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History of Tennessee/Jim Crow Laws and Customs (1886-1955)

=Jim Crow Laws= Between 1886 and 1955, Tennessee was one of many Southern states to enact a set of racial black- codes that were referred to as Jim Crow laws. White members of communities across the state of Tennessee, other southern states and even those who lived beyond the south, began to erect a number of barriers between themselves and members of other racial and ethnic groups, particularly African Americans that initiated extreme segregation and exclusion.

Under these Jim Crow laws, white people were seen as first-class citizens. Comparatively, African Americans were seen as inferior, second class citizens who did not deserve freedom or rights that they previously would have expected after the eradication of slavery. The Jim Crow Laws were able to go around the legislation during the time they were put into place to ensure their laws were applicable. The law of 1869 declared that no citizen could be excluded from the University of Tennessee because of race or color. While still including this law, the Jim Crow laws mandated that instructional facilities for black students be separated from those of white students, schools for the black were referred to as “Jim Crow Schools” and received a noticeably less amount of consideration and funding compared to white schools.

The legal system worked against all black citizens, the positions of police and judges were taken up by ex-confederate soldiers which made it almost impossible for African Americans to win any court systems and ensured the blacks all became victims of these black codes customs that promoted segregation. These laws dictated all aspects of a black individual's daily life including who may marry whom, who may live where, who may study where, who may work where, who may vote where, and who may travel how. They mainly consisted of developing “separate but equal” facilities to create a division between African Americans and their white counterparts, this included separate taxis, drinking fountains, prison cells, sports teams, bathrooms, and, as previously mentioned, schools.

Origin of Jim Crow
The character “Jim Crow” was once known as a popular folk trickster among black slaves but later turned into a term to describe racist segregation laws, rules, and customs. The name Jim Crow became an insult towards African Americans after Thomas D. Rice created a racist theatre character referred to as Jim Crow that gained quick success and popularity amongst whites.

After this theatre character gained attention, the name became an offensive term aimed towards blacks and later became the name of the racial laws of segregation that took away rights from African Americans which were once granted to them through the 13th, 14th, and 15th Amendments that freed millions of slaves, defined citizenship and protected voting rights.

After these amendments, white people felt that black individuals required more restriction and less freedom. They then created the Jim Crow laws and customs to outline when, where and how these newly freed slaves could work, created indentured servants out of the African Americans (practically made them slaves again), took away their voting rights, and controlled how or where they lived and traveled. The Jim Crow laws created an entire system based on segregation and enshrined African Americans as “second class” citizens.

Jim Crow Etiquette and Customs
Not only was Jim Crow a set of racist laws against African Americans, but it also included a particular set of etiquette and customs that outlined the appropriate interactions between blacks and whites as well as blacks with one another. This set of customs and etiquette would go so far as to say that blacks were expected to step aside for passing whites on the sidewalk. Other forms of etiquette that were given attention during this time included that blacks were only to be introduced to white individuals; meaning, a white should never be introduced to a black, whites should not use courtesy titles when referring to blacks (Mr., Mrs., Miss.), but blacks must use such titles when referring to whites as a sign of respect, if a car was being driven by a white person, they were given the right of way at intersections and if a black individual was in the vehicle with them, they had to sit in either the back seat or the trunk.

These Jim Crow laws and the Jim Crow customs were enforced through violence, both real violence and the threat of violence. Any blacks who violated Jim Crow laws and etiquette risked their property, jobs, freedom (the degree of which they had), and even their lives. It was evident that the Jim Crow laws stood to benefit white individuals.

The Jim Crow Coach
A “Jim Crow” coach was a separate train car that was specifically for African Americans. Within the slave states, slaves were required to ride in the Jim Crow car amongst other free black individuals when they had to travel somewhere with their owners. This isolated coach was meant to show the inferiority of one class of people over another. In these Jim Crow coaches, the trains were supposed to show equal treatment to both white and black people which created a "separate but equal" mentality. Everyone who rode the train was required to pay the same train fare in order to make everyone more equal. If Caucasian people received something; food, seating, washrooms, then black people must be able to obtain those things as well if they wished to. If black people did not receive equal treatment then whoever mistreated them, the conductor or server, could be charged.

There was a high degree of racism at this time causing black people to experience unjust treatment on these trains even with the implementation of these laws. For example, African American people were not allowed to leave the Jim Crow sections of the trains. However, white people were able to go wherever they pleased. Sometimes white people would use the Jim Crow car for their own space and many individuals would use it as a smoking car. The cars were dirty, and the conductor would often leave his stuff on a few of the seats in the Jim Crow coach. In the 1920s and 1930s, the black community was increasing in Tennessee and full-length Jim Crow coaches were being developed. Full-length Jim Crow cars were much longer and more spacious than the original Jim Crow sections on trains. Black individuals who refused to ride in the Jim Crow coaches were subject to violence and arrested immediately.

End of Jim Crow
The end of Jim Crow started after World War II in 1945 where a number of anti-segregation laws came into effect the following years due to an increase in civil right activities in black communities. By 1955 the Jim Crow laws were virtually no longer in effect.


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User talk:Blanc1100

Please take care
Hi, you seem to be making a number of edits that don't make a lot of sense to me, or appear to be of marginal value.

Please take more care, and use your sandbox if you wish to experiment.

Thanks -- Jules (Mrjulesd) 17:03, 20 November 2019 (UTC)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: User talk:Blanc1100


text:
Lentis/Cyber-attack Attribution

Cyber-attack Attribution
One of the first steps in mitigating and containing a cyberattack or a breach is attributing its source. Identifying the perpetrators early on and keeping track of their previous campaigns helps understand their motivation, determine the data they are after, and physically locate and apprehend them. On a more global level, associating an attack with a nation state can influence the policymakers in regard to foreign policy. Despite its importance, the current methods for attribution are nowhere near from perfect, and some decisions are made prematurely. This can lead to a variety of implications, and misattribution may cause countermeasures to harm victims rather than cybercriminals.

What is Attribution
Cyber-attack attribution describes the act of identifying the party responsible for a certain internet attack by correlating various indicators to trace the attack back to its source. Every kind of cyber operation leaves a trail, and analysts use this information, combined with a knowledge of previous events, to attempt identify the malicious actor. Knowing what data the attacker is after allows incident responders to predict the intruder's actions and concentrate their containment, mitigation, and preventative efforts. In terms of policy, it helps law enforcement differentiate criminal activity from state-sponsored attacks and act accordingly.

Attribution Methods
Cyber-attacks generally leave some form of logs on the system, so the information found in them or even the evidence of deleted logs can ring a bell. Any cyber-attack or regular use of a system interact with computer equipment through many services and on various networking levels, leaving some traces in almost every step. These indicators of compromise, such as failed login attempts, anomalous connections outside of business hours, IP addresses, records of traffic leaving a network with sensitive data, etc., point to malicious activity. Additionally, the demonstrated behavior and techniques used to conduct the attack are valuable because habits are harder to change than tools. Human factors such as socio-cultural references, language skills, and timezones help point to a country.

Attribution Difficulties
There is currently no standardized method for identifying the perpetrator. Attack methods and techniques are constantly evolving, each target system is unique, and both offense and defense require human actions that are difficult to generalize.

 * Pure technical indicators of compromise (such as IP addresses originating from a specific location) are no longer a reliable method of attribution . Threat actors can easily spoof their origin or use de-anonymization services such as the TOR network to make their traces meaningless. In some cases, the attackers may deliberately plant red-herrings to encourage missatribution.
 * Attackers reuse methods and tools from others, which leads to false positives. They can be obtained on the dark web or within white-hat hacking communities. For example, an attack called EternalBlue has become the standard method for exploiting Windows SMB service. It has been reportedly developed by the NSA.
 * An increased use of so-called "living off the land" tactics has allowed threat actors to perform attacks using only built-in tools already present in the target environment. This makes attacks more difficult to detect and avoids the use of any custom tools that would otherwise serve as valuable artifacts in investigations.
 * As internet usage grows, the number of web services increases and companies ramp up their infrastructure. This leads to an explosion of traffic volume that the security teams have to analyze and process. This makes it difficult to distinguish malicious traffic from benign, and automated solutions are not foolproof.
 * Attribution may result in disclosure of sensitive evidence or attribution methods, which would give the attacker an opportunity to obscure their trail . On the contrary, lack of released evidence hurts trust in the validity of the attribution.
 * Attribution methods that analyze attacker behavior and correlate independent attacks heavily rely on the knowledge of previous attacks. This requires strong and quick communication within and across the private and the public sectors, which is difficult due to the sensitivity of information, business competition, or reluctance to admit breaches.

Lack of government trust
In recent years, there has been a growing rift between the government and their constituents in the United States. According to a recent poll by the Pew Research Center, people are losing trust in the government to serve their best interests. This loss of trust consequently erodes the credibility of the government in the eyes of the public, which raises concerns on the process of cyber-attack attribution. When governments release official statements identifying the attack, many are skeptical. This distrust is not unreasonable. From unreasonable dairy consumption recommendations to the Vietnam war disaster, there are many historical examples of government dishonesty or poor decision making.

The government agencies are extremely reluctant to reveal their intelligence capabilities, sources, and methods. Some of this information is very sensitive to national security. In the case of the Sony Pictures hack in 2014, this could have revealed the details of NSA's attempts to eavesdrop on the North Korean government. Such disclosures also tell the threat actors what has been found and what needs to fixed in future attacks. Sometimes the public has no choice by to take government agencies at their word.

Findings motivated by political agendas
Political groups take advantage of the inherent difficulty in attributing cyber attacks and use this ambiguity in order to advance their own agendas. With the growing sophistication and aggressiveness of state-sponsored cyber attacks, nations are beginning to prioritize mitigating and responding to the threat of these attacks. This also gives political groups more opportunities to use these attacks in their favor, whether it be through premature attributing the attack to some particular group or implicating their opposition as liable or complicit.

Attributions by private companies
Private companies are gaining responsibility for cyber-attack attribution. This setup benefits government, which would typically be responsible for attributing cyber-attacks, by preserving options to retaliate and keeping intelligence capabilities secret. This setup benefits private security companies as well, since they gain valuable prestige and news coverage when making attributions. Since the standard of evidence to make an attribution is not clearly defined, private companies may make false premature attributions. Additionally, if different firms disagree on an attribution, then the trust and authority of attributions may break down further.

Geopolitical tensions
The power and potential damage of cyber attacks can be compared to that of actual physical weapons, so when a country threatens the US with a cyber attack, it is taken seriously. False accusations could raise unnecessary geopolitical tensions, and in some cases it might be rational to tolerate an attack rather than assign blame publicly and not be certain. In the aftermath of the Sony Hack, the White House proposed a 'proportional response' to North Korea in retribution, while many experts were not yet sure about the malicious actor. Another point of contention is that it is difficult to differentiate attacks carried out by a couple of lone hackers from ones backed by a nation-state military, and there is concerns that the government agencies have correlated independent attacks in the past without much evidence.

Grizzly Steppe
During the controversy about the 2016 presidential elections, Russia was blamed for meddling in the elections. This has been repeated many times by top government officials, but a lot of the detailed evidence remains unpublished, and some are skeptical of whether attribution to Russia is true for every attack. Over the years, there have been numerous reports that attributed individual small attacks to different malicious groups both in Russia and outside, which served as evidence to higher level reports that claimed all of those small attacks were the work of a single group tied to Kremlin.

