Today I want to look back (think or talk about something from my past, my personal history). Back in the mid-1970s, at the age of 11 or 12, I worked as a paperboy for the evening newspaper in St. Paul, the St. Paul Dispatch. A paperboy (or papergirl, although they were mostly young boys) is a child who sells newspapers on the street or (more commonly in the U.S.) delivers the newspaper to people's houses. It was very common for newspapers back in my youth (younger days) to have paperboys all throughout the city delivering newspapers. The newspapers would be dropped off (left, transported to) on a street corner early in the morning (5:00 AM) and during the mid-afternoon (4:00 PM), and the paperboy had to walk to the corner, pick up his 40 or 50 newspapers, put them in a small bag, and walk from house to house delivering the newspapers.
In the Twin Cities where I grew up (Minneapolis and St. Paul, Minnesota), each city had two daily newspapers – one in the morning, and one in the afternoon. Before the age of cable and satellite television, and long before the Internet, the only way you could get your news was watching one of the three local television stations or reading a newspaper. My parents received the daily paper, and I would always begin my day reading the paper as I ate breakfast (cold cereal, usually). I remember watching my father read the paper every morning and every evening. Like most children growing up, I wanted to act like the adults in my world, so I, too, read the newspaper every day. In fact, when I got my paper route, I spent part of the money I earned (made) on a subscription to the Minneapolis Star.
Then, sometime in the 1980s, when cable television became more and more popular, people started reading the newspapers less, so many morning and evening papers combined into one, single morning paper. The St. Paul Dispatch became the St. Paul Pioneer Press-Dispatch; the Minneapolis Star became the Minneapolis Star-Tribune, and so forth. All across America, newspapers were closing or combining.
Then came the Internet. Now, everyone could get almost all the news they wanted online – for free! So the number of subscribers to the newspapers began to decline even more. Combined with the current economic recession, many newspapers simply cannot survive. Several newspapers have closed in the past few months, some of them 150 or more years old. Some are moving to the Internet, such as this Seattle paper.
Many people welcome this change. They like getting their news whenever they want and the freedom to read any newspaper in the world that is online. But of course, newspapers need money to pay reporters who report the news. With few subscribers and Internet advertising that does not pay them enough, some newspapers – many newspapers? – will simply disappear in the next few years because they don't have the money to continue. This is especially true of smaller, local papers.
Although I do read news stories online, I still prefer to read an actual, physical paper. Every morning, just as I did when I was 11 years old, I get up and collect (pick up) my morning newspapers from my front steps, and sit down with a bowl of cold cereal to read the newspapers. I love the physical paper in my hands, and I will miss it when it's gone.
As to me, I prefer news by internet.
Tow words into The article ,I was certain the article is your work without checking the down bellow. Man , I am telling you ,you have a knack or better to say a flair for writting. If you ask for my oponion, You should think about starting a writing career. Ofcourse, Lucy has a strong aptitiude for it as well.
I am serious ,You can make tons of money. If only I could write even barely close to you or Lucy.
This is very interesting Jeff, I have read that this is the trend in the newspaper industry due to the internet as you explained very well, and this has been a topic which is being discussed in many journalist schools in universities all over the world.
Nowdays many people expend a lot of time in their jobs working with their pc, therefore is easier and faster to read the news in the net instead of reading physical papers, many workers sometimes don´t have time to read the news in the traditional way, in addition the information is fresh and updated each minute, it´s a challenge for newspaper media industry to adapt themselves to the new situation, I read t that he the New York Times was facing problems because of this (Incredible).
This is consecuence of the new technologies, I remember the book "The third wave" by Alvin Tofler who predicted all this changes and transformations the society, industries, countries, government, people will face with the use of advance technology, those who are interested in this matter will find this reading very useful….
Yes, I do think same as Peter.
one. We shall be fond to them that's for sure.
You really touch my heart as I was reading your memories, thank you so much Jeff.
Today is San Jose, so I want to wish a happy day to all Josephs, Joses, Pepes, Pepas, Maria Joses, and Josefas…..please, have a good day all of you.
Here also is father's day, so I want to wish happiness to all fathers and of course to all not fathers too.
It's a nice story. When I was reading it, I was seeing in my mind the same I had seen in many american movies. Really, when I was young I had liked to have had a work like that to be able to earn some money.
I don't know how the newspaper's information works in your country, but here in Spain you would need to read at least two of theirs. One of each political tendence. For instance, if you read the newspaper named "EL PAIS", you will think that you live in the best country of the world, thanks to Mr. Zapatero, socialist and President of Spain. However, if you read the newspaper named "EL MUNDO" you think that this country is governed by the worst Presidente of the world. Which should I believe?.
The question is a little difficult to answer. I think is a personal thing. What I want to say is that, if you are a person who have socialist ideas, you will only read what you want or like to hear. Otherwise, if you have conservative ideas you will only read the another newspaper. However, if you are independent or liberal or you don't have a prefixed idea, you should read as many newspapers as be posible to find out the truth.
This way, in Spain you had needed (using the same word that you used) to toss at least two newspapers at my house.
Certainly, this topic has a great similarity to when we have talked about mailservice's deliver. This is a new era, and something else like that it will change within more or less time, due to the fact of how the new technologies are affecting our daily life. As everything happens, we can consider it, like the well-known example, by looking at either a medium-full bottle or a medium-empty one. In this sense, undoubtely it´s a pain that such jobs tend to dissapear, but at the same time it's very good for all of us can enter on the internet and to be able to get practically for free any type of information in any newspaper online we want to choose.
The media sector I think is into a actual change, mainly for the internet's new habits of comsumption y many people believe that is going to change sooner than we could expect, probably by eliminating the chance of free access to every newspaper's web site and likely demanding previously, from the vast majority of them, a subscription or a payment to us.
Anyway, very interesting, Dr. Jeff.
Why you wrote would be dropped off ,not weren't dropped off? I know for a repeated action that happened for a period of time in the past we can use would ,but it is not the case here ,is it?
Preferably, I would use would ,like you did,but I don't know why.I just do it because I heard it a lot.And ,It is not a good ,solid grammer reason upon which one bases their sentence structures.
Hi! A Roumanian proverb says " who gets up early in the morning , far arrives " similar with the English proverb "It's the early bird that catches the worm " .
Only with huge effort you can be a special person. And all reporters are very , very special. Many reporters give / offer us their life for interesting news .
They leave their home , their friends just for good news . They are extraordinary .
So , I prefer to read a newspaper . You can feel better the reporter's soul .
It would be many comments to do, over all that I have read today,……. I spent more than two hours reading the blog, but in summary, it have been "A lot and interesting things" . And with a occasional "humorous touch"of Jeff, that always are wellcome.
Due to what I've seen up until now, there are many compatriots of mine, Spanish people, and I miss my loved land, Spain………: Greetings to all since "Beatiful Los Angeles California" and of course, Greeting, all people to the others parts of the world .
Thanks all the "ELS team" that makes possible this website.
Dear parham,as you seem to know the modal verb "would" is commonly used to talk about things in the past that happened often or always.But why?(you may be asking)Well,that's the way it is,whether you like it or not.Sometimes is better not to think about why some things are said in one way instead of the other.
This book was written by Charles Dickens (english)vs Este libro fue escrito por Charles Dickens(spanish).In this sentence I translate "by" as "por".
I drink coffee in the evenings (english)vs (Yo)bebo café por las tardes(spanish).In this sentence I translate "in" as "por",the same as in the previous sentence.
Well,when you are beginning to learn a new language you use your own language to learn another,like a kind of crutch,but when you are beginning to gain confident in the new language is much better to forget your own language as much as you can.The idea is trying to think only in english when you listen or read something and let's change gears.
Well, we all hope that in a few years time we'll still be able to listen to ESLpodcast because fortunately this was just a horror story,…or maybe it wasn't.
Good, Pedro, very good indeed, your English is going on so fast that I am impressed, and may be your dreams should be true along the years as I said before Jeff is nearly as good writer as teacher.
You write quite good also, congratulations, I have been amusing with your ideas about future, but sure he´ll don't leave us despite his great success as a writer.
That's for sure, I know it quite well.
He and Lucy love teaching and they know perfectly well that we their pupils, round the world, love them too.
This is priceless, few people get it along their lives.
Thanks for the input. the thing is ,the modal verb ,if you will, would is multifounctional. By that I mean, It acts like a chameleon ;it changes its character according to the concept it is in. Although I know the most part of it, just kidding I dont know squat about it , I just wanted to get either Jeff or Lucy to put more details to it. Believe you me , it would be useful for all of us. Mark my word my friend, the better you know would, The better your English get.
I really appriciate you sharing your insight with us,though.
Hi Loreto, welcome to the ESLPod Blog.
I'm Spaniard too, and I'm learning English like you, although it seems you know quite more than me.
Nice to meet you Loreto. I hope to read soon your comments.
hi everybody…. geez! Pedro, it was really nice of you to make it clearer the subject in case to our friend parham. I was about to take this matter and explained in my own way, but you did it well.
Actually, we all still not so sure about whether which preposition we should use at certain ordinary expressions in day to day speeches. But as you pointed it out, the KEY point is not atempt to fit a foreing language ( the second one you start to learn) into our own language's rules, which cause sometimes a tight in our heads.
As you also said right above… it is pretty wisely to use our native language to learn a second language like as a bridge of comprehension, So we gotta think in one sentence as a whole sense and not word by word since in english a single word usually has a different meaning than if it was placed inside of a expression.
Anyway, I welcome perham… to our eslblog and welcome also to Loreto to the eslpodcast. I hope you like so much of the eslpod and if it do, tell some else about it. By doing this you guys will be prevent us to have a such horrible nightmare or apocalypse that pedro just predicted it.
Well, however, I hope this never turn out!
well, I also would like to speak out about the topic in matter. This is a such terrible thing that one thing got to place over the other in order to make it better or modern. Perhaps, it is also too much contradictory; cause how you can feel more free and have easeir ways to have access to the news by the modern ways if the very modern ways are killing the sources from where the information are created come from.
In Brasil we still have this paperboys that jeff told about it; despite the growth majority has been getting born with a computer on the lap, there still being those who are growing older and still lover from physical newspaper.
By the way, jeff… thanks a lot for provide us with such a good memories from yours, I really enjoy a lot to hear them from you.
Actually, I was missing it alredy!
ELS Team have a good week!
Wellcome Loreto to this ESLPOD BLOG where all blogers are like friends to me.
So as I may understand reading your comment you are living in the beautiful city?. Tell us, is it as Jeff has told us so frecuently?
Any way, you are very wellcome to this place and I agree with you that there are a lot of spaniards learning English and wiriting in this blog. Why?
May be the mouth to mouth is a good way of sharing good news and we are very fond to do this way of telling everybody how good ESL is.
they would have also great fun what is even quite important.
I think also that we spaniards are very fond to debate about everything, and this is a good place to do it as Lucy and Jeff post to interest points continuously.
Fitters are survivors in this highly competitive society. Technology and continuous innovations are ruthless to obliterate the old memories of some people who like to cherish the past. Give and take is often the realistic hassle one has to figure out. Does it stand to reason that resisting against the cost-effective means of enjoyment in favour of embracing the obsolete stuff? Any day can be yesterday!
hello again everybody…. well I noticed that the matter of how used the modal verb " WOULD" is still shifting…. so I check out one of those english cafe that make some reference to this topic and I found it….
… so, Perham… if you were willing to take a look closier in this matter, you should check it out the ENGLISH CAFE # 142 , which one make some relative to the modal verb ( would).
I know it is quite a bit different of the subject in question ( would be X were /was ) , but you might be able to figure out what`s the differences are between the case of either use of it.
thanks a lot for you interest in lift up the doubt about this topic.
I got the massage you are trying to get across. Infact ,starting yesterday, Reading newspaper every single day is my task and making a habit out of it is my religion. clearly ,you are very well-read,no question about that. And ,I guess when you mention that you have been reading papers since always ,you are alluding that we must do so in order to get a better grasp of your mother tongue. Thanks bro ;as always ,I follow your lead.
Thanks Jeff and asociate for helping me snap out of it and realize such a blissful ignorance I was . Of course ,before joining you guys.
Kindly, Jeff presented us such nice and warm pictures through his experiences and memories.cereal – breakfast, father read the newspaper and so forth. also that makes me think of my childhood….even though time has gone! But with the new- tech era coming,all of us should apply to the new modern world,no matter how good or bad! anyway, everyone has his different thoughts. Believe the future will be better,and enjoy anything !
by the way, thanks for ESL – team hard working !
I think the disappearance of papers is a most distressing tendency. But it has one's good sides. The main of them – environmental protection, because you know pulp-and-paper industry damage to the environment. The less paper use the less forest cut down. Do you agree with me?


