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Caledonian University
Pop-up law clinic launches in city centre to court Glasgow shoppers
Students from Glasgow Caledonian University will provide free and confidential legal advice at the shopping centre for the next 10 weeks
Christina O'NeillDigital Journalist
A pop-up clinic has opened in one of Glasgow's busiest retail centres.
Shoppers can visit the GCU Law Clinic, a student-run initiative which provides free and confidential legal advice, will be based within the Buchanan Galleries for the next 10 weeks.
The students, who are studying law at Glasgow Caledonian University, will offer help to people who do not qualify for legal aid and cannot afford professional advice.
The team will be based in Good, on the first-floor of the centre, which showcases the work of community organisations across the west of Scotland.
Louise Thomson, of GCU Law Clinic, said: "We are delighted to be taking the clinic off campus and expanding our work in the community."
Hospices unite in Glasgow to launch new partnership
The law clinic provides general legal advice, can draft legal letters, represent clients in court and negotiate on behalf of clients in contractual disputes.
The students specialise in housing, employment, and contract law, but can provide assistance with other areas of law excluding criminal, family and immigration law.
All of the advice provided by the team of student volunteers is supervised by a qualified legal practitioner.
Half of older Scots say they aren't getting enough sex
As well as its case work with clients, the clinic has an outreach team which works with local schools, educating children on how the law impacts on their lives and their rights and protections.
Next month, the student-run initiative will compete against some of the biggest names in the legal profession for a prestigious award.
The clinic has been shortlisted in the Community Contribution category at the Scott & Co Legal Awards 2018 alongside Digby Brown Solicitors, Anderson Strathern and the Faculty of Advocates.
GCU Law Clinic will be available at Good every Wednesday, from 11am to 4pm, until the end of April.
Buchanan Street


We are unable to have the board meeting as planned for the first of February.
Many board members had other commitments on most of the other Wednesdays in February, so we now plan to hold the meeting on the last Wednesday, February, 29th, which is also the last day of February. I will send a reminder out the week before that.
Please e-mail back to let me know you received this message ok.


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On the night between January 21 and 22, a fierce thunder-storm struck the area where I live, destroying most of the electronic equipment.
I lost three TV sets, a VCR, a DVD and the charger of my chair lift. These six items had cost me exactly €1,165. The original receipts are still in my possession.
The tower of my computer, which is still under guarantee, was also damaged. Though this guarantee does/did not cover it against such incidents, SCAN, generously decided to repair it for free. For this, I thank them.
Since I was advised to back up all my documents lest they could be lost, I paid €35 to an individual who backed up all my documents.
As I am insured with Argus, the following Monday I visited their branch, a couple of minutes from where I live, and told them what had happened. They asked me to make my claim in writing and I did exactly that.
For several days, I was in contact with them asking and answering questions, giving details, delivering photocopies of all my receipts and also reports submitted by technicians. Then, when these ceremonies were over, I had frequent discussions with one of their representatives.
To cut a long story short, this clerk asked me if I would be satisfied with €400. I need not say what my answer was and so he suggested I go to the head office at Ta' Xbiex and meet some higher official.
When I went there, I was met by a female official who tried to be rather helpful. But it was more or less a repeat performance of what had happened in Mosta. Some 40 minutes of calculations, percentages, questions and answers, ifs and buts, depreciations, prices then and prices now… and a final offer of €500.
I had no option but to accept the offer. I spent these €500 on two TV sets and a new charger for my chairlift. This means I had insured three TV sets, a VCR, a DVD player and a charger without which the €9,000-stairlift would be useless.
Now, thanks to Argus insurance, I have only two TV sets, no VCR and no DVD. Is it worth insuring your property? If other insurance companies are like that, it certainly is not.


