equation was derived to simplify calculations in physics and is named after the physicist Pierre Simon Laplace.