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Rep. Eric Swalwell, D-Calif., on Monday became the first major Democratic presidential candidate to exit the face, as he announced he would suspend his campaign and focus instead on trying to win re-election to Congress next year.
Swalwell, who made last month's debate stage but risked not qualifying to participate in the second debate this month, has not registered over 1% in national polling. He will face a primary challenge in his bid to return to his House seat for a fifth term.
At a press conference in Dublin, California, Swalwell said he is "fired up" to return to Capitol Hill and continue the work he has been doing in Congress.
"Today ends our presidential campaign, but it is the beginning of an opportunity in Congress with a new perspective shaped by the lives that have touched mine and our campaign throughout these last three months to bring that promise of America to all Americans," Swalwell said.
As a presidential candidate, Swalwell, 38, offered himself as a fresh face from a younger generation to lead his party in its bid to unseat President Donald Trump and emphasized his commitment to reducing gun violence.
The most memorable moment of his short-lived campaign came during the first debate in Miami when he quoted former vice president Joe Biden from three decades ago saying it was time to "pass the torch" to a new generation of leadership.
Swalwell stoked speculation about his future in the race after cancelling a planned two-day trip to New Hampshire over the holiday weekend, saying he wanted to spend more time with his family.
Swalwell began considering a presidential bid after the 2016 election, making multiple trips to early primary states to campaign for down-ballot candidates and meet local Democrats. He traveled to Iowa, where he was born, more than a dozen times, and welcomed the speculation that he might seek the White House. If he ran, he said, he'd offer the sort of generational change that candidates like Biden and Sen. Bernie Sanders, I-Vt., could not.
"I don't think the same old leaders can be counted on to solve the same old problems," Swalwell said in an interview with The Washinton Post last year, before speaking to the liberal group Progress Iowa. "If Republicans can just frame the election as, 'Democrats want to go back to the third term of Obama,' I don't think we can win."
But as a candidate, Swalwell struggled to break through. As part of his focus on gun safety, he proposed a national program to ban assault weapons, then buy back and destroy them, but otherwise aligned himself with the other liberals in the race.
As he put together a presidential bid, Swalwell told reporters that he was leaving the House.
"I don't have a life insurance policy," he told the San Francisco Chronicle in February.
That inspired several Democrats to look at running in the safe Bay Area district, led by local city councilor Aisha Wahab, who would be the first Afghan-American elected to Congress. In an interview last month, Wahab said she would "reassess" her race if Swalwell returned.
Prior to launching his presidential bid, Swalwell had been a frequent guest on cable news programs because of his perch on the House Intelligence Committee, which provided an opportunity to opine on the investigation of special counsel Robert Mueller.
In Congress, Swalwell is known for launching the Future Forum, a group of young Democratic House members focused on issues important to millennials like reducing student debt. The organization, which he chaired from when it started in 2015 until this year, gave him the chance to visit more than 50 cities around the country.
The group nearly doubled in size to 50 members after the 2018 midterm elections ushered in a younger crop of new lawmakers.
Though he has made efforts to ally himself with Speaker Nancy Pelosi, D-Calif., Swalwell has frequently shown a willingness to go against the prevailing winds in his party.
He was one of the few Democratic House members to endorse a presidential candidate other than Hillary Clinton during the last election, instead backing former Maryland governor Martin O'Malley.
More significant, he successfully challenged Rep. Pete Stark in 2012, taking on a 40-year Democratic incumbent against the wishes of the party.
(c) 2019, The Washington Post · John Wagner, David Weigel


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Exercises Shed Fat And Speed Fat Loss
Basic training is a part of joining the military. However, it can deemed a big adjustment. Many new recruits are staying away for the first time, and but they are excited with what they are doing there is some apprehension and fret. Leaving behind family and friends is difficult, but by preparing in advance you can generate the transition easier.
Leg physical exercises are done by pushing the quads to optimal fatigue rates. Every set needs 8-12 reps, and every exercise needs between 2 to 4 sets. Retailer . major leg muscle needs anywhere between 2 to exercises for your development. However it is all going to depend upon your experience with working out, and the level of your tolerance.
Eliminate 5 foods which thought were healthy and would take care of belly extra weight. However, they are not healthy when they actually force your body to keep hold of body the calories! One such example are wheat accessories.
Do you perform almost not exercise each holiday? Does the couch have a permanent depression from your behind? Preference hear utilized push up does a bra taken up mind or how many reps utilized perform?
Next, why don't we move on to Cardio exercises. Cardio exercises are anything that involves movement. Some excellent cardio exercises to burn fat are running, sprinting, swimming, rowing, skipping, soccer, tennis, and also so on. If you have not been exercising for the time, you ought to stay within your intensity level that is most comfortable for someone. If you do not condition physical structure enough and jump by injection to high intensity cardio exercises, is that possible get injured.
I would like you to realize that you need to eat about 500 calories a day less than your body demands to keep its weight in investigate. This method alone will bring around a loss within a pound full week. I'm going to show you how to make that reduction in fat, not muscle. Never try and lose an excessive amount weight in a week because it can only slow down your as well as eat muscle tissues.
So, when you have room for doing it in your house and have enough money for it, the Ab Coaster is most excellent of ab machine. Just don't think it's some magic calorie burning machine. It is merely a stomach exerciser.

subset: commoncrawl


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Statistics and meaning of name Txu
Usage: 75% firstname, 25% surname.
Txu first name was found 6 times in 1 different countries. (USA)
Surname Txu is used at least 2 times in at least 1 countries. (USA)
Txu reversed is Uxt
Name contains 3 letters - 33.33% vowels and 66.67% consonants.
Anagrams: Tux Xut Xtu Utx
Misspells: Txua Tux Xtu
Rhymes: who pursue shoe zu shew
Meaning of this name is unknown.
This page has been visited from the following countries:
Txu.com - REGISTERED Txu.net - REGISTERED Txu.co.uk - REGISTERED Txu.de - REGISTERED
Writers: Txus di Fellatio
Recent searches: Labutis Otarola Ilaga Ilagan Grodsinsky Berdyshev Startzel Crocos Ghiordunescu Ciucioiu
Top searches: Dragos Davide Vitelaru Suchocki Cavalaro Ali Mudda Taltos Search Sporn
Random names: Dubrau Sibary Kaouach Quintel Meize Ghizuroiu Szokal Didkovskiy Guarizo Yamkovy
Do you know more about the meaning
or about the origins of this name?
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Simon Says Stamp BUNNY BUTT PEEK A BOO Wafer Dies sssd112120c Lucky To Know Yousssd112120c
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Fairlawn 29" Bar Stool Finish: Oak
In order to buy Fairlawn 29" Bar Stool Finish: Oak good deal. I'd like advice that you look into the price now for save your money. The Bar Stools Brayden Studio BRYS2937 is a great gift for you This Bar Stool is a full bar height stool that features a tall, large seat, plus a large footrest. It is lighter than it looks because of the bent plywood construction in birch hardwoods. Solid oak or walnut legs provide added strength, especially for...Bathroom Furniture And Bathroom Suite - Two Key Factors For A Bathroom
The bathroom could be the right place, which can be regarded as the absolute right place for an individual to wind down. This is a proper place where a person could get an ultimate freedom from all the strain and stress which they happen to be with the whole day. Baths include the most important mattes which has to be in the washroom. Therefore, it really is important to possess a proper washroom.