Virginia Business asked five Certified Public Accountants for their take on the state of the economy in Virginia. Read what Gary Wallace and four other Virginia CPAs had to say about their regions.
How is the economy faring in your part of the state?
We are seeing significant growth in all sectors of the economy. It is highlighted by new business growth, as well as a strong real estate market in the commercial sector [in neighborhoods like] Scott's Addition, Manchester and others.
How has the new tax law affected your clients?
Generally, a positive impact. It offered both initial tax relief for many corporations and also provided an economic sign of stability. What has yet to occur is the additional time to comply with the new provisions.


It seems odd there is no standard way of getting the name of a control rendered with the Html helper extensions in MVC3 Razor. I saw some proposed solutions, but none of the actually worked e.g. in partial views or list binding.
This solution gets it right, but is nonetheless just another hack. You can easily modify the regular expression to get the id instead of the name.


text:
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Republican senators consider turning to Trump as a trial trial that will begin
The late passenger caused a bomb scare in an attempt to take the flight
Samsung's new Galaxy S20 is not even as powerful as the 2-year-old iPhones, which is so embarrassing – BGR
Isaac Hayden breaks Chelsea's hearts with Newcastle's late winner | Football
Health strikes: NI unions thank the public while industrial action is suspended
By drbyosIn NewsPosted January 16, 2020 0 Comment(s)
Health worker unions have praised the public and patients for their patience and support during the strike.
Northern Ireland health unions suspended the strike on Thursday after talks with the Department of Health.
Unison, Unite and the Royal College of Nursing (RCN) organized industrial actions to protest salaries and staffing.
RCN members were to attack again on Monday, Wednesday and Friday of next week.
Anne Speed, director of negotiations for Unison, said members made "many sacrifices" during the industrial action and "really appreciated" the public's understanding.
Health unions will consider a payment offer of £ 30 million
The health minister, Robin Swann, said the news "would be well received by many, especially the patients and, of course, the staff who undertook industrial actions with a heavy heart."
On Tuesday, the unions had a "positive" meeting with the health minister, who offered £ 30 million to restore wage parity. Talks with the department continued until Wednesday night.
NIPSA has said it is not happy with the agreement and the BBC understands that it will not suspend its industrial action.
A source said the union was deeply concerned about the way the negotiations were conducted and the amount of money promised in the salary agreement.
Speed said the union will now vote for its members on the agreement
Speed said Unison will vote for its members with a recommendation to accept the agreement.
She said that while the agreement "is not perfect, there is always room for improvement, this is a great step forward."
Ms. Speed added that, in addition to the fact that there is now wage parity with England, there would be an additional £ 60 million for staffing and an estimated £ 10 million on the boat.
She said there would be 900 additional nursing places in practice and, although "it will take time," there will be a reduction in agency agency dependency.
"We need to put that money in the salaries of the nursing workforce."
Ms. Cullen said it has been a "long and difficult road" for nurses.
Pat Cullen, Director of the RCN in Northern Ireland, said strikes "have been a long and difficult road for nurses in Northern Ireland."
"Following the unprecedented decision to strike, our members finally have something concrete to consider in relation to restoring salary parity and personnel safety," he said.
The talks between senior union officials and the Department of Health lasted most of Wednesday.
Swann thanked the suspension of the strike and said it had been "a very difficult time."
"I want to pay a sincere tribute to our nurses and other health workers and the great work they do," he added.
"Yesterday, we saw figures that show that the number of compliments in our health service far exceeds the number of complaints."
'A lot of work to do'
"This illustrates the excellent medical care that is provided on a daily basis in Northern Ireland, despite all the serious problems the system faces. I recognize, of course, that there is still much work to be done."
Previously, Mr. Swann pledged to comply with union demands for wage parity after £ 30 million of additional funds from future funds were identified.
An agreement on how the department would implement the necessary processes to increase the number of staff working in the health and social care service has not yet been agreed.
As a result of the suspension, the five health trusts will not have to proceed with the cancellation of appointments and procedures next week.
Another positive move is that workers will not lose more wages.
drbyos
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Harry and Meghan will no longer use His and Her Royal Highness titles – CNN
Best Buy opens the probe to the CEO's personal conduct
Davis Bertans was pescatarian and became one of the best shooters in the league.
Jonny Kim, a former Navy SEAL and a Harvard-trained doctor, becomes a NASA astronaut
Where surgeons don't bother with checklists
Archynetys
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redpajama_set_name: RedPajamaCommonCrawl