The Department of Homeland Security and FBI did release a join report with a threat advisory containing a list of IP addresses and malware signatures that served as indicators of various Russian attacks, which does not constitute evidence. The intention was to help other agencies and private organizations protect themselves by spotting these IP addresses. Upon further analysis, of 875 IP addresses released and attributed to Russia, at least 425 (~49%) are IP addresses that correspond to Tor exit nodes. This means that anyone, any attacker or any benevolent user from any country can easily obtain those IP addresses both accidentally and on purpose. As such, these published indicators were both misleading and may suggest a level of technical inadequacy and a lack of trust in reports from the same agencies. This has led to missatributions as well.

For example, after December 2016 attack on a Vermont facility, officials publicly attributed it to the Russian military and civilian services. Vermont Representative Welch commented on the Russian involvement, "they will hack everywhere, even Vermont, in pursuit of opportunities to disrupt our country. We must remain vigilant, which is why I support President Obama’s sanctions against Russia and its attacks on our country and what it stands for." Despite these allegations, security experts could not claim that the attack was actually performed by the Russians. As a result, this Washington Post article was later modified to say:"'Editor’s Note: An earlier version of this story incorrectly said that Russian hackers had penetrated the U.S. electric grid. Authorities say there is no indication of that so far. The computer at Burlington Electric that was hacked was not attached to the grid.'"

DNC attack
In 2015 and 2016 the Democratic National Committee (DNC) network was infiltrated through a series of phishing attacks, resulting in thousands of sensitive documents being leaked online. The leaks destabilized Clinton's campaign, and many believe played a role in her eventual loss. In December of 2016, the U.S. Intelligence community concluded that Russian intelligence services were responsible for the leak. In the time before the attack was officially attributed, both candidates in the 2016 election were able to exploit the fact that the attacker's identity was unknown. As the attacks benefited the Republican Party at the expense of the DNC, Clinton continually asserted that Russia was responsible, and that Donald Trump was complicit in this attack. Even though officially the identity of the attacker was unknown, Clinton was able to use this fact to erode the credibility of Trump to further advance her campaign.

CrowdStrike Conspiracy
CrowdStrike, a private security company, revealed the DNC attack when hired to monitor the DNC servers. CrowdStrike then linked the attack to Russian hacking groups. When the FBI investigated the attack, CrowdStrike provided a digital copy of the server to the FBI but not the actual physical server which CrowdStrike themselves never possessed. The CrowdStrike conspiracy believes that CrowdStrike is hiding the physical server from the FBI in Ukraine (the server is physically located in Ukraine) because this physical server may contain evidence the attack came from Ukraine instead of Russia. The conspiracy highlights the potential consequences of mistrust of private company attributions even if the mistrust is unfounded.

Olympic Destroyer attack
The Olympic destroyer attack was a cyber attack against South Korea during the 2018 winter games. Initial reports said that the attack could have come from North Korea of Russia. It took nearly 8 months for researchers to attribute the attack definitively to Russia. The Russian hackers placed false flags to try to pin the attack on North Korea. The Olympic Destroyer attack occurred during a detente of the Korean conflict. Just one month after the Olympic games, the Singapore summit between North Korea and The United States would advance peace further. While misattribution of the Olympic Destroyer attack did not derail these talks, it is conceivable how misattribution could have serious geopolitical consequences in a situation like this.

title: Lentis/Cyber-attack Attribution


title: User:IngrownMink4


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Issues in Interdisciplinarity 2019-20/Truth in the Creation of the Universe

Introduction
There have long been discussions across the disciplines about the origin of the universe. This is a multifaceted debate and although it's generally represented as a dispute between science and theology, an appreciation of how disciplines including Art, Literature, Computer Science and Philosophy contribute to the question is important. Each discipline conveys their own truth surrounding creation, providing different types of evidence to strengthen their argument. The issue of 'power' also plays into the debate due to historical biases surrounding the validity of contrasting evidence and the long-standing power of the Church.

Cosmology and Philosophy
Examining the disciplines of cosmology and philosophy together, regarding creation, is integral. Modern cosmology originated in 1917 with Einstein's Static Model of the Universe, though evolved to incorporate physics, maths and astronomy. The practice of studying the cosmos originated in earlier societies; each establishing unique theories for understanding the heavens. Greek philosopher Aristotle argued there were four elements: fire, water, earth and air, and that all space was filled with one of the elements. Other theories include: the Brahmanda Cosmic Egg Theory (15th-12th century B.C) which depicted a cyclical universe containing a cosmic egg which continually expanded and collapsed, and the Einsteinian Theory (20th century) where the universe was identified as dynamically static. Today, cosmology is dominated by the Big Bang Theory (Edwin Hubble), which states the universe originated as a dense, hot mass around 14 billion years ago and from that point, it has been expanding. However, opposing theories such as the Steady State Theory and the Oscillating Theory currently coexist alongside the Big Bang Theory.

Cosmology is largely based on observation; it's impossible to run experiments on the entire universe. Thus, it requires philosophical debate. Cosmology is dealing with the concept of the beginning; the Big Bang is widely accepted as the prominent creation theory, but how do we conceptualise the "nothing" that preceded it? These are philosophical issues, therefore a combined understanding of philosophy and cosmology is necessary in addressing the origin of the universe.

Theology
In John 17:17 of the Bible, Jesus speaks to God on behalf of his disciples saying “Sanctify them by Your truth. Your word is truth”. Christianity teaches an absolute truth; truth is the word of God, as truth is the reality that God has created and defined. Truth in Christianity surrounding ‘the creation of the universe’ is presented through creationism.

Creationism states that “matter and all things were created... by an omnipotent Creator, and not gradually evolved or developed”. Many Christian denominations believe in biblical inerrancy; that the bible is “without error or fault in all its teaching”, meaning that the Genesis creation narrative, where God created the universe in six days, is interpreted as fact. Evidence for creationism has been argued through intelligent design theory: “that certain features of the universe and living things are best explained by an intelligent cause, not an undirected process such as natural selection". Thomas Aquinas, an 11th century Catholic philosopher, argued that "wherever complex design exists, there must have been a designer; nature is complex; therefore nature must have had an intelligent designer".

Contrastingly, there is also an argument for biblical allegory; a view that the bible should be interpreted rather than taken literally. This allows for belief in both a scientific and religious truth simultaneously, as they are no longer mutually exclusive.

Art and Literature
For over 6,000 years, man has been depicting the cosmos in art; illustrating versions of truth surrounding the creation of the universe. Michelangelo's depiction of the Book of Genesis on the ceiling of the Sistine Chapel portrays God creating the Earth in six days, resting on the seventh. These pieces of artwork align with the creation story told in Christian theology. Commissioned by Pope Julius II, this artwork demonstrates the power of the Church in representation of truth. In Literature, the creation stories have been frequently retold within both fiction and non-fiction writing. Milton's Paradise Lost portrays a universe where Heaven is at the top, Hell at the bottom, and chaos in-between. However, several scholars have commented on the parallels between science and religion within Milton's work. Paul Nurse draws on the relationship between Milton and Galileo, with Milton citing sunspots and other cosmological phenomena observed through Galileo's telescope within Paradise Lost; 'Milton slips seamlessly between the traditional religious views of the time and the modern 17th century thinking on science'. Karl Popper examines the similarities between science and faith, expressing the fact that 'Science avoids un-testable theories, unlike religion which takes matters on faith. However... science itself depends upon assumptions that come close to matters of faith'. Art and Literature have become spaces of discussion and innovation which are imperative to a more comprehensive and inclusive truth surrounding the question of creation.

Computer Science
Computer science has a positivist philosophy towards truth as it looks for an empirical truth that is rational, objective and derived from scientific methodology. Nick Bostrom suggested that either “(1) the human species is very likely to go extinct before reaching a “posthuman” stage; (2) any post-human civilisation is extremely unlikely to run a significant number of simulations of their evolutionary history... ; (3) we are almost certainly living in a computer simulation” concluding the likelihood of the latter was greatest. The theory of the universe being created as a computer simulation evolved from the idea of exponential human advancement. In 1972, the ‘Pong’ game was created which involved only basic pixelated shapes. In 2000 ‘The Sims’, a life simulation video game, was released, and now virtual reality games are gaming’s newest innovation. The prospect of a ‘posthuman’ civilisation existing who have created a computer-simulated universe is not so preposterous as first thought and this idea of a 'higher power' draws parallels with theological creationism.

There are numerous claims of supporting evidence. Firstly, the universe behaves mathematically (e.g. the Fibonacci sequence in nature), thus reflecting the computer code from which it was written. Furthermore, everything in the universe can be broken up into subatomic particles which imitates a pixilated video game. A simulation hypothesis also accounts for inconsistencies in quantum mechanics, such as the ‘measurement problem’, which is a paradox where measurements supporting quantum mechanics are theoretically impossible.

Conclusion
Many disciplines examined within this chapter are single-minded in regard to their version of truth surrounding creation, and the "absolute truths" of cosmology, theology and computer science produce significant tension. This is a prominent example of how the issue of truth prevents interdisciplinary research, which would inevitably lead to an enhanced understanding of creation. There are individuals within disciplines who acknowledge the potential role of numerous factors culminating in the creation of the universe; Scientists who believe in God and writers who portray both sides to the debate. However, as this specific case study is entrenched in power struggles and historical viewpoints, prominence for these academics is difficult. We have concluded that truth surrounding this question should not be categorised into one discipline, and that it's imperative not to prioritise scientific truth because it is quantifiable, but to recognise the potential role of many different versions of truth when examining the creation of the universe.


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Lentis/"Data is the new oil"

"Data is the new oil!" was the title of a talk given by data scientist Clive Humby in 2006 at an Association of National Advertisers conference. In his talk, Humby claimed that raw data has to be processed just like crude oil has to be refined in order to have value. Since then, the phrase has entered mass circulation to illustrate a myriad of other parallels between data and oil. Just as the Second Industrial Revolution of the late 1800s and early 1900s was fueled by oil, the digital revolution today is being powered by data.

Processing and Refinement
Clive Humby initially made the analogy to demonstrate that the raw forms of data and oil needed some intermediate processing step before they become sellable products. Just like oligopolistic oil companies which almost always own their own refineries, oligopolistic data companies often perform their own in-house data processing. These oligopolistic data companies should be referred to as data-driven companies, because they completely rely on their processed data for revenue.

Oligopoly
Like the oil industry, the data collection industry is limited to only a few large multinational companies which control the entire market. There are countless smaller companies that contribute in some small way to the macroscale supply chain operations of data/oil, but the large companies dominate the economy in terms of market share and overall sociotechnical influence. Examples of these larger companies in the oil industry include ExxonMobil, Chevron, BP, and Shell, and examples of their analogs in the data industry include Amazon, Google, Facebook, and YouTube. The world's first billionaire, John D. Rockefeller, was the CEO of the Standard Oil Company (the company that would eventually be split into companies such as ExxonMobil and Chevron), and the world's first centi-billionaire, Jeff P. Bezos, is the CEO of Amazon (currently the world's largest data-driven e-commerce platform). The parallels between the two industries are clear in terms of the amount of wealth and power contained in them.