A well decorated washroom always adds the lining great thing about a home plus it raises the property value also. But unfortunately, a lot of house owners are not aware of the best way to decorate the washroom. Fairlawn 29" bar stool finish: oak, Bathroom furniture and Fairlawn 29" Bar Stool Finish: Oak these are the two most significant things that can completely customize the outlook of the washroom.
Shelves, racks, toilet seats, mirrors are typical belong to the washroom furniture. When you are planning to purchase furniture for the washroom, it is vital that you remember something that it should be sized well for the washroom. A washroom must have enough space and this is the reason you need to execute proper space management skill within your washroom.
There you'll find different types of furniture for the washroom. You should always opt for wooden furniture that's completely waterproof. You must make certain that the piece of furniture is longer lasting.
Secondly, bathroom suite is the one other important matter for a washroom. Fairlawn 29" bar stool finish: oak, It is a complete package where you could get everything important for the washroom. Whether it is traditional or contemporary, it completely depends upon you which kind of suite you desire on your washroom.


text: circular kitchen circular kitchen circular kitchen was last modified: October 6th, 2013 by admin 0 Facebook Twitter Google + Pinterest previous post Hooked on Storage next post Pour water on top of lemons and freeze – perfect way to refresh pitchers of water You may also like love the exposed brick! reminds me of... January 6, 2014 traditional kitchen by Crisp Architects December 20, 2013 Make a place beautiful December 3, 2013 Fabulous Idea…bookcase built into the wall gives... February 18, 2014 Make your baseboard more dramatic…add small pieces... February 18, 2014 Cabinets behind/surrounding the toilet… Great use of... February 1, 2014 have a library in my home! yes... February 1, 2014 Mushroom Pillow December 3, 2013 For little girls rooms December 20, 2013 I ABSOLUTELY need this in my kitchen. February 1, 2014


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FHA Loans- APR calculation assumes a $153,918 loan ($150,000 base amount plus $3,918 for prepaid mortgage insurance) with a 3.5% down payment and borrower-paid finance charges of 0.862% of the base loan amount, plus origination fees if applicable.
Fha 500 Credit Score Bad Credit Fha Loan How to Qualify for an FHA Loan – FHA Loan Requirements and. – A bad credit score of 500 can already open the door to an FHA loan, but getting to 580 or above can make the path to homeownership more viable. Don't be discouraged by bad credit or newly established credit when it comes to buying a home. You can still realize the homeownership dream.Today, the minimum credit score on FHA loans is only 500. That means even if you have past credit problems, there is a good chance you may be eligible for a.
Comparing the current average 30- year FHA loan rate of 4.27% to the average conventional mortgage rate of 4.62%, we saw spreads widen relative to last year's mortgage rates, demonstrating that FHA rates have generally become more expensive versus conventional mortgage loans.
15 Year Fha Rates Orange County Fha Loan Limits 2019 Fha Loan New Construction fha 1 time close Construction Loan – FHA Home Loans – An FHA One Time Close Construction Loan is an all in one loan that allows you to get a construction loan and a permanent loan all wrapped into one loan. This is a huge advantage given the fact that most construction loans to build a home require two closings. So you will save time and money by doing a 1-time close. The Way FHA Construction Loan.The 2019 Home Equity Conversion Mortgage (HECM) limits in Orange County is $726,525. HECM limit does not depend on the size of the home. FHA and conventional Loan limits vary based on the number of living-units on the property. FHA loans are only allowed on 1 to 4 living-unit properties. orange county has standard limits which means loan limits are equal to the 2019 national floor.An FHA (Federal Housing Administration) loan is a government-backed home mortgage loan with more flexible lending requirements than conventional loans. Because of this, FHA mortgage interest rates may be somewhat higher. The buyer may also have to pay monthly mortgage insurance premiums, along with their monthly loan payments.
Not all lenders offer FHA, VA and USDA home loans or their options may be limited, so always ask about availability when comparison shopping. To see current interest rates from different lenders, use.
Please continue to check this page as rates move throughout the day and from day to day. Lenders can still reserve loans or extend existing rate locks on any loan program with an N/A. Details on reserving loans or extending rate locks can be found on CalHFA's Rates & Reservations webpage.
"Across loan types, the FHA delinquency rate posted the largest variance. By comparison, in January 2007, just before the start of the financial crisis, the current-to-30-day transition rate was.
First Time Home Owners Association Home owners 'lock in' low mortgage rates ahead of expected hike – reported by trade association UK Finance. "Competition amongst lenders, as well as near record low mortgage rates, has meant more and more home-owners are re-mortgaging and locking in deals," its.
FHA Mortgage Rates Are Lower. fha mortgage rates are typically.125% -.500% lower than the current interest rate on a conventional loan or low down payment mortgage program.
FHA mortgages, shows details Options for a loan with a down payment of (3.5%) As of. Note: This calculator assumes a 3.5% down payment for conforming FHA mortgages.
Compare current fha mortgage interest rates and save money on your FHA loan. Get free, customized FHA loan rate quotes in moments.
FHA mortgage rates hew closely to the mortgage rates on traditional home loans. If the average interest rate on a 30-year fixed-rate mortgage stands at 5.4 percent, you can figure that the average FHA mortgage rate is nearly the same. This makes these loans even more attractive.
Fixed interest rates stay the same for the entire loan term. adjustable rates have an initial fixed period (five or seven years is common), but will fluctuate after that period based on the current market rates for the remainder of the loan. Loan Amount. Your loan amount is not just the price of the home, but the total amount you'll need to.
Average Fha Loan Rates On Monday, Aug. 19, 2019, the average rate on a 30-year fixed-rate mortgage rose two basis points to 3.96%, the rate on the 15-year fixed went up three basis points to 3.47% and the rate on the 5.


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Summer has arrived, time to enjoy barbecues, bare legs, and warm evenings outside. However, you can also enjoy summer indoors by adding bright touches to your décor. You don't have to undergo a full house makeover, just add some touches of vibrant color and pattern to give a seasonal lift.
There are many great ways to achieve financial freedom. In this article, we will focus mainly on how to generate passive income safely. The way is cryptocurrencies. They have the same use as other payment methods. Economic operations are more secure and profitable, international, and decentralized currency. Be your bank after reading this article.
Updates on Justin Bieber's ascent to distinction spread as Wildfire in 2009 as a skilled vocalist who was found on YouTube for covering tunes. He broke the record as the principal craftsman to have 7 tunes on the Billboard Hot 100 and in 2010 his first studio collection with the single Baby was confirmed triple platinum in the US.
The skilled youthful vocalist was conceived on March 1, 1994, in London and experienced childhood in Stratford, Ontario. He went to a French primary school in Stratford called the Jeanne Sauvé Catholic School before continuing to St Micheal Stratford Catholic High School, Ontario, from which he graduated in 2012. During his youth days, he built up an enthusiasm for music which caused him to figure out how to play the piano, drums, guitar, and trumpet. At age 12, he sang the melody. So tired of Ne-Yo in a singing rivalry where he developed in the runner up and his mom. Intrigued by his singing, transferred the video to YouTube which ended up being a propensity in the next years.
Bieber's ability was found online by ScooterBraun, a previous So Def Recordings advertising leader who saw his video coincidentally, however, was very intrigued by the kid's ability that he needed to find him and get his mom's consent to take it to Atlanta, where Raymond Braun marked it. Media Group, a record mark claimed by Braun and Usher Raymond. Visit sites like omegle.