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Division of Administration
Division of Communications & Outreach
Use the form below to schedule a meeting with a member of the Executive Branch. Someone will reach out within 24-48 hours to schedule a meeting.
2020-2021 Executive Cabinet
Sabrina La Rosa
Student Body President, University Trustee
sga_pres@ucf.edu
¡Hablo Español!
The Student Body President is elected in the spring to represent more than 68,000 students for the following academic year. The President serves as the only student member on the UCF Board of Trustees. The President is the CEO of the UCF Student Government and is responsible for the allocation of the almost 20.4 million dollar Activity and Service Fee Budget. The President is advised by 16 Executive Cabinet members on matters of policy, and oversees various University departments and agencies.
Stephanie Blanco
Student Body Vice President
sga_vp@ucf.edu
The Student Body Vice President is elected in the spring alongside the Student Body President to serve for the following academic year. The Vice President and President work collaboratively in achieving the Platform goals they have set to accomplish. The Vice President sits on various committees appointed by the Student Body and university president. The Vice President is the chair of the Agency and Department Strategic Planning Board to oversee all organizations, agencies, and departments at the university level. The Vice President's role is to manage the internal operations of the Executive cabinet.
Ryan Regan
sga_chief@ucf.edu
The expectations of the Chief of Staff are to work directly with the President and support special projects. The Chief of Staff will also oversee and work directly with the Division of Student Affairs. This role includes advising and playing an essential role in decision making for the Executive Cabinet. Finally, the Chief of Staff will serve alongside the Deputy Chief of Staff, who will be the mirror of the position working with the Vice President.
Benjamin Rembaum
Deputy Chief of Staff
sgadeputychief@ucf.edu
The expectations of the Deputy Chief of Staff are to work directly with the Vice President and support special projects. The Deputy Chief of Staff will also oversee and work directly with the Division of Communications. This role includes advising and playing an essential role in decision making for the Executive Cabinet as well as being the direct Greek Liaison for all Greek Chapters at the University. Finally, the Deputy Chief of Staff will serve alongside the Chief of Staff.
Erving Rodriguez
Attorney General and Judicial Advisor
sga_ag@ucf.edu | sgajudicialadvisor@ucf.edu
The Attorney General shall be the Legal advisor to the president and shall hand down opinions on the constitution, statutes, and rights and responsibilities to any member of the student body upon request. It shall be the responsibility of the attorney general to undertake statutory revisions. The Attorney General is also responsible for advising students appearing before the SG Judicial Council, the Office of Student Conduct, or any other University sanctioned board. Statutory duties of the role are outlined here Chapter 402 and 403.
Joseph Tufo
sga_comp@ucf.edu
The Comptroller shall be the Chief Financial Officer (CFO) of the Student Senate and of Student Government in general, heading the $20.4 million budget paid by students through the Activity and Service Fee. The Comptroller shall serve as a liaison between student government and the entities receiving funds from the Activity and Services Fee including student organizations and SG agencies. The Comptroller shall be responsible to the student body, and the Student Body Senate, for accurate and up to date maintenance of records and transactions made. The Comptroller will also manage the Finance Office. Statutory duties of the role are outlined here under Chapter 801.
Director of Student Affairs
sgastaffairs@ucf.edu
The Director of Student Affairs is responsible for the management of the Division of Student Affairs. The Director will be the main point of contact for student on-campus events and student concerns. The director shall be responsible for planning and executing Universal Knights and Light Up UCF. The Director is also a liaison to Veterans Academic Resource Center (VARC), Student Accessibility Services (SAS), and Graduate Student Association (GSA).
Daniella Lopez
Diversity and Inclusion Coordinator
sga_diversity@ucf.edu
The Diversity and Inclusion Coordinator shall be in charge of promoting campus diversity and inclusion, interfaith relations, and mental health. This position will work closely with the Multicultural Student Center and serve as an advisor and a liaison. The Diversity & Inclusion Coordinator will also work with multicultural student organizations and Campus Faiths and Ministries to promote equity and acceptance within the university community. This person will also be required to host diversity education events throughout the year.
Rob Gray
Academic Affairs Coordinator
sgaacademics@ucf.edu
The Academic Affairs Coordinator shall be Student Government's link to UCF's academic colleges and academic programs. This position is responsible for completing objectives pertaining to academic services offered by the university and executing academic programming offered by Student Government. This position will continuously monitor all colleges of the university to determine how SG can help academic programs offered to students. They will also oversee the Student Government Leadership Council as the ELC Mentorship Program Coordinator and is required to attend all UCF summer orientations to provide information regarding Student Government to incoming students.
Emma Roehrig
Sustainability & Innovation Coordinator
sgainnovation@ucf.edu
This position shall be responsible for the implementation of environmental awareness programming on campus as well as working with campus entities and organizations to increase their environmental awareness. The Sustainability & Innovation Coordinator will also work with campus entities, such as the Student Union, the Recreation and Wellness Center, Wellness & Health Promotion Services, and the Health Center to develop more sustainable alternatives and practices to work towards achieving a healthier campus environment. The position will also oversee
research, technological improvements, and advancements at the university. This position will also research other methods for creating a more sustainable campus and promoting student wellness.
Alexandra Fabregas
Safety & Transportation Coordinator
sgatransportation@ucf.edu
The Safety & Transportation Coordinator shall be Student Government liaison with Parking & Transportation Services, and A&SF funded facilities. This position will focus on advocating for students in all matters related to facilities and transportation. The Safety & Transportation Coordinator will be responsible for improvements in areas such as shuttle services, facilities, operations, etc. This position will also be a voting member of the University Parking & Transportation Advisory Committee. Additionally, this position will work towards making UCF a safer campus for our students. This will include, but will not be limited to, working with UCF Police Department and other entities on campus.
Bryce Wyatt
Athletics Coordinator
sga_athletics@ucf.edu
The Athletics Coordinator shall be responsible for working closely with UCF Athletics to continue partnerships and create relationships with traditions-based agencies, the Alumni Association, and University Administration. This position will be responsible for planning/executing all of the SG Tailgates and all Student Government programming at athletic events.
Meg Hall
Governmental Affairs Coordinator
sgagovaffairs@ucf.edu
The Governmental Affairs Coordinator is responsible for ensuring that the lobbying concerns of the student body are being addressed at the university, local, state, and federal levels. The position will organize visits to the State Capital, such as SG's annual Day at the Capitol event, and local representatives at Day at City Hall, to voice the concerns of the student body. This position will be in charge of hosting events in conjunction with political action groups that bring local, state, and national leaders to UCF to educate the student body on prominent issues.
Vanessa Reyna
Director of Communications & Outreach
sga_comm@ucf.edu
The Director of Communications & Outreach shall be responsible for the management of the Division of Communications & Outreach. This Director shall be responsible for creating and executing a communication plan geared at advertising and educating students about SG events and services as well as focusing on student outreach and engagement. The Director of Communications & Outreach shall be responsible for the public perception of Student Government and will be required to build relationships with campus and local media to ensure the proper dissemination of information from Student Government to the students and local community. The Director will also be responsible for student outreach efforts across campus through the means of tabling, organization and classroom visits, as well as various workshops hosted by the Office of Student Involvement. The Director shall also serve as the Webmaster of the Student Government website. This position will collaborate with other Student Government branches as needed for joint ventures.
Colton Flynn
Graphic Design Coordinator
sga_gfx1@ucf.edu
TThe Graphic Design Coordinator will create graphics and promotional materials for Student Government, as well as Registered Student Organizations. This position will assist other Cabinet members with creative ventures. Knowledge of Photoshop or other graphic editing software is required, and experience in graphic design is preferred. Please submit previous work and be prepared to showcase a portfolio at the time of the interview.
Andrea Mungroo
Graphic Design Assistant
The Graphic Design Assistant will assist the Graphic Design Coordinator in creating graphics and promotional materials for Student Government, as well as Registered Student Organizations. This position will assist other Cabinet members with creative ventures.
Ayonna Sanders
Visual Media Coordinator
sgavisualmedia@ucf.edu
The Visual Media Coordinator will be responsible for creating videos and other content for the SG Communications & Outreach Division. This coordinator will be responsible for working with the Director to create outreach plans and partnerships with Registered Student Organizations and the Office of Student Involvement and create visual media for those groups. A background in graphics would be beneficial, but not required.


The contest is looking for innovative concepts for a new ride-on by chiccoRide on, baby! chicco is searching for innovation among ride-on toys, to help babies and kids develop their sense of movement and their physical skills. what we are looking for is a 360° exploration in the world of movement related to this category of products. link to the contest: http://d.


Lists employment statistics in agriculture, commerce, and manufacturing. Includes statistics on mining, trade, navigation, engineering, and "learned professions."
Lists each child by name and age.
Asks if parents were foreign born and if adult males were citizens.
Lists the occupation of any resident, regardless of gender.
Lists age and birthplace of each individual and lists birthplace of each person's mother and father.
Asks for year of immigration to the U.S. and naturalization status and if a different language was spoken before immigrating to the U.S.
Asks all residents if they were veterans, and if so, from which conflict.


text:
\section*{Introduction}
Financial markets can be thought of as complex systems, and are treated as such by the Economics community \cite{plerou2000econophysics, bonanno2001levels, rickles2008econophysics, fiedor2014mutual}. Economists however, approach financial markets from an economics view point, without frequently using information and tools available in other disciplines, such as in Physics and Mathematics \cite{fiedor2014mutual}.

Treating financial markets as a complex system helps in realising the relation to other complex systems and to common approaches in studying them. The performance and interaction of the individual components of such systems are expected to follow a non-linear, chaotic, rather than random, pattern of interaction with each other. It makes thus sense to study the system from the perspective of complex systems theory. Particularly, in this paper we consider data from financial markets world-wide and the European Union (EU). Financial markets consist of components, such as stock prices and currency exchange ratess. The connectivity between these components is usually unknown but highly desirable. The components form a network of nodes and connections, where the nodes are the individual index values, and the connections the interactions among them, usually non-linear and unknown! Here, we use a recently published information-mathematical theory for network inference based on the Mutual Information Rate (MIR) \cite{bianco2016successful} to infer the structure of financial networks coming from recorded data from financial markets. MIR is a measure of the amount of information per unit of time that can be transferred between any two nodes in a network or system \cite{10.1371/journal.pone.0046745,bianco2016successful}.

When network engineers need to infer the topology of a given computer network, they ping individual nodes to map the layout. In financial markets, the analogue to pinging is to using financial time-series data, whereby each component is recorded and tracked regularly in time. We then want to study these data to understand how information flows from node to node in the network and ultimately, its structure, with the links being in general either direct or indirect.

MIR was first introduced by Shannon in 1948 as a ``rate of actual transmission''  \cite{Shannon1948} and, it was redefined more rigorously in \cite{Dobrushin1959} and even later in \cite{Gray1980}. It represents the mutual information (MI) exchanged per unit of time between two dynamical, correlated, variables and is based on mutual information which quantifies linear and non-linear interdependencies between two systems or data sets. It is essentially a measure of how much information two systems or two data sets share. Even though MI is a very important quantity to understand various complex systems ranging from the brain \cite{antonopoulos2015brain} to chaotic systems, there are three main difficulties that need to be overcome: (a) MI in random memoryless processes does not consider the degree of memory that financial markets are proved to contain \cite{fiedor2014mutual,mohammadi2011behaviour,bordignon2008periodic}, (b) it is necessary to determine what is considered as significant event in the complex system under study as probabilities of significant events often need to be known prior to the calculation of MI, (c) due to the usually limited size of data sets, it is complicated to calculate these probabilities accurately. This might lead to a biased calculation of MI \cite{steuer2002mutual}. To overcome these challenges, it has been proposed in \cite{baptista2012mutual,fiedor2014mutual} to calculate the amount of information exchanged per unit of time between two nodes in a dynamical network, aka MIR. This rate permits a more reliable measure of the hierarchical dependency in networks. Other limitations are related to the market structure, that is to sectors and sub-sectors of economic activities for stock markets and geographical locations for market indices and foreign exchange markets. More information can be extracted when studying the causal relationships between markets, either by lead-lag effect (asymmetric correlations) or partial correlations. Another limitation is introduced by the use of linear measures for the relation between market elements, ignoring the complexity in such systems and the fact that financial markets present a non-linear behaviour with regard to stock returns. In \cite{fiedor2014partial, you2015network}, the authors have overcome the above limitations by performing a network analysis of financial markets based on partial mutual information.

In this paper, we are interested in identifying interdependencies between nodes in the said financial networks and not in inferring the directionality of information flow, i.e. which node is driving which (i.e. causality). We thus assume through out the paper that all networks are undirected, i.e., each connection between two nodes is bidirectional. The existence of such a connection means there is a bidirectional connection between them due to their interaction. The purpose of this work is focused on demonstrating how one can use a quantity based on MIR \cite{bianco2016successful} that we call the normalised MIR to infer the network structure in financial data. Particularly, we show how one can infer the underlying network connectivity among the nodes of the network of financial time-series data, such as foreign currency exchange rates and stock indices. We first apply a normalisation to the MIR using artificial data with given properties, compare the new methodology to work already done in \cite{bianco2016successful} and then apply it to financial market data based on \cite{fiedor2014mutual} for the economies of 14 world-wide countries and the EU. Our results show consistent relationships and structural properties among the 15 economies and, an explanation from the Economics view point is given that further supports these findings.

\section*{Materials and methods}
\subsection*{Information and network connectivity}

As is well-known, a system can produce information which can be transferred among its different components \cite{paluvs1996coarse, schreiber2000measuring, baptista2012mutual, marchiori2012energy, mandal2013maxwell, antonopoulos2014production, antonopoulos2015brain}. When information is transferred, there are at least two components involved that are physically interacting by either direct or indirect ways. These components can be either time-series data, modes, or related functions, and from the mathematical view point, they are defined on subspaces or projections of the state space of the system \cite{baptista2012mutual, antonopoulos2014production}. In this work, we will use a quantity based on MIR, the normalised MIR, to study the amount of information transferred per unit of time between any two components of a system, namely to determine whether a link between these components exists. Such an existence means there is an undirected connection between them attributed to their interaction.