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MEK ISIS Terrorist forces are used by Enemies. Iranian officials
BY Tehran Times, 2019/08/30
Tehran Times and Tasnim News, Tehran, August 29 2019:… Rouhani visit took place on Government Week. It commemorates the late President Mohammad Ali Rajaei and Prime Minister Mohammad Javad Bahonar along with other government officials who lost their lives in a terrorist bombing by MKO (also called MEK) in Tehran on August 30, 1981. Larijani also warned against the enemy's plot to employ the terrorist Mojahedin-e Khalq Organization (MKO or MEK) and the Daesh (ISIS) forces against Iran, urging the intelligence forces to keep a close watch on their activities. MEK ISIS Terrorist forces are used by Enemies. Iranian officials
Late Iranian President Mohammad Ali Rajaei (L) and Prime Minister Mohammad Javad Bahonar
1- President says unity will disappoint enemies
Tehran Times August 29 2019:
Link to the source
Rouhani, cabinet team renew loyalty to Imam Khomeini
TEHRAN – Iranian President Hassan Rouhani and his cabinet team on Saturday visited the shrine of Imam Khomeini in south of Tehran to renew their faithfulness to the founder of the Islamic Republic.
The visit took place on Government Week. It commemorates the late President Mohammad Ali Rajaei and Prime Minister Mohammad Javad Bahonar along with other government officials who lost their lives in a terrorist bombing by MKO (also called MEK) in Tehran on August 30, 1981.
Speaking while visiting the mausoleum, Rouhani said unity and resistance will disappoint enemies in the face of economic sanctions.
"Undoubtedly, we will pass this historic turn and make enemies disappointed through unity among the government, the people and all the highly talented ones. It is unity, endeavor and resistance that will make enemies disappointed," he said at the ceremony.
He said, "Enemies' plots will end when they are disappointed with causing harms to the system. Enemy's disappointment is our great objective. People's hope on future is another objective of ours. As long as this hope endures, this path will also be carried on."
Mujahedin e-Khalq MEK From Iraq to Albania
Elsewhere, he said that the government will focus on the people's well-being with all power.
He also told his opponents that they will not be "strengthened" by undermining his government.
"We applied all our power to have good relations with the East and the West, however, an amateur government caused our people many troubles… there is no way but unity and integrity to change the situation," Rouhani stated.
Rouhani said that despite the U.S. "increased pressure targeting the Iranian population over the past year", he believes the country's economic situation is showing signs of improvement.
"A recent bimonthly report along with statistics released yesterday by the Statistical Center of Iran reported that monthly inflation has decreased compared to previous months," Press TV quoted Rouhani as saying.
2- Intelligence Ties with Neighbors A Priority for Iran: Parliament Speaker
Tasnim News August 29 2019
TEHRAN (Tasnim) – Iranian Parliament Speaker Ali Larijani described intelligence cooperation with friendly and neighboring countries as one of the Islamic Republic's priorities.
Addressing a conference in Tehran on Wednesday, Larijani praised the Intelligence Ministry for the professional handling of issues, stressing the need for constant efforts to foil the enemy's plots.
He also called for the expansion of intelligence cooperation with friendly states.
"Friendship and creating connections with the neighbors is one of the priorities for the (Islamic Republic's) establishment," he added.
Larijani also warned against the enemy's plot to employ the terrorist Mojahedin-e Khalq Organization (MKO or MEK) and the Daesh (ISIS) forces against Iran, urging the intelligence forces to keep a close watch on their activities.
Camp Ashraf 3 Albania . MEK , ISIS , Al Qaeda Joint Terrorist Training Camp
In December 2017, in an address to a six-party conference on the threat of terrorism, held in Islamabad, Larijani called on his counterparts from Pakistan, Russia, China, Turkey and Afghanistan to attach more significance to interaction among their intelligence organizations and data sharing.
He had also proposed the idea of formation of a special committee to coordinate counterterrorism efforts and share intelligence data to ensure regional security.
A list of some MEK agents trained by Saddam's Republican Guard now operating in Tirana (In Persian)
ISIS Attacks in Tehran Expose US-Saudi Lies About Iran
Remember.Mojahedin Khalq (MKO, MEK, Rajavi cult) was one of the excuses of US attacking Iraq
BBC: Who are the Iranian dissident group MEK? (Mojahedin Khalq, MKO, PMOI, …)
Maryam Rajavi — MEK Propaganda Queen — Advertises Her Serives For Iran's Enemies
https://iran-interlink.org/wordpress/albania-mek-rebrands-by-assassinating-unwanted-members/
Albania: MEK rebrands by assassinating unwanted members
Massoud and Anne Khodabandeh, Balkans Post, June 22 2018:… The mysterious disappearance of a member of the Mojahedin Khalq (MEK) terrorist group in Albania has once again drawn attention to this controversial group. Malek Sharaee, 47, originally from Khuzestan Province in Iran, was reportedly drowned in the Rrotull village irrigation water reservoir. After three days, divers have not found his body even though the …
Massoud Keshmiri: Killed Iran's PM and President – last seen in Germany after escaping MEK
The mysterious disappearance of a member of the Mojahedin Khalq (MEK) terrorist group in Albania has once again drawn attention to this controversial group. Malek Sharaee, 47, originally from Khuzestan Province in Iran, was reportedly drowned in the Rrotull village irrigation water reservoir. After three days, divers have not found his body even though the water channel is only 3.5 meters deep. However, a MEK representative and three MEK witnesses say his clothes were found at the water's edge. Police are now investigating this as a possible criminal offense. Even so, unless they gain access to Camp Ashraf Three, the MEK's purpose-built training camp in Manez, they are unlikely to unearth the truth – MEK impunity is far greater than this small country can deal with or penetrate.
MEK (aka Saddam's Private Army) was unknown in Albania until they arrived after 2013. Their bizarre behavior and controversial activitiessoon became the focus of media attention.
But the MEK's dark history began long before this. Along with well-publicised military-style terrorist attacks on Iran since the 1980s, the MEK was also trained by Saddam Hussein's Mukhaberat (Secret Services) and later by Israel's MOSSAD, in intelligence gathering and secret operations. As a result, MEK has also conducted many covert terror acts and assassinations over the years. Several of these were deliberately staged to make it look like Iran was involved. Such as the 1994 bombing of the Jewish community center in Buenos Aires. In spite of extensive investigation, the primary evidence linking Iran came from four high ranking intelligence officers from MEK. In 2011, a man connected to Mexican drug dealers was arrested for the attempted murder of the Saudi Ambassadorto America. The US quickly accused Iran, but after two weeks the perpetrator was linked to MEK. In 2013, Israel arrested a Swedish Iranian man, Ali Mansouri, who 'confessed' to be spying for Iran in Tel Aviv. He turned out to be a MEK member.
The underlying pattern behind these events is of deception and callous, cynical murder. These examples are not unique. MEK has a long history of highly sophisticated and brutal undercover activity. However, the reported death of Malek Sharaee in Albania this week also points to a new phase in MEK covert activity. This time individual MEK members who were previously involved in known acts of violence are now themselves becoming victims of their own organization.
Internal assassinations are not new – Commander Ali Zarkesh was deliberately killedduring a military operation in 1988 because he had become critical of the leadership. There have been hundreds of reports of suspicious deaths and actual murders over the last three decades committed against critics and rivals.
In 2013, former MEK member Massoud Dalili was identified as the 53rd victim of a massacre at Camp Ashraf in Iraq. MEK only acknowledged his death when the Iraqi authorities formally identified him via his DNA. Dalili's body had been deliberately disfigured (his face and hands burned) to hide his identity. Massoud Dalili had been one of the personal security personnel for leader Massoud Rajavi. He had undergone training with Saddam's Republican Guards and the MEK's own specialist training. Before coming to Iraq, Dalili had headed a small MEK team in Gilan Province where he was responsible for scores of deaths, including civilians.
Massoud Dalili: Wanted for terrorism in Gilan Province – killed in Camp Ashraf, Iraq
Another victim killed during the same attack was Zohreh Ghaemi, She had commanded the assassination of General Sayad Shirazi in 1999. Of the other victims that day, at least ten are known to have participated in known acts of violence for MEK. No one claimed responsibility for the attack on Camp Ashraf.
Zohreh Ghaemi: Responsible for the assassination of General Shirazi – shot in Camp Ashraf, Iraq
In 2015, in the Netherlands, Mohamad Reza Kolahi was killed by a criminal gang on the order of MEK. Investigators confirmed that Kolahi was responsible for the 1981 bombing of the headquarters of the Islamic Republic Party in Tehran in which 72 high-ranking politicians and party members were killed.
Mohammad Reza Kolahi: Bombed Party headquarters in Iran – shot by gang members in the Netherlands while in hiding from MEK
Another MEK member, Massoud Keshmiri, responsible for the bombing which killed PM Bahonar and President Rajai in 1981, was last seen with MEK in Germany some years ago. He has since vanished and could be dead. Although these deaths cannot be said to be directly linked, there is a common thread whose purpose becomes clear when we remember 2016 when Prince Turki al-Faisal, former Saudi Intelligence chief, announced the death of MEK leader Massoud Rajavi. It is clear from this that MEK is being purged from top to bottom of all the individuals who have had involvement or are associated with its violent past – rebranding by assassination to make the group legally acceptable.
MEK has become the pattern for how other defeated terrorist groupings can be reinvigorated, rebranded and reused. The controversy following the Bulgarian PM's announcement that "Albania will become a coordination center for fighters returning from ISIS to the Balkans" may die down soon – Prime Minister Rama later dismissed this as fantasy, but he is not convincing.Instead, the history of the MEK in Albania indicates that the same scenario will be repeated. As ISIS fighters arrive and settle, the process will start just as it did with MEK. Expect more mysterious murders, suicides, disappearances, bizarre interventions to prevent investigations. And, of course, the involvement of Albanian citizens, politicians, and personalities for and against. Plus, not only abandoning any hope of joining the EU, but suffering more restrictions on the borders with the EU and Balkan countries.
Hossein Abrishamchi: Responsible for torture and destroying bodies, known as 'the Engineering Project'
in MEK – died in suspicious circumstances during a mortar attack on Camp Liberty, Iraq
Javad Ghadiri: Wanted for terrorism, including a failed assassination attempt against
Ayatollah Khamenei – whereabouts unknown, possibly in Camp Ashraf Three, Albania
MASSOUD AND ANNE KHODABANDEH
Massoud Khodabandeh is the Director of Middle East Strategy Consultants and has worked long-term with the authorities in Iraq to bring about a peaceful solution to the impasse at Camp Liberty and help rescue other victims of the Mojahedin-e Khalq cult. Khodabandeh co-authored the book 'The Life of Camp Ashraf – Victims of Many Masters' with his wife Anne Singleton.
Mujahedeen Khalq MEK in Washington
BY Iran Interlink, 2019/06/21
Reid Champlin Open secrets, June 21 2019:… Recent documents in accordance with the Foreign Agents Registration Act reveal that the council, the political arm of opposition group Mujahedeen Khalq or MEK, has been hosting opulent events at the National Press Club and elsewhere, publicizing itself through national and international media, and meeting with dozens of current and former government officials, all with the end goal of toppling the… MEK ISIS Terrorist forces are used by Enemies
US-Backed MEK Used for Manipulation
BY Nejat NGO, 2019/06/21
Nejat Society, June 21 2019:… Twitter has turned out to be a field for a "troll farm" of a thousand people inside the camps of Mujahedin Khalq (the MKO/ MEK/ PMOI/ Rajavi's Cult) who work in three shifts to launch anti Iran disinformation advocating war and sanctions against Iran. Former member of the group Hassan Heirani who left it in… MEK ISIS Terrorist forces are used by Enemies
Maryam Rajavi MEK Cult Fake Journalists
BY Global Research, 2019/06/21
Philip Giraldi, Global Research, June 21 2019:… MEK, which is financially supported by Saudi Arabia, stages events in the United States in Europe where it generously pays politicians like John Bolton, Rudy Giuliani and Elaine Chao to make fifteen-minute speeches praising the organization and everything it does. Maryam Rajavi MEK Cult Fake Journalists MEK Impunity Undermining Democracy Maryam Rajavi MEK… MEK ISIS Terrorist forces are used by Enemies
Edi Rama Maryam Rajavi sue Journalists?
Gjergji Thanasi, Gazeta Impakt, Tirana, Albania, June 18 2019:… Former terrorist Maryam Rajavi, leader of the MEK organization of the so-called Iranian Mojahedin of Camp Manez, has filed a lawsuit against German Der Spiegel journalist, Louisa Hommerich, for her article titled 'Prisoners of Their Own Rebellion' published by Der Spiegel on February 16, 2019. Among other things, the MEK organization… MEK ISIS Terrorist forces are used by Enemies
MEK B Team and the Rogue Gallery
BY The Iranain, 2019/06/18
M. K. Bhadrakumar, Iranian.com, June 18 2019:… In this rogues' gallery, apart from Israel, there is also Saudi Arabia and the UAE. Bolton, of course, is mentored by Israel and it is an established fact that he has received money for services rendered from the Mojahedin-e Khalq MEK, the anti-Iran terrorist group based in France, which espouses the overthrow of… MEK ISIS Terrorist forces are used by Enemies
MEK Impunity Undermining Democracy
BY Lobelog, 2019/06/18
Massoud Khodabandeh, Lobe Log, June 18 2019:… This is the tip of the iceberg. MEK interference in the internal affairs of America goes well beyond online attacks on Iran. In 2016, the Organization of Iranian American Communities in the US—a front for the MEK—announced a "General Elections Mobilization Effort," publicly urging its members to "fulfill their civic duty through active… MEK ISIS Terrorist forces are used by Enemies
Iran : Disinformation and Fake MEK Writers
BY Aljazeera, 2019/06/17
Aljazeera News, June 17 2019:… As the Trump administration continues with its hawkish talk on Iran, we need to look at how that story is being crafted and by whom: Heshmat Alavi was once cited by the White House as a credible commentator on Iran. Shame he doesn't exist. It turns out he is a fictional persona reportedly created by the Mujahedin-e-Khalq (MEK),… MEK ISIS Terrorist forces are used by Enemies
We lie We cheat We use MEK terrorists
BY Consortium News, 2019/06/16
Caitlin Johnstone, Consortum News, June 16 2019:… This would be the same John Bolton who has been paid exorbitant speaking fees by the pro-regime change MEK terror cult, promising the cult in a 2017 speech that they'd be celebrating regime change in Tehran together before 2019. This would also be the same John Bolton who once threatened to murder an… MEK ISIS Terrorist forces are used by Enemies
MEK Iran Disinfo and Heshmat Alavi
BY Russia Today, 2019/06/14
Helen Buyniski, Russia Today, June 14 2019:… Heshmat Alavi, a virulently anti-Iranian columnist promoted by the Trump administration and published in Forbes, the Hill, and several other outlets, was unmasked as a propaganda construct operated by the Mojahedin-e Khalq (MEK), a controversial Iranian exile group often called a cult that has only recently lobbied its way off the US' terror… MEK ISIS Terrorist forces are used by Enemies
MEK Fake Newsman is a Jihadi Front
The Young Turks, June 13 2019:… "Heshmat Alavi" is a front propaganda efforts of the People's Jihadis (Mojahedin-e Khalq or MEK). The MEK has committed numerous acts of terrorism in Iran and then collaborated with Iraqi dictator Saddam Hussein. It was listed as a terrorist organization the the US until a few years ago and it is mysterious why it… MEK ISIS Terrorist forces are used by Enemies
" Heshmat Alavi Gate " , Trump and MEK
BY The Independent, 2019/06/11
Negar Mortazavi and Borzou Daragahi , Independent, June 11 2019:…The "Heshmat Alavi" persona had a strong presence on Twitter and harassed Iranian journalists, academics, and activists who are critical of the Mujahedin-e-Khalq organisation, a one-time armed guerilla group now holed up in Albania. There is no known link between the Iran Disinfo programme and the fake persona. At least one… MEK ISIS Terrorist forces are used by Enemies
White House MEK Trolls and the Iran Case
BY The Washington Post, 2019/06/11
Jason Rezaian, Washinton Post, June 11 2019:… After the report, Twitter appears to have suspended the account. But the MEK, the organization that "Team Heshmat Alavi" represents, has a nasty history. It was on the State Department's list of foreign terrorist organizations for years before being removed in 2012. These days, it has no discernible popular support in Iran and… MEK ISIS Terrorist forces are used by Enemies
MEK Cult Operatives Undermining American Democracy
BY The Intercept, 2019/06/10
Murtaza Hussain, The Intercept, June 10 2019:… Alavi's articles also mixed criticisms of Iran and U.S. policy with overt advocacy for the MEK. His pieces in the Daily Caller, The Hill, and other outlets — though less numerous than his contributions to Forbes — employed a similar mix of advocacy against the Iranian regime and praise for the MEK. Though… MEK ISIS Terrorist forces are used by Enemies
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Mujahedin-e-Khalq After Trump
BY Press TV, 2021/01/20
Reza Alghurabi, Press TV, January 20 2021:... The MKO's worrisome track record has not deterred prominent Americans from endorsing the organization. In the months preceding the State Department's decision to delist the MKO, dozens of well-known advocates—primarily but not exclusively conservatives—lobbied on behalf of the group. Vocal supporters included former CIA directors R. James Woolsey Jr. and Porter Goss, former…
Biden Dilemma : Iranians Saw Trump As A Mad Man…
BY Massoud Khodabandeh, 2021/01/18
Anne and Massoud Khodabandeh, Iran Interlink, January 18 2021:... President Joe Biden will only have months to make a difference if he wants to pursue a diplomatic route. He must demonstrate through policies and actions that Trump was a hiccup, not the way things will be. Trump was not America. If Biden wants to start talking with Iran he must…
Maryam Rajavi Rudy Giuliani – Big Tech And Censorship
BY Olsi Jazexhi, 2021/01/17
Dr. Olsi Jazexhi and Gerjgji Thanasi, YouTube, January 17 2021:... The discussion of MEK's attack against freedom of media and democracy in Albania is compared with the censorship that Big Tech is doing against Rudy Giuliani and Trump in the United States. Olsi appeals to Rudy Giuliani to teach his good jihadi friend Maryam Rajavi to learn what democracy and…
Albanians shocked at MEK , Maryam Rajavi disregard for their…
Iran Interlink, January 15 2021:... As the number of deaths in the MEK camp at Manez in Albania due to COVID-19 continues to rise, the MEK have appropriated a section of the local cemetery to bury their dead. This week, residents of the town protested that a piece of land which had already been designated for expansion of the cemetery…
Iran Not Trusting USA For Good Reasons
Tehran Times, January 13 2021:... Iran cannot possibly be expected to trust the United States again after the unilateral withdrawal of the Trump administration from the JCPOA P5+1 nuclear deal, the subsequent American imposition of wartime economic sanctions on Iran, and obvious American involvement in and support for the [Gen.] Soleimani and Fakhrizadeh assassinations. I believe the more recent Fakhrizadeh…
Rejecting Regime Change For Good
BY The American Conservative, 2021/01/13
Daniel Larison, The American Conservative, January 13 20201:... the U.S. government doesn't understand the countries in question, it relies on bad information that is frequently offered to them by self-serving exiles and activists, and it doesn't know how to do state-and-institution-building on such a large scale in any case. The U.S. has expended vast resources for decades on some of…
The Mujahedin-e Khalq Organization (MKO) Intrusion in Albania, Threatens Regional…
Robert Fantina, Global research, January 11 2021:... It took at least three years, from 2013 – 2016, for the Iraqi government and political-social activists to oust the Mujahedin from Iraq. Few countries were willing to shelter the Mujahedin. However, with the support of the United States, they relocated to Albania. This was an interesting development, because the MKO is a…
Who is a mercenary ? MEK or critics ?
Mazda Parsi, Nejat Society, January 10 2021:... Two of the people who have recently labeled by the MEK propaganda as Iranian mercenaries are Dr. Olsi Jazexhi, the Albanian Canadian historian who is interested in the cases of families of the MEK members in the group's camp in Albania and Gjergji Thanasi, a journalist who pursues the case of MEK's illegal…
Mohammad Aq Atabay Complaint to the UN Committee on Enforced…
Nejat Society, January 09 2020:... Mr. Mohammad Aq Atabai, whose brother Hamid Mohammad Aq Atabai is being held in the Mojahedin-e Khalq (MEK) camp in Albania, has lodged a complaint against the Albanian government to the UN Committee on Enforced Disappearances under the UN Convention against the Protection of All Persons against Forced disappearance. While explaining his brother's story, he…
Maryam Rajavi Quarantines Herself To Avoid COVID-19 – Iran Interlink…
Iran Interlink, January 09 2021:... Coronavirus has infected the whole of Camp Ashraf 3 - everyone has it. Instead of using quarantine to control the spread, Maryam Rajavi and a few of her inner circle quarantined themselves to keep safe. She and the few members who don't have the virus are quarantined and have limited contact with a few safe…
With Trump gone , is the MEK…
Aawa Association (Mardom TV), November 21 2020:... With Trump gone…
MEK Problem For Albania Not Iran
Anne Khodabandeh (Singleton), The Iranian, October 20 2020:... From deep…
Lucrative MEK Fake Accounts Interfering In American…
Paul Brian and Arthur Bloom, The American Conservative, (First Published…
EU Denies Rajavi Entry (Originally published Aug…
Iran Interlink, August 28 2020:... Now Rajavi is trying to…
Rajavi House of Cards Destroyed By Ehsan…
Iran Interlink, August 16 2020:.. Olsi Jazexhi, a prominent Albanian…
Albania Accession TO EU – Open Letter…
Anne Khodabandeh, Iranian.com, July 17 2020:... MEK leader Maryam Rajavi…
Devil Lives In Tirana . Mujahedin-e Khalq…
Martin Franke, Frankfurter Allgemeine, June 10 2020:... In the camp…
Massoud Rajavi Biography
Terror Spring, May 20 2020:... Massoud Rajavi travelled to Jordan…
MEK Families Petition Addressing Albanian PM ,…
Anne Khodabandeh, Iranian.com, May 12 2020:... A petition addressed to Albania's…
MEK cult in Albania poses public health…
Massoud Khodabandeh, Responsible Statecraft (First Published April 25 2020) :...…
Pompeo and MEK isolated by response to…
Anne and Massoud Khodabandeh, Balkans Post, March 29 2020:... But,…
Mojahedin-e Khalq MEK Defectors Talk About The…
Murteza Hussain and Matthew Cole, The Intercept, March 22 2020:...…
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redpajama_set_name: RedPajamaCommonCrawl


