Bicycle tours can transform the way you see your destination. Rather than rushing through, bike trips give you the time to notice small details: the way the road bends, how the sun glints off the water, the scent of an olive grove.
Once you reach your destination and join the group, you'll find that many bike tours cover just about everything (lodging, meals, equipment). No matter your skill level, the tour provider will trail you with a vehicle for "support."
In other words, if you want to stop and linger, you can leave the group for a bit and not have to worry about the logistics, like "Who's got my bike?" or "Where's the awesome cheese I bought?"
Highlights: On this eight-day tour round-trip from Galway, the routes in this rugged area of west Ireland are away from the main tourist areas, so you can really enjoy the landscape at your own pace.
What's Included: Seven nights accommodation with full Irish breakfast; one evening meal; a bike rental; a guide in a support van; luggage transfers; all route details and maps; and a ferry ticket to the Aran Islands.
Highlights: Departing from St. Helens, you'll wind through coastal roads towards Bichero, where just before arrive you can feed--and maybe touch--Tasmanian devils. Other destinations and distractions include winneries, coastal fishing towns, white sandy beaches, and the rugged granite mountains of Freycinet National Park.
What's Included: Six nights' accommodation; some breakfasts; picnic style lunch, snacks, and drinks; park fees; tour leader; luggage and bicycle transfers; and a support minibus and purpose-built trailer.
Cost: From A$1,090 per person; add A$100 for bike rental.
Pace/Terrain: An average of 23-38 miles per day over rolling terrain for moderate to advanced cyclists.
Highlights: Located between the Green Mountains and the Adirondack Mountains, Lake Champlain is one of the largest lakes in the U.S. (after the Great Lakes). These bike tours include various routes that are ablaze in fall colors.
What's Included: Transfers from/to Burlington; five nights' lodging (historic inns and a lakeside resort); all meals except three lunches and one dinner; van support; a cruise across Lake Champlain; bike rental; and a tour of a local chocolatier to sample its gourmet chocolate.
Pace/Terrain: An average of 22-55 miles per day over rolling terrain for intermediate cyclists. However, a variety of routes are offered for recreational and more advanced cyclists.
Highlights: Dine on freshly caught seafood, experience Catalan cuisine, and enjoy the Mediterranean views while cycling from Barcelona, through the foothills of the Pyrénées, and to Salvador Dali's castle.
What's Included: Five nights accommodation; bike rentals; all breakfasts, four lunches, and four dinners; snacks and beverages in between meals; special events including wine tastings; a support van; and baggage transfers and handling.
Pace/Terrain: Backroads designs tours for all abilities, so you can bike 18-55 miles per day, depending on what route you choose.
Highlights: How about 3,750 miles of highlights over 45 days? The trip, which is brought to you by the same company that makes the well-regarded Trek bicycles, spans the U.S. from Portland, Oregon to Portland, Maine. Beaches, shopping, and historical site visits are also part of the trip.
What's Included: 44 nights' accommodation; daily breakfast, 39 lunches, and 36 dinners; all snacks and drinks for each day; six social hours; daily route support; Trek bicycle rentals, helmets, and other gear; luggage transfers; all transportaion; maps; all gratuities for special events, local guides, transfers, hotels and restaurants.
Pace/Terrain: Varied, but intense (20-134 miles per day; Trek Travel will interview you prior to booking to ensure you're prepared for such a strenuous journey).
Highlights: The backdrop includes pine forests, the Old West town of Deadwood, and abandoned railroads along Mickelson Trail. Ride through the Black Hills, hike the base of Mount Rushmore, and visit the monument dedicated to Crazy Horse.
What's Included: Five nights accommodation; all meals except one dinner; bikes, helmets and other equipment rentals; tour support; and luggage transfers.
Pace/Terrain: About 15-32 per day miles per day over rolling terrain.
Highlights: Mount McKinley towers above you at 20,000-plus feet, the largest mountain in North America. You'll also ride along Denali Highway, while hiking, canoeing, and sea kayaking provide alternate views of Alaska's glaciers, mountains, and wildlife. Get close to the source with a glacier cruise across Prince William Sound.
What's Included: Anchorage-hotel transfers; seven nights' lodging in cabins, inns, and hotels with all meals, snacks and beverages (except alcoholic ones); a Cannondale bicycle rental; support van; a glacier cruise; and the use of canoes and kayaks.
Pace/Terrain: About of 15-39 miles per day at an easy to moderate pace.
Highlights: Cycle through Lithuania, Latvia, and Estonia with this tour of the Baltic States. Stops and sights include churches and monestaries, castles, national parks and forests, beaches and snd dunes, medieval towns, and the summer palace of Peter the Great. It'll be an international crowd, so the tour is conducted in both Dutch and English.
What's Included: Eight nights in comfortable hotels with private showers; a bike rental, breakfast daily; city-to-city transport, and a tour leader who speaks Dutch and English.
Photo Caption: Get to know Riga and its striking architecture on Djoser USA's Baltics Bike Tour.
Highlights: This Kerala vacation puts you among tropical greenery, with routes along tea and spice plantations and stops at four wildlife reserves. One of your nights is on a houseboat.
What's Included: Thirteen nights' lodging (eco-lodges and historic hotels); all meals, snacks, water, and cold towels along the way; van support; local guides; and a cycling leader.
Highlights: Travel through virtually traffic-free roads of Italy's southeastern peninsula of Apulia, Puglia in Italian, to experience a land of caves, white villages, and olive groves. Other destinations and features include two UNESCO World Heritage Sites, Roman ruins, and Adriatic beaches.
What's Included: Seven nights accomodation with daily breakfast; luggage transfers; marked maps and/cue sheets and route notes; pre-loaded GPS unit.


Having a personal goal of a retirement account with $1 million in it is noble, even though 1,000,000 won't necessarily get many very far in retirement. Certainly not as far as it did 20, 30 or definitely 50 years ago. Nonetheless, that target figure is worth shooting for for the masses.
For the super elite, however, it's chump change.
According to Bloomberg, in the world of private bankers who cater to the uber-wealthy, having $25 million in investable wealth makes one considered rich and provides the "basic service" from private wealth bankers.
Business Class for the uber-wealthy in a private banker's eye takes $100 million; First Class, $200 million; and Private Jet Rich, $1 billion.
Set your goals as your needs dictate.
Once again it was the NASDAQ and Russell 2000 indices where positive strides were recorded last week.
If the indices are telling investors anything, it's to have a diversified portfolio.
Nothing exciting about that news except that it's always wise advice.
Netflix (NFLX) up 81.96%; Micron (MU) up 12.41%; and Align Technology (ALGN) up 42.69%.
DISH Network (DISH) down -36.15%, NetEase(NTES) down -35.37%; and Dentsply Sirona (XRAY) down -29.80%.
Below are the weekly and 1-year index performance results for four major indices— including the dates each reached new highs—according to CNBC.com based on prices at the close of business on Friday, May 25, 2018.
With the beginning of summer fast approaching, and the old saying reminding investors to sell in May and go away, that play hasn't been particularly a good one within the mutual fund arena—so far.
For example,the average performance of the funds under the U.S. Diversified Equity Funds heading was up 3.34% year-to-date at the close of business on Thursday, May 24, 2018. That's much higher than it was three weeks before– on May 3 it was 0.65%.
Also with heading averages up 10% or more year-to-date were Science & Technology Funds, 10.98%; Global Science & Technology funds, 10.79%; and Commodities Energy Funds, 12.99%.
Not sure who in Washington has noticed, but it has been apparent to most folks living across America that the cost of living is going up. Who hasn't noticed that the increased cost of a gallon of gas makes an impact in the amount of disposable cash one has in their pockets? Or that groceries, even at places like Aldi's, cost a little more? And that a buck or two increase in one's hourly pay doesn't translate to much?
So it may come as no surprise that people are using their credit cards more and more. And, not paying their balances off in full each month.
According to MyBudget360.com, there is more than $1 trillion in credit card debt outstanding in America these days. Most of that debt is on cards issued by smaller banks.


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Are deli meats safe? What you need to know about cold cuts
With its processed reputation, deli meat is not making many "clean eating" lists these days. But is it really that bad for you?
March 9, 2016, 4:38 PM UTC / Updated Oct. 7, 2019, 2:49 PM UTC / Source: TODAY
By Frances Largeman-Roth, RDN
Behold the sandwich, a beloved American icon. In fact, 50% of Americans eat one each day. But these days you may be thinking twice about grabbing a sandwich on your lunch break — and not just because of the carbs in the bread. With its processed reputation, deli meat (including sliced turkey, ham and roast beef) is not making many "clean eating" lists these days. But is it really that bad for you? The answer is, it depends. Let's take a closer look.
BLT? Grilled cheese? This is America's favorite sandwich
Aug. 12, 201901:58
If you do a quick Google search on deli meats, you'll find all kinds of articles that link the nitrites in these meats to certain types of cancer. OK, so why are nitrites in deli meat? According to the North American Meat Institute (NAMI), sodium nitrite is a salt and an antioxidant that is used to cure ham, bacon and hot dogs. It also stops the growth of botulism-causing bacteria, prevents spoilage and gives cured meats their color and flavor. Sodium nitrite also helps prevent the growth of Listeria monocytogenes, a bacteria that causes the foodborne illness listeriosis.
Listeriosis causes fever, headaches, fatigue, muscle aches, nausea and vomiting. It's especially dangerous for pregnant women because even if it doesn't make the mom feel sick, she can still pass it along to her baby, causing serious complications. Listeria is a tricky bacteria because it can grow under refrigeration. Deli meats and hot dogs are only safe for pregnant women if they are heated to 165°F, and that pretty much negates the idea of a quick sandwich.
Health & WellnessNutritionists share 9 tips to make a healthier sandwich
You may be wondering how "natural" and organic deli meats stay fresh. They use plant-based, naturally occurring nitrites, such as those found in celery, beets and sea salt. The label will likely say "No nitrates or nitrites added" and then in smaller type, "Except for those naturally occurring in sea salt and celery powder." So, you're getting nitrites in traditional deli meats, as well as the better-for-you stuff.
Overall safety
When nitrites combine with amines in meat they create nitrosamines, which some studies have found to be carcinogenic. Only about 5% of the nitrites we eat come from meat. We actually get most of them from plants and water. However, according to the World Health Organization (WHO), the consumption of processed meat is associated with small increases in the risk of cancer. And the more you eat, the greater the risk is. But WHO has not been able to determine what it is in processed meat, like deli meat, that actually increases cancer risk.
How to stock your fridge properly (and what NOT to put in it)
Feb. 8, 201703:06
Sodium overload
Processed foods tend to be high in sodium. The 2015-2020 Dietary Guidelines for Americans recommend eating less than 2300 milligrams of sodium per day. A 2-ounce serving of sliced roasted turkey breast contains between 360-590 milligrams of sodium. That's not so bad, but once you add two slices of bread (240 milligrams), a schmear of mustard (125 milligrams) and perhaps some mayo (85 milligrams), you're looking at a sodium total of 810 to 1040 for your sammie. As long as you balance out the rest of your day with fresh produce and other foods that are low in sodium, you're fine, but if dinner is take-out, you're very likely to exceed the sodium limit.
If sodium is a concern for you, look for low sodium deli meat, which has about 115 milligrams per 2-ounce serving, or try no salt added, which only contains 35 milligrams per serving.
FoodWhat's really in your deli meat? How to decode labels on packaged ham, turkey and more
How long do they last?
You buy a package of sliced ham at the grocery store, bring it home and put it in the refrigerator. Maybe you make a few sandwiches on the weekend and then you put the package back in the fridge. The clock is now ticking and you have three to five days to use them. They may not smell bad or look bad, but remember — listeria can grow in the refrigerator. If you purchase meat sliced fresh at the deli, it needs to be eaten within three days.
If you enjoy deli meat, eat it occasionally and refrigerate it properly. And it's safest to avoid eating it while pregnant.
Frances Largeman-Roth, RDN, is a nutrition expert, writer and best-selling author. Her books include Feed the Belly, The CarbLovers Diet and Eating in Color. Follow her @FrancesLRothRD.