MIR measures the rate of information exchanged per unit of time between any two non-random, correlated variables. Since variables in complex systems are not purely random, MIR is an appropriate quantity to measure the amount of information exchanged per unit of time among nodes in such systems. Its application to time-series data and to their functions is of primordial importance to our work and will be used to determine if an (undirected) link exists between any two nodes in the system. In this framework, the strength of such connections can also be inferred, in the sense that they will be compared to those from all other pairs of nodes in the network, so long as the available data collected from the financial markets are sufficient to allow for a discrimination between stronger and weaker connections.

\subsection*{Mutual information}

MI and MIR  were originally introduced by Shannon in 1948 \cite{Shannon1948}. Particularly, the MI of two random variables $X$ and $Y$ is a measure of their mutual dependence and quantifies the ``amount of information'' obtained about one random variable $X (Y)$, after observing the other random variable $Y (X)$. It is defined by \cite{Shannon1948,kullback1997}

\begin{equation}\label{IXY1}
I_{XY}\left ( N \right )=H_{X}+H_{Y}-H_{XY},
\end{equation}
where $N$ is the total number of random events in variables $X$ and $Y$. $H_{X}$ and $H_{Y}$ are the marginal entropies of $X$ and $Y$ (i.e. the Shannon entropies) respectively, defined by
\begin{equation} H_{X}=-\sum_{i=1}^{N}P_{X}(i) \log P_{X}(i) \end{equation}
and
\begin{equation} H_{Y}=-\sum_{j=1}^{N}P_{Y}(j) \log P_{Y}(j), \end{equation}
where $P_{X}(i)$ is the probability of a random event $i$ happening in $X$, and $P_{Y}(j)$ is the probability of a random event $j$ happening in $Y$.

The joint entropy, $H_{XY}$ in Eq. \eqref{IXY1} measures how much uncertainty there is in $X$ and $Y$ when taken together. It is defined as
\begin{equation}\label{HXY}
H_{XY}=-\sum_{i=1}^{N}\sum_{j=1}^{N}P_{XY}(i,j) \log P_{XY}(i,j),
\end{equation}
where $P_{XY}(i,j)$ is the joint probability of both events $i$ and $j$ occurring simultaneously in variables $X$ and $Y$.

Equivalently, we can define MI by
\begin{equation}
I_{XY}\left ( N \right )=\sum_{i=1}^{N}\sum_{j=1}^{N}P_{XY}\left ( i,j \right ) \log \left ( \frac{P_{XY}\left ( i,j \right )}{P_{X}(i)P_{Y}(i)} \right ).\label{IXY2}
\end{equation}
This equation provides a measure of the strength of the dependence between the two random variables, and the amount of information $X$ contains about $Y$, and vice versa \cite{kullback1997}. When MI is zero, $I_{XY}=0$, the strength of the dependence is null and thus $X$ and $Y$ are independent: knowing $X$ does not give any information about $Y$ and vice-versa. Note that MI is also symmetric, i.e. $I_{XY}=I_{YX}$.

The computation of $I_{XY}\left (N \right)$ from time-series data requires the calculation of probabilities in an appropriately defined probabilistic space on which a partition can be defined (see for example Eqs. \eqref{HXY} and \eqref{IXY2}). The probabilistic space is defined based on the $X$, $Y$ variables. $I_{XY}$ can be computed for any two components, or nodes $X$ and $Y$ of the same network and can then be compared with $I_{XY}$ of any other pair of nodes in the same network. However, $I_{XY}$ is not suited to compare between different systems as it is possible for different systems to have different correlation decay-times and time-scales \cite{eckmann1985ergodic, baptista2008finding, pinto2011density} in their dynamical evolution.

There are various methods to compute MI, depending on the method used to calculate the probabilities in Eq. \eqref{IXY2}. The main methods are the binning (histogram) method \cite{moddemeijer1989estimation}, the density-kernel method \cite{moon1995estimation}, and the estimation of probabilities from distances between closest neighbours \cite{kraskov2004estimating}. In this work, we use the binning (histogram) method \cite{bianco2016successful}. Binning tends to overestimate MI because of the finite length of recorded time-series data, and the finite resolution of non-Markovian partitions \cite{butte2000mutual, steuer2002mutual}. However, these errors are systematic and always present for any given non-Markovian partition. To avoid such errors, we apply a normalisation as proposed in \cite{bianco2016successful} when dealing with MIR, utilising the binning method to calculate the probabilities in Eq. \eqref{IXY2}.

\subsection*{Mutual Information Rate}

The Mutual Information Rate came about as a method to bypass problems associated with the resolution of non-Markovian partitions, specifically in calculating MI for such partitions. In \cite{baptista2012mutual}, it was shown how to calculate MIR for two finite length time-series irrespective of the partitions in the probabilistic space. MIR is invariant with respect to the resolution of the partition and is defined by
\begin{align}\label{MIR1}
\mbox{MIR}_{XY} &=\lim_{N\to\infty} \lim_{L\to\infty} \sum_{i=1}^{L-1} \frac{I_{XY}\left ( i+1,N \right )-I_{XY}\left ( i,N \right )}{L} \nonumber \\[1em]
  &=\lim_{N\to\infty} \lim_{L\to\infty}\frac{I_{XY}\left ( L,N \right )-I_{XY}\left ( 1,N \right )}{L} \nonumber \\[1em]
  &=\lim_{N\to\infty} \lim_{L\to\infty}\frac{I_{XY}\left ( L,N \right )}{L},
\end{align}
where $I_{XY}\left ( L,N \right )$ represents the MI of Eq. \eqref{IXY1} between two random variables $X$ and $Y$, considering trajectories of length $L$ that follow an itinerary over cells in an $N\times N$ grid of equally sized boxes defined in the probabilistic space. Note that the term $I_{XY}\left (1,N \right)/{L}$ tends to zero in the limit of infinitely long trajectories, (i.e. $L\rightarrow \infty$).

For finite-length time-series $X$ and $Y$, the definition in Eq. (6) can be further reduced, as demonstrated in \cite{baptista2012mutual}, to
\begin{equation}\label{MIR_def2}
\mbox{MIR}_{XY}=\frac{I_{XY}\left ( N \right )}{T\left ( N \right )}, \end{equation}
where $I_{XY}\left ( N \right )$ is defined as in Eqs. \eqref{IXY1} and \eqref{IXY2}, and $N$ is the total number of cells in a Markov partition of order $T$ for a particular grid-size $N$.

It is important to note that while $T$ and $N$ are both finite quantities, for statistically significant results, a sufficiently large number of points is required to ensure that the length of the time-series is sufficiently larger than $T$, and thus a more saturated distribution of data across the probabilistic space and its partitions can be achieved.

\section*{Demonstration of the method}

We first demonstrate the usefulness of the method to infer the structure of networks where the dynamics in each node is given by a chaotic map, before applying it to financial-markets data. Particularly, we apply the proposed methodology to networks with known structure given by their adjacency matrices, and different maps for the dynamics of their nodes, and assess the obtained results by comparing the inferred network with the original networks, i.e. by comparing their adjacency matrices to decide whether the inference was successful in terms of the number of correctly inferred connections expressed as a percentage. For example, 100\% successful inference means that the proposed methodology was able to infer all connections in the original network used to produce the recorded data with no spurious connections appearing.

We first generate time-series data using discrete-time dynamics for the nodes of networks with given binary adjacency matrices. For these data and for each considered system, we then calculate the normalised MIR for each pair of nodes in the network, and subsequently its adjacency matrix based on the computed values and an appropriately defined inference threshold. Finally, we compare the adjacency matrix of the inferred network with that of the initially used network to estimate the percentage of successful inference.

\subsection*{Circle map network}

Following \cite{bianco2016successful}, we first apply the methodology to the case of a topology, which we refer to as the circle map network (CMN) (see Fig~\ref{CMN_fig}a). The network is composed of 16 coupled nodes with dynamics given by \cite{kaneko1990clustering}
\begin{equation}\label{CMN}
x_{n+1}^{i}=\left ( 1-\alpha \right )f\left ( x_{n}^{i},r \right )+\frac{\alpha }{k_{i}} \sum_{j=1}^{M} A_{ij} f\left ( x_{n}^{j},r \right ),
\end{equation}
where $M=16$ is the total number of nodes, $x_{n}^{i}$ is the $n$-th iterate of map $i$ (where $i=1,2,\ldots,M$) and $\alpha \in \left [ 0,1 \right ]$ is the coupling strength. $(A_{ij})$ is the binary adjacency matrix of connectivities, containing entries of either 1 when there is a connection between nodes $i$ and $j$, or 0 when there is no such connection. Moreover, it is a symmetric matrix as we consider a connection between any two nodes to be undirected (i.e. bidirectional). $k_{i}=\displaystyle \sum_{j=1}^{M} A_{ij}$ is the node-degree, $r$ the parameter of each map, and $f\left (x_{n},r \right)$ the circle map, defined by
\begin{equation}\label{dynamics_coupling_equation}
f\left ( x_{n},r \right )=x_{n}+r-\frac{K}{2 \pi} \sin\left ( 2\pi x_{n} \right ) \mod 1.
\end{equation}

For the parameters in Eq. \eqref{CMN}, we follow \cite{bianco2016successful} and use $\alpha=0.03$ to create a weakly interacting system, and $r=0.35$ and $K=6.9115$ that correspond to fully developed chaos for each individiual map. $x_{0}^{i}$ is initialised randomly, and the first 1,000 iterations are discarded so that a random seed can be used to start the next 100,000 iterations generated from the dynamics in the network, which we recorded as the time-series data. Fig~\ref{CMN_fig}a shows the network topology described by the adjacency matrix $A$ with the 16 nodes.