text:
Health is Wealth, But Where is the Minimum Wealth to Create Health?
Public Health is the casualty again, with the promised health centres little more than an illusion
By DR. ADITHYAN G.S.
|6 Feb 2018 12:21 PM GMT
CHENNAI: The novel idea of revamping the primary healthcare scenario in India by transforming the sub-centre's into Health & Wellness Centres (HWC) is in the public health domain for quite some time. The concept was first reflected in the draft National Health Policy 2015 and later incorporated in the Cabinet approved National Health policy, 2017.
Union Finance Minister's budget speech to scale the numbers up to 150,000 Health and Wellness centres has brought the issue into focus.. This was one single model which got support from the public health professionals, general public and even the critics.
The recent tale of tragedies happening in the health sector- be it the Gorakhpur tragedy or the Max- Fortis scams underlines the criticality of the situation in India's primary healthcare. The only silver bullet solution for India's ailing health sector is strengthening public health through a comprehensive primary healthcare approach.
The whole concept of HWC received accolades against this backdrop. The proposed services provided under the HWCs move from selective to comprehensive primary health care and covers preventive, promotive, curative, rehabilitative and palliative care for a package of services related to RMNCH+A, communicable diseases, non- communicable diseases, ophthalmology, ENT, dental care, mental health, geriatric care, treatment for acute simple medical conditions and emergency and trauma services.
This is in contrast to the existing primary healthcare scenario in the country which is limited to selective interventions of immunization, maternal & child healthcare and few communicable diseases – which account for less than 15 % of the package.
The recently published Global Burden of Disease report "India: Health of the Nation's States - The India State-Level Disease Burden Initiative" shows clearly that seven of the top 10 causes of death among all age-groups in India are non-communicable diseases or injuries. Hence, the concept of HWC which moves beyond the usual MCH paradigm in India can be seen as a very good example of data-driven decision making in healthcare which is an unusual event in India.
However, several questions have now been raised after it was known that the budgetary allocation for the proposed 1.5 lakh HWC's was only Rs 1200 crores. This comes to a meagre sum of Rs 80,000 per Sub-Centre to be transformed into a HWC.
The Ministry of Health & Family Welfare has calculated that the amount needed for this transformation requires approximately 17.5 lakhs per centre including the training and HR cost. If we go by that calculation, only 6857 sub-centers in the country can be transformed to HWCs in the current year - less than 5 % of the total HWCs as promised by the Government.
Also, if the central government is determined to strengthen the primary health care system, the referral chain also needs to be strengthened- i.e. simultaneous strengthening of the higher centres of healthcare delivery including PHCs, CHCs and district hospitals.
The few successful models in this regard like the Jan Swashtya Sahyog's (JSS) program in rural and tribal areas of Bilaspur district in Chhattisgarh state is worth mentioning in this context. Other concerns relate to capacity building for the implementation of a mammoth scheme of this dimension. Remember the speech of the President in NIMHANS recently where he lamented the lack of qualified medical professionals and health workers in the country. So the lack of funds, lack of qualified manpower to attend to a vast population of ailing human beings and the absence of a well-managed health care system are worrisome aspects which cannot be swept under the carpet.
Everyone knows that the healthcare scenario cannot be tackled with a magic wand in the form of a budget proposal coming at the fag end of an outgoing government. But with a meagre increase of 2.5 % in the health budget in 2018-19 compared to the revised estimates in 2017-18, the governments aspirations in improving healthcare scenario is too far from reality. This hasn't come as a shocker nor a surprise as it basically reflects the government's commitment to health of the nation
The concept of HWCs has the potential to decentralize, democratize and communitize healthcare- which is indeed the need of the hour. Despite all the challenges and issues, HWC was seen as the window of opportunity for addressing this growing burden of chronic illness in a rapidly aging population in a resource poor setting such as India.
The conceptualization of HWC was seen by many as India's first right step in pursuit of achieving Universal Health Care (UHC). The rise in prices of shares of private hospital operators and insurance companies after the budget announcement clearly shows what the Government is up to in its pursuit of achieving UHC in India.
(Dr. Adithyan G.S is with the National Health Mission- Tamil Nadu. The opinions are strictly personal and do not reflect those of the organization/institution where he works)
DR. ADITHYAN G.S.