redpajama_set_name: RedPajamaCommonCrawl


The Saints seemed to be permanently looking for a full-back in the late 1930s. They signed Rochdale veteran Walter Gowers for a spell and also obtained the services of Bob Fraser, who played against the Saints in the 1930 Challenge Cup final for Widnes.
His first match against Salford showed him to be a class act. He appeared quite quick and he had excellent hands, even though, according to the St. Helens Newspaper, he tired in the second half. He could not be blamed for the 0-20 reversal, against a talented Salford team. Bob played his last game against Hull KR at Knowsley Road in a 9-2 success on 18th February 1937 and that was that. We do not know why he no longer figured on the team sheet after just 8 games, but it could well have been a result of further injury. Bob was an HGV Driver and in 1939 he was living in Russell Road, Runcorn. He died in February 1987 and his death was registered in Sefton North. If anyone can tell us more information, we would like to hear from you.


Jennifer is unhappy with the way some things are run in school. Would she dare to voice her opinions and risk offending the principal ? The workplace beckons again. After deciding to take a long absence from work, her bosses call and ask Durrani to reconsider taking even more responsibility at the law firm. Will the adrenalin of work lure Durrani away from her family once again ? Min Yi's father, Eddie decides it is time to make amends with Min Yi if he wants to get close to his grandson. Then again, maybe some wounds are just too deep to heal. Has Eddie lost the chance to be a part of his only grandchild's life ?


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Pittsburgh RE/MAX Agent Gives Back
May 26, 2020 | By lkriegel
Throughout the year, RE/MAX agents and offices across the United States raise funds from the Miracle Home Program, which allows agents to make donations and send honor cards to clients letting them know that they have made a donation in their name.
For one Pittsburgh RE/MAX agent, Deb Reddick, the contribution to the Miracle Home Program is more than just a donation to an amazing cause – it's personal.
At 11 months old, Deb's granddaughter, Lennox Hope Reddick, was on her way to taking her first steps – one of the most exciting milestones for a toddler and their families! It was October 28, 2019 when Lennox suddenly stopped standing or moving her legs. Her parents took her to the Emergency Department at UPMC Children's Hospital of Pittsburgh and were given heartbreaking news later that same night. An MRI showed that Lennox had a tumor that had grown into her spine and was pushing on her spinal cord. Emergency chemotherapy and steroids were administered. After several biopsies and scans over the days that followed, Lennox was diagnosed with stage 4 Neuroblastoma; not only did Lennox have a solid tumor, it had spread into her bone marrow.
Lennox championed through six rounds of chemotherapy, along with all the pokes, procedures, and scans that come with treatment of pediatric cancer. Post-chemo scans and a bone marrow biopsy in February 2020 showed no evidence of disease! The Reddick family could not be more grateful to the oncology team and others who treated their daughter at UPMC Children's. The doctors, nurses and staff, including child life, provided every comfort and support system to Lennox, her parents, and her older brother throughout the process. It is because of them that sweet, strong Lennox is now walking, reaching other amazing milestones, and providing HOPE to other families.
Thank you, Deb Reddick and RE/MAX 360 Realty, for helping kids in your community! You can learn more about the Deb Reddick Team by visiting: https://www.reddickteam.com
RE/MAX has been a proud supporter and partner of CMN Hospitals and have raised more than $500,000 for UPMC Children's Hospital of Pittsburgh since 1994.
Honor Cards
Miracle Home Program
Previous post: Panda Express Feeds Frontline Staff at UPMC Children's
Next post: Meet the Children's Miracle Network Hospitals Leading the Charge on LGBTQ Equality