\begin{figure}[!ht]
\centering\includegraphics[width=0.8\textwidth]{Figure1-1.eps}
\caption{\textbf{The CMN, distribution of data points and expansion of points in $\Omega$.} Panel (a): The CMN is composed of 16 coupled nodes as shown by its network. The dynamics in each node is given by Eqs. \eqref{CMN} and \eqref{dynamics_coupling_equation}. Panel (b): The distribution of points in $\Omega$ obtained from Eqs. \eqref{CMN} and \eqref{dynamics_coupling_equation}, plotted in a $10 \times 10$ grid of equally sized cells. Panel (c): The points belong initially to a cell of the same grid and expand to a larger portion of $\Omega$ after three iterations, occupying more than one cells. $\delta$ is the maximum distance in the initial cell and $\Delta$ the maximum distance after the points have expanded to a larger extend of $\Omega$.}
\label{CMN_fig}
\end{figure}
\FloatBarrier

We then calculate MI and MIR for a pair of nodes $X,Y$ by defining a 2-dimensional probability space $\Omega$ formed by the time-series data recorded for that pair (see Fig~\ref{CMN_fig}b). $\Omega$ is partitioned into a grid of $N\times N$ fixed-sized cells where the probability of an event $i$ in $X$ is
\begin{equation} P_{X}\left ( i \right )=\frac{\mbox{number of data points in column } i}{\mbox{total number of data points in } \Omega}, \end{equation}
and the probability of an event $j$ in $Y$ is
\begin{equation} P_{Y}\left ( j \right )=\frac{\mbox{number of data points in column } j}{\mbox{total number of data points in } \Omega}. \end{equation}
Similarly, the joint probability can be defined by the ratio of points in cell $\left ( i,j \right )$ of the same partition in $\Omega$ and is expressed by
\begin{equation}
P_{XY}\left (i,j \right )=\frac{\mbox{number of data points in cell } \left ( i,j \right )}{\mbox{total number of data points in } \Omega}.
\end{equation}
MI therefore can be calculated from Eq. \eqref{IXY2} for different values of grid sizes $N$.

To ensure there is always a sufficiently large number of data points in the cells of the $N \times N$ partition of $\Omega$, we require the average number of points in all occupied cells to be sufficiently larger than the number of occupied cells,
\begin{equation}
\left \langle N_{0}\left ( N \right ) \right \rangle \geq N_{oc},
\end{equation}
where $N_{oc}$ is the number of occupied cells and $\left \langle N_{0}\left ( N \right ) \right \rangle$ is the average number of points in all occupied cells in $\Omega$. For the CMN, we have used a data set of 100,000 points which guarantee that this condition is satisfied for grid sizes up to $N_{\max}=19$ and, thus we have calculated $I_{X,Y}$ for grid sizes ranging from $0.2N_{\max}$ to $N_{\max}$.

In order to compute MIR, as in Eq. \eqref{MIR_def2}, to infer the topology of the network, we first need to estimate $T(N)$, i.e. the correlation decay time. $T(N)$ can be calculated in many ways, e.g. by using the diameter of an associated itinerary graph $G$ \cite{bianco2016successful}, or the Lyapunov exponents of the dynamics or the largest expansion rates \cite{baptista2012mutual}. All these different ways of estimating $T(N)$ exploit the fact that the dynamics is chaotic and uses thus the assumption that the points expand to the whole extend of $\Omega$ after about $T(N)$ time.

For the itinerary graph $G$, each cell in $\Omega$ occupied by at least one point is represented as a node in $G$. If $G_{ij}=1$, there is at least one point that can move from node $i$ to node $j$. If $G_{ij}=0$, then there is no such point. The diameter of $G$ is the minimum distance (hops) required to cover the entire network. The correlation decay time, $T(N)$, is then the minimum time for points in any cell of $\Omega$ to spread to its whole extend, and can be expressed as the diameter of $G$. A drawback of this approach however is that the resulting correlation-decay time is integer and thus not as good approximation as the correlation-decay time estimated by the largest Lyapunov exponent or expansion rate, with profound consequences in the calculation of MIR.

Consequently, in this work, we estimate $T(N)$ by the largest expansion rate which is computed from the recorded data. Particularly,
\begin{equation}\label{TN}
T\left ( N \right )\approx \frac{1}{\lambda _{1}} \log \frac{1}{N},
\end{equation}
where $\lambda _{1}$ is the largest positive Lyapunov exponent of the dynamics \cite{Benettin1980}. However, in a system or for recorded data sets for which $\lambda _{1}$ cannot be estimated or computed, it can be replaced by the largest expansion rate $e_{1}$, defined by
\begin{equation}\label{e_1}
e_{1}=\frac{1}{N_{oc}}\sum_{i=1}^{N_{oc}} \frac{1}{t} \log L_{1}^{i}\left ( t \right ),
\end{equation}
with $e_1\leq\lambda_1$ in general. The equality holds when the system has constant Jacobian, is uniformly hyperbolic, and has a constant natural measure \cite{baptista2012mutual}.

In Eq. \eqref{e_1}, $L_{1}^{i}\left ( t \right )$ is the largest distance between pairs of points in cell $i$ at time $t$ divided by the largest distance between pairs of points in cell $i$ at time 0, and is expressed as
\begin{align}
L_{1}^{i}\left ( t \right ) &=\frac{\Delta }{\delta } \nonumber\\[1em]
    &=\frac{\mbox{largest distance between pairs of point in cell } i \mbox{ at time }t}{\mbox{largest distance between pairs of point in cell } i \mbox{ at time }0} .
\end{align}

In Fig~\ref{CMN_fig}c and Fig~\ref{CMN_fig}d we present an example of the expansion of points for the CMN that initially belong to a single cell (Fig~\ref{CMN_fig}c) and after three iterations of the dynamics, they expand to a larger portion of $\Omega$ (Fig~\ref{CMN_fig}d). In Fig~\ref{CMN_fig}c,  we denote by $\delta$ the maximum distance for a pair of points in the initial cell and in Fig~\ref{CMN_fig}d by $\Delta$ the maximum distance after they have expanded to a larger extend of $\Omega$.

In the calculation of MIR, since we are using partitions of fixed-size cells which are non-Markovian, errors will populate, causing a systematically biased computation towards larger MIR values. In such cases, MIR is partition-dependent, and we thus calculate it in a way that makes it partition-independent, following \cite{bianco2016successful}. Particularly, there is a systematic error coming from the non-Markovian nature of the equally-sized cells in the considered grids as a smaller $N$ is likely to create a partition which is significantly different to a Markovian one than a larger grid size $N$. Moreover, using the fact that $\mbox{MIR}_{XY}=\mbox{MIR}_{YX}$ and $\mbox{MIR}_{XX}=0$, we can narrow the number of $X$, $Y$ pairs from $M^2$ to $M\left ( M-1 \right )/{2}$. The method presented in \cite{bianco2016successful} to avoid such errors is to normalise MIR for each grid size $N$ as follows: For fixed $N$, $\mbox{MIR}_{XY}\left ( N \right )$ is first computed for all $M(M-1)/2$ pairs of nodes and is normalised with respect to their minimum and maximum values. The reason is that for unconnected pairs of nodes, MIR is numerically very close to zero, and doing so the new $\widehat{\mbox{MIR}}_{XY}$ in Eq. \eqref{MIR_wedge} will be in the interval $[0,1]$. This normalisation can be achieved by
\begin{equation}\label{MIR_wedge}
\widehat{\mbox{MIR}}_{XY}\left ( N \right )=\frac{\mbox{MIR}_{XY}\left ( N \right )-\min\left \{ \mbox{MIR}_{XY}\left ( N \right ) \right \}}{\max \left \{ \mbox{MIR}_{XY}\left ( N \right )\right \}-\min \left \{ \mbox{MIR}_{XY} \left ( N \right )\right \}},
\end{equation}
where $\mbox{MIR}_{XY}\left ( N \right )$ is the MIR calculated for nodes $X$ and $Y$, $\min \left \{ \mbox{MIR}_{XY} \left ( N \right ) \right \}$ is the minimum MIR of all $M(M-1)/2$ pairs, and similarly $\max \left \{ \mbox{MIR}_{XY} \left ( N \right ) \right \}$ is the maximum MIR over all MIR values of the $M(M-1)/2$ pairs. Since we use $\widehat{\mbox{MIR}}_{XY}\left ( N \right )$ for a range of grid sizes $N$, we can further normalise $\widehat{\mbox{MIR}}_{XY}$ by \cite{bianco2016successful}
\begin{equation}\label{MIR_bar}
\overline{\mbox{MIR}}_{XY}=\frac{\sum_{i} \widehat{\mbox{MIR}}_{XY}\left ( N_{i} \right ) }{\max \left \{ \sum_{i} \widehat{\mbox{MIR}}_{XY}\left ( N_{i} \right ) \right \} },
\end{equation}
where the maximum is now taken over the $N_{i}$ grid sizes considered in $[0.2N_{\max}$, $N_{\max}]$. This normalisation ensures again that $\overline{\mbox{MIR}}_{XY}$ values are in $[0,1]$.

To reconstruct a network using Eq. \eqref{MIR_bar}, we fix a threshold $\tau \in [0,1]$ and consider the pair $XY$ as connected if $\overline{\mbox{MIR}}_{XY} \geq \tau$. If so, then the corresponding entry in the adjacency matrix $A^{c}$ of the reconstructed network becomes 1, i.e. $A_{XY}^{c}=1$ or 0 if $\overline{\mbox{MIR}}_{XY} < \tau$ as the pair is considered as unconnected.

Choosing $\tau$ appropriately is crucial in successfully depicting the structure of the original network from the recorded data. If $\tau$ is set too high, real connections among nodes might be missed, while if set too low, spurious connections between nodes might appear in the inferred network. To address this, we follow \cite{rubido2014exact} and determine $\tau$ by first sorting all $\overline{\mbox{MIR}}_{XY}$ values in ascending order, and then identifying the first $XY$ pair for which $\overline{\mbox{MIR}}_{XY}$ increases more than 0.1 - which accounts for an abrupt change in $\overline{\mbox{MIR}}_{XY}$ values - and then set the threshold $\tau$ as the middle value of the $\overline{\mbox{MIR}}_{XY}$ for the identified pair and that for the immediately previous pair. This is based on the observation that there two main groups of $\overline{\mbox{MIR}}_{XY}$ values, i.e. for the connected and unconnected nodes (see Fig \ref{MIR_bar_CNM_fig}b).

\begin{figure}[!h]
  \centering
  \includegraphics[width=0.8\textwidth]{Figure2-1.eps}
\caption{\textbf{Estimation of the threshold $\mathbb{\tau}$ for the inference of CMN.} Panel (a) is the plot of the $\overline{\mbox{MIR}}_{XY}$ values before ordering in ascending order and (b) after being ordered. Following the approach in the text, $\tau\approx0.21$ plotted by the red dash line in both panels.}\label{MIR_bar_CNM_fig}
\end{figure}
\FloatBarrier

We demonstrate the application of this approach for network inference in Fig~\ref{MIR_bar_CNM_fig}. Particularly, in panel (a) we plot the $\overline{\mbox{MIR}}_{XY}$ values for all pairs $XY$ before ordering them in ascending order and in (b) the same values ordered. It is evident in panel (b) that there two groups of $\overline{\mbox{MIR}}_{XY}$ values, namely one for the unconnected pairs (left side of the plot with relatively low values) and another one for the group of connected pairs (right side of the plot with relatively big value), separated by an abrupt change in $\overline{\mbox{MIR}}_{XY}$ values in between. Following the proposed methodology, we have estimated that $\tau\approx0.21$ (plotted by the red dash line in both panels) which crosses the bar for which there is this abrupt change. This then leads to the 100\% successful inference of the original network shown in Fig~\ref{CMN_fig}a.