Data-Driven Companies
The business model of these companies is to collect data from their consumers and use the data to guide internal decision-making to improve sales by better targeting their products to individual consumers' preferences. A new variant of this business model accompanies the advent of the Internet, which makes it possible to use advertising as the sole means of revenue generation. In exchange for users' data, data-driven companies can offer their services to users for free.

Data-Brokering Companies
These companies profit almost exclusively by collecting and selling data to third-party companies and entities. They usually gather data by buying it, mining public records, and/or creating applications that collect user data. There are three main types: 1) companies that create an online marketplace where individual users can pay for information about other individuals (e.g. PeopleFinders, White Pages), 2) companies that sell data to entities that will use the data for marketing decisions (e.g. Acxiom and Cambridge Analytica), and 3) companies that sell data to entities using it for risk mitigation and identity verification (e.g. ID Analytics).

Leaks and Spills
Oil spills cause lasting damage, especially on marine ecosystems, and usually result in massive backlash from environmentalists and erosion of public trust in oil companies. Some notable examples in the United States include the 1989 Exxon Valdez and 2010 Deepwater Horizon oil spills. Similar to these spills, data leaks result in backlash from privacy advocates and erosion of public trust in the companies collecting consumer data.

Major Data Leaks
Countless data breaches have resulted in the non-consensual publishing of consumer personal data. Reasons for breaches include hacking, accidental uploading, and intentional leaking. Breaches affect both the private and public sectors and encompass a wide swath of data sets ranging from electronic medical records to location data to friends lists on social networking sites.

Facebook and Cambridge Analytica
Aleksandr Kogan, a researcher at Cambridge University, claimed to be conducting research and received Facebook approval for a personality quiz that thousands of Facebook users installed on their accounts. As many as 87 million Facebook users had their data given to Cambridge Analytica, a political data-brokering company hired by Donald Trump’s 2016 presidential campaign. Facebook's "terms of service" allows researchers to use the data for academic purposes. However, selling data is prohibited. Some claim that this scandal should prompt researchers to be more careful in how they distribute personal data. The principle of beneficence states that the researchers should place the well-being of the participants above all.

LocationSmart
LocationSmart is a company that works with U.S. wireless carriers to sell people's location data. Up until 2018, any LocationSmart user was able to find the real-time location of any phone in the United States. An anonymous hacker also breached the website of LocationSmart client Securus and accessed confidential law enforcement information.

Regulations
In the United States, domestic offshore oil drilling and oil pipeline transport are subject to regulation by the Environmental Protection Agency (EPA). Similar to how oil spills have engendered public uproar for increased regulations on oil companies, data leaks over the past twenty years have served as the impetus for data privacy regulations that are just beginning to emerge.

California Consumer Privacy Act
The California Consumer Privacy Act was originally passed in June 2018 under former Governor Jerry Brown. The main tenets of the law state that Californians now have the rights to 1) access what data is collected on them, 2) disallow collection of their data, and 3) delete their data. The law is one of the first examples of comprehensive state legislation on consumer data protection.

“Data Dividend”
Similar to how Alaskans receive a Permanent Fund dividend in part for oil drilling in their home state, some California officials (e.g. Gavin Newsom, Andrew Yang) are proposing that Californians receive a dividend for use/sale of their online data. Many argue this proposal is impractical. Former Facebook executive Antonio Martínez contends that Amazon, Google, and Facebook do not believe they owe their consumers anything, because they provide their services for free in exchange for user data that can be used to generate advertising revenue.

General Data Protection Regulation
The General Data Protection Regulation is a European Union law passed in April 2016. Like California's CCPA, it mandates transparency in data collection with a focus on user control over data. It is viewed as an exemplary law in global data protection policy. The Electronic Privacy Information Center (EPIC), a data privacy watchdog, contends that the U.S. needs a similar law to the GDPR or a regulatory body (e.g. Data Protection Agency).

Social Effects
Both data and oil affect billions of lives on a daily basis. It is impossible to understand their technological impacts without first considering their social effects.

A Useful Commodity
Both oil and data have been valuable resources for humanity. Trade and harvest data have been collected for thousands of years. One of the first examples of big data being used to drastically improve human life was in 1663 when John Graunt used mortality data to make predictions about impending bubonic plague outbreaks. Governments have been collecting and utilizing data through the census to allocate funding properly and help ensure democracy. Businesses realized how collected data can be used to improve their efficiency and customer experience, with Clive Humby's consulting firm Dunnhumby and British grocer Tesco being some of the pioneering companies. In the twenty-first century, as soon as data's worth became apparent, the data business exploded. Like collection of data, oil use also has a long history, dating back to the Sumerians efforts to make fire. Modern use of oil started in mid-1850s, when a significant quantity of oil was discovered in the U.S. Oil’s utility began as a light and heat source, but went on to powering transportation and an economic revolution. Companies in the industry became very wealthy as a result.

Dependence
Automobiles that run on petroleum are the primary modes of transportation in most countries. The modern economy, especially with the advent of e-commerce, is dependent on the shipping industry, and thus, automobiles. Many plastics are petroleum-based, and plastics lie at the heart of packaging and retail. Our society is reliant on oil today, and some contend that will continue, but a new dependence may be emerging: data. According to Google's Ngram Viewer, the phrase "data science" entered the vernacular somewhere during the early 1900s. Internal data helps companies optimize supply chain operations, and consumer data helps companies make decisions to improve customer experience. In other words, data collection does not always have to be negative or privacy-invading. Despite myriad opinions regarding acceptable data collection practices, data collection lies at the heart of our digital economy.

Equality
We can surmise that data collection rose concurrently with the Internet, as one notices a steep climb in the "data science" Ngram curve around 1990. One may hypothesize this was the result of the Internet's streamlining of data collection. At the touch of a button, it is now possible to collect data across different locations, socioeconomic strata, and cultures. More diverse social groups have their data represented than ever before; data may act as a great equalizer. Similarly, with cheap oil and refining methods came the freedom for many more people to drive.

Accuracy of the Analogy
Overall, data has both similarities and difference with oil, but the phrase "data is the new oil" exerts a social effect of its own. It communicates data's power and lucrativeness in the Digital Age. It also hearkens back to the troubled history of oil, which is perhaps appropriate, as we presently grapple with the privacy threats of data collection. The phrase teaches us that words have power, since the phrase itself has dissenters and disciples. We also witness that emergent technologies, especially disruptive ones like the Internet and mass data collection, raise many unanswered economic, regulatory, and ethical questions. Data science is still in its infancy, but we already see data's massive impact on our modern world.

title: Lentis/"Data is the new oil"


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LMIs in Control/pages/LMIforMinimizingMatrixNorm

The System

\begin{align} A(x) = A_{0}+A_{1}x_{1}+ ... + A_{n}x_{n} \end{align}$$

Note that $$ \begin{align} A_{i}, \quad i=1, 2, ..., n \end{align}$$ are symmetric matrices.

The Data

\begin{align} A_{0}, A_{1}, ..., A_{n} \quad \text{are given matrices.} \end{align}$$

The Optimization Problem
Find

$$\begin{align} x = [x_{1} \quad x_{2} ... x_{n}] \end{align}$$

to minimize,

$$\begin{align} J(x) = ||A(x)||_{2} \end{align}$$

According to Lemma 1.2 in [1] page 11, the following statements are equivalent

$$\begin{align} A^{T}A - t^{2}I \leq 0 \iff \begin{bmatrix} -tI & A \\ A^{T} & -tI\end{bmatrix} \leq 0\\ \end{align}$$

The LMI: LMI for matrix norm minimization
Mathematical description of the LMI formulation:

\begin{align} \text{min} \; \quad t:&\\ \text{s.t.} \quad \begin{bmatrix} -tI & A(x) \\ A(x)^{T} & -tI\end{bmatrix} \leq 0\\ \end{align}$$

Conclusion:
This problem is a slight generalization of the eigenvalue minimization problem for a matrix.

$$\begin{align} x_{i}, \quad i =1, 2, ... , n \quad \text{and} \quad t > 0 \end{align}$$ are parameters to be optimized

Implementation
A link to Matlab codes for this problem in the Github repository:

https://github.com/asalimil/LMI-for-Matrix-Norm-Minimization

Related LMIs
LMI for Eigenvalue Minimization


text:
LMIs in Control/page/Quadratic Polytopic Hinf- Optimal State Feedback Control/

The System: Consider System with following state-space representation.

\begin{align} \dot x(t)&=Ax(t)+B_1 q(t) + B_2 w(t)\\ p(t)& = C_1 x(t) + D_{11} q(t) + D_{12} w(t)\\ z(t)& = C_2 x(t) + D_{21} q(t) + D_{22} w(t)\\ \end{align}$$

Add uncertainty to system matrices

A, B_1, B_2, C_1, C_2, D_{11}, D_{12} $$

New state-space representation

\begin{align} \dot x(t)&=(A+A_i)x(t)+(B_1+B_i) q(t) + (B_2+B_i) w(t)\\ p(t)& = (C_1+C_i) x(t) + (D_{11}+D_i) q(t) + (D_{12}+D_i) w(t)\\ z(t)& = C_2 x(t) + D_{21} q(t) + D_{22} w(t)\\ \end{align}$$

The Optimization Problem: Recall the closed-loop in state feedback is: $$ S(P,K) = $$

\begin{align} \begin{bmatrix} A + B_22F&&B_1\\C_1 + D_{12}F&& D_{11}\end{bmatrix}\\ \end{align}$$ This problem can be formulated as $$ H\infty $$ optimal state-feedback, where K is a controller gain matrix.

The LMI: An LMI for Quadratic Polytopic $$ H\infty $$ Optimal State-Feedback Control $$ ||S(P(\Delta),K(0,0,0,F))||_{H\infty} \leq \gamma $$ $$ Y > 0 $$

\begin{align} \begin{bmatrix} Y(A+A_i)^T+(A+A_i)Y+Z^T(B_2+B_{1,i})^T+(B_2+B_{1,i})Z&&*^T&&*^T \\(B_1+B_{1,i})^T&&-\gamma I&&*^T \\(C_1+C_{1,i})Y+(D_{12}+D_{12,i})Z&&(D_{11}+D_{11,i})&&-\gamma I \end{bmatrix} < 0 \end{align}$$

Conclusion:

The $$ H\infty $$ Optimal State-Feedback Controller is recovered by $$ F = ZY^{-1} $$

Implementation: https://github.com/JalpeshBhadra/LMI/tree/master

Related LMIs:


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LMIs in Control/pages/Continuous Time D-Stability Controller

Continuous-Time D-Stability Controller

In some control problems, people are still interested in designing a controller whose poles are located in specific regions of a complex plane while also simultaneously ensuring that the controller is stable. One such method with which this can be possible is called D-stability.

The System
Suppose we were given the continuous-time system

\begin{align} \dot x(t)&=Ax(t)+Bu(t)\\ y(t)&=Cx(t)+Du(t) \end{align}$$

whose stability was not known, and where $$A\in\mathbb{R}^{mxm}$$, $$B\in\mathbb{R}^{mxn}$$, $$C\in\mathbb{R}^{pxm}$$, and $$D\in\mathbb{R}^{qxn}$$ for any $$t\in\mathbb{R}$$. Then the controller that simultaneously stabilizes the above system while ensuring that the poles are at their desired location can be achieved by the controller $$u(t)=Kx(t)$$.