Food Corporation of India (FCI) has issued the call letter for written exam for the recruitment of Junior Engineer (JE) (Civil Engineering / Electrical Engineering / Mechanical Engineering), Assistant Grade-II (Hindi), Typist (Hindi) and Assistant Grade-III (General / Accounts / Technical / Depot) in 2015-16. You can now download the admit card.
How to download FCI JE, AG Gr.-II/ III Exam Call Letter?
Please Note : The admit cards for rest of the zones have not been released. It will be released in due course. We will update further details on our website.
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QFC's Capital Market Report 2020
Qatar Financial Centre (QFC), one of the world's leading and fastest growing onshore business and financial centers, launched its landmark 'Qatar Capital Market Report 2020,' which highlights major capital market advances in Qatar, identifies how comparator markets have forged ahead on their developmental journeys and how QFC and its regulatory partners can build further on its successes to become a regional world-class financial hub
Source of the report: Qatar Financial Centre - Qatar
Investment in the State of Qatar
Qatar represents an attractive destination for foreign investments, offering investors political and social stability in addition to a full range of benefits to energy-consuming industries. The State of Qatar has the largest gas reserves in the world and enjoys a strategic location within the Gulf region. Furthermore, the State of Qatar has adopted a free market economy and enacted laws and legislations that are being continuously revised to ensure an open economy with no business restrictions, customs burdens or limitations on money transfers in addition to providing incentives and facilities to bolster investors' profits.
Source of the report: Ministry of Economy and Commerce - Qatar
Qatar Monthly Statistics
Qatar Monthly Statistics" Bulletin is released on a Monthly basis by the Planning and Statistics Authority. It contains the previous Month's preliminary data
Source of the report: Planning and Statistics Authority - Qatar
Qatar National Vision 2030
Qatar National Vision 2030 builds a bridge between the present and the future. It envisages a vibrant and prosperous country in which there is economic and social justice for all, and in which nature and man are in harmony. We need to galvanize our collective energies and direct them toward these aspirations. Strong Islamic and family values will provide our moral and ethical compass
Source of the report: General Secretariat for Development Planning
The Annual Report 2019 from The World Bank
Ending Poverty, Investing in Opportunity
Source of the report: The World Bank
Monthly Press Release on Foreign Merchandise Trade Statistics – February 2020
The Planning and Statistics Authority has released preliminary figures of the value of exports of domestic goods, re-exports, and imports for February 2020.
Press Release on Monthly Producer Price Index February 2020
QCB Financial Stability Report 2018
The current FSR provides a review of events influenced Qatar's financial sector during the year 2018. The review also summarizes the activities of Qatar Financial Centre (QFC) and Qatar Financial Markets Authority (QFMA) for
completeness. The analysis is based on the data up to the financial year 2018
Source of the report: Qatar Central Bank
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Tag Archives: Argentina
Foreign Policy, International Organizations, South America
A New Weapon in the South Atlantic
2016-08-01 Jon Nielsen	3 Comments
In 1982, a continuing dispute over a few small, sparsely populated islands in the South Atlantic became the catalyst for a brief war between Argentina and the United Kingdom, resulting in a decisive British victory. Paying tribute to the long and complicated history of these islands, Museo Las Malvinas (Malvinas Museum) is located on the grounds of the former Naval Officer Mechanic School and is now one of the newest and most prized museums in the Argentine capital of Buenos Aires. Since its opening, the museum watchtower has maintained vigil over the main highway running through Buenos Aires, proudly displaying the word "Sovereignty", and reminding commuters of Argentina's enduring ambition to govern these otherwise undistinguished islands in the South Atlantic.
According to the March 2016 United Nations Commission on the Limits of the Continental Shelf (CLCS), the Falklands/Malvinas lie within Argentina's maritime borders. Thirty-four years after fighting to keep the islands, the British see this as a new threat to their continuing sovereignty over the islands and their surrounding waters. Learning from past mistakes, Argentina has introduced a new weapon in the struggle over the South Atlantic –not a machine of war, but a potentially more terrifying and effective tool: lawyers.
Argentina's claimed maritime territory in the South Atlantic. Photo Credit: http://news.bbc.co.uk/2/hi/business/8520038.stm
Miscalculation
The struggle for sovereignty of the islands is nearly two centuries old. English Captain John Strong discovered the islands in 1690 and the first British settlement followed in 1766. For decades British control of the islands waxed and waned during multiple international wars, no easy feat as the Falklands/Malvinas are located a daunting 7,939 miles from London and a mere 415 miles east of the Argentine city of Rio Gallegos. Finally in 1833, after several disputes with the fledgling Argentine Confederation, the British solidified control and have maintained their hold on the islands despite the expense of supporting the territory over such a vast distance. With only 2932 English residents, the Falklands/Malvinas are one of the most expensive foreign territories per capita in the world.
The pursuit of sovereignty over the Falklands/Malvinas was a costly decision for Argentina's military dictatorship. In 1982, daily strikes by labor unions and anti-government supporters were a result of the loss of confidence in the leadership of Lt. General Leopoldo Galtieri who had assumed command of the junta after a 1976 coup that deposed President Isabel Perón. Unemployment rates were skyrocketing, and the inflation rate ballooned to more than 600 percent. In an effort to distract the population from the collapsing economy and to restore national pride and support for the government, Galtieri ordered the invasion of the Falklands/Malvinas on 2 April 1982.
The initial days of fighting saw great success for the Argentine military. The first 4,000 soldiers arriving on the islands met minimal resistance and quickly took control, raising their flag over the captial city, Port Stanley. Their victory ignited strong nationalism, pride, and support for the military by ordinary Argentines that flooded the streets of every city in the country. The military government fostered this sentiment by publishing propaganda and positive reports promoting the success of their military. Triumphant claims —"Estamos Ganando" (We Are Winning) graced the covers of newspapers and magazines nationwide.
Across the Atlantic, the British government rapidly prepared a response force to take back the islands. Less than three weeks after the initial invasion, the UK launched a counterattack with more than 120 ships, 160 aircraft, and multiple Special Air Service (SAS) and commando units. The British quickly gained the initiative, and by 14 June 1982 the 3-month war was over. In the end, a little more than two months of combat resulted in the deaths of 648 Argentine, 255 British service members, and three civilians. Most of the Argentine casualties –and the fighting spirit of the Argentine Navy– lay at the bottom of the Atlantic with the ARA Belgrano, sunk by a British torpedo. The islands have remained securely in Britain's hands ever since.
A few months after the war, the international community legitimized the British presence in the South Atlantic. The 1982 UN Convention on the Law of Sea established the limits of the continental shelf and solidified British rights to the water and resources surrounding the Falklands/Malvinas. In 1998, after tentative discoveries 20 years earlier, the British began drilling the first offshore oil wells, discovering large reserves in the area with two fields, Sea Lion and Isobel Elaine, thought to hold half a billion barrels of recoverable oil each. These, and many other repositories around the islands, have the potential to make the tiny population there one of the richest communities per capita in the world.
The discovery of oil intensified debate on both sides of the Atlantic but the situation on the ground remains quite complex. Though a majority of Argentines believe that the Falklands/Malvinas belong to Argentina, multiple referenda reveal the actual residents of the islands want to remain under the British crown. Citing concerns about stability and security, the British maintain a substantial military presence that includes strike aircraft, warships, and more than 1,300 service members. The Argentine government sees the presence of such a large and active military force as a threat and has argued this point continuously and unsuccessfully to the international community.
UNCLCS decision of March 2016 includes the islands within Argentina's maritime borders. Photo Credit : http://en.mercopress.com/2016/06/03/delimitation-of-the-argentine-continental-shelf
Changing the Game
In 2015, Argentina began to use a different approach that avoids direct confrontation with Britain. Filing a petition with the United Nations, the Argentine leadership decided to pursue international arbitration to prove the islands reside within the maritime territory of Argentina. In March 2016, after more than nine months of debate, the CLCS extended the maritime territory of Argentina by 35%. By doing so, the UN acknowledged Argentina's claims that the Falklands/Malvinas lie within its maritime territory.
Victorious on the battlefield and secure on the basic question of governance of the islands, Britain now faces an unusual challenge to its supremacy in the South Atlantic –an internationally-arbitrated legal battle over resources. Historically committed to international cooperation and the rule of law, the UK would face significant challenges should it choose to ignore the UN, especially as doing so would set a precedent for other states looking to circumvent international arbitration.
Argentina may not yet have achieved the lofty goal of "Sovereignty" as displayed atop the watchtower at the Las Malvinas Museum, but it has found traction in pursuing a legal resolution to the territorial dispute. Now more than ever, the Falklands/Malvinas are an economically and strategically significant territory for the UK, and it is unlikely Britain will let the islands go easily. However, after nearly two centuries of struggle, the balance of power relationship in the South Atlantic may finally shift to favor Argentina thanks to a new tactic that neutralizes the otherwise superior power of the British. Perhaps the pen is truly mightier than the sword.
CPT Jonathan Nielsen is a U.S. Army Infantry Officer with combat experience in multiple countries in the Middle East and extensive multinational training experience. He is currently attending the University of Belgrano in Buenos Aires. The views expressed in this article are those of the author and do not reflect the official policy or position of the U.S. Army, or the Department of Defense.
ArgentinaBritainCLCSFalklandsMalvinasUKUN
Let's Change: Argentine Voters Speak
2016-01-05 Jon Nielsen	Leave a comment
In the first run-off election in the history of Argentina, the people's voice demonstrated a drastic change for the future of Argentina and potentially the international community.
On 9 August 2015, all seemed certain that Frente Para La Victoria (FPV) candidate Daniel Scioli was destined to continue the current 12-year reign of the Peronist Party in Argentina. However, after the presidential primary elections were held the assurance that Argentina would continue marching under the same political party rapidly disintegrated with every passing day. As the days and months grew nearer to the presidential elections on 25 October 2015, the voice of the Argentine voters echoed one unified idea: change. The people of Argentina rallied behind a new hope and a new image of an improved future. The face of that future rested in the ideas, initiatives, and spirit of one man that led not only a young political party, but a significant social movement. That man was Mauricio Macri.
Top: Presidential Elections, 9 August 2015
Bottom: Presidential Elections, 25 October 2015
A Rising Opposition
Macri was the face of the political opposition, "Cambiemos," (Lets Change) and rightfully so as he started the party just eight years ago when he ran for and was elected mayor of the City of Buenos Aires. Many would describe Macri as a well-connected businessman whose party was considered by so many as having a long shot to win the elections and lacking the political influence to govern the country. However, with every passing election in 2015, "Cambiemos" demonstrated the power of a socially connected and driven movement.
First, Horacio Larreta maintained the influence of the Cambiemos party within the city of Buenos Aires when he was elected mayor on 19 July 2015. A few months later during the primary elections, Maria Vidal defeated Anibel Fernandez (FPV) to claim the governorship of the Province of Buenos Aires. So as the first national run-off election approached on 22 November 2015, the strength and the support for the Cambiemos movement should not have come as a surprise to the FPV Party. However, even with the energy behind the Cambiemos Party and Macri's continual climb, he was substantially aided by three significant flaws made by Scioli, the FPV Party, and the current government.
The Lead up to the Election
The first of those errors occurred immediately following the primary elections in August 2015 when the Province of Buenos Aires suffered a significant natural disaster. In the two weeks that followed the primary elections, the Province of Buenos Aires received more than 14 inches of rain causing water levels in some areas to raise more than 30 inches and forcing the evacuation of more than 30,000 residents. The storm was one of the worst in history. As the storms started, then Governor of Buenos Aires, Daniel Scioli, departed for Italy to receive treatment on his prosthetic arm while leaving little to no plan to resolve the dire situation. So as Scioli remained in Italy, the floods continued to worsen and Macri remained as the strongest voice of support to aid those in need and provide a plan to assist with the situation.
The second event that continued to deflate Scioli's campaign occurred on 5 October 2015 during the first-ever presidential debate. The debate consisted of all Presidential candidates except Scioli who elected not to participate. In the events leading up to the debate, Scioli stated that nothing good comes from a debate. A portion of that comment proved to be true, but the reality was that nothing good came for Scioli. As Scioli watched from a distance, the other five candidates used the forum to promote their ideas and highlight the flaws of the current frontrunner, Scioli, resulting in a significant drop in polls for Scioli the following week.
Results of the run-off election showed Macri defeated Scioli and signaled a change in Argentine Politics.
The third and most powerful of the issues that led to Scioli's defeat was his relationship, or better said, lack of relationship, with the FPV Party and the current President Cristina Fernandez de Kirchner. With every passing day after the primary elections, Scioli attempted to separate himself from the criticisms of the Kirchner government in order to obtain more of the votes needed to achieve 45% of the popular vote and avoid a runoff election. However, in doing so, he continued to lose the confidence and support of his party, so much so that President Kirchner would not publicly endorse her support for him as a candidate, an endorsement she did with many other FPV candidates. Furthermore, a week before the runoff election, President Kirchner affirmed that if Scioli was to lose, his decline was not a fault of her or the FPV Party, but that of the actions of Scioli. And as predicted by many, Scioli was defeated. Scioli started as a candidate who presented a grand idea for the future of Argentina, but ultimately lacked the plan, conformity, and energy to continue the reign of the FPV party.
Daniel Scioli's collapse was fast and essentially unstoppable after the first initial polls in July when he garnered close to 50% of the vote with more than 10 presidential candidates. However, at the end of November when there were only two candidates, he was unable to achieve 50% of the vote to secure the presidency. Thus, Scioli fell to Macri in the runoff election by close to 4% of the popular.
The future of change now rests with President Macri.
The defeat of the FPV Party and the victory of the Cambiemos Party signal a new future in the domestic and international relations of Argentina. As President Macri donned the presidential sash and grabbed hold of the presidential scepter, he takes office with grand ideas to improve the international relations of Argentina most notably with the United States and European leaders. Furthermore, President Macri vows to improve the security situation surrounding the increased drug trade in Argentina as well as improve critical infrastructure along major routes throughout the country. Lastly, among many issues that Macri strives to improve, he looks to take strong and swift action to improve the rapidly devaluating peso that has plagued the people of Argentina for the past two years.
Many of Macri's right-wing ideas demonstrate a significant shift in the mentality and actions of the future government of Argentina. The question that remains is whether that shift will provide that "change" that Argentines voted for during the country's first runoff election. Nevertheless, one can expect to see a different domestic and international Argentina in the years to come.
CPT Jonathan Nielsen is a U.S. Army Infantry Officer with combat experience in multiple countries in the Middle East and extensive multinational training experience with various NATO partners. He is currently attending the University of Belgrano in Buenos Aires. The views expressed in this article are those of the author and do not reflect the official policy or position of the U.S. Army, the Department of Defense, or the U.S. Government.
Argentinaelections
Some More Equal than Others
2015-06-19 Jared Wilhelm	3 Comments
Argentine President Nestor Kirchner, left, transfering power to his wife Cristina on her inauguration day in 2007 (Infolatam)
In the midst of presidential election season, life in Argentina today is a bizarre mixture of cries for equality and human rights, contrasted with insidious government propaganda, limits on personal freedom and frustrating consumer choice.
By Jared Wilhelm
In October 2015, the citizens of Argentina will elect a new president. For the past twelve years, the large and resource-rich South American nation was headed by a member of the Kirchner family: first Nestor in 2003 and then his wife, Cristina since 2007. Much in the vein of famous Argentine first lady Eva "Evita" Peron, Cristina is a charismatic, populist figure within in the nation, exerting tight control over monetary policy, the media, and those who oppose her controversial policies.
The election will prove to be important not only for the nation, but also for the world. While Cristina can't run due to term limits, a victory for one of her Kirchnerismo Party candidates might signal a continuation of some of her edicts, like allowing China to build it's first overseas military base in Argentine Patagonia, cozying up to Vladimir Putin's Russia and his houseguest Edward Snowden, and alleged suspicious dealings with Venezuela and Iran.
Uncertain future aside, it is interesting to look past the sensational headlines of corruption and international intrigue to consider the day-to-day life of the average Argentine who lives with the oddities of Kirchnerite rule. Would Argentina's Founding Fathers- who modeled Argentina's Constitution almost exactly after the United States of America's in 1853– recognize the life that Cristina has imposed on the average citizen?
Free Fútbol for Everyone: A Captive Audience
While Argentina is famous for its grass-fed beef, wine and Tango, the key to the average Argentine's heart is soccer, or fútbol. The Argentine league is the third oldest in the world, and no Sunday afternoon family meal is complete without watching one of the local or national clubs on television.
Since 2009, Argentines don't need a satellite dish or a cable subscription to see their favorite local team, international tournaments or even the World Cup. Instead of tuning in to one of the major local networks, fans turn to the government-run TV Publica. Outbidding traditional media conglomerates with a contract nearly double the size of what private companies previously paid, Cristina uses taxpayer dollars to ensure there will be Football for Everyone broadcast on her own personal station.
Since there are sometimes weekends with no national soccer games, in addition to Fútbol para Todos, there are Boxing for Everyone and Racing for Everyone as well. Anyone used to paying $99.99 for a blockbuster boxing match will be shocked to see the broadcast free for all.
Under the guise of providing equal access across the class spectrum and in an effort to compete with opposition media tycoons, Cristina and her propaganda machine waste no time taking advantage of their captive audience. In place of selling advertising time to the highest bidder, the government uses these precious opportunities to reach the people using spots that tout the government's achievements, or to advertise for the state-owned airline, Aerolineas Argentinas, or the state-owned oil company, YPF. Both of these companies were expropriated from private businesses after takeovers by Spaniards in the 1990s turned sour.
In an almost eerie dichotomy with everyday life, the government's slickly produced propaganda spots show a country where everything works beautifully and everyone is happy. No matter if the commercials are one-sided campaign ads, showcases of nuclear energy projects, satellite launches or simple advertisements for upcoming free sports broadcasts, one clear message is always sent during the final few seconds of each spot: Brought to you by the President.
While taxpayers are paying for the production and airtime for the Ministries of Education, Tourism, Health or Science and Technology, each commercial includes a not-so-subtle reminder of who is behind it all. See an example here.
Journalists estimated in January 2015 that during its six-year existence, the Argentine people bankrolled a staggering $793 million US dollars worth of government propaganda through the program. Compared to a world where the market demand determines availability and private advertising dollars or subscriptions pay for broadcasts, taxpayer-paid government control of the majority of soccer broadcasting is a small price to pay for the Presidenta to spread her message on a daily basis to households across the nation.
Ever since Cristina expropriated the national petroleum company from Spain in 2012, the government controls the price of gas at the pump. Citizens no longer have to worry about the whims of supply and demand, world events or the decisions of the OPEC nations; instead, Cristina's government ministers decide the price of a liter and then negotiate with the rest of the privately-owned companies to cap prices for six months at a time.
Price controls don't stop at the gas pump. In 2014, Cristina launched a nationwide campaign to control prices and combat double-digit inflation on some 470 items in grocery stores, restaurants, airports, and even motorcycle dealerships. Cristina's economic team decided that instead of letting market forces drive the price of certain items in stores, the government would set the price. What does the Price Guarding program mean to the consumer?
When a shopper visits any grocery chain or Wal-Mart Argentina, a large list of every Price-Guarded item is posted at the front of the store. The store shelves look the same, except there are special Price Guard logos on certain brands of the milk, hotdogs, cereals… even beer. A pack of cookies that should cost $2 is only $1.50- a good savings for the generic, tasteless cookies. Unfortunately, these price controls have unintended consequences. If you don't like generic, tasteless cookies and instead prefer made-in-Argentina Oreos- well the Oreos that should cost $2.50 now can cost $4.50 or $5.
While businesses are not forced to comply with this scheme, more than 100 nation-wide chains have agreed to participate to maintain competitive market share. But Cristina and her team do not trust big business, so they encourage every day citizens to verify Price Guard prices are followed. During soccer broadcasts, government propaganda spots encourage shoppers to print out the Price Guard list and check to make sure the prices are set at the government's rate. If not, Cristina has set up a 1-800 number and smartphone-app that allows everyone to report businesses for charging extra. When someone calls to make a citizen's report, the odds are they probably won't be using an iPhone.
A Nation without iPhones
Since 2011, Argentina has not permitted the legal import and sale of Apple iPhones. Thanks to Cristina, only cell phones that are locally made can be sold on the legal market. Cristina and her economic team have decided that in order to spark national growth, imports of all kinds will be limited and the local industries will be protected. The idea is to create jobs: now instead of being assembled in Asia, Samsung phone parts are shipped across the Pacific so that they can be assembled in country by Argentines and then considered a legal product. The same goes for televisions, refrigerators, even cars.
If someone really, really wants an iPhone, there are options. Besides the healthy trade on the black market, one could order the phone (or any other prohibited foreign item) through a site like the US's Amazon. If the phone costs $300, the buyer is responsible for paying the $300 cost of the phone and shipping to Amazon, and then an additional 50% tax to the Argentine government. A $300 phone will cost around $500 and require a trip to a local customs office for pickup, because Cristina doesn't allow foreign deliveries to be sent directly to your home. Home delivery makes 50% tax collection complicated.
So if the average citizen of the Argentina has decided to save $300 of his or her hard-earned salary to buy an iPhone, unfortunately that citizen can't buy one in a local store. Instead, he or she must save $200 extra to order from overseas, or cross the border to Chile or Uruguay to buy one there. Many weekends, especially holidays, there are six to eight hour lines at international border crossings, allowing citizens to purchase Apple Products, Kitchen-Aid Mixers, X-Boxes or Lego toys for their children. On the way back into Argentina, they of course need to hide these products from customs so they don't have to pay the 50% tax.
While the import substitution policy protects local workers and the economy in theory, in practice it creates a class-based unfairness that even eclipses the most pure free-market capitalist society. The wealthy, mobile political class can and do afford travel overseas to buy superior products. The average worker has no choice but to buy inferior or obsolete products, based on the rules imposed by the supposedly well-meaning political class. In true Animal Farm style, political leaders artificially determine the market in the name of the "good of the economy," while jet-setting to the United States or Europe to buy the same products they keep from the people.
The Future of Argentina
Supporters of the three consecutive Kirchner administrations trumpet accomplishments on human rights, reparations from the most recent military junta, equality, social justice and Argentine sovereignty. While Cristina and her husband do deserve some credit, it is not enough to outweigh the restrictions imposed on personal freedom of action and thought as illustrated above. Surprisingly enough, there are many more examples that would elicit similar reactions.
In 1853, Argentina's forefathers based the Argentine Republic's Constitution on the model of the Constitution of the United States of America. By 1914, Argentina had become one of the world's richest nations. A century later, Argentina was ranked second out of 108 nations in the CATO Institute's 2015 economic "Misery Index," only less miserable than Venezuela and slightly more miserable than Syria, Ukraine and Iran. Ten more pages would be required to even start to explain how a nation- a Republic- based on Constitutional freedoms is embroiled in a "separation of powers crisis" and now looks more like an Orwellian, Big-Brother State. Through a complicated history filled with crises, coups and collapses, Argentina is again in trouble despite its many natural resources and deep cultural diversity.
In addition to continued government meddling, there is also a risk of the pendulum swinging too far in the opposite direction as it has in the past. After twelve years of Kirchnerite rule, international investors are licking their chops, imagining a presidential victory for an opposition candidate that would reopen Argentina to the world market. Some pundits fear a repeat of the 1990s, when neoliberal policies allowed international corporations and investors to take advantage of industry privatizations that did not benefit the everyday Argentine.
Whatever the outcome of the election, an unbiased observer can maintain an cautious optimism for the nation of Argentina that the next executive can prioritize the rights and freedoms of its people above personal ambition, enrichment and control. It may seem trivial to focus on fútbol, price controls and consumer purchasing freedom when the Kirchner administration has been accused of large-scale corruption, repression, and even murder. But the reality for the everyday Argentine citizen speaks more to the state of the republic than the high-level political scandals. In a nation based on the US model of individual rights and freedoms, the political devolution of all-powerful populist executives has left life in Argentina today far from what her founders envisioned.
Jared Wilhelm is a Naval Aviator who served two deployments in Africa, Central America and Europe as a P-3C Orion Maritime Patrol Pilot. He is currently attending the National University of Cuyo in Argentina. The views expressed are those of the author and do not reflect the official policy or position of the US Navy, the Department of Defense, or the U.S. Government.
ArgentinaEconomySouth America