MATLAB provides the necessary functionality for basic video processing tasks using a limited number of video formats. Not very long ago, the only video formats supported by built-in MATLAB functions was only the AVI, through functions such as aviread, avifile, movie2avi, & aviinfo. Moreover, the support was OS dependent & limited only to a few video codecs. Then with newer versions of MATLAB, new library functions like mmreader, was introduced to extend video support of video formats such as AVI, MPEG, and WMV (But it was true for only Windows Platform).
MATLAB represent the monochrome & colored video by creating the manipulative, 3 dimensional or 4 dimensional matrix, but provided that the video sequences are short, i.e., only few minutes of it.
When a frame needs to be processed individually, it can be easily converted into an image using the "frame2im" function, which can then be processed using any of the functions available in the Image Processing Toolbox of MATLAB.
• aviread: to read an AVI video file & store the frames into a MATLAB video structure, which may me actually 3D or 4D matrices, containing information about the monochrome or colored frames.
• mmreader: constructs a multimedia [here mm stands for multimedia] object that can read video data from a variety of multimedia file formats.
1. Convert frame to an image using frame2im.
2. Process the image using any function as required by the image processing task.
3. Then Convert the result back into a frame using im2frame function.
• implay: a fully functional built in image & video player with many options.
• movie2avi: creates an AVI file, from MATLAB Frame Sequence.


text:
\section{\label{sec:introduction}Introduction}

Reductionism is one of the classic principles of science. At the same time, science itself forms a diverse tree with elements at different spatiotemporal scales, such as quantum waves in physics, molecules in chemistry, cells in biology, all the way up to macroeconomics and sociology. Macroscale descriptions like biophysical models of cells, the machine code in computers, or organisms operating within a food web, are generally treated as if they reflect some intrinsic scale of function that cannot be neatly improved by reduction. The result is a contradiction between the theory and practice of science \citep{fodor1974special, hoel2018agent}.

One resolution to this contradiction is the "null hypothesis" of reductionism: that all macroscale descriptions, which broadly are some form of dimension reduction such as coarse-graining, are only useful due to computational constraints. This is because, according to this hypothesis, their underlying microscales contain all the information. That is, \textit{information compression} is the only true benefit to analyzing, modeling, or understanding a system at a macroscopic level. Compression of a given information source can be lossless or lossy, but can never lead to an overall gain of information \citep{cover2012elements}. Without any gain of information at a macroscale some have argued that macroscales cannot add anything above or beyond their underlying microscales and therefore should be considered epiphenomenal \citep{kim1998mind, bontly2002supervenience}.

An alternative resolution to the contradiction between universal reductionism and science as practiced is a formal theory of emergence. Such a formal theory should a) directly and fairly compare microscales to macroscales, b) offer a quantitative measurement of what a macroscale is providing in terms of information gain above and beyond compression, and c) enable the means to identify emergent scales in a given system or data set. A non-trivial formal theory of emergence should allow for reduction or emergence on a case-by-case basis. Such a theory of emergence can solve longstanding problems in model choice for scientists, since it reveals the intrinsic scale of function of systems.

The first contemporary formal theory of emergence took the form of "causal emergence" \citep{hoel2013quantifying}. It made use of the effective information ($EI$), which is the mutual information between a set of interventions by an experimenter and their effects. More specifically, the effective information is the mutual information following an experimenter intervening to set a system or part of a system to maximum entropy. Since the interventions cannot have any common causes, any correlations of future system states to that injected noise must be caused by that noise. Furthermore, the effective information has been shown mathematically to capture the causally-relevant information in a system by being sensitive to the determinism (lack of uncertainty in state transitions) and degeneracy (similar or identical state transitions or dynamics, e.g., the necessity of a given state transition). Dimension reductions like coarse-graining (grouping elements of states in macro-elements or macro-states) or black-boxing (leaving elements or states exogenous) can increase the effective information \citep{hoel2017map}. Overall, the argument was that since macro-states were more deterministic and less degenerate, they constrained the past/future of the system to a greater degree, and the effective information was able to identify the spatiotemporal scale at which this constraint peaked. This peak indicated that this scale, whether macro or micro, was the most causally-relevant scale.

One possible criticism of the theory is that the effective information is only one specific measure of causation. Originally proposed by Giulio Tononi and Olaf Sporns in 2003 \citep{tononi2003measuring}, effective information is mathematically well-described \citep{balduzzi2008integrated, balduzzi2011information, hoel2013quantifying}. While it keeps being re-invented as a measure of causation \citep{korb2009information, griffiths2015measuring}, generally without acknowledgement of previous formulations or ongoing lines of research, the measure has not yet been proven to be the unique measure of causation, and there are alternative proposals (many of which are mathematically related) for how to measure causation using information theory \citep{albantakis2019caused}. Indeed, the same general phenomena of causal emergence has been shown in integrated information \citep{hoel2016can, marshall2018black} as the $\phi$ measure can increase at macroscales due to similar reasons of increasing determinism and decreasing degeneracy of state transitions. Measures that are not directly causal but capture aspects of causation, like assessing the entropy of random walkers on networks (indicating uncertainty of transitions), can also improve at macroscales in that random walkers are more deterministic in their dynamics \citep{klein2020emergence}. This all indicates there is a broader phenomenon at work. Specifically, there is somehow more causally-relevant information at macroscales (although it is currently unclear if this is captured by a unique measure of causation, or is better captured by a set of common measurements). How is such information gain possible?

Information cannot be created \textit{ex nihilo}. Here we propose that emergence is a form of \textit{information conversion} at a higher scales. When measured in its totality in a given system, total information measures like the entropy of the distribution system states, the Kolmogorov information for describing the entirety of the system, or the total correlation in the form of the mutual information, all necessarily decrease at a macroscale (or at best, do not decrease, but can never increase). However, information can be converted from one type to another, with no change except for what scale the system is being modeled at, meaning there can be a gain of specific types of information at macroscales.

Herein we demonstrate this phenomenon of information conversation making use of the classic and well-understood measure of mutual information. Specifically, we consider the mutual information between the past and future of Boolean networks \citep{kauffman1969homeostasis}. While total mutual information always decreases or remains constant at a macroscale (no information \textit{ex nihilo}), this is not the full story, since the information itself can be decomposed into a set of partial information ``atoms" (PI atoms) that quantify how the total information is distributed over all of the elements of the system \citep{williams_nonnegative_2010}. Herein we show that, after coarse-graining, redundant information at the microscale can be converted into synergistic information at macroscales in an overall movement of information up the PI lattice, and that this effect exists even when no mutual information is lost. This indicates that the ``structure" of the mutual information is truly being converted from redundant to synergistic at macroscales.

In Section \ref{sec:MI_PID}, we overview the how we are applying the mutual information in our model system, and also its decomposition in multi-element systems. We introduce a novel measure of redundancy/synergy bias in the mutual information, based on how PI-atoms are distributed across the PI-lattice. In Section \ref{sec:macroscales},
we examine systems of Boolean networks across scales and accompanying changes in the partial information decomposition. First we look at common macroscales of logic gates and find a clear shift toward synergies in some systems (e.g., we show how an XOR is more synergistic than its underlying microscale logic gates, none of which are XORs). Next, we examine sets of Boolean networks wherein the mutual information is identical at both micro and macroscales, and show that even in these cases there can be an increase in synergy bias at the macroscale. This shows direct evidence of information conversion at macroscales in that the mutual information becomes more dominated by its synergistic component without changing its total bit value, just its decomposition. In Section \ref{sec:CE_as_information_conversion}, we connect our work to previous research by demonstrating how causal emergence can be thought of as a form of information conversion, as the total entropy of transitions is converted to the causally-relevant form of effective information via dimension reduction. Overall, we conclude that information conversion offers an agnostic umbrella explanation for theories of emergence based in information theory.