The Data
In order to properly define the acceptable region of the poles in the complex plane, we need the following pieces of data:

 * matrices $$A$$, $$B$$, $$C$$, $$D$$
 * rise time ($$t_r$$)
 * settling time ($$t_s$$)
 * percent overshoot ($$M_p$$)

Having these pieces of information will now help us in formulating the optimization problem.

The Optimization Problem
Using the data given above, we can now define our optimization problem. In order to do that, we have to first define the acceptable region in the complex plane that the poles can lie on using the following inequality constraints:

Rise Time: $$\omega_n{\leq}{1.8 \over t_r}$$

Settling Time: $$\sigma{\leq}{-4.6 \over t_s}$$

Percent Overshoot: $$\sigma{\leq}{-ln({M_p}) \over {\pi}}|{\omega_d}|$$

Assume that $$z$$ is the complex pole location, then:

\begin{align} {\omega_n}^2=\|z\|^2&=z^{*} z\\ {\omega_d}=Im{z}&={(z-z^{*}) \over 2}\\ {\sigma}=Re{z}&={(z+z^{*}) \over 2} \end{align}$$

This then allows us to modify our inequality constraints as:

Rise Time: $$z^{*} z-{1.8^2 \over {t_r}^2}{\leq}0$$

Settling Time: $${(z+z^{*}) \over 2}+{4.6 \over t_s}{\leq}0$$

Percent Overshoot: $$z-z^{*}+{{\pi} \over ln({M_p})}|z+z^{*}|{\leq}0$$

which not only allows us to map the relationship between complex pole locations and inequality constraints, but it also now allows us to easily formulate our LMIs for this problem.

The LMI: The Continuous-Time Controller D-Stability
Keeping the above inequalities in mind, we observe the following:

Suppose there now exists a symmetric matrix $$P>0$$ and matrix $$Z$$, we can now determine the controller the following LMIs:

\begin{align} \begin{bmatrix} -rP&&AP+BZ\\(AP+BZ)^T&&-rP\end{bmatrix}&<0\\ AP+BZ+(AP+BZ)^T +2\alpha P&<0, and\\ \begin{bmatrix} AP+BZ+(AP+BZ)^T&&c(AP+BZ-(AP+BZ)^T)\\c((AP+BZ)^T -(AP+BZ))&&AP+BZ+(AP+BZ)^T\end{bmatrix}&<0 \end{align}$$

Conclusion:
Given the resulting controller $$K=ZP^{-1}$$, we can now determine that the pole locations $$z\in\mathbb{C}$$ of $$A+BK$$ satisfies the inequality constraints $$|x|{\leq}r$$, $$Re(x){\leq}-{\alpha}$$ and $$z+z^{*}{\leq}-c|z-z^{*}|$$.

Implementation

 * Example Code - A GitHub link that contains code (titled "ControllerDStability.m") that demonstrates how this LMI can be implemented using MATLAB-YALMIP.

Related LMIs

 * ../Continuous Time D-Stability Observer/ - Equivalent D-stability LMI for a continuous-time observer.
 * ../D stabilization/ - LMI for $$\mathbb{D}_{(q,r)}$$-stabilization.


text:
LMIs in Control/pages/Discrete-Time H2-Optimal Observer

In many applications, perhaps even most, the state of the system cannot be directly known. In this case, you will need to strategically to measure key system outputs that will make the system states indirectly observable. Observers need to converge much faster than the system dynamics in order for their estimations to be accurate. Optimal observer synthesis is therefore advantageous. In this LMI, we seek to optimize the H2 norm, which conceptually is minimizing the average magnitude of error in the observer.

The System

\begin{align}

x_{k+1}&=A_{d}x_{k}+B_{d1}w_{k},\\

y_{k}&=C_{c2}x_{k} + D_{d21}w_{k}\\ \end{align} $$

where $$ x \in R^{n}$$  and is the state vector,  $$ A \in R^{n\times n}$$  and is the state matrix,  $$ B \in R^{n\times r}$$  and is the input matrix,  $$ w \in R^{r}$$  and is the exogenous input,  $$ C \in R^{m\times n}$$  and is the output matrix,  $$ D \in R^{m\times r}$$  and is the feedthrough matrix,  $$ y \in R^{m}$$ and is the output, and it is assumed that $$(A_{d}, C_{d2})$$ is detectable.

The Data
The matrices $$ A_{d}, B_{d1}, C_{cd2}, C_{cd1}, D_{d21} $$.

The Optimization Problem
An observer of the form:

\begin{align}

\hat x_{k+1}&=A_{d}\hat x_{k}+ L_{d}(y_{k}-\hat y_{k}),\\

\hat y_{k}&=C_{d2} \hat x_{k} \\ \end{align} $$

is to be designed, where $$ L_{d} \in R^{n_{x}\times n_{y}}$$ is the observer gain.

Defining the error state $$ e_{k} = x_{k} - \hat x_{k}$$, the error dynamics are found to be

$$e_{k+1} = (A_{d} - L_{d}C_{d2} e_{k} + (B_{d1} - L_{d}D_{d21})w_{k}$$,

and the performance output is defined as

$$z_{k} = C_{d1}e_{k}$$.

The observer gain $$L_{d}$$ is to be designed such that the $$H_{2}$$ of the transfer matrix from $$w_{k}$$ to $$z_{k}$$, given by

$$L_{d} \begin{align} T(z) &= C_{d1}(z1 - (A_{d} - L_{d}C_{d2}))^{-1}(B_{d1} - L_{d}D_{d21}),\\ \end{align} $$

is minimized.

The LMI: Discrete-Time H2-Optimal Observer
The discrete-time $$H_{2}$$-optimal observer gain is synthesized by solving for $$P \in S^{n_{x}}$$, $$Z \in S^{n_{z}}$$, $$G_{d} \in R^{n_{x}\times n_{y}}$$, and $$v \in R_{>0}$$ that minimize $$J(v) = v$$ subject to $$P>0, Z>0$$,

\begin{align}

\begin{bmatrix}P & PA_{d}-G_{d}C_{d2} & PB_{d1}-G_{d}D_{d21}\\
 * & P & 0\\
 * & * & 1\end{bmatrix}&>0,\\

\begin{bmatrix} Z & PC_{d1}\\
 * & P\end{bmatrix}&>0,\\

\operatorname{tr}Z<v

\end{align}$$ where $$\operatorname{tr}$$ refers to the trace of a matrix.

Conclusion:
The $$H_{2}$$-optimal observer gain is recovered by $$L_{d} = P^{-1}G_{d}$$ and the $$H_{2}$$ norm of $$T(z)$$ is $$\mu = \sqrt{v}$$. The $$L_{d} $$ matrix is the observer gains that can be used to form the optimal observer:

\begin{align}

\hat x_{k+1}&=A_{d}\hat x_{k}+ L_{d}(y_{k}-\hat y_{k}),\\

\hat y_{k}&=C_{d2} \hat x_{k} \\ \end{align} $$

Implementation
This implementation requires Yalmip and Sedumi.

https://github.com/rezajamesahmed/LMImatlabcode/blob/master/Discrete_Time_H2_Optimal_Observer_LMIs_Wikibook_Example.m

Related LMIs
Mixed H2-Hinfinity discrete time observer

Discrete-Time_Hinfinity-Optimal_Observer


text:
LMIs in Control/pages/Maximum Singular Value of a Complex Matrix

Maximum Singular Value of a Complex Matrix

The System
Consider $$ A \in \mathbb{R}^{n \times m} $$ as well as $$ \gamma $$. A maximum singular value of a matrix $$ A $$ is less than $$ \gamma $$ if and only if $$ AA^H < \gamma^2I $$, where $$ A^H $$ is the conjugate transpose or Hermitian transpose of the matrix $$ A $$.

The Data
The matrix $$ A $$ is the only data required.

The LMI: Maximum Singular Value of a Complex Matrix
Using the Shur complement procedure, the following LMIs can be constructed:

\begin{align} \bar{\sigma}(A) < \gamma\\ \begin{bmatrix} \gamma I & A \\ A^H & \gamma I\end{bmatrix}&>0\\ \end{align}$$

The following LMI is also equivalent:

\begin{align} \bar{\sigma}(A) < \gamma\\ \begin{bmatrix} \gamma I & A^H \\ A & \gamma I\end{bmatrix}&>0\\ \end{align}$$

Conclusion:
The results from this LMI will give the maximum complex value of the matrix $$ A $$:

\begin{align} \bar{\sigma}(A) < \gamma \end{align}$$

Implementation

 * Maximum Singular Value Example

Related LMIs

 * Minimum singular value of a complex matrix


text:
LMIs in Control/pages/MatrixEigenValueMinimization

LMI for Minimizing Eigenvalue of a Matrix

Synthesizing the eigenvalues of a matrix plays an important role in designing controllers for linear systems. The eigenvalues of the state matrix of a linear time-invariant system determine if the system is stable or not. The system is stable if all the eigenvalues of the state matrix are located in the left half of the complex plane. Thus, we may desire to minimize the maximal eigenvalue of the state matrix such that the minimized eigenvalue is placed in the left half-plane, which guarantees that the system is stable.

The System
Assume that we have a matrix function of variables $$x$$:

$$ \begin{align} A(x) = A_{0}+A_{1}x_{1}+ ... + A_{n}x_{n} \end{align}$$

where $$ \begin{align} A_{i}, \quad i=1, 2, ..., n \end{align}$$ are symmetric matrices.

The Data
The symmetric matrices $$A_{i}$$ ($$ \begin{align} A_{0}, A_{1}, ..., A_{n}\end{align}$$) are given.

The Optimization Problem
The optimization problem is to find the variables $$\begin{align} x = [x_{1} \quad x_{2} ... x_{n}] \end{align}$$ to minimize the following cost function:

$$\begin{align} J(x) = \lambda_{\text{max}}(A(x)) \end{align}$$

where $$J(x)$$ is the cost function and $$\lambda_{\text{max}}(.)$$ indicates the maximim eigenvalue of a matrix.

According to Lemma 1.1 in LMI in Control Systems Analysis, Design and Applications (page 10), the following statements are equivalent

$$\begin{align} \lambda_{max}(A(x)) \leq t \iff A(x)-tI \leq 0 \end{align}$$

where $$t$$ is defined as the maximim eigenvalue of the matrix $$A$$.

The LMI: LMI for eigenvalue minimization
This optimization problem can be converted to an LMI problem.

The mathematical description of the LMI formulation can be written as follows:

$$ \begin{align} &\text{min} \quad t\\ &\text{s.t.} \quad A(x)-tI \leq 0 \end{align}$$

Conclusion:
As a result, the variables $$ x_{i}, \quad i =1, 2, ..., n $$ after solving this LMI problem. Moreover, we obtain the maximum eigenvalue, $$ t $$, of the matrix $$A(x)$$.

Implementation
A link to Matlab codes for this problem in the Github repository:

https://github.com/asalimil/LMI-for-Minimizing-the-Maximum-Eigenvalue-of-Matrix

Related LMIs
LMI for Generalized Eigenvalue Problem

LMI for Matrix Norm Minimization

LMI for Maximum Singular Value of a Complex Matrix

LMI for Matrix Positivity

Return to Main Page
LMIs in Control/Tools


text:
LMIs in Control/pages/Quadratic Polytopic Hinf- Optimal State Feedback Control

Quadratic Polytopic Full State Feedback Optimal $$H_{\infty}$$ Control
For a system having polytopic uncertainties, Full State Feedback is a control technique that attempts to place the system's closed-loop system poles in specified locations based off of performance specifications given. $$H_{\infty}$$ methods formulate this task as an optimization problem and attempt to minimize the $$H_{\infty}$$ norm of the system.