text:
\section{Synonyms}
 Relational learning,
 statistical relational models,
 statistical relational learning,
 relational data mining

\section{Glossary}

\begin{description}

   \item[\textbf{Entities}]  are (abstract) objects. We denote an entity by a lowercase $e$. An actor in a social network can be modelled as an entity.
    There can be multiple types of entities in a domain  (e.g., individuals, cities, companies), entity attributes (e.g., income, gender) and relationships between entities (e.g., knows, likes, brother, sister).
   Entities, relationships and attributes are defined in the entity-relationship model, which is used in the design of  a formal relational model

 \item[\textbf{Relation}] A {relation or relation instance $I(R)$} is a  set of tuples. A tuple $t$  is an ordered list of elements $(e_1, e_2, \ldots, e_{\textit{arity}})$, which, in the context of this discussion, represent entities. {The \textit{arity} of a relation is the number of elements in each of its tuples, e.g., a relation might be unary, binary or higher order. }
      $R$ is the name or type of the relation. For example, \textit{(Jack, Mary)} might be a tuple of the relation instance \textit{knows}, indicating that Jack knows Mary.
     A database instance (or world) is a {set of relation instances. } {For example, a database instance  might contain
      instances of the unary relations     \textit{student, teacher, male, female},  and instances of the
      binary relations \textit{knows, likes, brother, sister}} (see Figure~\ref{fig:rdb})