\section{\label{sec:MI_PID}Mutual information and its decomposition in discrete systems}

Here we detail our application of the mutual information to probabilistic Boolean networks. Systems can be viewed as passing information from the past to the future over the channel of the present. The quantification of this information flow can be done by calculating the mutual information between the future and past joint states of the variables that make up a network. Specifically, $X$ represents the past states of the Boolean network, while $Y$ represents the future states of the network. The calculation of $I(X,Y)$ quantifies how much knowledge of the past state of the system reduces our uncertainty about the future state of the system. Specifically:

\begin{equation}
\label{eq:mi}
    I(X ; Y) = \sum_{x\in\mathcal{X}}\sum_{y\in\mathcal{Y}} P(x, y) \log_2\big(\frac{P(x,y)}{P(x)P(y)}\big)
\end{equation}

This calculation requires defining the distributions $P(X)$, $P(Y)$, and $P(X,Y)$. The joint distribution is given by the transition probability matrix (TPM) of the system (with each row weighted by the probability of that state), and $P(X)$ is an ``input" distribution. To not bias our measurements, $P(X)$ is the stationary distribution of the system (in cases of multiple stationary distributions we use the one with the largest attractor, or design Boolean networks such that all states are included in a single attractor). The ``output" distribution ($P(Y)$) can then be calculated as the matrix-multiplication of $P(X)$ and $P(X,Y)$. Note that every ``state" in the support sets $\mathcal{X}$ and $\mathcal{Y}$ actually represents the joint state of multiple variables in the underlying Boolean network.

This application of the mutual information captures the total amount of information in the dynamics of the system (the calculation of which requires a the system's stationary dynamics $P(X)$). For example, in networks where the stationary distribution contains only a single state in the form of a point attractor the mutual information is zero, since the system is like a source that sends only a single message over a channel: there is no uncertainty about the future to be reduced by knowledge of the past. In networks where each state is visited equally in the stationary distribution, and each state deterministically transitions to a unique state, the mutual information would be maximized as $log_2(n)$, as every "message" the system sends is as informative as possible.

\subsection{Partial information decomposition}
A core limitation of mutual information when assessing systems with more than two variables is that it gives little direct insight into \textit{how} information is distributed over sets of multiple interacting variables. Consider the classic case of two elements $X_1$ and $X_2$ that regulate a third variable $Y$: it is easy to determine the information shared between either $X_i$ and $Y$ as $I(X_i ; Y)$, and it is possible calculate the joint mutual information $I(X_1, X_2 ; Y)$, however these measures leave it ambiguous what information is associated with which combination of variables. For example, if $X_1 \not{\bot} X_2$, then there is necessarily some information about $Y$ that is redundantly shared between both $X_1$ and $X_2$. Similarly, it is possible that there is \textit{synergistic} information about $Y$ that is only disclosed by the joint states of $X_1$ and $X_2$ together and not retrievable from either variable independently (for example if all elements are binary and $Y=X_1 \oplus X_2$, then $I(X_1 ; Y) = I(X_2 ; Y) = 0 \text{ bit}$ but $I(X_1, X_2 ; Y) = 1 \text{ bit}$).

To address this issue, the Partial Information Decomposition (PID) framework was introduced \citep{williams_nonnegative_2010}. It provides a method by which the mutual information between the joint state of multiple sources variables and a single target variable can itself be decomposed. For the example case detailed above, with two ``source" variables ($X_1$ and $X_2$) and a single ``target" variable $Y$, the full partial information decomposition breaks $I(X_1, X_2 ; Y)$ down into the following additive combination of ``partial information atoms":

\begin{equation}
\label{eq_pid1}
    I(X_1, X_2 ; Y) = Red(X_1, X_2 ; Y) + Unq(X_1 ; Y | X_2) + Unq(X_2 ; Y | X_1) + Syn(X_1, X_2 ; Y).
\end{equation}

Where $Red(X_1, X_2 ; Y)$ is the information about $Y$ that is \textit{redundantly shared} between $X_1$ and $X_2$ (i.e. an observer could learn the same information about $Y$ examining either $X_1$ or $X_2$), $Unq(X_1 ; Y | X_2)$ refers to the information about $Y$ that is uniquely present in $X_1$ and not in $X_2$, and $Syn(X_1, X_2 ; Y)$ is the information about $Y$ that is \textit{only} disclosed by the joint states of $X_1$ and $X_2$ considered together. Furthermore, the bivariate mutual informations can also be decomposed:

\begin{equation}
\label{eq_pid2}
    I(X_1 ; Y) = Red(X_1, X_2 ; Y) + Unq(X_1 ; Y | X_2)
\end{equation}
\begin{equation}
\label{eq_pid3}
    I(X_2 ; Y) = Red(X_1, X_2 ; Y) + Unq(X_2 ; Y | X_1)
\end{equation}

The result is that Eqs. \ref{eq_pid1}, \ref{eq_pid2}, and \ref{eq_pid3} form an under-determined system of three equations with four unknowns ($Red$, $Unq_1$, $Unq_2$, $Syn$). Given an appropriate function with which to compute any of these three, the rest are trivial.

As the number of sources informing about a single target grows, the number of combinations of sources that must be considered grows super-exponentially. The seminal contribution of Williams and Beer was to realize that it is not necessary to brute-force search every combination in the power-set of sources, but rather, that meaningful combinations of sources are naturally structured into a partially-ordered lattice, known as the partial-information (PI) lattice. Furthermore, for a particular set of  sources $\alpha$, the value of the associated partial information atom $\Pi_\alpha$ can be calculated recursively as the difference between the information redundantly shared across the sources of interest, and the sum of all PI-atoms lower down on the lattice:

\begin{equation}
\label{eq:mobius}
\Pi(\alpha, Y) = Red(\alpha, Y) - \sum_{\beta \prec \alpha}\Pi(\beta, Y)
\end{equation}

Where $Red(\alpha, Y)$ is the \textit{redundancy function}, which quantifies the information about $Y$ that is redundantly present in every element of $\alpha$.
For readers interested in the deeper mathematical details of the construction of the PI lattice, we refer to \citep{williams_nonnegative_2010}, and more recently \citep{gutknecht_bits_2020} for a more in-depth discussion. For our purposes, it suffices to understand that there exists a partial ordering of PI atoms, with ``more redundant" atoms towards the bottom. For example, in the case of three sources, the bottom of the PI-lattice is given by $\{0\}\{1\}\{2\}$, which refers to the information about the target that is redundantly present in all three sources. At the top of the lattice is $\{012\}$, which gives the information about the target that is only accessible when considering the joint state of all three sources jointly, and not disclosed by any ``simpler" combination of sources. It is important to note that, for systems with more than two sources, the PI-atoms no-longer break down into neatly intuitive categories of ``redundant", ``unique", and ``synergistic": more exotic combinations of sources appear, for example: $\{0\}\{12\}$, which gives the information about the target that is redundantly present in $X_0$ and the joint state of $X_1$ and $X_2$ together. However, in general the lower down on the PI-lattice a PI-atom is, the more redundant the information is, while the higher on the lattice, the more synergistic.

While the PID framework provides the scaffold on which information can be decomposed, it fails to provide the specific keystone necessary to actually calculate it: the redundancy function that forms the base of the PI-lattice. Williams and Beer proposed the \textit{specific information} as a plausible redundancy function typically denoted as $I_{WB}$:

\begin{equation}
    I_{WB}(\textbf{X} ; Y) = \sum_{y\in\mathcal{Y}}P(y)\min_{X_i \in \textbf{X}}I(X_i ; y)
\end{equation}

The specific information quantifies the average minimal amount every element of \textbf{X} discloses about \textit{Y}. The term $\min_{X_i \in \textbf{X}}I(X_i ; y)$ calculates the minimal amount of information any $X_i \in \textbf{X}$ provides about the specific state $Y=y$. Across all $y \in \mathcal{Y}$, $I_{WB}$ quantifies the expected minimum amount of information that \textbf{X} will disclose about $Y$. As a redundancy function, $I_{WB}$ has a number of appealing quantities: in contrast to other redundancy functions, it will only return values greater than or equal to zero bit. Given the perennial difficulties of interpreting negatively-valued information quantities, this is a key property, one not shared by most other redundancy functions. Furthermore, $I_{WB}$ is both conceptually and computationally simple: being based on the specific information, it is a ``pure" information theoretic measure and does not require leveraging theory or algorithms from fields like information geometry \citep{harder_bivariate_2013}, game theory \citep{ay_information_2019}, or decision theory \citep{bertschinger_quantifying_2014,kolchinsky_novel_2019} and is one of the fastest-running functions in the \textit{dit} toolbox. $I_{WB}$ is also arguably the most well-used redundancy function in the scientific literature, having been the function of choice in \citep{timme_synergy_2014,timme_high-degree_2016,faber_computation_2018,sherrill_correlated_2020,tax_partial_2017}.

Williams and Beer's redundancy function has been critiqued for behaving in an ``unintuitive" manner in some cases \citep{griffith_quantifying_2014}, and does not readily localize the way that the mutual information function does \citep{finn_pointwise_2018}. Since PID was initially proposed, considerable work has gone into developing an ``optimal" redundancy function, resulting in a plethora of measures \citep{bertschinger_shared_2013,harder_bivariate_2013,griffith_intersection_2014,griffith_quantifying_2014,olbrich_information_2015,goodwell_temporal_2017,ince_measuring_2017,finn_pointwise_2018,james_unique_2018}. So far, no single measure has emerged as the accepted ``gold standard", although they share many commonalities. Each function comes with it's own limitations (for example, only being defined for two variables, or occasionally returning negative quantities of partial information, or violating some intuitions about how such a measure ``should" behave), so care is necessary when deciding which one to use.

In this work, we used the original measure put forth by Williams and Beer, as it was necessary that whatever measure we chose never return negative quantities of information, be applicable to systems with more than two sources, and this measure remains widely used and the most studied. We used the Discrete Information Theory package \citep{james_dit_2018} for the implementation of $I_{WB}$ and all PID calculations.

\subsection{PID of temporal mutual information}

Partial information decomposition is usually applied to situations like those given in the example above, where a set of sources (neurons, perceptrons, etc) synapse onto a single target and is often applied in such cases \citep{tax_partial_2017,faber_computation_2018}. Here we detail our application of the partial information decomposition of the mutual information between the past and the future in Boolean networks.