The System
Consider System with following state-space representation.

\begin{align} \dot x(t)&=Ax(t)+B_1 q(t) + B_2 w(t)\\ p(t)& = C_1 x(t) + D_{11} q(t) + D_{12} w(t)\\ z(t)& = C_2 x(t) + D_{21} q(t) + D_{22} w(t)\\ \end{align}$$

where $$x\in\mathbb{R}^{m}$$, $$q\in\mathbb{R}^{n}$$ , $$w\in\mathbb{R}^{g}$$, $$A\in\mathbb{R}^{mxm}$$, $$B_1\in\mathbb{R}^{mxn}$$, $$B_2\in\mathbb{R}^{mxg}$$, $$p\in\mathbb{R}^{p}$$ , $$C_1\in\mathbb{R}^{pxm}$$, $$D_{11}\in\mathbb{R}^{pxn}$$,  $$D_{12}\in\mathbb{R}^{pxg}$$, $$z\in\mathbb{R}^{s}$$, $$C_2\in\mathbb{R}^{sxm}$$, $$D_{21}\in\mathbb{R}^{sxn}$$ ,  $$D_{22}\in\mathbb{R}^{sxg}$$ for any $$t\in\mathbb{R}$$.

Add uncertainty to system matrices

A, B_1, B_2, C_1, C_2, D_{11}, D_{12} $$

New state-space representation

\begin{align} \dot x(t)&=(A+A_i)x(t)+(B_1+B_i) q(t) + (B_2+B_i) w(t)\\ p(t)& = (C_1+C_i) x(t) + (D_{11}+D_i) q(t) + (D_{12}+D_i) w(t)\\ z(t)& = C_2 x(t) + D_{21} q(t) + D_{22} w(t)\\ \end{align}$$

The Optimization Problem:
Recall the closed-loop in state feedback is: $$ S(P,K) = $$

\begin{align} \begin{bmatrix} A + B_{2}F&&B_1\\C_1 + D_{12}F&& D_{11}\end{bmatrix}\\ \end{align}$$ This problem can be formulated as $$ H\infty $$ optimal state-feedback, where K is a controller gain matrix.

The LMI:
An LMI for Quadratic Polytopic $$ H\infty $$ Optimal State-Feedback Control $$ ||S(P(\Delta),K(0,0,0,F))||_{H\infty} \leq \gamma $$ $$ Y > 0 $$

\begin{align} \begin{bmatrix} Y(A+A_i)^T+(A+A_i)Y+Z^T(B_2+B_{1,i})^T+(B_2+B_{1,i})Z&&*^T&&*^T \\(B_1+B_{1,i})^T&&-\gamma I&&*^T \\(C_1+C_{1,i})Y+(D_{12}+D_{12,i})Z&&(D_{11}+D_{11,i})&&-\gamma I \end{bmatrix} < 0 \end{align}$$

Conclusion:
The $$ H\infty $$ Optimal State-Feedback Controller is recovered by $$ F = ZY^{-1} $$ Controller will determine the bound $$\gamma$$ on the $$H_{\infty}$$ norm of the system.

Implementation:
https://github.com/JalpeshBhadra/LMI/tree/master

Related LMIs
Full State Feedback Optimal $H_{\infty}$ Controller


text:
LMIs in Control/pages/MatrixNormMinimization

LMI for Matrix Norm Minimization

This problem is a slight generalization of the eigenvalue minimization problem for a matrix. Calculating norm of a matrix is necessary in designing an $$H_{2}$$ or an $$H_{\infty}$$ optimal controller for linear time-invariant systems. In those cases, we need to compute the norm of the matrix of the closed-loop system. Moreover, we desire to design the controller so as to minimize the closed-loop matrix norm.

The System
Assume that we have a matrix function of variables $$x$$:

$$ \begin{align} A(x) = A_{0}+A_{1}x_{1}+ ... + A_{n}x_{n} \end{align}$$

where $$ \begin{align} A_{i}, \quad i=1, 2, ..., n \end{align}$$ are symmetric matrices.

The Data
The symmetric matrices $$A_{i}$$ ($$ \begin{align} A_{0}, A_{1}, ..., A_{n}\end{align}$$) are given.

The Optimization Problem
The optimization problem is to find the variables $$\begin{align} x = [x_{1} \quad x_{2} ... x_{n}] \end{align}$$ in order to minimize the following cost function:

$$\begin{align} J(x) = ||A(x)||_{2} \end{align}$$

where $$J(x)$$ is the cost function and $$||.||_{2}$$ indicates the norm of the matrix function $$A$$.

According to Lemma 1.1 in LMI in Control Systems Analysis, Design and Applications (page 10), the following statements are equivalent:

$$\begin{align} A^{T}A - t^{2}I \leq 0 \iff \begin{bmatrix} -tI & A \\ A^{T} & -tI\end{bmatrix} \leq 0\\ \end{align}$$

The LMI: LMI for matrix norm minimization
This optimization problem can be converted to an LMI problem.

The mathematical description of the LMI formulation can be written as follows:

$$ \begin{align} &\text{min} \quad t&\\ &\text{s.t.} \quad \begin{bmatrix} -tI & A(x) \\ A(x)^{T} & -tI\end{bmatrix} \leq 0\\ \end{align}$$

Conclusion:
As a result, the variables $$ x_{i}, \quad i =1, 2, ..., n $$ after solving this LMI problem and we obtain $$ t $$ that is the norm of matrix function $$A(x)$$.

Implementation
A link to Matlab codes for this problem in the Github repository:

https://github.com/asalimil/LMI-for-Matrix-Norm-Minimization

Related LMIs
LMI for Matrix Norm Minimization

LMI for Generalized Eigenvalue Problem

LMI for Maximum Singular Value of a Complex Matrix

LMI for Matrix Positivity

Return to Main Page
LMIs in Control/Tools


text:
LMIs in Control/pages/KYP Lemma QSR

The Concept
In systems theory the concept of dissipativity was first introduced by Willems which describes dynamical systems by input-output properties. Considering a dynamical system described by its state $$ x(t) $$, its input $$u(t)$$ and its output $$y(t)$$, the input-output correlation is given a supply rate $$ w(u(t),y(t))$$. A system is said to be dissipative with respect to a supply rate if there exists a continuously differentiable storage function $$ V(x(t))$$ such that $$V(0)=0$$, $$V(x(t))\ge 0 $$ and

 * $$ \dot{V}(x(t)) \le w(u(t),y(t))$$

As a special case of dissipativity, a system is said to be passive if the above dissipativity inequality holds with respect to the passivity supply rate $$ w(u(t),y(t)) = u(t)^Ty(t) $$.

The physical interpretation is that $$V(x)$$ is the energy stored in the system, whereas $$w(u(t),y(t))$$ is the energy that is supplied to the system.

This notion has a strong connection with Lyapunov stability, where the storage functions may play, under certain conditions of controllability and observability of the dynamical system, the role of Lyapunov functions.

Roughly speaking, dissipativity theory is useful for the design of feedback control laws for linear and nonlinear systems. Dissipative systems theory has been discussed by Vasile M. Popov, Jan Camiel Willems, D.J. Hill, and P. Moylan. In the case of linear invariant systems, this is known as positive real transfer functions, and a fundamental tool is the so-called Kalman–Yakubovich–Popov lemma which relates the state space and the frequency domain properties of positive real systems.Dissipative systems are still an active field of research in systems and control, due to their important applications.

The System
Consider a contiuous-time LTI system, $$\mathcal{G} : \mathcal{L}_{2e} \rightarrow \mathcal{L}_{2e}$$, with minimal state-space realization (A, B, C, D), where $$\mathcal{A} \in \mathcal{R}^{n\times n}, \mathcal{B} \in \mathcal{R}^{n\times m}, \mathcal{C} \in \mathcal{R}^{p\times n},$$ and $$\mathcal{D} \in \mathcal{R}^{p\times m} $$.

\begin{align} \dot x(t)&=Ax(t)+Bu(t),\\ y(t)&=Cx(t)+Du(t)\\ \end{align}$$

The Data
The matrices $$ A,B,C $$ and $$D $$ which defines the state space of the system

The Optimization Problem
The system $$\mathcal{G}$$ is QSR disipative if

 * $$\int_0^T (y^T(t)Qy(t) + 2y^TSu(t) + u^T(t)Ru(t))\,dt \ge 0, \forall u \in \mathcal{L}_{2e}, \forall T \ge 0 $$

where $$u(t)$$ is the input to $$\mathcal{G}, y(t)$$ is the output of $$\mathcal{G}, Q \in \mathcal{S}^{p}, S \in \mathcal{R}^{p\times m},$$ and $$\mathcal{R} \in \mathcal{S}^{m}$$.

LMI : KYP Lemma for QSR Dissipative Systems
The system $$\mathcal{G}$$ is also QSR dissipative if and only if there exists $$ P \in \mathcal{S}^{n},$$ where $$ P > 0,$$ such that

 * $$\begin{bmatrix}

PA + A^{T}P - C^{T}QC & PB - C^{T}S - C^{T}QD \\ (PB - C^{T}S - C^{T}QD)^{T} &  -D^{T}QD - (D^{T}S + S^{T}D) - R \end{bmatrix}\le 0.$$

Conclusion:
If there exist a positive definite $$P$$ for the the selected Q,S and R matrices then the system $$\mathcal{G}$$ is QSR dissipative.

Implementation
Code for implementation of this LMI using MATLAB. https://github.com/VJanand25/LMI

Related LMIs
KYP Lemma

title: LMIs in Control/pages/KYP Lemma QSR


text:
LMIs in Control/pages/Schur Stabilization

LMI for Schur Stabilization

Similar to the stability of continuous-time systems, one can analyze the stability of discrete-time systems. A discrete-time system is said to be stable if all roots of its characteristic equation lie in the open unit disk. This provides a condition for the stability of discrete-time linear systems and a linear time-invariant system with this property is called a Schur stable system.

The System
We consider the following system:

$$ \begin{align} x(k+1) = Ax(k) + Bu(k) \end{align}$$

where the matrices $$ A \in \mathbb{R}^{n\times n} $$, $$B \in \mathbb{R}^{n \times r} $$, $$ x \in \mathbb{R}^{n} $$, and $$ u \in \mathbb{R}^{r} $$ are the state matrix, input matrix, state vector, and the input vector, respectively.

Moreover, $$k$$ represents time in the discrete-time system and $$k+1$$ is the next time step.

The state feedback control law is defined as follows:

$$ \begin{align} u(k) = Kx(k) \end{align}$$

where $$K \in \mathbb{R}^{n\times r} $$ is the controller gain. Thus, the closed-loop system is given by:

$$ \begin{align} x(k+1) = (A + BK)x(k) \end{align}$$

The Data
The matrices $$ A $$ and $$B $$ are given.

We define the scalar as $$ \gamma $$ with the range of $$ 0 < \gamma \leq 1 $$.