The youthful craftsman has delivered various hit tunes. For example, Someone to Love, Never Say Never, Boyfriend, What Do You Mean, I'm Sorry, and Love Yourself. He has likewise worked with significant specialists, for example, R. Kelly, DJ Khaled, Luis Fonsi, Daddy Yankee, Chance the Rapper, Lil Wayne, Future, Big Sean, and Diplo. As an account craftsman, he has likewise won various honors including the 2010 and 2012 American Music Award for Artist of the Year, the 2016 Grammy Award for Best Dance Recording, and the Latin Grammy Award for Best Urban Fusion and Performance for the remix. from Despacito in 2017. Bieber has been named in Forbes Magazines' Top Ten Most Powerful Celebrities in 2011, 2012, and 2013. He is additionally the Guinness World Record holder for the most youthful male craftsman to make a big appearance on the Billboard Hot 100 with the main single. for your melody. What you mean.
Justin Bieber's mother and father
The music star is Jeremy Jack Biebery's child Patricia Mallette. He was raised by his mom as his dad, who was not hitched to her, was supposed to be very youthful and had never been near. Pattie, as the mother is affectionately called, is Bieber's greatest fan and figured out how to put forth a strong effort while growing up, despite the fact that she wasn't gaining a significant pay when Bieber turned 17. Her mom even composed a book regarding the matter. troubles you confronted while bringing up your kid. Bieber, be that as it may, stayed in contact with his dad as a youngster. And since he came into the spotlight, the couple has gotten along very well.
All through his ascent to distinction, Bieber's was said to have fought with the mother as he saw her less frequently. He clarified in a meeting that the circumstance was not all that awful and that they were working things out. He was additionally observed with his dad hanging out. And later posted a photograph on Instagram saying that he adored gathering his dad. And working through difficult stretches to make them great. His dad has two other kids named Jazmyn conceived in 2009 and Jaxon conceived in 2010.
Who is Justin Bieber dating?
Bieber's sexual direction is clearly straight since he has just been dating ladies. The star's most genuine relationship has been with artist lyricist Selena Gomez in 2010. Their relationship endured around 2 years before they split in 2012. From that point forward, Biber was seen dating various young ladies despite the fact that she called them just companions. Nonetheless, it was made authority that he dated Yovanna Ventura, Hailey Baldwin, and Sofia Richie.
A get-together and conceivably a stroll a few doors down is really hypothesis among Bieber and Selena. As the two have seen each other a ton since late 2016. Alongside the way that they have both said a final farewell to their individual accomplices.
Justin Bieber Height
The attractive youngster who has had the option to draw in fans from both the teenager gathering. And the grown-up bunch remains at a masculine height of 5 feet 9 inches.
According to statistics, the number of people purchasing homes has dropped significantly. There are many reasons for this drop. For one thing, people have trouble getting the money they need to put money down on a home.
If you are someone who has boarded the Kratom bandwagon recently, the good news is that there are many along with you and rightly so. Kratom has been gaining in popularity gradually, mostly because of its positive effects and properties. Some are even contemplating putting it at a higher pedestal than normal tea and coffee in the ranking for useful beverages. Since Kratom has been traditionally around many years, its introduction into the commercial market was met with a lot of positivity.
The Kratoms that are available in the market are mainly classified based on the color of the medial part of the leaves. The three most common and powerful strains are white, green, and red ones. Even in these strains, some subtypes are highly potent. The Red Vein Bali is one such product. If you are a beginner in Kratom consumption and are looking for some intel with respect to the Red Vein Bali Kratom, here are some excerpts from the description-
Red Vein Bali Kratom
If you ask any Kratom enthusiast about the most popular red strain, he or she will definitely take the name of the Red Bali Kratom. It has established itself as being extremely potent and efficient in functioning. It has been grossly misunderstood too. It is not a huge energy booster like the white type of Kratom, nor does it produce a lot of sedation and euphoria. However, the one thing on which Red Vein Bali really specializes is the pain-relieving or analgesic effect that it has.
It truly remains to its name and is routinely used by people in chronic pain. Imagine experiencing a lot of back pain and inflammation of the joints of the body due to old age. It can become challenging to continue with day to day activities, and productivity also decreases. The good news is that you can say goodbye to all these problems by using just one product, the Red Vein Bali Kratom. Some are even touting it to be the best replacement for opium in situations like this.
It is indigenous to the species that grow in Bali exclusively and are supplied to different parts of the world. The red ball kratom type also serves other functions, but they are relatively small. For example, it helps in fighting lethargy and gaining energy while remaining focussed for longer periods. Some might argue that other Kratom products are more useful. Therefore, you have to choose the benefits that you want out of it. If pain relief is the main one, then go for the Red Vein Bali Kratom without any doubt.
The dosage for beginners should be anywhere between 1 to 2 grams per day, while the modest dosage can go up to 4 grams. If you are looking for some more action, than shooting up to 7 grams in order contemplated to be adequate.
Each Kratom is different and unique. The Red Vein Bali is an excellent kratom to start with, and you get a moderate dose of what it represents. Taking the right amount plays a crucial role in the kind of effect that you want to reap out if.


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We remember Deep CK from the early days of the Amapiano sound, the producer is here with a new extended play, this one he called "Amapiano Is Not A Movement".
The four-track EP features Lady Du, Samzin and Phindi Duke. Grab below.
DOWNLOAD: Deep CK – Amapiano Is Not A Movement


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Your contributions will help us continue to deliver the stories that are important to you
He also accused Fine Gael of 'targeting' Fianna Fáil members.
The incident has been reported to the PSNI.
"The same two cars suffered three accidents in front of us."
Chicago's Jackie Robinson West won the 2014 tournament for children between the ages of 11 and 13.
A novel prank, we must say.


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- Does tenant pay service charge UK?
- Should service charge be paid?
- What does a flat service charge include?
- What notice must accompany a demand for service charge?
- Can I get help with service charges?
- Can I refuse to pay service charge property?
- What is the difference between a sinking fund and a reserve fund?
- What are the 3 types of reserves?
- How are reserve funds calculated?
- How much is service charge on a flat UK?
- Can my landlord increase my service charge?
- How does a reserve fund work?
Does tenant pay service charge UK?
When a rental property is contained in an apartment block or housing development, either the tenant or the landlord may be expected to pay an annual service charge.
This charge will usually cover maintenance and repair work in communal areas..
Should service charge be paid?
"The government has approved guidelines on service charge. As per the guidelines, service charge is totally voluntary and not mandatory now," Paswan said in a tweet. … "If there is mandatory levy of service charge, customers can file a complaint in the Consumer Court," a senior Consumer Affairs Ministry official told.
What does a flat service charge include?
Service charges are payments by the leaseholder to the landlord for all the services the landlord provides. These will include maintenance and repairs, insurance of the building and, in some cases, provision of central heating, lifts, porterage, estate staff, lighting and cleaning of common areas etc.
What notice must accompany a demand for service charge?
Demands for service charges which you must pay to your landlord must be in writing and must contain your landlord's name and address. If your landlord's name and address are not on the demand, you do not have to pay the service charge until you are given this information.
Can I get help with service charges?
A leaseholder may be able get help with paying certain service charges if s/he is claiming income support, income-based jobseeker's allowance, income-related employment support allowance, pension credit or universal credit.
Can I refuse to pay service charge property?
If you can't pay your service charge, or you've fallen into arrears, you should contact the landlord or management company of your property to discuss your options for repaying the arrears. If you don't take steps to deal with the arrears, the freeholder could take court action and you could lose your home.