\section*{Inferring networks using data from additional sources}

\subsection*{Introducing an additional node with uniform, uncorrelated, random data}

One of the main difficulties in inferring the structure of a network is the estimation of $\tau$. The notion of an abrupt change might be subjective (more than one abrupt changes) or even not clear-cut, especially when dealing with data from real experiments and/or when the number of nodes in the network is big. Here, we introduce another approach to improve network inference by setting $\tau$ according to the $\overline{\mbox{MIR}}_{XY}$ value of a pair of, disconnected from the network, nodes with data with known properties. This $\overline{\mbox{MIR}}_{XY}$ value will then be used to set the threshold for network inference.

To demonstrate this, we will use uniformly random, uncorrelated, data as the additional source. One would expect that the $\overline{\mbox{MIR}}_{XY}$ and correlation decay time, $T(N)$, between any two nodes of a network of such data would be close to zero and one, respectively. This idea can be appreciated by using a network of isolated nodes. To this end, we set $\alpha = 0$ in Eq.~(\ref{CMN}), $N=6$ nodes and $f(x_n,r)$ be the logistic map in its chaotic regime (i.e. $r=4$),
\begin{equation}\label{LMAP}
f_{i}\left ( x_{n}^{i},r \right )=rx_{n}^{i}\left ( 1-x_{n}^{i} \right ).
\end{equation}
The normalisation process casts the $\overline{\mbox{MIR}}_{XY}$ values in $[0,1]$ for all pairs of nodes $XY$. Often, the resulting ordered $\overline{\mbox{MIR}}_{XY}$ values do not allow for a clear determination of $\tau$ as evidenced in Fig~\ref{fig:rand_ref}a. Without the use of an additional source of uniformly random, uncorrelated data for a pair of nodes disconnected from the rest of the network, one would compute $\tau\approx0.27$ and would result in the inferred network in panel (b) which is clearly not the original one which consists of only isolated, disconnected nodes! In contrast, when using the idea of introducing uniformly random, uncorrelated data for a pair of nodes disconnected from the rest of the network, and applying the same methodology, panel (c) shows an abrupt change in the ordered $\overline{\mbox{MIR}}_{XY}$ values that can be exploited to infer the network structure. In this case, $\tau\approx0.5$ and leads to the successful network inference shown in panel (d). The black bars correspond to the $\overline{\mbox{MIR}}_{XY}$ values for the pair of introduced nodes with other nodes in the network.

\begin{figure}[!ht]
 \centering
  \includegraphics[width=0.8\textwidth]{Figure3-1.eps}
\caption{\textbf{Results for the case of an additional source of data from a pair of nodes with uniformly random, uncorrelated data.} Panel (a) shows the estimation of $\tau\approx0.27$ based on the ordered $\overline{\mbox{MIR}}_{XY}$ values for a network of 6 isolated nodes without the introduction of the pair of nodes with uniformly random, uncorrelated data. Panel (b) is the unsuccessfully inferred network based on panel (a). Panel (c) shows the ordered $\overline{\mbox{MIR}}_{XY}$ values for the same network with the nodes of random data added. The black bars are the $\overline{\mbox{MIR}}_{XY}$ values that come from the pair of additional nodes. Panel (d) shows the resulting successfully inferred network of isolated, disconnected nodes. In panels (a) and (c), we plot $\tau$ by a red dash line where $\tau\approx0.27$ in (a) and $\tau\approx 0.5$ in (c).
}\label{fig:rand_ref}
\end{figure}
\FloatBarrier

\subsection*{Indirect information exchange and bidirectional connections}

Next, we will examine a network of six weakly coupled ($\alpha=0.1$) logistic maps \eqref{LMAP} seen in Fig~\ref{logmap_fig}a that corresponds to the following adjacency matrix
$$
\left(A_{ij}\right) = \begin{bmatrix}
0&1&0&0&0&0\\
1&0&1&0&0&0\\
0&1&0&0&0&0\\
0&0&0&0&1&0\\
0&0&0&1&0&1\\
0&0&0&0&1&0
\end{bmatrix}.
$$
This network consists of two triplets of nodes which are disconnected from each other. In each triplet, the two end nodes do not exchange information directly, but only indirectly through the intermediate nodes (i.e. 2 and 5, respectively). Applying the proposed methodology for network inference without the use of any additional nodes will result in picking up the indirect exchange of information as a direct one, where {$\tau\approx0.16$}, depicted as the red dash line in Fig~\ref{logmap_fig}b, leading thus to a spurious direct connection between the end point nodes (see Fig~\ref{logmap_fig}c).

\begin{figure}[!ht]
 \centering
  \includegraphics[width=0.8\textwidth]{Figure4-1.eps}
\caption{\textbf{Network inference in the case of two, disconencted from each other, triplets of nodes.} Panel (a) shows the network of weakly coupled ($\alpha=0.1$) logistic maps. Nodes 1 and 3 interact indirectly through node 2, and similarly, nodes 4 and 6 through node 5 only. Panel (b) shows the ordered $\overline{\mbox{MIR}}_{XY}$ and threshold $\tau$ (red dash line) when no additional nodes are introduced to the network. The red dash line corresponds to $\tau\approx0.16$. Panel (c) shows the unsuccessfully inferred network when using only the data from the network in panel (a). Panel (d) shows the ordered $\overline{\mbox{MIR}}_{XY}$ values for the same network as in (a) with the additional data from the pair of directed nodes (see text). The blue dash line represents the threshold computed for the pair of directed nodes ($\tau\approx0.69$) and the red dash line corresponds to $\tau\approx0.16$ from panel (b). Finally, panel (e) shows the successfully inferred network by considering as connected nodes only those with $\overline{\mbox{MIR}}_{XY}$ bigger than the blue dash threshold.
} \label{logmap_fig}
\end{figure}
\FloatBarrier

The addition of a new pair of nodes with uniformly random, uncorrelated data disconnected from the rest of the network will not help either in inferring the network structure successfully in this case as the chaotic dynamics data for which we want to infer the network structure are also uncorrelated in time. An alternative approach is  to add a pair of directed nodes to the network, again disconnected from the main network. These nodes will be represented by chaotic logistic maps (i.e. with $r=4$ and $\alpha=0.1$ in Eq. \eqref{LMAP}) with the adjacency matrix
$$
\left(A_{ij}\right) = \begin{bmatrix}
 0&1\\
 0&0
\end{bmatrix}.
$$
Only the second node is coupled to the first and, thus, the information exchange is unidirectional from the second to the first, and the $\overline{\mbox{MIR}}_{XY}$ would not be as high as for a bidirectional connection between them. Consequently, we may assume that any information exchange value smaller than the $\overline{\mbox{MIR}}_{XY}$ value for this particular pair will not be considered as a connection and will be represented by 0 in the corresponding inferred adjacency matrix (see Fig~\ref{logmap_fig}d where the new threshold, depicted as the blue dash line, is now set at $\tau\approx0.69$ and the old one is represented by the red dash line at $\tau\approx0.16$). Following the proposed methodology for network inference for the augmented data and for $\tau\approx0.69$, we arrive at the  100\% successfully inferred network in Fig~\ref{logmap_fig}e which is the same as in Fig~\ref{logmap_fig}a.

\subsection*{Application to correlated normal variates data}

An interesting application of the proposed methodology is to correlated normal variates data. In most cases, data collected from real-world applications have no obvious dynamical system equations to help relate the different variables involved. For example, global financial markets of different countries are correlated, however the underlying dynamics are not known \cite{junior2015dependency}. The proposed method would be able to provide clues on the interrelations among the financial markets.

To demonstrate this, we generated three groups of correlated normal variates data. Each group $i=1,2,3$ consists of three correlated normal variates specified by a covariance matrix $\Sigma_i$ and the three groups are uncorrelated with each other. Fig~\ref{cor_fig}a shows the scatter matrix of the three groups of data (first group: x1,$\ldots$,x3, second group: x4,$\ldots$,x6 and third group: x7,$\ldots$,x9) with covariance matrices
$$ \Sigma_1 =
\begin{bmatrix}
  3.40& -2.75& -2.00 \\
 -2.75& 5.50& 1.50 \\
 -2.00& 1.50& 1.25
\end{bmatrix}\hspace{-0.1cm},
\;
\Sigma_2 =
\begin{bmatrix}
 1.0& 0.5& 0.3\\
 0.5& 0.5& 0.3\\
 0.3& 0.3& 0.3
\end{bmatrix}\hspace{-0.1cm},
\;
\Sigma_3 =
\begin{bmatrix}
 1.40& -2.75& -2.00\\
 -2.75& 5.50& -1.00\\
  -2.00& -1.00& 3.25
 \end{bmatrix}.
$$
A circular pattern for a pair of data sets indicates they are independent (or weakly correlated), whereas an elongated one strong correlation among them, either positive or negative depending on the orientation. For example, in Fig~\ref{cor_fig}a data sets x8 and x9 are weakly correlated and thus one would not expect to see a connection between them in the inferred network. In contrast, since data sets x1 and x3 are strongly anti-correlated, one would expect to see a connection in the inferred network.