The Optimization Problem
The optimization problem is to find a matrix $$\begin{align} K \in \mathbb{R}^{r\times n} \end{align}$$ such that:

$$\begin{align} ||A + BK||_{2} < \gamma \end{align}$$

According to the definition of the spectral norms of matrices, this condition becomes equivalent to:

$$\begin{align} (A + BK)^{T}(A + BK) < \gamma^{2}I \end{align}$$

Using the Lemma 1.2 in LMI in Control Systems Analysis, Design and Applications (page 14), the aforementioned inequality can be converted into:

$$\begin{align} \begin{bmatrix} -\gamma I & (A + BK) \\ (A + BK)^{T} & -\gamma I\end{bmatrix} < 0\\ \end{align}$$

The LMI: LMI for Schur stabilization
The LMI for Schur stabilization can be written as minimization of the scalar, $$\gamma$$, in the following constraints:

$$ \begin{align} & \text{min} \quad \gamma\\ & \text{s.t.} \quad \begin{bmatrix} -\gamma I & (A + BK) \\ (A + BK)^{T} & -\gamma I\end{bmatrix} < 0\\ \end{align}$$

Conclusion:
After solving the LMI problem, we obtain the controller gain $$K$$ and the minimized parameter $$\gamma$$. This problem is a special case of Intensive Disk Region Design (page 230 in [1]). This problem may not have a solution even when the system is stabilizable. In other words, once there exists a solution, the solution is robust in the sense that when there are parameter perturbations, the closed-loop system's eigenvalues are not easy to go outside of a circle region within the unit circle [1].

Implementation
A link to Matlab codes for this problem in the Github repository:

https://github.com/asalimil/LMI-for-Schur-Stability

Related LMIs
LMI for Hurwitz stability

Return to Main Page
LMIs in Control/Tools


text:
LMIs in Control/pages/Polytopic stability

An important result to determine the stability of the system with uncertainties

The System:
Consider the system with Affine Time-Varying uncertainty (No input)

\begin{align} \dot x(t)&=(A_0+\Delta A(t))x(t)\\ \end{align}$$

where

\begin{align} \Delta A(t) = A_1 \delta_1 (t)+....+A_k \delta_k (t)\\ \end{align}$$ where $$

\delta_i (t) $$ lies in either the intervals

\begin{align} \delta_i \in [\delta_i^-,\delta_i^+] \\ \end{align} $$ or the simplex

\begin{align} \delta(t) \in { \delta : \Sigma \alpha_i = 1, \alpha \geq 0 } \end{align} $$

where $$x\in\mathbb{R}^{m}$$ and $$A\in\mathbb{R}^{mxm}$$

The Data
The matrix A and $$\Delta_{A(t)}$$ are known

The Optimization
The Definitions: Quadratic Stability for Dynamic Uncertainty

The system

\begin{align} \dot x(t)&=(A_0+\Delta A(t))x(t)\\ \end{align}$$ is Quadraticallly Stable over $$ \Delta $$ if there exists a P > 0

Theorem $$ (A+\Delta,\boldsymbol{\Delta}) $$ is quadratically stable over $$ \boldsymbol{\Delta} :=Co(A_1,...,A_k) $$ if and only if there exists a P > 0 such that

\begin{align} (A+A_i)^TP + P(A+A_i) < 0 \quad

for \quad all \quad i = 1,....,k \end{align} $$ The theorem says the LMI only needs to hold at the EXTREMAL POINTS or VERTICES of the polytope.
 * Quadratic Stability MUST be expressed as an LMI

The LMI

\begin{align} (A+\Delta)^TP + P(A+\Delta ) < 0 \quad for \quad all \quad

\Delta \in \boldsymbol{\Delta} \end{align} $$

Conclusion:
Quadratic Stability Implies Stability of trajectories for any $$ \Delta $$ with $$ \Delta \in \boldsymbol{\Delta} $$ for all $$ t \geq 0 $$ Quadratic Stability is CONSERVATIVE. There are Stable System which are not Quadratically stable. Quadratic Stability is sometimes referred to as an "infinite-dimensional LMI"
 * Meaning it represents an infinite number of LMI constraints.
 * One for each possible value $$ \Delta $$ with $$ \Delta \in \boldsymbol{\Delta} $$
 * Also called a parameterized LMI
 * Such LMIs are not tractable.
 * For polytopic sets, the LMI can be made finite.

Implementation
A link to implementation of the LMI https://github.com/JalpeshBhadra/LMI/blob/master/polytopicstability.m

Related LMIs

 * Parametric Norm Bounded Uncertain System Quadratic Stability


text:
LMIs in Control/pages/Discrete-Time Lyapunov Stability

Discrete-Time Lyapunov Stability

The System
Discrete-Time System

\begin{align} x(t)_{k+1}&=A_dx(t)_k,&A_d \in \bf{R^{n*n}} \;\\ \end{align}$$

The Data
The matrices $$ System A_d,P $$.

The Optimization Problem
The following feasibility problem should be solved:

Find P obeying the LMI constraints.

The LMI:
Discrete-Time Lyapunov Stability

The LMI formulation

\begin{align} P \in \bf{S^{n}}\\ Find \; &P>0,\\ \begin{bmatrix} A_d^T P A_d - P\end{bmatrix}&\leq0 \end{align}$$

Conclusion:
If there exists a $$P \in \bf{S^{n}}$$ satisfying the LMI then, $$|\lambda_i(A_d)|\leq 1, \forall i = 1,2,...,n;$$ and the equilibrium point $$\bar{x}=0$$  of the system is Lyapunov stable.

Implementation

 * [] - MATLAB implementation of the LMI.

Related LMIs
Continuous_Time_Lyapunov_Inequality - Lyapunov_Inequality


text:
LMIs in Control/pages/Deduced LMI Conditions for H-infinity Index

H-infinity Index Deduced LMI

Although the KYP Lemma, also known as the Bounded Real Lemma, is a basic condition to evaluate an upper bound on the H∞, the verification of the bound on the H∞-gain of the system can be done via the deduced condition.

The System
A state-space representation of a linear system as given below:

\begin{align} \dot x(t)&=Ax(t)+Bw(t)\\ y(t)&=Cx(t)+Dw(t) \end{align}$$

where $$x(t)\in \R^n$$, $$y(t)\in \R^m$$ and $$w(t)\in \R^r$$ are the system state, output, and the disturbance vector respectively. The transfer function of such a system can be evaluated as:

\begin{align} G(s)=C(sI-A)^{-1}B+D \end{align}$$

The Data
Number of states n, number of outputs m and number of external noise channels r need to be known. Moreover, the system matrices A,B,C,D are also required to be known.

The Feasibility LMI
For an arbitrary $$ \gamma $$, the transfer function G(s) satisfies

 * $$ \left\| G(s) \right\|_{\infty} < \gamma $$

if and only if there exists a symmetric matrix X > 0 and a matrix $$ \Omega .$$ such that:

\begin{align} \text{Find} \; X, \Omega :&\\ \Theta + \Phi^\top \Omega \Psi + \Psi^\top \Omega^\top \Phi < 0 \end{align}$$

where:

\begin{align}

\Theta &= \begin{bmatrix} 0 & X & 0 & 0 & 0 \\ X & -X & 0 & 0 & 0 \\ 0 & 0 & -\gamma I_m & 0 & 0\\ 0 & 0 & 0 & -X & 0 \\ 0 & 0 & 0 & 0 & \gamma I_r \end{bmatrix} \\ \Phi &= \begin{bmatrix} -I_n & A^\top & C^\top & I_n & 0 \\ 0 & B^\top & D^\top & 0 & -\gamma I_r\end{bmatrix} \\ \Psi &= \begin{bmatrix} I_n & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & I_r\end{bmatrix} \end{align}$$

The above LMI can be combined with the bisection method to find minimum $$ \gamma $$ to find the minimum upper bound on the H∞ gain of $$ G(s) $$.

Conclusion:
If there is a feasible solution to the aforementioned LMI, then the $$ \gamma $$ upper bounds the infinity norm of the system G(s).

Implementation
To solve the feasibility LMI, YALMIP toolbox is required for setting up the feasibility problem, and SeDuMi is required to solve the problem. The following link showcases an example of the feasibility problem:

https://github.com/smhassaan/LMI-Examples/blob/master/Deduced_hinf_example.m

Related LMIs
Bounded Real Lemma


text:
Finite Model Theory/FO Localities

Basic idea: bringing neighbourhoods in relational structures together with queries written in certain languages. Like an area of land and a watchtower with a visual range, a neighbourhood has a sort of sprawl that it covers, and queries may have a sort of operating distance over which they can relate elements.

Such queries are said to have the locality property. This concept now can be expanded to whole languages. For example, for FO it can be shown that all FO-queries have the locality property.

Neighbourhood
universe with 1 relation: distance-1-neighbour

expand a: distance-2-neighbour

expand b: 2 relations with d-1-neighb

expand a+b: ...

Gaifman graph
We define a Gaifman graph as a sort of overlay of all relations of a structure S, and then make use of it for measuring a distance between elements of S, i.o.w. how far it is to walk from one element to the other, on a path paved by the relations of S.

Definition Gaifman Graph
Let S be a relational structure with a universe U and relations Ri. A graph G (N, E) is said to be the Gaifman Graph of S iff N = U, for all e exists a r in some Ri or e = (u, u).

Remarks

 * 1) G is reflexive
 * 2) the orientation of the edges in Ri doesn't matter in G

Examples

 * 1) If S is a graph, G is basically the same plus reflexivity
 * 2) If S is a digraph, G is basically the same plus reflexivity, plus symmetry (i.e. edges lose orientation)
 * 3) If S has only the relations R1 and R2, G is their union plus reflexivity, plus symmetry
 * 4) In the example image a Gaifman graph (a) is derived from the 2 relations (digraphs) Blue (b) and Green (c) by 'overlaying' the edges, 'forgetting' orientation and adding loops.

Definition Distance between elements
Let S be a structure with universe U with elements u1, u2 and Gaifman graph G. The length of a shortest path between u1 and u2 in G with ... is said to be the distance of u1 and u2 (in S).

Definition Distance between sets
Let S be a structure with universe U with subsets U1, U2 and Gaifman graph G. Then the minimal distance btw elms of U1 and U2 is said to be the distance btw U1 and U2 (in S).

Remarks

 * 1) Example elms: shorter path, infinite, zero
 * 2) Example sets: path, zero?

Neighbourhood
First, a ball is defined as a subset of elements of a structure S. Next, a neighbourhood is defined as a partial structure of S, based on a ball.

Definition Ball
Let S be a relational structures with a universe U, a be a sequence, r a natural. A set of elements of U is said to be a ball of radius r around a, B(S, r; a), iff

B(S, r; a) = { u | d(a, u) le r }

Definition Neighbourhood
Let B(S, r; a) be a Ball around a. Then a structure N(Sn, r; a) is said to be the r-neighbourhood of a in Sn, with Sn having
 * 1) the universe B(S, r; a)
 * 2) each relation from S, restricted to B
 * 3) the elements of a as additional constants a1, ..., a_n

Remarks

 * 1) examples of 'locally look the same'
 * 2) Isomorphism: ai -> bi - overlap?
 * 3) => equi classes decompose with increasing radius - no swap of class?

Neighbourhoods & Queries
1-Neighbourhood: {1, 6,7}, {2, 3,5},{4}, {8, 9}

2-Neighbourhood: {1, 6}, {7}, {2}, {3}, {5}, {4}, {8, 9}

title: Finite Model Theory/FO Localities


text:
Issues in Interdisciplinarity 2019-20/Evidence in Religious Experience

Religious experiences can range from ineffable out of body sensations to interactions with the divine as if through the use of our normal bodily senses. This chapter considers an interdisciplinary approach to understanding these experiences, and how this is challenged by the variances in methodologies and forms of evidence across numerous disciplines.