What is the difference between a sinking fund and a reserve fund?
A sinking fund is similar to a reserve fund as a means of collecting funds for more specific long term purposes. A reserve fund can account for an overspend in the accounting year, a buffer for the unexpected, or funds required to be built up over a short period of time such as a year or two.
What are the 3 types of reserves?
There are different types of reserves used in financial accounting like capital reserves, revenue reserves, statutory reserves, realized reserves, unrealized reserves.
How are reserve funds calculated?
The formula used is 1-(1+R)-n/R. This will be used to reflect the time value of periodic investments accumulated over the entire lifetime of the project. Thirdly, multiply required funds (RF) by the discount figure (D) to determine the present value (PV) of the Reserve Fund.
How much is service charge on a flat UK?
On this basis the average amount a Leaseholder can expect to be asked to pay as a Service Charge would be between £1,000 or £2,000 a year (£80 to £160 per month) depending on what type of Landlord they have and the type of property they lease.
Can my landlord increase my service charge?
If it is variable it can go up or down and so your landlord can increase your service charge. However, such increase should be reasonable and the law gives leaseholders the right to challenge the increase if this is not reasonable. More information you might find useful: Service Charges and other issues.
How does a reserve fund work?
How a Reserve Fund Works. A reserve fund sets aside money for covering scheduled, routine and unscheduled expenses that would otherwise be drawn from a general fund. … Because expenses may arise unexpectedly, a reserve fund is typically kept in a highly liquid account, such as a savings account.


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Ian Dury
Greatest Hits
If you need to know before ordering, contact us via @BanquetRecords on Twitter or send us an email email@example.com.
Ian Dury, like Ray Davies and Paul Weller was a very English talent. The colourful, metropolis inhabiting characters like Billericay Dickie or Plaistow Patricia he sung about and the ever so cockney music hall accent he chose to sing in were a big part of that. But most of all it was the detailed, poetic way he catalogued English life and the melting pot of musical styles used to do so, from punk to funk to jazz and rock 'n' roll, which left the listener in no doubt that Dury's work could only come from one place: The very heart of England.
Released on yellow coloured heavyweight vinyl, this is all killer and no filler. With Dury's most loved songs, including 4 UK Top 40 singles and the number 1 smash, Hit Me With Your Rhythm Stick
More Items From Ian DurySee all
Customers Who Bought This Also Picked Up…
- 1.Sex & Drugs & Rock & Roll
- 2.Wake Up And Make Love With Me
- 3.Reasons To Be Cheerful Part 3
- 4.Hit Me With your Rhythm Stick
- 5.My Old Man
- 6.Sweet Gone Vincent
- 7.What A Waste
- 8.There Ain't Half Been Some Clever Bastards
- 9.Inbetweenies
- 10.I Want To Be Straight
- 11.Sueperman's Big Sister
- 12.Billericay Dickie
- 13.Clevor Trever
Item code: DEMREC280
Label: Demon Records
Info correct on: 2/5/2018


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Food Wisdom Repository: Information Resources for Native Nutritional Health
This project undertakes outreach activities to further development of, and facilitate access to, a public knowledge repository of information on Indigenous nutritional health. The Research for Indigenous Community Health (RICH) Center at the University of Minnesota is building a curated database of reliable information resources relevant to Native nutritional health, accessed via a public website. Drawing from multiple sources of "food wisdom"—including experiential, community-based, and cross-disciplinary academic knowledge—this searchable repository provides a directory of experts, a bibliography of scholarly publications and grey literature, and information on relevant projects, programs, and resources. Supported by a gift from the Shakopee Mdewakanton Sioux Community's Seeds of Native Health initiative, and guided by first-round needs assessment, the RICH Center has built a proof-of-concept test site. The proposed project will further development of the repository into a fully-functioning, publicly-accessible prototype, and maximize its accessibility, through collection and analysis of additional user data. In the first half of the award period, advised by the External Consultants and the University of Minnesota's Usability Lab, the Project Director and Outreach Associate will conduct needs assessment and user testing with selected target users: health workers (health practitioners, health and nutrition educators, program managers, project coordinators, and other public and community health workers) who are working most directly within Native communities to foster health and well-being through food and nutrition. Analysis of user data will guide content development, database structure, and interface design for the prototype. The proposed project also includes assessment of the need for training resources to help target users access and utilize the repository's resources. Advised by the External Consultants, the Project Director and Outreach Associate will collaborate with the Outreach Librarian for the University of Minnesota's Health Sciences Library to assess the training needs of two audiences: health workers, and information professionals who serve Native communities. In the second half of the award period, they will analyze data collected on training needs, identify priority needs, and plan for development of high-priority training resources.
Identify any specific population(s) this project will serve:
Alaska Natives
American Indian
Native Hawaiians
Identify roles of participants this project will serve:
Educator, K-12
General public
Health care provider
Library or information professional
Public health professional
Student, college & post-grad
Student, K-12
Project Lead:
Julie Davis
Funding Source:
Project Funding:
Federal Fiscal Year:
Funding Amount:
Funding Period:
May 1, 2018 to Apr 30, 2019
Project Status:


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Aurora Cannabis and the $2.17 Billion Magic Trick
Though it's been a wild journey for the North American marijuana industry, cannabis could well be one of the fastest-growing industries this decade. A green wave on election night pushed the number of legalized medical marijuana states up to 36 – 15 of which also allow for the consumption and/or retail sale of adult-use weed. Meanwhile, Canada hit its two-year anniversary of recreational marijuana legalization in October.
The table is set for the legal North American pot industry to mature, and for a handful of standout marijuana stocks to shine.
Image source: Getty Images.


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We show the practicality of two existing meta-learning algorithms Model- Agnostic Meta-Learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propose two novel quantum adaptations of those algorithms using continuous quantum neural networks, for learning to trade portfolios of stocks on the stock market. The goal of meta-learning is to train a model on a variety of tasks, such that it can solve new learning tasks using only a small number of training samples. In our classical approach, we trained our meta-learning models on a variety of portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a pool of 60. In our quantum approach, we trained our quantum meta-learning models on a simulated quantum computer with portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings suggest that both classical models could learn a new portfolio with 0.01% of the number of training samples to learn the original portfolios and can achieve a comparable performance within 0.1% Return on Investment of the Buy and Hold strategy. We also show that our much smaller quantum meta-learned models with only 60 model parameters and 25 training epochs have a similar learning pattern to our much larger classical meta-learned models that have over 250,000 model parameters and 2500 training epochs. Given these findings , we also discuss the benefits of scaling up our experiments from a simulated quantum computer to a real quantum computer. To the best of our knowledge, we are the first to apply the ideas of both classical meta-learning as well as quantum meta-learning to enhance stock trading.
Profitable stock trading is vital to investment companies. In order for stock trading to be profitable, an investment strategy must be in place to maximize performance, which can be measured in expected return. However, many different factors must be considered to measure expected return, such as future estimated value or risk of loss, which makes it difficult for market analysts to make quick decisions about how to trade their many stocks profitably in their portfolios.