 \item  [{\textbf{Predicate}}] A {predicate $R$} is a mapping of tuples to true or false. $R(t)$ is a \textit{ground} predicate and is true when $t \in I(R)$, otherwise it is false. Note that we do not distinguish between the relation name $R$ and the predicate name $R$.  Example: \textit{knows} is a predicate and \textit{knows(Jack, Mary)} returns \textit{True} if  it is true that Jack knows Mary, i.e., that $\textit{(Jack, Mary) }\in I(\textit{knows})$. The convention is that relations and predicates are written in lowercase and entities in uppercase

 \item[\textbf{Probabilistic Database}]
A (possible) world corresponds to a database instance.
   In a probabilistic database,   a probability distribution is defined over all possible worlds under consideration. Probabilistic databases with potentially complex  dependencies can be  described by  probabilistic graphical models.
In a canonical representation,  one assigns a binary random  variable $X_{R, t}$ to each possible tuple $t$ in each relation $R$. Then
\[
t \in I(R) \;  \Leftrightarrow \; R(t)= \textit{True} \; \Leftrightarrow  \;  X_{R, t} = 1
\]
and
\[
t  \notin I(R) \;  \Leftrightarrow  \; R(t)= \textit{False} \; \Leftrightarrow \;  X_{R, t} = 0   .
\]
The probability for a world $x$ is written as  $P( X = x)$
where $X = \{ X_{R, t} \}_{R, t}$ is the set of random variables and $x$ denotes their values in the world (see Figure~\ref{fig:rdb})

\item[\textbf{Triple Database}]  A triple database consists of  binary relations represented as  subject-predicate-object triples. An example of a triple is:  \textit{(Jack, knows, Mary)}. A triple database can be represented as a knowledge graph with entities as nodes and predicates as directed links, pointing  from the subject node to the object node.  The
      Resource Description Framework (RDF)  is triple based and is the basic data model of the Semantic Web's Linked Open Data.
      In social network analysis, nodes would be individuals or actors and links would correspond to ties

  \item [\textbf{Linked Data}]  Linked Open Data describes a method for publishing structured data so that it can be interlinked and can be exploited by machines.
        Linked Open Data uses the RDF data model

  \item[\textbf{Collective learning}] {refers to the effect that
  an entity's relationships, attributes or class membership can be predicted  not only from  its attributes  but also from { its (social)}  network environment
}

  \item[\textbf{Collective classification}] A special case of collective learning: The class membership of entities can be predicted from the class memberships  of entities in their {(social)} network environment. Example: {Individuals}' income classes  can be predicted from those of  their friends

  \item[\textbf{Relationship prediction}] The prediction of  the existence of a relationship between entities,  for example friendship between {individuals}. A relationship is typically modelled as a binary relation

  \item[\textbf{Entity resolution}] The task of predicting if  two constants refer to the {same} entity

    \item[\textbf{Homophily}] The tendency of {an individual} to associate  with similar others

  \item[\textbf{Graphical models}] A graphical description of a probabilistic domain where nodes represent random variables and edges represent direct probabilistic dependencies

  \item[\textbf{Latent Variables}] Latent variables are quantities which are not measured directly and whose states are inferred from data

\end{description}

\section{Definition}

 {Relational models} are machine-learning models that are able to truthfully {represent}  some or all distinguishing features of a relational domain such as  long-range dependencies over multiple relationships.  Typical examples for relational domains include social networks and knowledge bases.   Relational models concern  nontrivial relational domains  with at least one relation with an arity of two or larger  that  describes the relationship between entities, e.g.,\textit{ knows, likes, dislikes}. In the following we will focus on  nontrivial relational domains.

\section{Introduction}

 {Social networks can be modelled  as graphs, where actors correspond to nodes  and where relationships between actors such as friendship, kinship, organizational position, or sexual relationships are represented by directed labelled links (or ties) between the respective nodes.}
{  Typical machine learning tasks would concern the prediction of
  unknown relationship instances between actors, as well as the prediction of actors's attributes and class labels.}
{ In addition, one might be interested in a  clustering of actors.}
{  To obtain best results, machine learning
  should take an actors's network environment into account. Thus two individuals might appear in the same cluster because  they have common friends.}

  \emph{Relational learning} is a branch of machine learning that is concerned {with these tasks, i.e.~to learn efficiently from data where information is represented in form of relationships between entities.}

 \emph{Relational models} are machine learning models that  truthfully model some or all distinguishing features of relational data such as long-range dependencies propagated via  relational chains and homophily, i.e. the fact that  entities with similar attributes  are  neighbors  in the relationship structure.
In addition to social network analysis, relational models are used to model  {knowledge graphs,} preference networks, citation networks, and biomedical networks such as gene-disease networks or protein-protein interaction networks.
{Relational models can be used to solve   the aforementioned  machine learning tasks, i.e., classification, attribute prediction, clustering.}  Moreover, relational models can be used to solve {additional relational learning tasks  such as   relationship prediction and entity resolution.}
{Relational models are derived from  directed and undirected graphical models or  latent variable models and typically  define a probability distribution over a relational domain.}

\section{Key Points}

Statistical relational learning is a subfield of machine learning.  Relational models learn a probabilistic model of a complete networked {domain  by taking into account global dependencies in the data}.  Relational models can lead to more accurate predictions if compared to non-relational machine learning approaches. Relational models typically are based on probabilistic graphical models, e.g., Bayesian networks, Markov networks, or latent variable models.

\section{Historical Background}

 {Inductive logic programming (ILP)   was  maybe  the first machine learning effort  that  seriously focussed on a relational representation.} It gained attention in the early  1990s and focusses on learning deterministic or close-to-deterministic dependencies, {with representations derived from   first order logic.}
  As a field, ILP was {introduced in a} seminal paper by  Muggleton~\cite{Muggleton91}. A very early and still very influential algorithm is Quinlan's FOIL~\cite{DBLP:journals/ml/Quinlan90}. ILP will not be a focus in the following, since social networks exhibit primarily statistical dependencies.
 Statistical relational learning started around  the beginning of the millennium  with the work by
 Koller, Pfeffer, Getoor and Friedman~\cite{Koller:89,DBLP:conf/ijcai/FriedmanGKP99}.
 Since then many combinations of  ILP and relational learning have been explored. The Semantic Web, Linked Open Data are producing vast quantities of relational data and~\cite{Tresp:09,Nickel2012} describe the
application of  statistical relational learning to these emerging fields.
{Relational learning has been applied to the learning of knowledge graphs, which model large domains as triple databases. \cite{nickel2016review} is a recent review on the application of relational learning to knowledge graphs. An interesting application is the semi-automatic completion  of knowledge graphs by analysing information from the Web and other sources,  in combination with relational learning, which  exploits the information already present on the knowledge graph~\cite{dong2014knowledge}.}

\section{Machine Learning in  Relational Domains}

\subsection{Relational Domains}

Relational domains are domains that can truthfully be represented by relational databases.  The glossary defines the key terms such as a relation, a predicate,  a tuple and a database. Nontrivial relational domains contain
 at least one relation with an arity of two or larger that  describes the relationship between entities, e.g.,\textit{ knows, likes, dislikes}.  The main focus here is on   nontrivial relational domains.

Social networks are typical relational domains, where information is
{represented by  multiple types of relationships (e.g., \textit{knows, likes, dislikes}) between entities (here: actors), as well as through the attributes of entities.}

\subsection{Generative Models for a Relational Database}

{Typically, relational} models can exploit   long-range or even global dependencies   and have principled ways of dealing with missing data.
 Relational models are often displayed as  probabilistic graphical  models and  can be thought of as relational  versions of regular
graphical models, e.g., Bayesian networks, Markov networks,  and latent variable models. The approaches often have a ``Bayesian flavor'' but  a fully Bayesian statistical  treatment is  not always performed.

The following section describes common relational graphical models.

\subsection{Non-relational Learning}

Although we are mostly concerned with relational learning, it is instructive to analyse the special case of non-relational learning. Consider a database with a key entity class \textit{actor} with elements $e_i$ and with only unary relations; thus we are considering a trivial relational domain. Then one can partition the random variables into independent disjoint sets according to the entities, and  the joint distribution factorizes as
\[
 \prod_i P(\{X_{R, e_i}\}_{R})
\]
where the binary random variable  $X_{R, e_i}$  is assigned to tuple $e_i$ in unary relation $R$ (see glossary).

Thus the set of random variables can be reduced to  non-overlapping independent sets of random variables. This is the common non-relational learning setting with \textit{i.i.d.} instances, corresponding to the different actors.

\subsection{Non-relational Learning in a Relational Domain}

An common approximation to a relational model is to model unary relations of key entities in a similar way as in a non-relational model as
\[
 \prod_i P(\{X_{R, e_i}\}_{R} \;  | \; \mathbf{f}_i )
\]
where $\mathbf{f}_i$ is a vector of relational features that are derived from the relational network environment of the actor $i$. Relational features provide additional information to support learning and prediction tasks. For instance, the average income of {an individual's friends might be a good covariate to predict {an individual's} income in a social network}. {The underlying mechanism that forms these patterns might be homophily, the tendency of individuals to associate  with similar others.   The goal of this approach is to be able to use \textit{i.i.d.} machine learning by exploiting some of the relational information. This approach is commonly used  in applications where probabilistic models are computationally too expensive.
The application of  non-relational machine learning to relational domains is sometimes referred to as  \textit{propositionalization}.

 Relational features are often high-dimensional and sparse (e.g., there are many people, but only a small number of them are {an individual}'s friends; there are many items but {an individual} has only bought a small number of them) and in some domains it can be easier to define useful  kernels than to define useful features. Relational kernels often reflect the similarity of entities with regard to the network topology. For example a kernel can be defined based on counting the substructures of interest in the  intersection of two graphs defined by  neighborhoods of the  two entities~\cite{DBLP:conf/esws/LoschBR12} (see also  the discussion on RDF graphs further down).