This is a case where there is a clear distinction between correlated and non correlated pairs as the data have been constructed as such. This can then be exploited further to set the threshold $\tau$ to identify connectivity in terms of the correlated pairs. Particularly, we set $\tau$ for $\overline{\mbox{MIR}}_{XY}$ so as to depict all correlated pairs of nodes in the data. The results in Fig~\ref{cor_fig}c show that $\overline{\mbox{MIR}}_{XY}$ can be used to infer successfully the network from correlated normal variates. One can see that the data are successfully classified into three distinct groups, and that, the connection between nodes x8 and x9 is missing as they are weakly correlated and the connection between nodes x1 and x3 is present as they are strongly anti-correlated. Since, this pair is also the strongest correlated among all, its $\overline{\mbox{MIR}}_{XY}$ value is also maximal and corresponds to the highest bar in Fig~\ref{cor_fig}b which is equal to 1!

Here, we have shown that the $\overline{\mbox{MIR}}_{XY}$ can depict correctly the number and pairs of correlated data and thus, infer successfully the underlying network structure. Since, global financial data of different economies and countries are also found to be correlated \cite{junior2015dependency}, we will use a similar approach in the next section to infer the network structure of currency exchange rates to USD and stock indices for 14 countries world-wide and the EU.

\begin{figure}[!ht]
  \centering
 \includegraphics[width=0.8\textwidth]{Figure5-1.eps}
 \caption{\textbf{Application of the proposed method to correlated normal variates data.} Panel (a) shows the scatter matrix of nine data sets split into three groups (first group: x1,$\ldots$,x3, second group: x4,$\ldots$,x6 and third group: x7,$\ldots$,x9). Each group consists of three correlated normal variates with zero correlation among the groups. Fig~\ref{cor_fig}a shows the scatter matrix of the three groups of data (x1,$\ldots$,x9) . The circular pattern indicates that the two nodes are independent (or weakly correlated), whereas an elongated one shows strong correlation, either positive or negative depending on the slope. Panel (b) is the ordered  $\overline{\mbox{MIR}}_{XY}$ for the correlated variates. The red dash line corresponds to $\tau\approx 0.06$.  Panel (c) shows the successfully inferred networ resulting from (b).}\label{cor_fig}
\end{figure}
\FloatBarrier

\section*{Application to financial data}

So far, we have demonstrated the applicability of the method to infer successfully the network structure for artificial data, and we now use it to infer the connectivity in networks of financial-markets data.

Particularly, we have applied the proposed methodology to infer the financial relations among 14 countries world-wide and the EU using the currency exchange rates to US Dollar (USD) and stock indices. The information for the local currencies and stock indices for the 14 countries and the EU are shown in Table \ref{tab:countries}. The data used are daily exchange rates of the local currencies to USD, ranging from January 2000 to August 2016 (taken from the Datastream, Thomson Reuters, database) and stock-indices data, ranging from January 2000 to December 2016 (taken from Bloomberg). We have transformed the daily data points $p_t$ from exchange rates and stock indices to log-return values $r_t = \ln (p_t/p_{t-1})$ where $t$ is the index of the data point in the time-series, as this is a common assumption and practise in Quantitative Finance \cite{hudson2015calculating}.

\begin{table}[!ht]
 \centering
 \begin{tabular}{clll}
  \hline
  Label & Countries/Economies & Local currencies & Stock indices\\
  \hline
  1 & JPN - Japan & Yen & Nikkei 225\\
  2 & EUR - European Union & Euro & Euro Stoxx 50\\
  3 & CAN - Canada & Canadian Dollar &S\&P/TSX Composite Index\\
  4 & TWN - Taiwan & New Taiwan Dollar & TSEC weighted Index \\
  5 & CHE - Switzerland & Swiss Franc & SMI Index\\
  6 & IND - India & Rupee & Bombay BSE 30 \\
  7 & KOR - South Korea & Won & Seoul Composite KS11\\
  8 & BRA - Brazil & Brazilian Real & Bovespa \\
  9 & MEX - Mexico & Mexican Peso & MXX Bolsa Index\\
  10 & NOR - Norway & Norwegian Krone & Oslo OBX Index\\
  11 & SWE - Sweden & Swedish Krona & OMX Stockholm 30 Index\\
  12 & SGP - Singapore & Singaporean Dollar & Straits Times Index\\
  13 & ZAF - South Africa & Rand & FTSE/JSE All-Share Index \\
  14 & THA - Thailand & Thai Baht & SET Index\\
  15 & DNK - Denmark & Danish Krone & OMX Copenhagen 20\\
  \hline
 \end{tabular}
 \caption{The local currencies and stock-indices information for the 14 countries and the EU. The first column is the node labels, the second the corresponding 14 countries and the EU (second row), the third their local currency exchange rates with USD and the fourth, the stock indices. Notice that the first column is the node label seen in the networks in Fig~\ref{finance_fig}e and \ref{finance_fig}f.}
 \label{tab:countries}
\end{table}

Fig~\ref{finance_fig}a and \ref{finance_fig}b present the scatter plots for the daily log-returns of the exchange rates and stock-indices based on the 15 economies, respectively. In both scatter plots, the strongest correlated pair is the EU-Sweden (EUR-SWE) pair. This strong interrelation is manifested by the highest bars in the ordered $\overline{\mbox{MIR}}_{XY}$ plots in Fig~\ref{finance_fig}c and \ref{finance_fig}d. The second highest, and rest of the bars in Fig~\ref{finance_fig}c do not correspond to the same pairs in Fig~\ref{finance_fig}d, indicating that the connectivity in the two inferred networks for the exchange rates and indices will be different.

To infer the networks from the $\overline{\mbox{MIR}}_{XY}$ plots, we need to set the thresholds appropriately in Fig~\ref{finance_fig}c and \ref{finance_fig}d. Since for the financial data, there is no clear-cut distinction between correlated and non correlated pairs (see Fig~\ref{finance_fig}a and \ref{finance_fig}b), we use the same idea as for the indirect information exchange data and introduce an additional pair of \textit{unidirectional} interacting nodes with chaotic logistic map dynamics to the financial data nodes. The pair is the same for both the forex and stock-indices data and is disconnected from the financial data networks to avoid indirect influences. Again, we assume that any pair of nodes with $\overline{\mbox{MIR}}_{XY}$ bigger than the $\overline{\mbox{MIR}}_{XY}$ of the unidirectional interacting nodes can be regarded as connected. Following this approach, we have found that $\tau\approx0.03$ and $0.05$ for the exchange-rates and stock-indices data, respectively. The difference in the two thresholds comes from the normalisation in Eq. \eqref{MIR_wedge} as the maximum values of $\overline{\mbox{MIR}}_{XY}$ for the forex and stock-indices data are different and they are representing information exchange in different networks. Subsequently, in Fig~\ref{finance_fig}e and \ref{finance_fig}f we present the resulting inferred networks for the daily exchange rates and indices based on the $\overline{\mbox{MIR}}_{XY}$ values for the $105(=15(15-1)/2)$ possible, unique pairs of world-wide economies. Doing so, we have been able to infer most of the weakly and all strongly correlated pairs of economies in both financial data-sets.

To complement our analysis, we have performed a structural analsyis to shed light on the properties of the two inferred networks. Particularly, we have found that both are small-world networks with small-world measures \cite{Basettetal2006,antonopoulos2015brain} of $\sigma\approx5$ for the exchange rates and $\sigma\approx3$ for the stock-indices, respectively. The higher is $\sigma$ from unity, the better it displays the small-world property, with values of $\sigma<1$ indicating a random network. In our case, we find that the stock-indices network is closer to a random network than the exchange-rates network. We have also found that the exchange rates network is a dissasortative (mixing by degree) network (with coefficient of assortativity $r\approx-0.17$) whereas the stock-indices network is assortative with $r\approx0.1$. Assortative mixing by degree is the tendency of nodes with high degree to connect to others with high degree, and similarly for low degree, whereas dissasortative mixing by degree is the tendency of nodes with high degree to connect to other nodes with low degree \cite{2003PhRvE67b6126N}. This is a qualitative difference between the two networks as economies well-connected in terms of their exchange rates prefer to connect with economies less well-connected in terms of the exchange rates as opposed to the stock-indices behaviour where the preference is toward the well connected nodes! Interestingly, our study also reveals that there are 32 bidirectional connections with non-trivial information exchange in the exchange-rates network and 49 in the stock-indices network. Moreover, both networks have a relatively small modularity (i.e. $Q\approx0.12$ and $Q\approx0.14$ for the exchange rates and stock indices, repsectively) indicating that the strength of division of both networks into modules is small, meaning that both networks have sparse connections between the nodes within modules and denser connections between nodes in different modules.

\begin{figure}[!ht]
  \centering
 \includegraphics[width=0.8\textwidth]{Figure6-1.eps}
\caption{\textbf{The scatter plots and inferred networks for the financial-markets data.} Panels (a), (c) and (e) show the scatter matrix, ordered $\overline{\text{MIR}}_{XY}$  values and network of the 15 currency exchange rates. Panels (b), (d) and (f) similarly for the 15 stock indices. The red dash line in (c) corresponds to $\tau \approx 0.03$ and in (d) to $\tau\approx 0.05$. Note that in panels (e) and (f), the nodes represent the currency exchange rates and stock indices for the 15 economies, respectively, as in Table \ref{tab:countries}.}
\label{finance_fig}
\end{figure}
\FloatBarrier

The results in Fig~\ref{finance_fig}e show that the Indian Rupee is not connected to any currency because it is a non-convertible currency, also known as a blocked currency, where the currency cannot be freely traded on the international exchange market, generally as a result of government restrictions. A similar pattern is evident in other non-convertible currencies in this study, namely South Korean Won, Taiwan Dollar and Brazilian Real. The results show that the South Korean Won has no connection with other currencies, except with regional neighbour's currency, Taiwan Dollar. Emerging market currencies, especially those in the same region, tend to mirror each other \cite{kritzer2013forex}. The Brazilian Real and the Mexican Peso have come to epitomise this kind of relationship. Other than Mexican Peso, the Brazilian Real is also connected to South Africa Rand, a fully convertible currency with excellent connection with most currencies around the world.

Our results also confirm that the Euro is the predominant currency in Europe, including countries not in the European Union such as Norway and Switzerland. Two of the three Nordic currencies under study, namely the Swedish Krone and Norwegian Krone, and the Swiss Franc form a network of connections among other and the Euro. The Euro-Swedish Krone pair has the highest $\overline{\mbox{MIR}}_{XY}$ value, close to 1. All Nordic countries considered in this study and Switzerland, are nonetheless intrinsically linked to the European socio-economic and political situations.