The problem of trading profitably on the stock market has been solved with some success with the recent developments of Deep Reinforcement Learning (DRL) [
One of the largest issues with training deep learning algorithms to trade on the stock market is that once they have successfully learned to trade a certain set of stocks, that learning does not translate to other stocks, even if those stocks are very similar to the ones it has previously learned. This problem is called overfitting, and is one of the biggest hurdles in training DRL models [
In order to solve this problem, we propose a novel meta-learning approach to trading on the stock market. Meta-learning is a field of reinforcement learning that enables the learning agent to generalize previous knowledge to similar areas it has learned before which enables it to learn new environments with very little training time. We propose to use the meta-learning algorithms Mo- del-Agnostic Meta-Learning (MAML) [
Furthermore, the stock market is a very complex environment with many variables at play and classical computers can struggle to learn effectively with this much data. The recent developments of Quantum Computing is a promising solution to train an algorithm quickly on the large amounts of data the stock market provides, which has the potential to increase our performance when trading. With these exciting developments in mind, we also explore quantum implementations of the meta-learning algorithms MAML and CAVIA on a simulated quantum machine using our novel quantum algorithms Q-MAML and Q-CAVIA. To the best of our knowledge we are also the first to create quantum meta-learning algorithms. In summary our main contributions of this paper are as follows:
· We provide a new way to increase training performance when learning new stock portfolios via meta-learning.
· We create new quantum implementations of these meta-learning algorithms used to enhance stock trading.
· As far as we know, we are the first to explore both meta-learning to enhance stock trading and the first to create a quantum implementation of the meta-learning algorithms MAML and CAVIA.
In this section we give a brief background in the fields of reinforcement learning, evolutionary strategies, meta-learning, and continuous variable quantum computing.
Reinforcement learning (RL) is one area of machine learning in which an agent interacts with an environment to learn how to maximize rewards by taking certain actions within that environment [
J ( π ) = E q 0 , q , π [ ∑ t = 0 H − 1 γ t r ( s t , a t , s t + 1 ) ] (1)
where H ∈ ℕ is the horizon and γ ∈ [ 0,1 ] is the discount factor which determines how much the agent cares about rewards in the distant future relative to those in the immediate future.
RL is very useful, especially when we are trying to learn complicated environments that would be too difficult to write instructions for by hand. By only defining the rewards the agent will seek, it will teach itself how to best navigate the environment and maximize its reward. All methods of RL have this same goal, which is to find the optimal policy to maximize reward.
RL algorithms that use neural networks are defined as Deep Reinforcement Learning algorithms. Neural networks are a technique of function approximation that can learn continuously complex environments faster and more effectively. Neural Networks are able to do this via activation functions which enables them to learn on these more complex environments [
Evolutionary Strategies (ES) are an alternative to Stochastic Gradient Descent to optimize deep learning models. These strategies were created from the family of Evolutionary Algorithms (EA). The idea behind these algorithms is to mimic what natural selection does to species in biology. Over time, natural selection works by naturally propagating "good" genes. Organisms that have these "good" genes are more likely to survive from predators and perpetually pass these genes to their offspring. This cycle continues until all the organisms of the species have these "good" genes.
We can apply this same idea of natural selection to optimize a deep learning model. Recall that in deep learning, the goal is to reach the optimal policy that receives the greatest cumulative reward by updating the parameters of the neural network. In other words, we can find the optimal configuration of parameters θ to find the optimal policy π for any state x, which is described as π ( x ) . This alternative way for optimizing parameters is shown in Algorithm 1 [
There are a few important lines to note in Algorithm 1. First, we apply a "jitter" to each of the samples from step 4. The "jitter" is created through random variance based on a hyper parameter, σ , which controls how much random variance we have in "jittering". Then, based on the samples chosen with this random variation we can evaluate the fitness of them in line 5 in accordance with our fitness function f at state x i , and then perform our update to our parameters θ toward the optimal policy π via the computed log-derivatives shown in step 9.
Furthermore, ES is effective for deep reinforcement learning. It has been
shown that ES scales very well with multiple CPUs for parallelization, even with up to 1000 CPUs [
Reinforcement Learning struggles with the problem of overfitting, or learning one task very well while failing to generalize what it has learned to other tasks, even ones that are very similar. One of the things that humans can do very well is generalize things that they have learned previously to new tasks. One such example is that once a human learns to ride a bicycle, it takes little or no instructions to figure out how to ride a motorcycle. These two tasks are very similar in practice, and our human minds can automatically detect the similarities between these two tasks and transfer our previous knowledge to learn the new task quickly. This is impossible to do for traditional RL algorithms, which require completely different training sessions for each individual task, even if the two tasks are very similar.
The field of meta-reinforcement learning hopes to solve this problem. The goal of meta-reinforcement learning is to create good models that are capable of adapting or generalizing to new tasks and environments that it never encountered during training time. In order to adapt to these new tasks, the meta-rein- forcement learning algorithm is shown small batches of this new task with limited exposure. Eventually, the algorithm will be able to perform well on this new task with much less training. The research field of meta-learning is very broad whose goal is to learn how to learn, but for the purposes of our paper, whenever we say meta-learning we mean meta-reinforcement learning, which applies meta-learning ideas to reinforcement learning algorithms.
Some examples of how meta-learning could be used in the real world are:
· A image classifier trained to detect cat images can learn to detect non-cat images quickly with only a few images [
· A game bot learning to play checkers after it has already learned chess.
· A meta learned regression algorithm can learn shapes of new sine curves with only seeing a few of them [
The goal of few-shot meta-learning, or learning with very few training steps, is to enable the agent to quickly discover the optimal policy for a new task using only a small amount of experience from the new test setting. However, gradient-based optimization is designed to work with large sample sizes, not the small amount of training required for meta-learning to take place. MAML and CAVIA are two algorithms that look to solve this problem using the optimization approach for meta-learning.
MAML stands for Model-Agnostic Meta-Learning and its purpose is to be a general meta-learning algorithm that is compatible with all models that use some form of gradient descent to update learning parameters [
MAML has been shown to be a successful way to quickly train a model on a variety of popular reinforcement learning environments such as the 2D Navigation environment and the complex MuJoCO 3D quadruped environment. In both cases, MAML achieved the desired goal for both environments with only a few training iterations [
Fast context adaptation via meta-learning, or CAVIA, is an extension of MAML which claims to be less prone to meta-overfitting, which has been shown to be a problem for MAML [
In MAML, the gradients of the model parameters θ are calculated before the inner-loop update, which means that the outer-loop update involves a higher order derivative of θ . This increases complexity of the algorithm and decreases performance while training. CAVIA removes the higher order derivative calculation
by adding the context parameters ϕ which are calculated within the inner loop for each task, and is separate from the model parameters θ which are meta-leaned and calculated in the outer loop and shared across tasks. This is done by updating the model parameters θ with the average of the context parameters ϕ , which means that the higher order gradients are already included in θ due to its dependence on ϕ . The full algorithm can be found in more detail in Algorithm 3 [
A recently popular model of quantum computing is continuous variable (CV) quantum computing which serves as a continuous method of computation and leverages wavelike properties found in nature where quantum information is not encoded in bits but in the quantum states of fields, such as the electromagnetic field. The particles that encode this information are called qumodes, which carry more information than bits and are more powerful due to their quantum properties. For example, qumodes can be in a quantum superposition of multiple states at the same time. The state of the qumodes are manipulated using quantum gates and multiple gates applied successively to qumodes make up a quantum algorithm.