\subsection{Learning Rule-Premises  in Inductive Logic Programming}

Some researchers apply a systematic search for good features and  consider this as an essential distinction between {\emph{relational} learning} and {\emph{non-relational}} learning:  in non-relational learning features are essentially defined prior to the training phase whereas relational learning includes a systematic and automatic search for features in the relational context of the involved entities.
  Inductive logic programming (ILP) is a form of relational learning with the goal of finding deterministic or close-to-deterministic {dependencies, which are described in logical form such as Horn clauses. Traditionally, ILP involves}  a  systematic search for sensible relational features that form the rule premises~\cite{Dz:07}.

\begin{figure}
\center
  \includegraphics[width= 1.0 \textwidth,angle=0]{2016Schema.pdf} %
\caption{A database instance (world) with  4 unary relations \textit{(student, teacher, male, female)} and 4 binary relations \textit{(knows, likes, brother, sister)}.  As examples: $(\textit{Jack, Mary}) \in I(\textit{knows})$. Thus $\textit{knows(Jack, Mary)=True}$, and $X_{knows, (\textit{Jack, Mary})}=1$.  $(\textit{Jack, Michael}) \notin I(\textit{knows})$. Thus $\textit{knows(Jack, Michael)}=\textit{False}$, and $X_{\textit{knows, (Jack, Michel)}}=0$.}
\label{fig:rdb}
\end{figure}

\section{Relational Models}

In this section we describe the most important relational models in some detail. These are based on probabilistic graphical models, which efficiently model high-dimensional probability distributions by exploiting independencies between random variables}. In particular, we consider Bayesian networks, Markov networks and latent variable models.  We start with a more detailed discussion on possible world models for relational domains and with a discussion on the dual structures of
the  triple graph and the
probabilistic graph.

\subsection{Random Variables  for Relational Models}

As mentioned before,   a probabilistic database defines a  probability distribution  over the possible worlds under consideration. The goal of relational learning is to derive a model of this probability distribution.

In a canonical representation,  we assign a binary random  variable $X_{R, t}$ to each possible tuple in each relation. Then
\[
t \in I(R) \;  \Leftrightarrow \; R(t)= \textit{True} \; \Leftrightarrow  \;  X_{R, t} = 1
\]
and
\[
t  \notin I(R) \;  \Leftrightarrow  \; R(t)= \textit{False} \; \Leftrightarrow \;  X_{R, t} = 0   .
\]
The probability for a world $x$ is written as  $P( X = x)$
where $X = \{ X_{R, t} \}_{R, t}$ is the set of random variables and $x$ denotes their values in the world (see Figure~\ref{fig:rdb}).
What we have just described corresponds to a closed-world assumption where all tuples, which are  not part of  the database instance,  map to   $R(t)= \textit{False}$ and thus $X_{R, t} = 0$. In contrast  in an open world assumption, we would consider the corresponding truth values and states as being unknown and the database instance as being only partially observed. Often in machine learning some form of a \textit{local} closed-world assumption is applied with a mixture of true,  false and unknown ground predicates~\cite{dong2014knowledge,krompass2015type}. For example one might assume that, if at least one child of an individual is specified, it implies that all children are specified (closed-world), whereas if no child is specified, children are considered unknown (open-world). Another aspect is that type constraint imply that certain ground predicates are false. For example, only individuals can get married, but neither cities or buildings. Other types of background knowledge might materialize tuples that are not explicitly specified. For example, if individuals live in Munich, by simple reasoning one can conclude that they also live in Bavaria and Germany.
 The corresponding  tuples can be added to the database.

Based on background knowledge, one might want to modify the canonical representation, which uses  only binary random variables.
  For example, {discrete random variables with $N$ states are} often  used to {implement the constraint that} exactly one out {off} $N$ ground predicates is true, e.g. that {an individual} belongs exactly to one out of $N$ income classes or age classes. It is also possible to extend the model towards continuous variables.

So far we have considered an underlying probabilistic model and an observed world. In probabilistic databases one often assumes a noise process between the actual database instance and the observed database instance by specifying a conditional probability
\[
P(Y_{R, t} | X_{R, t}) .
\]
Thus only $Y_{R, t}$ is observed whereas the real interest is on $X_{R, t}$:
One observes a $t \in I^y(R) \;  \Leftrightarrow  \;  Y_{R, t} = 1$ from which one can infer for the database instance
$P(t \in I(R)) \;  \Leftrightarrow  \;  P(X_{R, t} = 1)$. With an observed  $Y_{R, t} = 1$,  there is a certain probability that $X_{R, t} = 0$ (error in the database) and with an observed
$Y_{R, t} = 0$ there is a certain probability that $X_{R, t} = 1$ (missing tuples).

 The theory of probabilistic databases focussed on the issues of complex query answering under a probabilistic model. In probabilistic databases~\cite{DBLP:series/synthesis/2011Suciu} the canonical representation is used in tuple-independent {databases, while multi-state}  random variables are used in block-independent-disjoint (BID) databases.

Most relational models assume that all entities (or constants) and all predicates are known and fixed (domain closure assumption).  In general these constraints can be relaxed, for example if one needs to include new individuals in the model. Also, latent variables derived from a cluster or a  factor analysis can be interpreted as new ``invented'' predicates.

\subsection{Triple Graphs and Probabilistic Graphical Networks}

 A triple database consists of  binary relations represented as  subject-predicate-object triples. An example of a triple is:  \textit{(Jack, knows, Mary)}. A triple database can be represented as a knowledge graph with entities as nodes and predicates as directed links, pointing  from the subject node to the object node.
  Triple databases are able to represent web-scale   knowledge bases and  sociograms that allow multiple types of directed links.  Relations of higher order can be reduced to binary relations by introducing auxiliary entities (``blank nodes'').  Figure~\ref{fig:rdf}  shows an example of a triple graph.
  The
      Resource Description Framework (RDF)  is triple based and is the basic data model of the Semantic Web's Linked Open Data.
      In social network analysis, nodes would be individuals or actors and links would correspond to ties.

For each triple a random variable is introduced.
  In {Figure~\ref{fig:rdf}} these random variables are represented as elliptical  red nodes.
The binary random variable associated with the tripe $(s=i, p=k, o=j)$ will be denoted as $X_{k(i,j)}$.

\begin{figure}
\center
  \includegraphics[width= 1.0 \textwidth,angle=0]{RDFandRVs1-2Rev.pdf} %
\caption{The figure clarifies the relationship between the triple graph and the probabilistic graphical network.
 The round nodes stand for entities in the domain, square nodes stand for attributes,   and the labelled links stand for triples.  Thus we assume that it is known that \emph{Jack} is friends with \emph{John} and that \emph{John} likes the book
 \emph{HarryPotter}.  The oval nodes stand for random variables and their states represent the existence (value 1) of non-existence (value 0) of a given labelled link; see for example the node $X_{likes, (John,  HarryPotter)}$ which represents  the ground predicate  \emph{likes(John,  HarryPotter)}. Striped oval nodes stand for random variables with many states, useful for attribute nodes (exactly one out of many ground predicates is true). The unary relations \textit{hasAgeOld, hasAgeMiddle,} and \textit{hasAgeYoung} are represented by the random variable $X_{hasAge}$ which has three states. Relational models assume a probabilistic dependency between the probabilistic nodes. So the relational model might learn that \emph{Jack} also likes \emph{HarryPotter} since his friend \emph{Jack} likes it (homophily). Also $X_{likes, (John,  HarryPotter)}$ might correlate with the  age of John.  The direct dependencies are indicated by the red  edges between the elliptical nodes. In PRMs the edges are  directed (as shown)  and in Markov logic networks they are undirected. The elliptical random nodes and their quantified edges form a probabilistic graphical model. Note that the probabilistic  network is dual to the triple  graph in the sense that links in the triple graph become nodes in the probabilistic  network.
}
\label{fig:rdf}
\end{figure}

\subsection{Directed Relational Models}
\label{sec:DRGM}

The  probability distribution of a directed relational model, {i.e.~a}  relational
Bayesian model, can be written as
\begin{equation}
P \left(\{ X_{R, t} \}_{R, t} \right) = \prod_{R, t} P(X_{R, t} | \textit{par}(X_{R, t})) .
\label{eq:drm}
\end{equation}
Here { $\{ X_{R, t} \}_{R, t}$ refers to the set of random variables  in the directed relational model, while $X_{R, t}$ denotes a particular random variable}. In a graphical representation, directed arcs are
pointing from all parent nodes $\textit{par}(X_{R, t})$ to the node $X_{R, t} $ (Figure~\ref{fig:rdf}). As {Equation~\ref{eq:drm} indicates the model requires the specification of the parents of a node and the specification of the probabilistic dependency of a node, given the states of  its parent nodes. In specifying the former,  one often follows a causal ordering of the nodes, i.e., one assumes that the parent nodes causally influence  child nodes and their descendents. An important constraint is that the resulting directed graph is not permitted to have directed {loops, i.e.~that it is a directed acyclic graph}. A major challenge is to specify  $P(X_{R, t} | \textit{par}(X_{R, t}))$, which might require the calculation of  complex aggregational features as intermediate steps.

\subsubsection{Probabilistic Relational Models}

Probabilistic relational models (PRMs) were one of the first published  directed relational models and found great interest
in the statistical machine learning
community~\cite{Koller:89,Getoor:07}. An example of a PRM is shown in Figure~\ref{fig:prm}. PRMs combine a frame-based (i.e., object-oriented)
logical representation with probabilistic semantics based on
directed graphical models.  The PRM provides a template for specifying the graphical probabilistic structure and the quantification of the probabilistic dependencies for any ground PRM.
In the basic PRM models   only the entities' attributes are uncertain whereas the relationships between
entities are {assumed to be known}.  Naturally, this assumption  greatly simplifies
the model.  Subsequently, PRMs have been extended to also consider the case that
relationships between entities are unknown, which is
called \textit{structural uncertainty} in the PRM
framework~\cite{Getoor:07}.

In PRMs one can distinguish parameter learning and structural learning.
 In the simplest case the dependency structure is known and the truth values of all ground predicates are known as well in the training data. {In this case,  parameter}  learning consists of estimating parameters in the conditional probabilities.  If the dependency structure is unknown,
structural
learning is applied, which  optimizes an appropriate cost function and typically  uses a greedy search strategy to find the optimal dependency structure. In structural learning,  one needs
to guarantee that the ground Bayesian network does not contain
directed loops.