There is no set circumstance for an individual to have a religious experience (partly due to different practices across world faiths) thereby leading to conflicts between disciplines in how evidence of religious experiences are collected since there is much scope for contention. There is also a paradox underlying an interdisciplinary approach: scientists seek empirical evidence for religious experiences, yet to others the experiences themselves constitute evidence — evidence for the existence of a higher power. This therefore exacerbates the issue of evidence and perpetuates the age-old divide between religion and science that prevents researchers from understanding the root causes of religious experience.

Interpretivist Evidence
Interpretivism centres around a contextualised, in-depth insight into respondents’ lives, their different understandings of objective reality and individual motivations. In this way, religious experiences aren’t homogenous but dynamically shaped by culture. In the fields of theology and the social sciences, interpretivist evidence is used and this approach avoids imposing pre-conceived understandings upon evidence, instead examining experiences in isolation.

Theology
Theologists focus on the recount of personal experiences, believing that evidence from this is self-explanatory and non-refutable. They argue that due to the large quantity of religious experiences recorded throughout history, it would be unreasonable to suggest that they were all false. Hence, to an extent, the existence of religious experiences can be seen as evidence of some kind of God. The common phrase used when describing religious experience of ‘seeing God’ itself is linguistically suggestive of an evidential occurrence as to see is an objective action. They propose that although emotions or feelings dominate religious experiences, there must be elements of judgement and perception for the witness to view the experience as a true event. Since there is no test that can be done to prove someone’s account as true, often the quantity of people reporting similar experiences is used as a way to measure its reliability as evidence.

Social sciences
Eminent cultural anthropologist Clifford Geertz termed the study of human life "not an experimental science in search of laws but an interpretive one in search of meaning”. Historically, anthropologists and sociologists have used a mix of quantitative (closed-ended surveys and statistical methods) and qualitative (open-ended surveys or interviews) evidence when studying religious experience. However focus has recently shifted towards examining how meaning is created retrospectively through the construction of narratives, how events given a moral ordering according to overarching themes drawn from culture. Since the objective existence of religious experiences are only fleeting, the memory, interpretation, linguistification, recounting, emplotment and narrativization is the “data” which sociologists must study.

Positivist Evidence
Positivist evidence elucidates religious experience in terms of observable, measurable and empirical factors. This approach attempts to uncover generalisable laws of nature and human behaviour that can explain religious experience, often trying to express the inexpressible. Theories formed in the disciplines of psychology and neuroscience which use positivist evidence ultimately rely on hard evidence collected.

Psychology
Psychologists have examined the cognitive structures of individuals who have encountered religious experiences. Subjects who reported religious experiences had higher scores on the Repression-Sensitization scale, which measures one's openness to unusual, reality-threatening aspects of experience. There is a relation between religious experiences and schizotypy, which is also about openness to non-logical aspects of experience. Physiological explanations for this finding is due to the activation of the right brain hemisphere. Intense arousal produced by ecstatic dancing and singing could also lead to religious experiences. Additionally, it was found that individuals were often in a state of stress or despair before their experiences.

Neuroscience
Laboratory studies have concluded that religious experiences are linked to the biological construct of human brains. The brain was studied during significant spiritual experiences, such as observing the grey matter of praying Franciscan nuns and meditating Tibetan monks. Images showed increased activity in the frontal lobes, the attention area, and decreased activity in the posterior superior parietal lobe. This area of the brain primarily orients us in space, helping us to generate an awareness of the physical limits of the self. The decrease in activity in the area could mean that the individual fails to find the borderline between the self and the outside world, and hence such a distinction does not exist. Thus, the brain perceives that the self is endless and intimately interwoven with everyone and everything the mind senses, giving an unquestionably real feeling of spiritual transcendence.

Tensions and conflicts
Different forms of evidence create friction between disciplines. When quantitative fields gather evidence to support their research, this gives rise to the problem of determining the subjective state of the individual being tested. Measuring spiritual states empirically while not disturbing such states is almost impossible. An interpretivist approach would challenge this methodology given the idiosyncratic nature of religious experiences. Many instead believe that the study of religious experiences should primarily focus on individual accounts, rather than biological, psychological or sociological explanations which may diminish their importance.

However, empirical researchers would disagree with the above given that testimonies differ so greatly it becomes difficult to reach a common understanding. The variation in intensity, duration, and multiple influences one's surroundings may have on the experience, will undeniably give rise to unique religious experiences. Coupled with the differences in the way each individual perceives such experiences, recounts of incidents will differ vastly. Primary data gathered in interpretivist studies therefore cannot be generalised or replicated, perhaps lessening its validity. Other weaknesses of interpretivism include the untrustworthiness of those retelling these accounts  and bias on behalf of the researcher, whose cultural background and religious beliefs might skew how the qualitative evidence is gathered, interpreted and presented. Lastly the use of religious language amongst evidence presents an issue. Since positivism restricts assertible reality to the realm of sense experience, religious language such as ‘holy spirit’ is deemed meaningless as it’s not moored in our experience of the physical world.

Synthesising approaches
Positivist and interpretivist evidence addresses fundamentally different questions: one seeks to quantify, summarise and generalise whereas the other offers meaning and perspective. To fully understand the complexity of religious experiences, there should be a synthesis of positivist and interpretivist evidence. Rather than presenting religion as a neurological illusion and product of biology, science can play a supportive role to theology, thereby providing a more comprehensive understanding of religious experiences. This has led to the emergence of neurotheology, which seeks to understand the relationship between brain science and religion. Certain numbers are emphasized in religious traditions, such as 40 in the Bible (40 days and nights of the flood; the Jews wandered the desert for 40 years) and multiples of five in the Quran (Five Pillars of Islam and Ten Ancillaries of the Faith). The quantitative processes of the brain strengthen one's belief in the message associated with the numbers. Hence, it is evident how a more holistic understanding of religious experiences can be achieved by incorporating findings from seemingly contrasting fields.


text:
Annotated Republic of China Laws/Criminal Code of the Armed Forces/Article 75

Promulgated full amendment on 2001-09-28
Effective on 2001-10-02

 * Article 75
 * A person who gambles in the area of military base, vessel, structures or other military places shall be punished with imprisonment for not more than six months, detention, or a fine not more than 50,000 yuan may be imposed unless the item which he gambles is the one to provide temporary entertainment.
 * A commander who harbors a subordinate who gambles or assembles persons to gamble shall be punished with imprisonment for not more than five years.
 * Gambling apparatus at the site found at the gambling table or place for exchange of gambling tokens shall be confiscated whether or not it belongs to the offender.

Promulgated amendment on 2017-04-19

 * Article 75
 * A person who gambles in the area of military base, vessel, structures or other military places shall be punished with imprisonment for not more than six months, detention, or a fine not more than 50,000 yuan may be imposed unless the item which he gambles is the one to provide temporary entertainment.
 * A commander who harbors a subordinate who gambles or assembles persons to gamble shall be punished with imprisonment for not more than five years.
 * Gambling apparatus at the site found at the gambling table or place for exchange of gambling tokens shall be confiscated whether or not it belongs to the offender.

中華民國法律註解/陸海空軍刑法/第75條


text:
Annotated Republic of China Laws/Criminal Code of the Armed Forces/Article 72

Promulgated full amendment on 2001-09-28
Effective on 2001-10-02

 * Article 54
 * A person who fabricates a military rumor with intent and intends of disperse the public shall be punished with imprisonment for not more than three years, detention, or a fine of not more than 300,000 yuan.

Promulgated amendment on 2019-11-20
(awaiting governmental translation)
 * Article 72

中華民國法律註解/陸海空軍刑法/第72條


text:
Wikibooks:Reading room/Administrative Assistance/Archives/2019/October

special:contribs/103.113.15.11
Please block him. Thank you. --Hasley (discuss • contribs) 17:49, 1 October 2019 (UTC)
 * ✅ By Pi zero. JackPotte (discuss • contribs) 20:19, 1 October 2019 (UTC)

Special:Contributions/159.146.10.58
Currently running riot adding spurious text. -- Jules (Mrjulesd) 10:35, 5 October 2019 (UTC)
 * anyone? 大 诺 史 ( Talk/留言/토론/Discussion ) 11:14, 5 October 2019 (UTC)
 * Do help. 大 诺 史 ( Talk/留言/토론/Discussion ) 11:39, 5 October 2019 (UTC)
 * ✅ By MarcoAurelio. JackPotte (discuss • contribs) 12:11, 5 October 2019 (UTC)

 * Delete also the following diffs by this vandal. You missed quite a few there... Thanks