Qumodes can be represented by the wavefunction representation, where we specify a single continuous variable, say x, and represent the state of the qumode through a complex-valued function of this variable called the wavefunction ψ ( x ) . The single continuous variable x can also be interpreted as a position coordinate, and | ψ ( x ) | 2 as the probability density of a particle (photon) being located at x. Based on elementary quantum theory, we can use a wavefunction based on a conjugate momentum variable, ϕ ( p ) . The position x and the momentum p can also be pictured as the real and imaginary parts of a quantum field, such as light [
The CV model is largely unexplored when it comes to machine learning, but there have been some recent research that have shown the usefulness of the continuous nature of a CV quantum circuit being used as a kernel-based classifier [
networks [
A quantum neural network is made with a specific set of quantum gates that manipulate qumodes as they pass through them. Gates can either be Gaussian or not. Gaussian gates are the "easy" operations for a CV quantum computer. The rotation R ( ϕ ) , displacement D ( α ) , and squeezing S ( r ) gates are Gaussian operations and are applied to one qumode. Another Gaussian gate, called beam- splitter B S ( θ ) , can be understood as a rotation between two qumodes. These gates can be represented as matrix transformations on phase space and are as follows,
R ( ϕ ) : [ x p ] ↦ [ c o s ( ϕ ) s i n ( ϕ ) − s i n ( ϕ ) c o s ( ϕ ) ] [ x p ] ,
D ( α ) : [ x p ] ↦ [ x + R e ( α ) p + I m ( α ) ] ,
S ( r ) : [ x p ] ↦ [ e − r 0 0 e r ] [ x p ] ,
B S ( θ ) : [ x 1 x 2 p 1 p 2 ] ↦ [ c o s ( θ ) − s i n ( θ ) 0 0 s i n ( θ ) c o s ( θ ) 0 0 0 0 c o s ( θ ) − s i n ( θ ) 0 0 s i n ( θ ) c o s ( θ ) ] [ x 1 x 2 p 1 p 2 ] .
The ranges for the parameter values are ϕ , θ ∈ [ 0,2 π ] , α ∈ ℂ ≅ ℝ 2 and r ∈ ℝ . To help visualize what these Gaussian transformations do, we can map the phase space as a quasi probability density, therefore simulating what each of the gates look like when they are applied to a qumode.
One more non-Gaussian gate is used to build quantum neural networks, the Kerr gate which is represented by the function K ( κ ) = exp ( i κ n ^ 2 ) . A universal gate set for CV quantum computing consists of all of the Gaussian transformations shown above, and one non-Gaussian gate such as the Kerr gate. With a universal gate set, we can approximate any function with CV quantum computing. This universal gate set is visualized in
These gates can then make up a neural network algorithm that is similar to how classical neural networks operate. We can use these quantum neural networks in place of the classical neural networks to speed up computation time on a quantum computer. In the future, we could use a classical computer to run the RL algorithms together with a quantum chip that runs quantum gate operations to compute the neural networks very quickly [
Instead of trying to teach a reinforcement learning agent to master a game on the Atari, which is a very popular and standard way to benchmark RL performance, we are going to train a RL agent to trade on the stock market. The stock market is an attractive alternative environment for testing RL performance because it offers a more practical application for learning than playing a video game. Also, all of the data on the stock market is publicly available and easily accessible. The goal of this paper is to not create an algorithm to make a lot of money on the stock market but to show how a combination of reinforcement learning, meta learning, and quantum computing can effectively learn to trade on a practical environment like the stock market.
In our paper, we will train an agent to manage m multiple portfolios at the same time, each with n number of stocks. Then, using meta reinforcement learning our agent will learn to trade a single portfolio that contains completely different stocks than what it has learned to trade with before with much less training required.
In order to train the RL agent we must model the stock trading process as a MDP which is the baseline assumption required for all of the RL methods described above. This frames the problem as a maximization problem, where the goal of the RL agent is to maximize the amount of trading profits it will receive when trading on the stock market.
· State s = m t : which includes a vector of size n + 1 , where n is the number of stocks in the portfolio plus 1 for the amount of total cash we hold. The closing prices p ∈ ℝ for all of the stocks in each portfolio m have time-step measure t, where t denotes the day we are trading.
· Action a: which is the action the agent can perform on each portfolio m. The actions are encoded by the portfolio weights w t ∈ [ 0,1 ] which describe the percentage amount each stock makes up the portfolio at each timestep t where the sum of all w t in the portfolio m is 1.
· Reward r ( s , a , s ′ ) : which is the change of the portfolio value when action a
is taken at state s and arrives at the new state s ′ . The total portfolio value is the sum of the closing prices in all held stocks in the portfolio plus the total remaining cash we are holding that is not put in stocks. The goal of the agent is to maximize r for a given time frame.
· Policy π ( s ) : which denotes the trading strategy of stocks at state s, which is the probability distribution of a at state s.
· Action-value function Q π ( s , a ) : which is the expected reward achieved by action a at state s following policy π .
Each portfolio is composed of a vector of weights w t ∈ [ 0,1 ] , which describes how much percentage the portfolio is composed of each asset at timestep t. These weights are then the output of our agent, and how it decides how much of each asset in the portfolio to hold, which always sum up to one by definition, ∑ i w t , i = 1 . The first weight is special as it describes how much cash is being held. At timestep 0, the cash weight is always 1 because no trading has occurred yet so all of our assets are in cash. The rate of return at timestep t is then:
p t : = y t ⋅ w t − 1 − 1 (2)
where at time t, p t is the rate of return of the portfolio, y t is the vector of closing prices of each stock in the portfolio, and w t is the assigned vector of weights by the agent. The corresponding logarithmic rate of return is
r t : = ln y t ⋅ w t − 1 . (3)
The logarithmic rate of return is used to normalize the reward function so that it is easier for the agent to learn. This ensures that when the model goes to update its gradients, that the gradients all get updated on the same scale. This makes training more stable which should also increase performance of our model.
If there is no transaction cost, the final portfolio value will be
p f = p 0 exp ( ∑ t = 1 t f + 1 r t ) = p 0 ∏ t = 1 t f + 1 y t ⋅ w t − 1 , (4)
where p 0 is the initial investment amount. The goal of the reinforcement learning agent is to maximize the portfolio value p f for a given time frame.Assumptions
In this work, only back-test tradings are considered. This means that our model pretends to be "back in time" at a point in market history and then trades on unknown "future" market data. In order to perform backtesting we must make a couple of assumptions:
1) Zero slippage: The liquidity of all market assets is high enough that each trade can be carried out immediately at the last price when an order is placed. This also includes the ability to always place orders immediately at the end of the day.
2) Zero market impact: The capital invested by the agent is insignificant enough to not influence the market.
In a real-world trading environment, if the volume in the market is high enough, then these two assumptions are near to reality. The Consumer Cyclical stocks that we will be trading are all high volume stocks so our assumptions are justified. Furthermore, we assume that there are no transaction costs to trading. In the modern world of stock trading there are many online brokerages that offer no cost trading, several of which are Robinhood, Fidelity, and E-Trade. Since we will only trade stocks once per day we can assume no transaction costs will be charged if one of these online brokerages are used.
Algorithmic trading on the stock market is a very active research domain. Here we will focus on those algorithms which use different deep reinforcement learning techniques.