In general the data will contain missing information, i.e., not all truth values of all ground predicates are known in the available data.
 For some PRMs, regularities in the PRM structure can be exploited (encapsulation) and even exact inference to estimate the missing information
is possible. Large PRMs require approximate inference; commonly,
loopy belief propagation is being used.

\begin{figure}
\center
  \includegraphics[width= 1.0 \textwidth,angle=0]{RDFandRVs4Rev.pdf} %

\caption{A PRM with domain predicates
 \emph{Professor(ProfID, TeachingAbility, Popularity)},
 \emph{Course(CourseID, ProfID, Rating, Difficulty)},
 \emph{Student(StuID, Intelligence, Ranking)}, and
 \emph{Registration(RegID, CourseID, StuID,  Satisfaction, Grade)}.  Dotted lines indicate foreign keys, i.e. entities defined in another relational instance.
 The directed edges indicate direct probabilistic dependencies on the template level.
   Also shown is a probabilistic table of the random variable \emph{Grade} (with states \emph{A, B, C}) given its parents \emph{Difficulty} and \emph{Intelligence}. Note that some probabilistic dependencies work on multisets and require some form of aggregation: for example  different students might have different numbers of registrations and the ranking of a student might depend on the (aggregated) average grade from different registrations. Note the complexity in the dependency structure which can involve several entities: for example the \emph{Satisfaction} of a \emph{ Registration} depends on the the \emph{TeachingAbility} of the \emph{Professor} teaching the \emph{Course} associated with  the \emph{Registration}.  Consider the additional complexity when structural uncertainty is present, e.g., if the \emph{Professor} teaching the \emph{Course} is unknown.
}
\label{fig:prm}
\end{figure}

\subsubsection{More Directed Relational Graphical Models}

A {Bayesian logic program} is defined as a set of Bayesian clauses~\cite{Kersting:01}. A Bayesian clause specifies the conditional probability distribution of  a random variable given its parents. A special feature is that, for a given random variable, \emph{several} such
conditional probability distributions might {be given and combined}  based on
various combination rules (e.g., noisy-or). In a Bayesian logic
program, for each clause there is one conditional probability
distribution and for each random variable
there is one combination rule.
\textit{Relational Bayesian
networks}~\cite{Jaeger:97} are related to Bayesian logic programs
and use probability formulae for specifying conditional
probabilities.
The probabilistic entity-relationship (PER) models~\cite{Heck:07} are related to the PRM framework and use the entity-relationship model as a basis, which is often used in the design of a relational database.
{Relational dependency networks~\cite{Neville:2004} also belong to the
 family of directed relational  models and  learn the dependency of a node given its Markov blanket (the smallest node set that make the node of interest independent of the remaining network). Relational dependency networks are generalizations of dependency networks as introduced by~\cite{Heck:00, Hof:97}.  A  relational dependency networks typically contains directed loops and thus is not a proper Bayesian network.

\subsection{Undirected Relational Graphical Models}

The probability distribution of an undirected graphical model, i.e. a Markov network, is written as a log-linear model in the form
\[
P\left(X  = x  \right) =
 \frac{1}{Z} \exp \sum_{i}  w_i f_i (x_{i})
\]
where the feature functions $f_i$ can be any real-valued function
on the set  $x_{i} \subseteq  x$ and where $w_i \in \mathbb{R}$. In a probabilistic graphical representation one forms undirected edges between  all nodes that jointly appear in a feature function.
 {Consequently, all nodes that appear jointly in a function will form a \emph{clique} in the graphical representation.}
 $Z$ is the partition function normalizing the
distribution.

A major advantage is that undirected graphical models can elegantly model symmetrical dependencies,  which are common in  social networks.

\subsubsection{Markov Logic Network (MLN)}

 A Markov logic network (MLN) is a probabilistic logic which combines Markov networks with first-order logic.
 In MLNs the random variables, representing ground predicates, are part of a Markov network, whose dependency structure is derived from a {set of first-order} logic formulae (Figure~\ref{fig:mln}).

 Formally, a MLN $L$
 is defined as follows: Let $F_i$ be a first-order formula, (i.e., {a logical  expression containing constants, variables, functions and predicates) and let $w_i \in \mathbb{R}$ be a weight attached to each formula. Then  $L$ is defined as a set of pairs
 $(F_i, w_i)$~\cite{Richardson:06, Domingos:07}.

 From $L$  the ground Markov network $M_{L, C}$   is generated as follows.  First, one generates nodes (random variables) by introducing a binary node for each possible grounding of each predicate  appearing in $L$  given a set of
constants $c_1, \ldots, c_{|C|}$ (see the discussion on the canonical probabilistic representation).
The state of a node is equal to one if the ground  predicate is true, and zero otherwise.
The feature functions $f_i$, which define the probabilistic dependencies in the Markov network,  are derived from the formulae by grounding them in a domain. For
formulae that are universally quantified,  grounding  is an assignment of constants to the variables in the
formula.
If a
formula contains $N$ variables, then there are $|C|^{N}$ such
assignments.
The  feature function  $f_i$ is equal to one if the ground formula is true,  and  zero otherwise.
The  probability distribution of the $M_{L, C}$  can then be written as
\[
P\left(X = x \right) = \frac{1}{Z}
 \exp\left(
 \sum_i w_i n_i(x)
 \right) ,
 \]
where $n_i(x)$ is the number of formula groundings that are
true for $F_i$  and where the weight $w_i$ is associated with formula
 $F_i$ in $L$.

  {The joint distribution $P\left(X = x\right)$} {will be maximized when large weights are assigned to formulae that are frequently true}.
   In fact,  the larger the weight, the higher is
the confidence that a formula is true for many groundings.
Learning in MLNs consists of estimating the weights $w_i$ from data. In learning, MLN makes a
closed-world assumption and employs a  pseudo-likelihood cost
function, which is the product of the probabilities of each node
given its Markov blanket. Optimization is performed using a limited
memory BFGS algorithm.

The simplest form of {inference in a MLN} concerns the prediction of the truth
value of a ground predicate given the truth values of other
ground {predicates. For this task an efficient algorithm can be derived:  In
	the first phase of the algorithm, the}
minimal subset of the ground Markov network
{is computed that}
 is
required to calculate the conditional {probability of the queried ground predicate}. It is essential
that this subset is small since in the worst case, inference could
involve all nodes.  In the second {phase, the conditional probability is then computed by applying Gibbs sampling to the reduced network}.

Finally, there is the issue of structural learning, which, in this
context, means the learning {of first} order formulae.
Formulae can be learned by directly optimizing the
pseudo-likelihood cost function or by  using ILP algorithms. For the
latter, the authors use CLAUDIAN~\cite{Raedt:97}, which can learn
arbitrary first-order clauses (not just Horn clauses, as in many other
ILP approaches).

An advantage of MLNs is that the features and thus the dependency structure is defined using a well-established  logical representation. On the other hand, many people are unfamiliar with logical formulae and might consider the PRM framework to be more intuitive.

\begin{figure}
\center
  \includegraphics[width= 1.0 \textwidth,angle=0]{RDFandRVs12Rev.pdf} %

\caption{Left: An example of a MLN. The domain has two entities (constants) \emph{A} and \emph{B }   and the unary relations \emph{smokes} and \emph{cancer} and the binary relation \emph{friends}. The 8 elliptical nodes are the ground predicates. Then there are two logical expressions $\forall x \; smokes(x) \rightarrow cancer(x)$  (someone who smokes has cancer) and $\forall x\forall y \; friends(x, y) \rightarrow (smokes(x) \leftrightarrow smokes(y)) $ (friends either both smoke or both do not smoke).  Obviously and fortunately both expressions are not always true and learned weights on both formulae  will assume finite values.
 There are two groundings of the first formula (explaining the edges between the \emph{smokes} and \emph{cancer} nodes) and four groundings of the second formula, explaining the remaining edges. The corresponding features are equal to one if the logical expressions are true and are zero else. The weights on the features are adapted according to the actual statistics in the data. Redrawn from~\cite{Domingos:07}.
 Right: The corresponding triple graph for two individuals (blue) and the dual ground Markov network (red).
}
\label{fig:mln}
\end{figure}

\subsubsection{Relational Markov Networks (RMNs)}

RMNs generalize many concepts of PRMs to undirected
relational  models~\cite{DBLP:conf/uai/TaskerPK02}. RMNs use conjunctive database queries as
clique templates, where a clique  in an undirected graph is a subset of its nodes such that every two nodes in the subset are connected by an edge. RMNs are mostly trained discriminately. In contrast to {MLNs and similarly to PRMs,
RMNs do} not make a closed-world assumption during
learning.

\subsection{Relational  Latent Variable Models}
\label{IHRM}

In  the approaches described so far, the structures in the  graphical models were either defined using expert knowledge or were learned directly from data using some form of structural learning.
Both can be problematic since appropriate expert domain knowledge might not be {available, while structural learning can be very time consuming and possibly results} in local optima which are difficult to interpret.
{In this context, the advantage {of relational latent variable models} is} that the structure in the associated  graphical models is purely defined by the entities and relations in the domain.

  The additional complexity of working with a latent representation is counterbalanced by the great simplification by avoiding  structural learning. In the following discussion, we assume that data is in triple format;  generalizations to relational databases haven been described~\cite{Xu:06, krompass2014probabilistic}.

 \begin{figure}
\center
    \includegraphics[width= 1.0 \textwidth,angle=0]{2016Fig.pdf} %

\caption{
The architecture of RESCAL and the IHRM. In the bottom layer (input) the index units for subject $s=i$ and object $o=j$ are activated (one-hot encoding). $A$ is a weight matrix.  The second layer  calculates the latent representations $\mathbf{a}_i$ and $\mathbf{a}_j$. The following layer forms componentwise products.
The output layer then calculates
$
f(i, j, k) = \sum_{r, q} a(i, r) a(j, q) g(k, r, q)
 $ for predicate $p=k$.
 For RESCAL,  $P(X_{k(i,j)}| i, j) = \textit{sig}(f(i, j, k))$. In the IHRM, we identify $P(r|i) = a(i, r)$,
$P(q|j) = a(j, q)$, $P(X_{k(i,j)}|r, q) = g(k, r, q)$ and
 $P(X_{k(i,j)} | i, j) = f(i, j, k)$.
For the IHRM, the factors must be nonnegative and properly normalized. $R$ is the number of latent dimension and $K$ is the number of relations.
}
\label{fig:rdflatent}
\end{figure}

\subsubsection{The IHRM: A Latent Class Model}

The  infinite hidden relational model (IHRM)~\cite{Xu:06} (a.k.a infinite relational model~\cite{Kemp:06}) is a generalization to a probabilistic mixture model where a latent variable with states $1, \ldots, R$ is assigned to each entity  $e_i$. If the latent variable for subject $s=i$ is in state $r$ and   the latent variable for object $o=j$ is in state $q$, then the triple $(s=i, p=k, o=j)$ exists with probability    $P(X_{k(i,j)}|r, q)$. Since the latent states are unobserved, we obtain
\[
P(X_{k(i,j)}| i, j) = \sum_{r, q} P(r|i) P(q|j) P( X_{k(i,j)}   |r, q)
\]
which can be implemented as the sum-product network of  Figure~\ref{fig:rdflatent}.