Another interesting finding is that, the Danish Krone, the only Nordic currency evaluated in this study, is not connected with the Euro, although Denmark is a member of the European Union. One reason that can explain this finding could be that Danish Krone is pegged to the Euro, so there is no currency risk that may be affected by economic, monetary or political developments in Europe. In effect, this meant that there are no bidirectional connections in the cases with pegged currency regimes. This can be appreciated in view of Brexit, where the Euro, Swedish Krone, Norwegian Krone and Swiss Franc are slumped against most other currencies, while the Danish Krone, which is pegged to the Euro, is strengthening post-Brexit \cite{wienberg_levring_2016}.

Moreover, our results confirm past studies \cite{wu1998dynamic, bhattacharyya2004integration} that most of the world's major stock indices are integrated into international markets. Fig~\ref{finance_fig}f shows that some of the largest stock-market indices (by market capitalisation) under study, i.e., Nikkei 225 (Japan), Euro Stoxx 50 (European Union) and SMI Index (Switzerland) are connected with most other stock indices. Euro Stoxx 50 and OMX Stockholm 30 Index (Sweden) pair of stock indices has the closest $\overline{\mbox{MIR}}_{XY}$ value to 1 and hence, share the largest rate of information exchange. The Straits Time Index (Singapore) is also well connected with other stock indices internationally, because it is one of the world's most diversified benchmark indices with a mix of stocks that are both domestic and globally focused. The results also show that the SET Index (Thailand) is only connected with one stock index. This is not surprising given that a study by \cite{valadkhani2008dynamic} found no cointegration between stock prices indices of Thailand and its major trading partners.

These networks of currency exchange-rates and stock-indices are relevant for risk managers to use as an investment strategy employed in portfolio management as they give an indication of the mix of assets to hold in order to form a well-diversified portfolio.

\section*{Conclusions}
In this paper, we used the normalised Mutual Information Rate to infer the network structure in artificial and financial data ranging from 2000 to 2016. Particularly, we showed how the underlying network connectivity among the nodes of financial time-series data, such as foreign currency exchange-rates and stock-indices can be inferred. We first demonstrated the applicability of the method by applying it to artificial data from chaotic dynamics and to cases of correlated normal variates. Our results for the artificial data showed that the method can be used to successfully infer the underlying network structures with only using the data recorded from the coupled dynamics assuming no previous knowledge of the adjacency matrices but only to estimate the percentage of successful network inference.

We then applied the method to infer the underlying connectivity structure of currency exchange rates and stock market indices for the economies of 14 worldwide countries and the European Union and, performed an analysis for both networks to identify their structural properties. Our results showed that both are small-world networks with the stock-indices network being assortative by mixing degree and closer to a random network than the exchange-rates network, which was found to be dissasortative by mixing degree. This is a qualitative difference between the two networks as well-connected economies in terms of their exchange rates prefer to connect with economies less well-connected in terms of the exchange rates as opposed to the stock-indices behaviour where the preference is toward the well connected nodes! Interestingly, our study also revealed that both networks have a relatively small modularity, suggesting that both networks have sparse connections between the nodes within modules and denser connections between nodes in different modules.

Our analysis showed that the normalised Mutual Information Rate is a mathematical method that can be used to infer the network structure in complex systems. Since variables in complex systems are not purely random, it is a method to estimate the amount of information exchanged among nodes in systems such as financial markets and identify their connectivity. In our study, the method allowed us to infer two networks and how the world-wide economies are connected to each other and the structural properties of the inferred networks. The currency exchange-rates and stock-indices networks are relevant for risk managers to use as an investment strategy employed in portfolio management as they give an indication of the mix of assets to hold in order to form a well-diversified portfolio.

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CALEB KRAFT from Make shows us the Z6 Riot Control Baton that he built. The build cost would be low since most of the construction is out of PVC piping. The result is a great looking weapon. It has actually been enhanced with a bit of high voltage danger for some extra fun. An internal spring allows the unit to spring into action.


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Cyrtomium pachyphyllum är en träjonväxtart som först beskrevs av Eduard Rosenstock, och fick sitt nu gällande namn av Carl Frederik Albert Christensen. Cyrtomium pachyphyllum ingår i släktet Cyrtomium och familjen Dryopteridaceae. Inga underarter finns listade.

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text:
For Sake of the Forest
Parks | Education | Blogs | Notes From The Field | July 2015
Once upon a time, the land that would become Ohio was a wild place. Two-hundred and sixty-five years ago, nearly 100% of "Ohio" land was covered in dense, unclaimed forest. Your nearest shopping center was not even a glimmer in someone's mind. Elk, bobcat, black bear, gray wolf, and even American bison thrived here. Most appealing though, were plentiful beavers, whose fur was literally worth its weight in gold, serving as the driving force behind European exploration of Ohio during the mid-18th century.
After the Revolutionary War, the land we know as "Ohio" was officially named the Ohio Country or Ohio Territory. Enticed by affordable land, anti-slavery legislation, and a massive freshwater lake, migrants from New England, Pennsylvania, and New York rapidly settled the Ohio Country. Like the traders, they continued to hunt game for their fur, but also began to clear forests for valuable lumber and farmland, build mills and factories on Lake Erie and its rivers, and ultimately force many larger predators westward and out of Ohio. As the Industrial Revolution progressed onward, it solidified the mentality that man truly could tame and own the earth. By 1850, all the bobcat, gray wolf, and beavers were gone from Ohio, as were most of the native peoples. Ohio was no longer a wild place, but instead home to two-million settlers, and two of the country's most powerful cities, Cleveland and Cincinnati.
Today, many people think of Ohio and the Midwest as being quite tame, known more for for mini-vans and golden retrievers than bears and bobcats. It can be difficult to recognize remnants of Ohio's former wilderness around you. However, thanks to stringent legislation and environmental activism, 31% of Ohio is forested. "Forested," however, does not mean we have rampant, ecologically rich wilderness like we did 265 years ago. Only 1% of the forest is protected in parks and reservations; the other 30% is timberland or privately owned by Ohio citizens. In fact, though Cleveland Metroparks is immensely distinguished in the scheme of park systems, our Emerald Necklace is a thin ribbon compared to the privately-owned development surrounding it.
Since you are reading this blog, it's likely that you have an interest in nature and consider it valuable in more ways than the fur traders did. With that in mind, fathom this statement carefully: Nearly 5-million people control the 94-million privately-owned acres of forest in Ohio, the 30% not formally protected from development. This means that the health of our forests is mostly influenced by people who can possibly choose to develop their land in the future. The truth is, with the price of lumber and land on the rise, it will be tempting for people to consider selling their property for development. Ecological changes are imminent; however, if you are one of those people who look upon nature dearly, there are things you can do now and in the future to ensure Ohio will always have backwoods.
Interestingly, you need not own acres of forest to encourage ecological prosperity. Nature can work with us, after all, we are part of it. Much of the forest in Ohio isn't actually "forest" per se. Forest, according to the Ohio Division of Wildlife, can be a few trees in a fenced-in backyard with a mowed lawn. Even on official land-cover maps of Ohio, Cleveland Heights to Akron is colored green for "mixed forest." That being said, Ohio doesn't have as much forest as "31% forest coverage" leads you to believe. However, if you are one of those people with a manicured lawn and an oak tree, you absolutely still matter in the scheme of ecological development and you don't have to make drastic sacrifices! For example, consider planting native wildflowers and plants in your yard. Doing so will create mini-habitats for critters, especially attractive ones like butterflies and birds. The smaller critters are very important in ecology because they support larger fauna and often act as pollinators. Additionally, if you have fenced property, consider leaving a small opening in the fence, perhaps even just 6 inches high and a foot long, near the ground to allow squirrels and raccoons to pass from yard to yard. Doing so will connect your property to your neighbors, thereby expanding suburban habitat for wildlife, and still allowing you to preserve your privacy. Otherwise, our pockets of "forest" are not even part of the same system. However, if you are one of those people willing to make a very forward contribution to preserving wild places, you may want to consider donating your land to a protected park system or creating a plan for loved ones to take care of your land in the future.
Being a steward of the forest and rivers need not exist only in the realm of private property. Always respect and follow guidelines posted in protected wildlife areas, such as never taking or damaging any property or wild creatures. However, please do enjoy them and allow them to thrive in wild Ohio!
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Drs. Carson Murray, Catherine Markham and Maggie Stanton from George Washington University will be on hand with touchable objects to answer questions and talk about their research.
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text:
Q: Stabilized propensity weights: intuition and ATT formula The average treatment effect (ATE) of binary treatment T on outcome Y can be estimated using inverse propensity weights:
\begin{equation}\nonumber
\frac{\sum_{i=1}^{N}t_i\hat{\pi}_i^{-1}y_i}{\sum_{i=1}^{N}t_i\hat{\pi}_i^{-1}}-\frac{\sum_{i=1}^{N}(1-t_i)(1-\hat{\pi}_i)^{-1}y_i}{\sum_{i=1}^{N}(1-t_i)(1-\hat{\pi}_i)^{-1}} \xrightarrow{p} E[Y^1]-E[Y^0]
\end{equation}
where $\hat{\pi}_i$ is the estimated propensity for individual $i$, $Y^1$ is the outcome under treatment and $Y^0$ the outcome under the control condition.
To avoid extreme weights, there is a literature that suggests replacing the numerator of the treated weights with the marginal probability of treatment, $p(t=1)$, and the numerator of the control weights with $1-p(t=1)$. I see how this makes the weights milder, but why those particular numerators? What is the intution of this stabilization?
Also, the average treatment effect on the treated (ATT) can be estimated by weighting control units with the odds:
\begin{equation}\nonumber
\frac{\sum_{i=1}^{N}t_iy_i}{\sum_{i=1}^{N}t_i}-\frac{\sum_{i=1}^{N}(1-t_i)\hat{\pi}_i(1-\hat{\pi}_i)^{-1}y_i}{\sum_{i=1}^{N}(1-t_i)\hat{\pi}_i(1-\hat{\pi}_i)^{-1}} \xrightarrow{p} E[Y^1|T=1]-E[Y^0|T=1]
\end{equation}
How should one stabilize these $\hat{\pi}_i(1-\hat{\pi}_i)^{-1}$ weights?

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