One deep reinforcement learning strategy used a Portfolio-Vector Memory (PVT) technique to form the problem as optimizing a portfolio of assets, where each asset in the portfolio has an assigned weight given to it by the learning agent [
Utilizing quantum computing for algorithmic trading is a new field of research but is increasing in activity. One method explores using a Quantum-ins- pired tabu search to find trading rules that will optimize profits when trading on the stock market [
Another branch of algorithmic trading with quantum computers explores using quantum artificial neural networks (QuANNs) as a way to build and simulate financial market models with adaptive selection of trading rules [
At the time of writing, there is no current research that uses continuous quantum neural networks to learn to trade on financial markets. Furthermore, there is also no research that looks into applying the field of meta-learning algorithms to better improve the agent's learning of financial markets. Our research is the first to combine both the fields of meta-learning and continuous quantum neural networks to teach an agent to trade on financial markets.
Our trading experiments were done on stocks that are classified as Consumer Cyclical (CC) stocks. Consumer Cyclical is a category of stocks that rely heavily on the business cycle and economic conditions. It includes industries such as automotive, housing, entertainment, and retail.
We extracted a total of 60 different CC stocks from finance.yahoo.com. In our experiments, our agents trained on m multiple portfolios, each having a vector of size n + 1, where n is the number of stocks inside the portfolios and where n + 1 includes the amount of cash the agent has available to purchase stocks with. To fill the portfolios during training time, we randomly sample n stocks from 55 of the 60 stocks available. The random sampling is done so that the meta-lear- ning algorithm learns how to trade CC stocks as a whole and not just individual stocks. For our classical experiments m and n were set to 5. This means that our agent trained on 5 different portfolios, each containing 5 randomly chosen CC stocks.
We then split this data into a training set that contains the first 70% of the data and a testing set that contains the last 30% of the data. Then, to test our agents performance, we train the agent with only a few training iterations on 1 portfolio that contains the last 5 of the stocks that were not included in the random sampling during training. This test portfolio contained the same set of CC stocks across all tests. These stocks were ABC, AKAM, APD, AVP, and BAC. The test portfolio data is also split with the same 70/30 train/test split like the training set. We then meta-train the agent with only a few training iterations on the 70% test set, and then run a back test on the last 30% of the test portfolio to validate the performance of the agent on data it has not seen before. Back testing is a standard way to measure how well a strategy or model would have done at some time period in the past. In our case, our period of back testing is our 30% split of our testing dataset. The stock data we used were from the dates 1/2/2001 to 6/12/2002. Therefore, with the 70/30 train/test spit described above, the agent trained on stock data from 1/2/2001 to 1/4/2002 and was back-tested on stock data from 1/7/2002 to 6/12/2002. This is shown in
Like mentioned before, each portfolio is a vector of size n + 1 where n is the number of stocks in the portfolio plus one for the amount of cash the agent currently has to trade with. The data available to us for each day includes the opening price, the highest price in the day, the lowest price in the day, the closing price of the day, and the volume or the amount of stock traded in the day. In order to make the problem simpler and to reduce the total amount of data the agent needs to process to make training time quicker, we only use the closing price of the day. While giving the agent more data to train on has the potential to increase its trading performance, for the purposes of this paper, we think that the closing price offers enough information to show the potential of meta-learning on trading on the stock market. Future work could include using more data to further boost performance.
In order for the agent to more effectively learn from the data, we group multiple days together as one input into the model. We do this because it would be near impossible for the model to make a decision from just one days closing price. With only one days worth of information, the model doesn't know if the stock price is going up or down and therefore cannot make a judgement on if its a good time to purchase the stock in order to make a profit. To solve this, we input ws stocks at a time, where ws is called the window size. In our experiments, the window size is 10. This means that at every timestep t, the agent is given the data from the past 10 days from t, or t − 9, for each of the stocks in the portfolio, which can be seen in
for each day of training m × n × ws.
Another very common strategy to improve the stability of learning is to normalize the data that it receives so that variation between the data is reduced. In order to do this, we apply two strategies. First, we have selected to train and test only on stocks that have a price between $0 and $50. This way, there are no very large stocks that could cause a high variance in training our model. Second, we also normalize our data by using the logarithmic rate of return shown in Equation (3).
In this section, we will discuss how our models were created and how learning takes place. We have a total of 4 algorithms being tested: MAML, CAVIA, and our quantum implementations of these algorithms Quantum MAML (Q-MAML) and Quantum CAVIA (Q-CAVIA). All of these algorithms use the NES model for updating the gradients. Normally, MAML and CAVIA use gradient descent to update the gradients, but they are both general purpose meaning that any method to update the gradients of our model parameters will work. We chose to use the NES algorithm to replace the derivative calculations in MAML and CAVIA because it is has been shown to avoid overfitting and scales well with parallel computing [
In our implementation of MAML and CAVIA using NES, we have a neural network with 2 hidden layers of size 50 × 100 and 500 × 500, and an input layer of size 1 × 50 and an output layer of size 500 × 5 + 1 (see
The NES algorithm updates each of these weights following Algorithm 1, where our fitness function f is the reward function, or the expected portfolio value, our learning rate is set to 0.03, our σ which applies the "jitter" is set to 0.1, and our population size is set to 15.
Our goal in meta-learning is not to learn to trade one portfolio, but to learn how to trade CC stocks as a whole. Because of this, our training loop trains on 5 separate portfolios, each containing 5 CC stocks. In order to do this, we loop through each portfolio, updating our agents parameters on each portfolio one at a time. This means we have one set of parameters for all of our portfolios. Training in this way makes the training essentially maximize the reward over all of the stocks, or in other words, it learns the average best policy for all of the portfolios that contain CC stocks. We do this for 2500 epochs, meaning that we train the algorithm over each portfolio 2500 times. We chose 2500 epochs because training progress slowed down significantly around this point.
Then, we train the meta-algorithm on 1 previously unseen portfolio with only 20 epochs, which is significantly less epochs required than during training time. How the meta-algorithm is trained is different for each MAML and CAVIA algorithms.
The MAML algorithm requires a separate set of model parameters θ ′ to be trained separately along with the main parameters θ . In our implementation θ ′ is initialized with the same structure as the normal parameters and they are also updated via the NES algorithm. The difference between these parameters is when they are updated. After each θ parameter is updated for each of the portfolios, we save the gradients from each of these updates. We then use each of these gradients from θ to update our meta-gradients which are then used to update our meta-parameters θ ′ . These parameters are then used to speed up our training on our new unseen test portfolio, which only requires around 20 epochs to learn.
In summary, MAML requires two different gradients to be computed which increases computational complexity due to having to compute 2nd order gradients. This is done to train the θ ′ parameters which are essentially an average of the normal θ parameters and are used to dramatically increase training speed on an environment that is similar to what the θ parameters were trained on.
CAVIA aims to increase the performance of MAML by taking away the 2nd order gradients and replacing it with context parameters ϕ . The context parameters are initialized with a vector of size 5, which gives one context parameter for each stock in the portfolios. Training takes place in 2 separate loops, the inner loop and the outer loop. In the inner loop the context parameters are initialized to 0, as described in the CAVIA paper, and are updated in accordance to how close the θ parameters are to the optimal policy via an NES update of the gradients. These context parameters are then concatenated to the input layer along with the portfolio's stock closing price data. This means that the size of our input layer doubles. Then in the outer loop, the model parameters θ are updated like usual, but with the addition of the context parameters from the inner loop being concatenated onto the inputs to the model.
In summary, CAVIA removes the 2nd order gradients because the context parameters are not dependant on any gradients computed before, but are instead computed before the weights are updated in the inner loop. The context parameters are then added to the input where then the θ parameters are trained like normal through the model via the NES algorithm. Because there is one context parameter for each stock in the portfolio, and these context parameters are updated over time for each portfolio through θ , the context parameters serve to boost training speed when we train on another environment that is similar to what was learned before.