 * https://en.wikibooks.org/w/index.php?title=Getting_Started_as_an_Entrepreneur%2FMarket%2FShow_Me_the_Numbers&type=revision&diff=3583608&oldid=3583562
 * https://en.wikibooks.org/w/index.php?title=Managing_Groups_and_Teams/Process_Losses&diff=prev&oldid=3583560
 * https://en.wikibooks.org/w/index.php?title=Electronics_Handbook/Components/Inductors/Series_Configuration&diff=prev&oldid=3583558
 * https://en.wikibooks.org/w/index.php?title=Cookbook:Shakshouka&diff=prev&oldid=3583556
 * https://en.wikibooks.org/w/index.php?title=X-Plane_Flight_Simulator/Part_Two&diff=prev&oldid=3583554
 * https://en.wikibooks.org/w/index.php?title=False_Friends_of_the_Slavist/Lower_Sorbian-Macedonian&diff=prev&oldid=3583552
 * https://en.wikibooks.org/w/index.php?title=Molecular_Cloning/Extraction_of_DNA&diff=prev&oldid=3583550
 * https://en.wikibooks.org/w/index.php?title=Assistive_Technology_in_Education/Speech_Recognition_Software&diff=prev&oldid=3583548
 * https://en.wikibooks.org/w/index.php?title=Croatian/Main_Contents/Level_2/Lesson_5&diff=prev&oldid=3583547
 * https://en.wikibooks.org/w/index.php?title=Cookbook:Buuz&diff=prev&oldid=3583545
 * https://en.wikibooks.org/w/index.php?title=Pinyin/Afghanistan&diff=prev&oldid=3583543
 * https://en.wikibooks.org/w/index.php?title=Cookbook:Creme_Brulee_Cheesecake&diff=prev&oldid=3583542
 * https://en.wikibooks.org/w/index.php?title=Pinyin/Mumbai_shrine_to_allow_entry_to_women_(2016-10-24)&diff=prev&oldid=3583540
 * https://en.wikibooks.org/w/index.php?title=Cookbook:Indian_Curry_Marinade&diff=prev&oldid=3583538
 * https://en.wikibooks.org/w/index.php?title=Wikijunior:Languages/Inuktitut&diff=prev&oldid=3583536
 * https://en.wikibooks.org/w/index.php?title=More_C%2B%2B_Idioms/Int-To-Type&diff=prev&oldid=3583535
 * https://en.wikibooks.org/w/index.php?title=Order_Theory/Series-parallel_order&diff=prev&oldid=3583533
 * https://en.wikibooks.org/w/index.php?title=BarCamp_-_How_to_Run_Your_Own/Organizing_an_International_Barcamp&diff=prev&oldid=3583532
 * https://en.wikibooks.org/w/index.php?title=BIM_for_Construction_Management&diff=prev&oldid=3583529
 * https://en.wikibooks.org/w/index.php?title=OpenSSH/Cookbook/Kerberos&diff=prev&oldid=3583525
 * https://en.wikibooks.org/w/index.php?title=Exercise_as_it_relates_to_Disease/Exercise_effects_on_Cystic_Fibrosis&diff=prev&oldid=3583523
 * https://en.wikibooks.org/w/index.php?title=Rueda_de_Casino/Centro&diff=prev&oldid=3583522
 * https://en.wikibooks.org/w/index.php?title=International_Postage_Meter_Stamp_Catalog/Madeira_Islands&diff=prev&oldid=3583520
 * https://en.wikibooks.org/w/index.php?title=Celestia/Celx_Scripting/CELX_Lua_Methods/CEL_command_set&diff=prev&oldid=3583518
 * https://en.wikibooks.org/w/index.php?title=Horticulture/Packera_obovata&diff=prev&oldid=3583517
 * https://en.wikibooks.org/w/index.php?title=Radiation_Oncology/NSCLC/PCI&diff=prev&oldid=3583516
 * https://en.wikibooks.org/w/index.php?title=HydroGeoSphere/Array_Dimensioning&diff=prev&oldid=3583515
 * https://en.wikibooks.org/w/index.php?title=Hiking_in_the_Canadian_Rockies/Day_Hikes/Kananaskis/Canmore&diff=prev&oldid=3583514
 * https://en.wikibooks.org/w/index.php?title=Rebol_Programming/next&diff=prev&oldid=3583512
 * https://en.wikibooks.org/w/index.php?title=Fluid_Mechanics_Applications/B14:_Fluid_flow_in_turbine&diff=prev&oldid=3583513
 * https://en.wikibooks.org/w/index.php?title=Chinese_Medicinal_Formulas/Prescriptions_for_Eliminating_Dampness&diff=prev&oldid=3583511
 * https://en.wikibooks.org/w/index.php?title=MIPS_Assembly/Exceptions&diff=prev&oldid=3583509
 * https://en.wikibooks.org/w/index.php?title=GLSL_Programming/Unity/Transparency&diff=prev&oldid=3583504
 * https://en.wikibooks.org/w/index.php?title=The_implementation_of_E-Government/E-Government_by_country/Thailand/Ministry_of_public_health&diff=prev&oldid=3583499
 * https://en.wikibooks.org/w/index.php?title=Cookbook:Barbecue_Sauce&diff=prev&oldid=3583491
 * https://en.wikibooks.org/w/index.php?title=Scribunto:_An_Introduction/Using_invoke&diff=prev&oldid=3583490
 * https://en.wikibooks.org/w/index.php?title=Statistical_Thermodynamics_and_Rate_Theories/Diffusion&diff=prev&oldid=3583489
 * https://en.wikibooks.org/w/index.php?title=Traditional_Chinese_Medicine/Orifice-Opening_Prescriptions&diff=prev&oldid=3583488

Getting tired of the vandal attack by Encik Tekateki (discuss • contribs) 14:55, 5 October 2019 (UTC)

 * Done by, looks like, Mrjulesd, Larsnooden. --Pi zero (discuss • contribs) 15:54, 5 October 2019 (UTC)

special:contribs/165.139.22.113
Please consider to block him. Thank you. --Hasley (discuss • contribs) 17:29, 7 October 2019 (UTC)
 * Done. --Pi zero (discuss • contribs) 17:37, 7 October 2019 (UTC)

Special:Contributions/37.154.168.48

 * Please block this IP (range might be better). 大 诺 史 ( Talk/留言/토론/Discussion ) 15:33, 8 October 2019 (UTC)
 * pinging. 大 诺 史 ( Talk/留言/토론/Discussion ) 15:35, 8 October 2019 (UTC)
 * Blocked and nuked that IP. --Pi zero (discuss • contribs) 15:50, 8 October 2019 (UTC)
 * Seems like you forgot to remove talk page access. 大 诺 史 ( Talk/留言/토론/Discussion ) 15:57, 8 October 2019 (UTC)
 * Done. --Pi zero (discuss • contribs) 16:00, 8 October 2019 (UTC)
 * +1 @ Special:Contributions/37.154.205.110. 大 诺 史 ( Talk/留言/토론/Discussion ) 16:02, 8 October 2019 (UTC)
 * QU took care of it and I later removed talk privs. --Pi zero (discuss • contribs) 17:02, 8 October 2019 (UTC)

Special:Contribs/159.146.14.55
Please, block him. Thank you. --Hasley (discuss • contribs) 22:54, 8 October 2019 (UTC)
 * Short global range block by Bsadowski1. --Pi zero (discuss • contribs) 23:36, 8 October 2019 (UTC)

Special:Contribs/159.146.10.52

 * pinging. Please block. 大 诺 史 ( Talk/留言/토론/Discussion ) 02:15, 9 October 2019 (UTC)
 * Blocked, nuked. --Pi zero (discuss • contribs) 02:18, 9 October 2019 (UTC)
 * Special:Contribs/159.146.18.27. 大 诺 史 ( Talk/留言/토론/Discussion ) 02:23, 9 October 2019 (UTC)
 * Special:Contribs/159.146.18.70. 大 诺 史 ( Talk/留言/토론/Discussion ) 02:25, 9 October 2019 (UTC)
 * Range blocked and nuked. --Pi zero (discuss • contribs) 02:29, 9 October 2019 (UTC)
 * Thanks. 大 诺 史 ( Talk/留言/토론/Discussion ) 02:29, 9 October 2019 (UTC)


text: Category:Book:NVQ Carpentry Level 3

title: Category:Book:NVQ Carpentry Level 3


text:
Issues in Interdisciplinarity 2019-20/Power in Global Development

This piece will discuss the power within the interdisciplinary field of global development, using research into—as an example. The majority of research focuses on .... Often associated with a North–South binary


text:
Issues in Interdisciplinarity 2019-20/Truth in the Formation of Social Groups

Human interaction and development are governed by our social groupings. Commonly, a social group is defined as two or more people who interact with one another, share similar characteristics, and collectively have a sense of unity. Social groups are considered to have followed a gradual evolution from need-based relations like in hunter-gatherer groups to comfort-based relations as in friendships; their existence and progression is undeniable, but how and why do they form?

Disciplines such as geography, biology, psychology and sociology have come up with different explanations for this phenomenon. While some of these are sources of contradiction, others complement one another and help us understand where truth resides in the formation of social groups.

Geography
The physical features of a location affect formation of groups in multiple ways. According to Edward C. Hayes structures like forests and swamps are barriers preventing contact between people, whereas mountain passes and rivers are ideal for intercommunication, and thus for social development: his claim being that ‘isolation tends to stagnation’. Similarly, bigger groups tend to form (and thrive) around areas where food is plentiful. Another influence of geographic location is that it dictates societal norms and cultural practices such as sport and art. Just as siestas are uncommon in cool climates where daylight might be limited, figure skating is not particularly common in tropical climates. Hence it can be seen that geographical factors play an important role in determining how a given social group might appear and behave.

Proximity
It is apparent that human social groupings are largely limited by who can be found in close proximity - that is, we tend to form groups with the people who happen to be around us. The regularity of interaction brought about as a result of being nearer to one another creates a sense of familiarity and comfort, thus leading to the formation of a social group. This idea was documented by Theodore Newcomb and is known as the proximity principle.

Genetic Influences
Humans are biological beings; this fact alone makes biological sciences relevant to social behaviour and, therefore, to our predisposition to commit to social groups - which can be studied from an empirical point of view. Positivist approaches have been used in order to determine the molecular analyses of social interaction, including a broad array of animal models, and the use of Williams syndrome to study the influence of a selected group of genes on our social behaviour. In the first case, experiments in fruit flies concluded that genes influence the social behaviour of an individual through their effects on brain development and physiology, this linkage being sensitive to both genetic and environmental variation, and to their interaction.

In the latter case, scientists used schizophrenia disorders patient-derived stem cell models to investigate the true cellular effects of our genes that influence our social behaviour, and therefore, our need to engage in a social group. This further helped scientists to identify which genes have an impact on our social behaviour and they found that common polymorphisms in the gene GTF2I found in the Williams syndrome deletion area, are associated with reduced anxiety in the general population.

Although biological studies on humans relating to the formation of social groups haven't been conclusive thus far, biology plays an incontestable part in human behaviour and therefore in all its manifestations.

The need to belong
The need to belong is an intrinsic motivation for humans to socially interact and be accepted, also causing phenomena such as social comparison and self-representation as a means to conform to social groups they wish to join. According to psychologist Abraham Maslow’s Hierarchy of Needs, belongingness, along with physiological, security, and esteem needs are characterised as a “deficiency need”, a basic need that must be satisfied to achieve a comfortable life.

The need to form social bonds and groups is part of our instincts and as important as securing necessities such as food, and shelter. The phenomena can be seen in ages as early as infants, who seek to form social bonds without knowledge of their social world. Furthermore, people experience a reluctance to break social bonds even when the relationship is a wholly negative experience. The need to establish social bonds stems from the theory that forming social groups drastically increased survival, whether it be that it provided security in times of danger or it was easier to reproduce.

Solidarity and Social Cohesion
The evolution of social groups and their different types can be studied in sociology, the focus here, however, will be on what maintains these groupings.

According to, traditional societies are relatively homogenous and essentially based on kinship, age and sex. is founded on mechanical solidarity, which relies on resemblance between individuals  and their conformity to traditional social norms, values and roles. Individuals who do not fit the social standards are usually excluded and depicted as the internal enemy, occasionally leading to phenomena such as “witch hunts”. However, as first pointed out by Durkheim and developed by, this form of deviance can actually play an important role in keeping the social order intact. In complex societies, often provokes the replacement of mechanical solidarity to. People become both more specialised and independent. Individual differences are not only tolerated they become crucial to social cohesion.

As a, Durkheim wanted sociology to be considered as the science which studies the objective reality of social facts. However, staying neutral in sociology is challenging, his theories were undeniably influenced by the socio-historical context of his time. Additionally, as mentioned by, methods adopted from physical sciences (in an attempt to be as close to the truth as possible) aren't always able to explain social behaviour and laws. Finally, different truths exist in sociology and theories regarding the formation of social groups, for example  and others have criticised Durkheim for overlooking how aspects of mechanical solidarity persist in modern societies.

Collating truths
Studying the formation of social groups through the perspectives of multiple disciplines, a dizzying array of often contradictory conclusions can be found. These disagreements arise from the individual “truths” of each discipline and their varying positivist and interpretivist views and allow us to observe social groups with a more holistic view.

Geography, psychology, and sociology take on a positivist attitude when regarding their "truths"; however, they tend to draw conclusions from trends in empirical data - ignoring phenomena that deviate from the trend. For example, proximity is a positive factor in social grouping, yet it can also cause social division. These holes in "truths", however, can be filled by looking at the sociological perspective of the human tendency to form social groups with people who share a commonality. On why social groups form at all Biology might answer: because it is coded in our genes to do so, with Psychology reinforcing this with evolutionary perspectives. When looking at each disciplines' conclusions at a glance, they may seem unconnected or even contradictory, but upon deeper inspection, their truths fit together to reveal a bigger whole.