Consider a Markovian system with two interacting elements that is evolving in time. Following the convention given above, we will say that $\textbf{X}=\{X_1, X_2\}$ indicates the past states of both elements of our system, and $\textbf{Y}=\{Y_1,Y_2\}$ indicates the future states of both elements of our system. We can then adapt the classic PID framework by defining our ``sources" as every $X_i \in \textbf{X}$, and our single target as the joint future state \textbf{Y}. The PID of this two-element dynamical system is then given by:

\begin{equation}
I(\textbf{X},\textbf{Y}) = Red(X_1, X_2 ; \textbf{Y}) + Unq(X_1 ; \textbf{Y} | X_2) + Unq(X_2 ; \textbf{Y} | X_1) + Syn(X_1, X_2 ; \textbf{Y})
\end{equation}

This decomposition details how information about the next joint-state of the system is distributed  \citep{mediano_beyond_2019}.

\subsection{\label{sec:synergy_bias}Introducing synergy and redundancy biases}

\begin{figure}
    \centering
    \includegraphics[scale=0.75]{figures/fig_1.png}
    \caption{\textbf{The partial information spectrum.} The PI lattices for two, three-element systems. For each system, from the PI lattice we can create a PI spectrum, which gives the proportion of the total mutual information present in all PI atoms at a given ``height" on the lattice. \textbf{Left}: this system has a low synergy-bias (high redundancy bias): the majority of the mutual information about the joint-future state is redundantly shared across all the elements (\{0\}\{1\}\{2\}), or other highly-redundant PI atoms (e.g. \{0\}\{1\}). \textbf{Right}: this 3-element system has a high degree of synergy bias, with the majority of the information about the joint future present only in the joint state of all three elements (\{012\}).
    }
    \label{fig:pi_spectra}
\end{figure}

In our two toy examples above, we relied heavily on the categorical distinction between redundant, unique, and synergistic information. These classifications are useful for building intuition, but do not readily generalize to systems with more than two elements. To address this, we introduce the construct of a \textit{partial information spectrum}, from which one can calculate the relative top- or bottom-heaviness of a PI lattice without directly having to define well-delineated ``pools" of redundant, unique, or synergistic information.

Recall that the value of a given PI atom is calculated recursively from the sums of every PI atom lower than it down on the lattice; PI atoms higher on the lattice contain information that is increasingly synergistic and cannot be extracted from any simpler combination of sources. Because the PI lattice is partially ordered, there are collections of PI atoms that are at the same ``height" on the lattice relative to the bottom (the maximum redundancy atom) or the top (the maximum synergy atom). We claim that these atoms comprise a ``layer" of the lattice defined by some ratio of redundancy to synergy. The PI spectrum $S$ is then defined as an ordered list where the $i^{th}$ bin in the spectrum is given by the proportion of total mutual information present in all PI-atoms in the $i^{th}$ layer.

Once the PI spectrum ($S$) has been calculated, it is easy to determine how top-heavy it is using a measure similar to the Earth Mover's Distance. We define the synergy bias ($B_{syn}(S)$) as the amount of normalized partial information in each layer, weighted by the number of steps that layer is from the bottom.
\begin{equation}
    B_{syn}(S) = \sum_{i=0}^{|S|} \frac{i}{|S|}S_i
\end{equation}

Where $i$ indexes the layer (starting from bottom, maximally redundant layer) and $|S|$ gives the total number of layers in the lattice. By normalizing by the total number of layers, we can compare the synergy bias between two different sized lattices, since we are looking at the proportion of the total lattice height moved, rather than counting the actual number of layers.

The redundancy bias is defined equivalently, although the reference point is the top of the lattice, rather than the bottom:

\begin{equation}
    B_{red}(S) = 1 - \sum_{i=0}^{|S|} \frac{i}{|S|}S_i
\end{equation}

Conveniently, $B_{syn} + B_{red} = 1$, so we only ever have to calculate one.

The synergy and redundancy biases allow us to compare how top- and bottom-heavy two different PI lattices are: a high synergy bias indicates that most of the partial information is present in synergistic relationships between elements, while a high redundancy bias indicates that most of the partial information is redundantly present across multiple individual elements. Since it is a normalized measure, we can compare the top- and bottom-heaviness of systems with different numbers of elements (and consequently, different sized lattices) and thus can measure synergy/redundancy bias across scales.

\section{\label{sec:macroscales}Evidence of information conversion across scales}

\subsection{\label{sec:logic_gates}Macroscales can increase the synergy bias of information}

We begin with a well-known type of macroscale as our model system: that of a single logic gate, which itself is some dimension reduction of a larger collection of networked gates. By breaking down three basic logic gates with distinct mechanisms (AND, OR, and XOR) into collections of microscale gates with simpler mechanisms, we can directly and fairly compare the microscales and macroscales in terms of their respective distribution of partial information. This provides a first showcase of information conversion across scales.

Note that here we are technically leaving elements exogenous in time in our macroscale (since the microscale networks require multiple timesteps to run), and all mechanisms have been coarse-grained into single mechanism (again, broadly these are all referred to as forms of dimension reduction). Such example systems of logic gates have no stationary dynamics, since they are not closed, but are open systems. Therefore, to calculate the mutual information, in all cases (both micro and macro) the same input distribution to either the macroscale mechanism's inputs or the inputs to the network of microscale logic gates that underlies them. For solely explanatory purposes we make use of the maximum entropy as our input distribution in all cases, and this means that $P(X)$ is identical for both macro and microscales in our comparisons.

By calculating the mutual information of the macro and microscales with the same inputs, and decomposing the result using PID, we can see that the synergy bias of the system is not constant between scales. Consider the XOR gate: which can be decomposed into a network of one NAND gate, one AND gate and one OR gate (as well as two inputs A and B). The resulting system has five elements compared to the macroscale, which has 3 elements (XOR, A and B). We found that, at the micro-cale, the system had a mutual information of 2.5 bit, while it had the expected 1 bit of mutual information at the macro scale. However, while the total mutual information decreased for the XOR gate macroscale, the synergy of that information increased, from 0.52 at the microscale to 0.83 at the macroscale. The same was true for both the OR and the AND gates, although to a lesser degree (final values can be found in Table \ref{tab:logic}). Note that while the AND and OR gates have the same macroscale mutual information and synergy bias (since they are isomorphic), they have different microscale values, which reflects the different number and structure of NAND gates required to implement them.

This suggests that, while dimension reduction reduces the overall amount of information in a system, the "leftover" information can move higher on the macroscale PID lattice. This can be seen directly in Fig. \ref{fig:logic_gates}, which shows the PI spectra based off of the PI lattices for three macroscale mechanisms and their underlying microscales of networked logic gates.

When considering our logic gate result, it is clear that dimension reductions like coarse-grainings can alter the distribution of information in the PI lattice of a system, even when both scales are simply a different description of the same system. Note that this result fits intuitively with the idea that something is being gained by modeling an XOR gate as an actual XOR gate, even if it is made of a set of underlying logic gates that, like NAND gates, are not themselves XORs. What is gained is a distinctive bias toward synergy in the information flowing through the system. Additionally, it is intuitive that XORs should greatly demonstrate this effect, like ANDs and ORs demonstrate it to only a slight degree. While not shown, it should be obvious that there can be reverse cases; for example, some macroscales may be more redundant than their underlying microscales, since there is no limit to how complex a microscale can be.

\begin{figure}
    \centering
    \includegraphics[scale=0.75]{figures/and_pid_fig.png}
    \includegraphics[scale=0.75]{figures/or_pid_fig.png}
    \includegraphics[scale=0.75]{figures/xor_pid_fig.png}
    \caption{\textbf{Partial-Information Spectra for Logic Gates.} Partial information spectra for three logic gates: AND (\textbf{top}), OR (\textbf{middle}), XOR (\textbf{bottom}) at the macroscale (\textbf{right}) and the microscale (\textbf{left}). The synergy biases and temporal mutual information values can be viewed in Table \ref{tab:logic}.}
    \label{fig:logic_gates}
\end{figure}

\begin{table}[]
\begin{tabular}{@{}lllll@{}}
\toprule
\textbf{Gate} & \textbf{Microscale MI} & \textbf{Macroscale MI} & \textbf{Microscale Syn. Bias} & \textbf{Macroscale Syn. Bias} \\ \midrule
AND           & 1.623 bit               & 0.811 bit               & 0.533                             & 0.578                             \\
OR            & 2.811 bit               & 0.811 bit               & 0.518                             & 0.578                             \\
XOR           & 2.5 bit                 & 1 bit                   & 0.52                              & 0.833                             \\ \bottomrule
\end{tabular}
\label{tab:logic}
\caption{\textbf{The macro and microscale temporal mutual informations and their respective synergy biases.} For three logic gates (AND, OR, and XOR), this table shows the effects that going up a level of abstraction has on the temporal mutual informations and the synergy biases. It's important to understand that while the micro and macroscale implement the same function, the temporal mutual information can be very different.}
\end{table}

\begin{figure}
    \centering
    \includegraphics[scale=0.7]{figures/fig_2.png}
    \caption{\textbf{Multi-scale analysis}. Here we can see how to construct equivalence class microscales from a given macroscale such that mutual information is fixed. \textbf{Left}: a three-element system (top), and its associated TPM. We select a single node ($A$) to expand. \textbf{Middle}: expanding node $A$ into nodes $\alpha$ and $\beta$ results in a four-element system, which crucially preserves the mutual information from the joint-past to the joint-future. We can select another node ($\alpha$) to expand again, resulting in \textbf{Right}: the final microscale expansion of our system. Note that the original node $A$ has been expanded twice, while the overall mutual information dynamics are preserved in all cases.}
    \label{fig:multi_scale}
\end{figure}

\subsection{\label{sec:modeling_across_scales}Redundant to synergistic information conversion}

In the cases of classic logic gate composition above in Section \ref{sec:logic_gates}, it could be argued by a skeptic that while the synergy biases are indeed increasing at the macroscale, this is because all the information at the bottom of the lattice is being removed by dimension reduction. This may be true in some instances. Luckily, we can provide direct evidence the effect we've observed is not just that some types of information (like redundant information at the bottom of the lattice) is lost at macroscales. Rather, there is evidence that information is being converted from one type to another (or more precisely, information is moving up the PI spectrum from redundant to more synergistic at the macroscale).