In the {IHRM  the number of states (latent classes) in each latent variable is allowed to be
infinite   and  fully Bayesian learning is performed based on  a    Dirichlet process mixture model. For inference  Gibbs sampling is employed where only a small number  of the infinite states are
occupied in sampling, leading to a clustering solution where the number of
states in the latent variables
  is automatically determined. Models with a finite number of states have been studied as stochastic block models~\cite{nowicki2001estimation}.

Since the dependency structure in the ground Bayesian network is
local, one might get the impression that only local information
influences prediction. This is not true, since
latent representations are shared and
in the ground
Bayesian network the latter are parents to the random network   variables $X_{k, (i, j)}$. Thus
common children  with evidence lead to
interactions between the parent latent variables. Thus information
can propagate in the network of latent variables.

The IHRM has  a number of key advantages. First, no structural
learning  is required, since the directed arcs in the ground
Bayesian network are directly given by the structure of the triple graph. Second, the IHRM model can be thought of as an infinite
relational mixture model, realizing hierarchical Bayesian modeling.
 Third, the mixture model can be used for   a cluster  analysis providing insight into  the relational domain.

The IHRM has been applied to social networks, recommender systems, for gene function
prediction and to develop medical recommender systems. The IHRM was
the first relational  model applied to trust
learning~\cite{Rettinger:08}.

In~\cite{AiroldiBFX08} the IHRM is generalized to a mixed-membership stochastic block model, where entities can belong to several classes.

\subsubsection{RESCAL: A Latent Factor Model}

The RESCAL model was introduced in~\cite{Nickel2011} and  follows a similar dependency structure as the IHRM as shown in Figure~\ref{fig:rdflatent}. The main differences are  that, first,  the latent variables do not describe entity classes but are latent entity factors and that, second,  there are no nonnegativity or normalization constraints on the factors.
The probability of a triple  is calculated with
\begin{equation}\label{eq:tens}
  f(i, j, k) = \sum_{r, q} a(i, r) a(j, q) g(k, r, q)
\end{equation}
as
\[
P(X_{k(i,j)}| i, j) = \textit{sig} \left( f(i, j, k)\right)
\]
where $\textit{sig}(x) = 1/(1 + \exp -x)$.

As in the IHRM, factors are unique to entities which leads to
interactions between the factors in  the ground
Bayesian network, enabling  the propagation of information
in the network of latent factors.
The relation-specific matrix $G^{R} = g(k, :, :)$ encodes the factor interactions  for a specific relation and its asymmetry permits the representation of directed relationships.

The calculation of {the latent factors is} based on the factorization of a multi-relational adjacency tensor where two modes represent the entities in the domain and the third mode represents the relation type (Figure~\ref{fig:RESCAL}).
With a closed-world assumption and a squared-error cost function, efficient alternating least squares (ALS) algorithm can be used; for local closed world assumptions and open world assumptions,  stochastic gradient descent is being used.

{The relational learning capabilities of the RESCAL model} have been demonstrated on classification tasks and entity resolution tasks, i.e., the mapping of entities between knowledge bases.  One of the great advantages of the RESCAL model is its scalability: RESCAL has been applied to the YAGO ontology~\cite{DBLP:conf/www/SuchanekKW07} with several million entities and 40 relation types~\cite{Nickel2012}! The YAGO ontology, closely related to DBpedia~\cite{DBLP:conf/semweb/AuerBKLCI07} and the Google Knowledge Graph~\cite{Singhal},  contains formalized knowledge from Wikipedia and other sources.

RESCAL is part of a tradition on relation prediction using factorization of matrices and tensors. \cite{YuCYTX06} describes a Gaussian process-based approach for predicting a single relation type, which has been generalized to a mutli-relational setting in~\cite{XuKT09}.

 \begin{figure}
\center
  \includegraphics[width= 1.0 \textwidth,angle=0]{model2.pdf} %

\caption{In RESCAL, Equation~\ref{eq:tens} describes a tensor decomposition of the tensor $\mathcal{F} = f(:, :, :)$ into the factor matrix $A= a(:, :)$ and core tensor $\mathcal{G} = g(:, :, :)$.
In the  multi-relational adjacency tensor on the left,   two modes represent the entities in the domain and the third mode represents the relation type. The $i$-th row of the matrix $A$ contains the factors of the $i$-th entity.  $G^R$ is a slice in the $\mathcal{G}$-tensor and encodes the relation-type specific factor interactions. The factorization can be interpreted as a  constrained Tucker2 decomposition.
}
\label{fig:RESCAL}
\end{figure}

A number of variations and  extensions exist. The SUNS approach~\cite{Tresp:09} is based on a Tucker1 decomposition of the adjacency tensor,  which can be computed  by a singular value decomposition (SVD). The Neural Tensor Network ~\cite{socher2013reasoning} combines several tensor decompositions. Approaches with a smaller memory footprint are  TransE~\cite{bordes2013translating} and HolE~\cite{nickel2015holographic}. The multiway neural network in the Knowledge Vault project~\cite{dong2014knowledge} combines the strengths of latent factor models and neural networks and was successfully used in semi-automatic completion of knowledge graphs. \cite{nickel2016review} is a recent review on the application of relational learning to knowledge graphs.

\section{Key Applications}

Typical applications of relational models are in  social networks analysis, knowledge graphs, bioinformatics, recommendation  systems, natural language processing, medical decision support,   and  Linked Open Data.

\section{Future Directions}

As a number of publications have shown, best results can be achieved by committee solutions integrating factorization approaches with user defined or learned rule patterns~\cite{nickel2014reducing, dong2014knowledge}. The most interesting application in recent years was in projects involving large knowledge graphs, where performance and scalability could clearly be demonstrated~\cite{dong2014knowledge,nickel2016review}.  The application of relational learning to sequential data and time series opens up new application areas, for example in clinical decision support and sensor networks~\cite{esteban2015predicting,esteban2016coev}. ~\cite{tresp2015learning} studies the relevance of relational learning to cognitive brain functions.

\bibliographystyle{plain}

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The latest earnings from Johnson & Johnson and JPMorgan Chase both beat Wall Street's expectations.
Stock futures edged higher Tuesday as investors digested mixed U.S. retail sales data and several corporate earnings reports. The latest earnings from Johnson & Johnson and JPMorgan Chase both beat Wall Street's expectations.
KEEPING SCORE: Dow Jones industrial average futures rose 30 points, or 0.2 percent, to 17,008 as of 9:20 a.m. Eastern time. Standard & Poor's 500 index futures rose two points, or 0.1 percent, to 1,973, while Nasdaq 100 futures added eight points, or 0.2 percent, to 3,927.
SPENDING BAROMETER: The Commerce Department reported Tuesday that retail sales rose just 0.2 percent last month, held back by a sharp drop at building materials and garden supply stores. Sales also fell at restaurants and at auto dealers. The latest figures suggest that Americans are reluctant to spend freely, which could slow growth in the April-June quarter.
BEATING THE STREET: JPMorgan Chase, the nation's largest bank by assets, said Tuesday its second-quarter earnings fell 9 percent as revenue at its investment banking and mortgage businesses dropped. Even so, the results trumped the forecasts of financial analysts. JPMorgan's stock rose $1.61, or 2.9 percent, to $57.90 in premarket trading.
GOOD MEDICINE: Johnson & Johnson's second-quarter profit jumped 13 percent, aided by strong sales of several new medicines. The results topped analysts' expectations. The maker of Band-Aids, medical devices and biologic drugs raised its 2014 profit forecast for the second time this year. J&J rose 14 cents to $105.52 in premarket trading.
OVERSEAS MARKETS: European stocks were mixed. Germany's DAX and Britain's FTSE 100 were little changed. France's CAC-40 fell 0.1 percent. In Asia, Japan's Nikkei 225 rose 0.6 percent after a central bank policy meeting ended as expected with no changes to Tokyo's ultra-loose monetary policy. Hong Kong's Hang Seng gained 0.5 percent and South Korea's Kospi rose 0.9 percent.
BONDS & COMMODITIES: In the market for U.S. government bonds, the yield on the 10-year Treasury note was little changed at 2.55 percent. The price of oil slipped 0.9 percent to $99.98.


Hydrology Research (2009) 40 (6): 564-579.
The combination of climate change and natural periodicities in meteorological variables are demonstrating significant impacts on the water resources of Lake Superior within the Laurentian Great Lakes system of North America. Statistical analyses of long-term records are used to demonstrate how changes over time may be interpreted very differently, depending upon the timeframe over which the analyses are made. Non-linear regression modelling shows that, while increasing trends in overland and overlake precipitation, flows and runoff occurred during the first decades of the twentieth century, very different trends are apparent for the period 1970–2005. For this latter period, increasing rates of air overlake temperature and lake evaporation are occurring but all other parameters are demonstrating decreasing trends. The result is a decline in water levels in Lake Superior at the rate of approximately 1 cm per year over the last 35 years. The results are used to show that to avoid decreasing water levels in Lake Superior, the discharge through St Mary's River must be decreased to approximately one-half the long-term annual average, the results of which will have dramatic implications for ships' cargo levels and hydroelectric energy generation.


text:
Rakoveț (în ) este o comună în raionul Zbaraj, regiunea Ternopil, Ucraina, formată din satele Rakoveț (reședința) și Zașleahom.

Demografie

Conform recensământului din 2001, majoritatea populației comunei Rakoveț era vorbitoare de ucraineană (%), existând în minoritate și vorbitori de rusă (%).

Note

Rakoveț, Zbaraj, Ternopil
Rakoveț, Zbaraj, Ternopil
Rakoveț, Zbaraj, Ternopil

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