To show cases of information conversion, we developed a method by which the mutual information can be kept constant across scales. Since the mutual information in terms of total bits is not decreasing at the macroscale, any change in synergy bias must be from the conversion of information, not its loss.

First, it is important to note that a neglected aspect of fairly comparing micro and macroscales is making sure that the macroscales are viable models of the system. It is therefore critical that the dynamics between a macroscale model and a microscale model are either identical (as in our cases), or highly similar. That is, dimension reduction shouldn't lead to significant differences in dynamics, nor to responses to interventions, or else the macroscale is a poor model of the system. This has been called "consistency" and has been explored in structural equation modeling specifically of equivalence classes \citep{rubenstein2017causal}. Given that precise consistently is not always possible, it is possible to measure the amount of inconsistency as the difference between the dynamics of the microscale and that of the macroscale, and an informational measure of inconsistency was therefore introduced that can analyze how consistent a wide variety of systems are \citep{klein2020emergence}. It is worth noting that in this work, because of the use of equivalence classes in our method of expansion, in addition to the mutual information being constant, all scales used here also have zero inconsistency according to this measure. Such perfectly consistent macroscales do not necessarily need to be constructed via equivalence classes in order to ensure consistency, as we do here, since there are various types of macroscales that can give complete consistency \citep{klein2020emergence}.

What follows is the description of our method to hold the mutual information constant and ensure consistency between scales. This "expansion method" introduced here is based on generating a Boolean network (represented by some TPM). Assuming this Boolean network is a macroscale, we can then bifurcate nodes in the network in such a way to create an equivalence class: that is, \textit{n} elements that have the same function in that they have the same inputs and the same outputs. Effectively, this allows for the generation of microscales from a given macroscale, an inversion of the normal process of finding macroscales from a given microscale \citep{hoel2013quantifying}. Relevant Python code can be found in the supplementary materials attached to this manuscript. This process allows us to create different arbitrary microscales for a given system, while the mutual information remains fixed between the two scales. Therefore any change in bias on the PI spectrum comes from the conversion of information from one type to another.

To illustrate this, we constructed two hundred TPMs that were positive-Gaussian by initially generating $8\times8$ random Gaussian matrices from a distribution with a mean of 0 and a standard deviation of 1, taking the absolute value of every entry, and finally normalizing the rows to define discrete probability distributions. The resulting TPMs describe the stochastic dynamics of 200 distinct, fully connected three element systems with binary states. These are our starting macroscales. For each of these 200 systems, we split one element to create a 4-element system, two of which are from our initial macroscale, and two of which are ``children" of the initial split macroscale element. We then re-calculate the PI spectrum and synergy bias for our new microscale.  We can do this process of creating children in an equivalence class more than once: if so, we call the 4-node network a "mesoscale" and the 5-node network a "microscale." An example system is shown in Fig. \ref{fig:multi_scale}, as well as details of the entire process as it is expanded into a mesoscale and then microscale (non-macroscale nodes in Fig. \ref{fig:multi_scale} are referred to as $\alpha$ and $\beta$ in this process).

First, all of the 200 Gaussian systems showed an increased bias toward synergy at the macroscale, despite the mutual information being unchanged across scales. In general, our hypothesis was that the higher the synergy bias of the macroscale, the more that synergy would be lost at the microscale. This appears to be true in these model systems; in Fig. \ref{fig:change_syn}, we correlated the gain in synergy bias following the conversion of the microscale to the macroscale, against the macroscale synergy bias. Pearson's product moment correlation found a highly significant positive correlation between macroscale synergy bias and the gain in synergy bias under repeated coarse grainings of the microscales (see Fig. \ref{fig:change_syn}, Left, $\rho = 0.819$ $p<10^{-10}$). This suggests that, for random stochastic systems, even when total mutual information is constant across scales, the systems have more redundant information at the microscale than the macroscale. This is proof that dimensionality reductions exist that increase the overall synergy of the system by converting information to be more synergistic.

In addition to the Gaussian matrices, we also constructed 185 ``deterministic" systems, where a single joint state lead predictably to another single joint state with probability 0.99 (the remaining probability was evenly distributed to ensure the system was ergodic, did not have fixed point attractor, and that the stationary distribution involved all system states), which we expanded in the same manner as described above into both ``meso" and microscales. By exploring both highly stochastic and deterministic systems, we can generate a richer sample of the space of all three-element Boolean systems and identify more universal patterns.

We found similar results with the deterministic systems, although several clear differences are immediately apparent upon inspection (see Fig \ref{fig:change_syn}, Right). First, it's clear that deterministic systems of the kind we are creating have a lower synergy bias overall than using the Gaussian method of creating systems. In cases where the macroscales are indeed synergistic, like they are in Gaussian systems (above 0.5 in synergy bias), then it is also the case that the underlying microscales are more redundant. However, in deterministic macroscales that are biased toward redundancy (below 0.5 in synergy bias) they can actually be expanded into microscales that are themselves more synergy biased (and the correlation between the change in synergy and the macroscale synergy remained positive).

\begin{figure}
    \centering
    \includegraphics[scale=0.75]{figures/fig_3.png}
    \caption{\textbf{Change in synergy bias across scales}. \textbf{Right:} the change in synergy from microscale to macroscale plotted against the starting macroscale synergy for Gaussian systems. There is a clear positive correlation. Indeed, all systems show an increase in synergy at the macroscale and an increase in redundancy at the microscale. The equivalence class structure used to construct the systems holds the mutual information steady across scales, so an increase in synergistic information at the macroscale can only come from a decrease in the redundant information of the microscale. \textbf{Left}: the same plot, although here we display both the Gaussian and deterministic systems. Note that, in contrast to the Gaussian systems, the deterministic systems start at a much lower synergy bias. Typically these lower synergy systems actually \textit{lose} synergy bias after coarse-graining, although the linear relationship between how synergistic the macroscale is and how much synergy is lost at the microscale remains. Interestingly, visual examination suggests that for both classes of system, the relationship between the macroscale synergy bias and the change in synergy bias is generally linear and, for both systems, lies along a common line of best fit. This suggests that, while different systems behave differently under coarse-graining, the broader relationship is preserved.}
    \label{fig:change_syn}
\end{figure}

\section{\label{sec:CE_as_information_conversion}Causal emergence as information conversion}

Given the evidence that information conversion can occur at macroscales, even in a well-understood baseline measure like the mutual information. How does it play out in measures like the effective information, which was specifically designed to capture causal influence and can peak at a macroscale?

A hint comes from the already well-established fact that the effective information ($EI$) changes across scales due to a change in determinism and/or degeneracy \citep{hoel2013quantifying, hoel2017map,klein2020emergence}. Indeed, it is already proven that:

\begin{equation}
\label{eq:mi}
    EI(X ; Y) = \sum_{x\in\mathcal{X}}\sum_{y\in\mathcal{Y}} P(x, y) \log_2\big(\frac{P(x,y)}{P(x)P(y)}\big)|P(X) = H^{max}
\end{equation}

which differs from the normal mutual information calculation in that $P(X)$ is set to $H^{max}$, and can be rewritten as
\begin{equation}
\label{eq:ei}
    \text{Effective information} = \text{determinism} - \text{degeneracy}.
\end{equation}

In this interpretation of the effective information, the determinism is based on the information lost via uncertainty in state transitions:

\begin{equation}
    \text{determinism} = \log_2(N) - \langle H(p(y)|P(X)=H^{max}) \rangle
\end{equation}

The term $\log_2(N)$ can be understood as the uncertainty about the future state of a maximally entropic system with $N$ unique states. The second term $\langle H(p(y)|P(X)=H^{max}) \rangle $ gives the average uncertainty about the future state of our real system $X$ (note that this is applied over a single timestep, e.g., $t$ to $t_{+1}$). The average uncertainty is a function of the noise, wherein $\langle H(p(y)|P(X)=H^{max}) \rangle $ is zero if there is no noise in any transition (and the system is therefore deterministic).

The difference between the two terms (the hypothetical maximum entropy and the empirical entropy) gives us a measure of how much how much better are we at predicting the future of $X$ than we would be in the ``worst case scenario." If effective information is increasing at a macroscale due to an increase in the determinism term, then the entropy term in the determinism must itself be necessarily decreasing. This is because $log2(N)$ also necessarily decreases at the macroscale, so therefore any increases in the determinism term must come from a greater decrease in $\langle H(p(y)|P(X)=H^{max}) \rangle $ than $log2(N)$. Figure 4 (left) shows this decrease in the information (the uncertainty of transitions), which can lead to an increase in effective information at a macroscale.

The degeneracy contains a similar entropy term and a size term:

\begin{equation}
    \text{degeneracy} = \log_2(N) - H(\langle p(y)|P(X)=H^{max} \rangle)
\end{equation}

The degeneracy term is very similar structurally to the determinism term: once again the $\log_2(N)$ represents the maximally entropic ``reference" system. The term $H\langle (p(y)|P(X)=H^{max})\rangle$ quantifies the uncertainty in retrodiction of a previous state, given a current state. That is, degeneracy is the amount of information lost (in a single timestep) if the system is "reversed" in time.

The degeneracy can be thought of as the amount of information about the past that is lost when multiple causal paths ``run together." For instance, we can imagine a system where two unique states both lead to the same subsequent state. In this case, information about the past is lost because it is impossible to determine which of the two prior states preceded the current one from just the current state alone. It can also be thought of as a quantification of how different each state's transitions are. If in a system every state has a unique transition, then the degeneracy $H\langle( p(y)|P(X)=H^{max} \rangle)$ term is zero, and degeneracy maximal.

The case of degeneracy is more complicated, since here the entropy term can increase at macroscales. However, as in determinism, the $\log_2(N)$ term always decreases. And it is actually the decreasing of $\log_2(N)$ that can lead to a decrease in degeneracy at the macroscale. E.g., if two states both deterministically transition to a single state, then a grouping over those (or an equivalent drop out of one) leads to an decrease in in degeneracy since $\log_2(N)$ decreases. This leads to an increase in $EI$ because the degeneracy term itself is subtracted in the $EI$ equation.

Notably, it is well-known that the effective information cannot increase at a macroscale if determinism is maximal and degeneracy is minimal. Why? Because there is no information to convert, neither in terms of the size of the system $\log_2(N)$ nor in terms of the uncertainty of transitions $\langle H(p(y)|P(X)=H^{max}) \rangle $. Therefore, causal emergence can be conceived as the conversion of causally-irrelevant information (like the uncertainty of state transitions) to causally-relevant information (the effective information), fitting into the umbrella theory of emergence as information conversion.

\section{\label{sec:discussion}Discussion}

We have offered forth an umbrella theory of emergence based on how changes in scale lead to the conversion of one type of information to another. That is, dimension reduction does not necessarily leave all types of information invariant. We claim that the best way to consider these questions is to examine how changes in modeling, such as dimension reduction, change information type. While some information measures, like the total correlation between past and future measured by the mutual information, can only stay constant or decrease at macroscales, such measures can still demonstrate information conversion (such as here from redundant to synergistic information). We have shown this effect in Boolean networks of logic gates, such as comparing a macroscale XOR to its underlying logic gates, none of which are XORs at the microscale. We have also shown it in cases of equivalence classes where the mutual information is held constant across scales and yet still information can become more biased toward synergy at the macroscale, proving information conversion.

Further future work may be examining things like at what scale synergistic information peaks, or how to find scales that maximally convert information while minimally losing information. Though we have shown evidence that some redundant information must become synergistic (or vice versa), it remains to be understood exactly \textit{which} information changes form. Recent work on decomposing the local mutual information into directed local entropies may provide an interesting path forward \citep{finn_probability_2018}. Another promising future direction of research would be to introduce local information analysis to this pipeline \citep{lizier_local_2013,finn_pointwise_2018}.

More broadly, this umbrella theory of emergence reveals that there are measurable benefits to macroscale models. If so, this is likely to have been selected for in science, i.e., members of the special sciences can be viewed as converting redundant information into a more useful form. Indeed, it has even been shown that synergistic information processing can be key to certain games, like to a successful poker strategy \citep{frey2018synergistic}. Our hypothesis is that the special sciences, and macroscale models in general, involve the conversion of redundant information into synergistic and unique information, making such macroscales useful for experimenters above and beyond their degree of compression.

Tying this to previous research, macroscale modeling can also convert causally-irrelevant information to causally-relevant information by making causal relationships between variables in a model more dependent (by increasing determinism or decreasing degeneracy), i.e., causal emergence.
Note that there are clear advantages to identifying scales at which variables are more dependent. For instance, it has been shown that biological networks show more causal emergence than comparable technological or social networks \citep{klein2020emergence}. This is probably because there are multiple advantages for macroscales, ranging from a lower entropy of random walkers to greater global efficiency at macroscales \citep{griebenow2019finding}. Some preliminary research examining the protein interactomes of over 1,000 species shows that macroscales have become more likely to demonstrate causal emergence over evolutionary time \citep{hoel2020evolution}. This may even be one reason that controlling biological systems is so difficult: they are cryptic by having an intrinsic functional scale be a difficult-to-discover macroscale, making biological networks more robust to failure and less likely to be controlled from the outside \citep{hoel2020emergence}.

The sort of above analyses are just a fraction of the applications of a formal theory of emergence, which is ultimately a toolkit for identifying the intrinsic scale of function of complex systems. This issue of identifying intrinsic scale crops up all across the sciences, such as modeling gene regulatory networks in biology \citep{hoel2020emergence}, understanding whether the brain functions at the scale of neurons or minicolumns \citep{buxhoeveden2002minicolumn, yuste2015neuron}, answering what level of abstraction is appropriate for modeling and comparing deep neural networks \citep{cao2021explanatory}, or even examining the best scale to model cardiac systems at \citep{ashikaga2018causal}. Previous research has shown how intrinsic scale comes about via growth rules, e.g., networks that develop causally-emergent macroscales only do so once the network is no longer growing in a "scale-free" manner \citep{klein2020emergence}. Ultimately, by tracking information conversion, experimenters and modelers can close in on the intrinsic scale of function for a given system.

\subsection{\label{sec:comparison_to_others}Comparison to other theories of emergence}

To help the nascent field of formal theories of emergence, it is important to discuss exactly what type of emergence we mean here, and compare and contrast to other definitions. For example, there is no doubt that simple laws and interactions in systems can lead to the emergence of complex, interesting, or unexpected dynamics, such as in cases of symmetry breaking \citep{anderson1972more} or simple rule iteration \citep{wolfram2002new}. Sometimes this is referred to as "emergence." However, this phenomena of complexity emerging from simplicity is not conceptually mysterious, and is already well-understood mathematically.

Another use of the term "emergence" comes from thinking about joint effects, which is a "whole vs. parts" emergence. Ultimately, this is motivated by the fact that elements in a system can exhibit behavior, dynamics, or functions that would not take place if they were partitioned or isolated from the rest of the system. One such measure that captures how much information is lost by partitioning individual elements off from a given system is Integrated Information Theory \citep{tononi2008consciousness, oizumi2014phenomenology}. A similar proposition is the ``integrated information decomposition" ($\Phi$ID) \citep{mediano_beyond_2019,rosas_reconciling_2020}. As with the work described here, the $\Phi$ID framework actually takes as its starting point the decomposition of the mutual information between past and future. However, the $\Phi$ID framework constructs a double PI lattice that describes how information moves from one PI-atom to another through time. In this framework, the authors define ``emergent" information as information that remains synergistically present across the joint state of many elements through time. They also refer to information in the joint state of the whole that constrains the evolution of a particular element as ``downward causation." However, as in IIT both of these are just joint information flow over sets of elements at a single scale, and there is an absence of any kind of macroscale vs. microscale comparison. It's worth noting that sets of elements with joint effects may be good candidates for macroscales. However, the mere fact that joint sets of elements behave differently compared to isolated elements in terms of effects or information flow does not by itself capture what is lost by reduction to some lower scale of explanation.

The "macro vs. micro emergence" kind discussed here resembles that which is traditionally discussed in analytic philosophy, which involves issues of supervenience, multiple-realizablity, and causality \citep{humphreys2016emergence}. There it is sometimes called "synchronic" emergence \citep{humphreys2008synchronic}, although we eschew this term as confusing for scientific usage. However, as we have shown, a mathematical theory of emergence is not put forward to resolve metaphysical problems, but is part of modeler and experimenter toolkits when it comes to identifying intrinsic scale of function, as well as modeling practices. This process involves explicit modeling of different scales of model or physical systems, followed by their comparison. While related to philosophical discussions of emergence, note that our proposal of emergence as information conversion does not fit cleanly into the traditional strong/weak emergence dichotomies in philosophy \citep{chalmers2006strong}. Supervenience is not violated when information is converted from one type to another (such as redundant mutual information becoming synergistic, uncertainty of transitions being transformed into effective information, etc). In this view, reduction is always possible when supervenience holds, and therefore there is always an identifiable procedure to map one scale to another. However, such reduction can lead to a real and measurable loss of a given type of information. This offers a subtle but powerful explanation as to what advantages macroscale models (such as the existence of the special sciences), provide above and beyond compression.

\section{\label{sec:acknowledgments}Acknowledgments}

This work was supported and made possible by the USA Army Research Office (proposal 77111-PH-II) and the NSF-NRT grant 1735095, Interdisciplinary Training in Complex Networks and Systems at Indiana University Bloomington.

\bibliographystyle{plainnat}

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Q: Unable to load curl library on WampServer I really cant seem to find a solution to the cURL library not being loaded on PHP via WampServer. Am in windows 8.1 (64 bit operating system) with WampServer Apache 2.4.17, PHP 5.6.16 and mySql 5.7.9. I have tried many fixes, such as: un-commenting the ini files both in the apache and php folders. I see that php_curl is ticked when i click on the wampserver icon. However, my WampServer can't even start on its own. The light never turns green. I have to start it via the task manager, and it starts cause I get the homepage and am able to run some simple scripts on server. So, I know that it is working. Also, on the ini file there are two extension_dir: extension_dir = "C:/php/ext"
extension_dir ="C:/wamp64/bin/php/php5.6.16/ext/"
I've checked that the php_curl.dll file is placed on C:/wamp64/bin/php/php5.6.16/ext/. I've even moved it to C:/Windows and the apache folder but no success after restarting WampServer and checking the error logs.
I just might have to re-install WampServer one more time and take it from there.
Any advice would be helpful.
thank you

A: After placing the file in he extensions folder, you have to enable it in the php.ini file for the effect to take place
Change your php.ini :
extension_dir = "./extensions/"

And uncomment php_curl.dll in your php.ini files
extension=php_curl.dll

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