Our quantum implementation is created using the Python package Pennylane from Xanadu. This package simulates a quantum machine on a classical computer and gives support for quantum machine learning. It is important to note that Pennylane simulates a Continuous Variable Quantum Machine. We use this package to create our quantum neural networks. We then use those networks instead of our classical networks in our MAML and CAVIA algorithms. Because of how the quantum neural networks work, the inputs and outputs of the network have to be changed slightly in order for learning to take place in the quantum network. Everything else from the classical algorithms remains the same, including how we update the gradients via NES.
We decided to test two different implementations of a quantum neural network, one that uses the beamsplitter gate that entangles qumodes, and one that does not use the beamsplitter gate. Quantum entanglement is a property of quantum computers where two quantum particles are united in a perfectly shared existence, even at immense distance. Harnessing this quantum property has the potential to dramatically increase the amount of calculations we can do in parallel [
Both of our quantum neural networks have 4 layers of gates. These gates are applied to the "wires" of the simulated quantum computer, and each gate has a parameter which controls how the gates behave to map the inputs to the outputs. These parameters are updated through the NES algorithm in the same way that the classical models parameters were updated. The input data, the closing price of stocks, is applied through each of these wires individually via a displacement gate which encodes the input data into a quantum state so that the quantum computer can apply other operations to it. Because each bit of input data must be encoded onto its own separate wire, the data has to be structured differently than in the classical case so that the quantum neural network can properly utilize the data for learning. The quantum neural network outputs the same vector of voting weights as the classical neural network after mean displacement in the phase space along the x axis of each wire [
Another thing to note is that simulating a quantum computer on a classical computer is a very computationally heavy task. With each additional wire the task becomes exponentially more difficult, and exponentially more RAM is required to store all of the quantum states and the interactions between them. For this reason, with the computer hardware available to us, we were only able to simulate a quantum computer with up to 5 qumodes, or 5 wires. With a budget of only 5 wires, we could only simulate portfolios that contained 2 stocks in them. Furthermore, the number of portfolios the quantum agents trained on was 2, so the quantum agents trained on 2 portfolios that each contained 2 randomly sampled CC stocks. This was done in order to reduce training time to a manageable level. These 2 stocks in the portfolios each had to take up their own wire, along with an additional wire that was used for the cash bias layer. The other wires in our budget had to be used in order to implement both MAML and CAVIA. To reduce training time even further, our data set included only 180 days instead of 360. Because we trained our quantum agents on portfolios of size 2, our testing portfolios had to be the same size, so our test portfolio only contained the stocks ABC and AKAM. Using the same 70/30 train/test split, the quantum training data set were between the dates 1/2/2001 and 6/29/2001, and the quantum testing data set were between the dates 7/2/2001 and 9/21/2001.
Our Q-MAML implementation used a total of 3 wires: 2 wires for each stock in the portfolio and 1 wire for the cash bias layer. Our Q-MAML implementations can be seen in
Our Q-CAVIA implementation used a total of 5 wires: 2 wires for each stock in the portfolio, 1 wire for the cash bias layer, and another 2 wires to encode the context parameters. Recall that in the CAVIA algorithm we have one context parameter for each stock. Our CAVIA Quantum implementations can be seen in
Even though we have more wires in the budget for our Q-MAML implementation that we could use to trade more stocks, we wanted the input data to be the same for both the Q-MAML and Q-CAVIA so that we could compare their performances. Q-CAVIA was limited to trading on only 2 stocks, so we trained the Q-MAML on the same 2 stocks so that we could appropriately compare their performance.
The performances of all of our meta-learned and quantum meta-learned models were compared to that of what we call the market value of the portfolio. The market value is measured by simply equally spreading the total initial amount of
the fund into each asset in the portfolio and holding them without making any purchases or sales until the end [
ROI = End Portfolio Value − Initial Amount Invested Initial Amount Invested ∗ 100 (5)
For the classical experiments, we ran each of the experiments 12 times in order to reduce the chances that our results are an anomaly. The training and market test plots in this section show the mean and ± standard deviation from the mean of our portfolio values over all 12 experiments. The mean value is shown in the dark blue line and the ± standard deviation from the mean is shown in the light blue background.
For our Classical Experiments, recall that we trained both MAML and CAVIA over 2500 epochs on 5 portfolios each with 5 stocks randomly sampled from a pool of 50 CC stocks over the dates between 1/4/2001 and 1/7/2002. For
Recall that for testing our classical models, the portfolio contained the stocks
ABC, AKAM, APD, AVP, and BAC, and was back tested between 1/7/2002 and 6/12/2002. The market value of this portfolio over this time period starting with $10,000 was $10,085.50, or 0.86% ROI.
For the quantum experiments, we ran each of the experiments 8 times to reduce the chances that our results are an anomaly. The plots below show the mean and ± standard deviation from the mean of our portfolio values over all 8 experiments. We chose to run them 8 times rather than 12 in order to reduce the amount of time spent training to a manageable amount.
For our quantum experiments, recall that we trained Q-MAML and Q-CAVIA with 25 epochs on 2 portfolios each of size 2 that were randomly sampled from a pool of 50 CC stocks over the dates between 1/2/2001 and 6/29/2001. For reference, Figures 13-16 show model training for both Q-MAML and Q-CAVIA, where the plot on the left are trained on the CC stocks and the plot on the right show the meta-model training on the test portfolio containing ABC and AKAM. Note that the plots on the right we set our meta-model training epochs to 5 instead of 20 because our training on the CC stocks (plots on the left) were only trained for 25 epochs. This was done so that our meta-learning training epochs were set as a smaller fraction of the training on the CC stocks. If the number of epochs was larger, then we run the risk of overfitting our model on the particular
test portfolio. Remember that due to computational limitations we only trained on portfolios that contained 2 stocks instead of 5 for the quantum models. We also compared two variations of our quantum models, one constructed with the beamsplitter gates (
Recall that for testing our quantum models, the portfolio contained the stocks ABC and AKAM, and was back tested between 7/2/2001 and 9/21/2001. The market value of this portfolio over this time period starting with $10,000 was $8734, or −14.38% ROI.
The Quantum model performances can be seen in
note is that at trading days 10 - 25 in
It is more difficult to compare the classical results and quantum results for multiple different reasons. First of all, they both do not use the same test portfolio so they are trading on different stocks. The classical models were back tested with a portfolio that contained the stocks ABC, AKAM, APD, AVP, and BAC, where the quantum models were back tested with a portfolio that contained the stocks ABC and AKAM. Second, during training the classical models were trained on up to 25 different CC stocks (5 portfolios each with 5 randomly sampled CC stocks). Because of hardware constraints, our quantum models were trained on up to 4 different CC stocks (2 portfolios each with 2 randomly sampled CC stocks). We say up to x different CC stocks because random sampling could choose the same stock twice to be included in different portfolios. This means that our classical models were given the opportunity to better learn how CC stocks behave, and thus could then utilize that information better to increase its trading performance on its test portfolio. However, even with these differences we can make some meaningful comparisons between how the classical models and quantum models behaved.
Our Q-MAML and Q-CAVIA trading graphs in
Another reason this could happen is how the quantum neural networks are constructed. Our largest quantum neural network, Q-CAVIA with beamsplitter, had a total of 65 model parameters. Our classical models had 257,500 model parameters. In general, the more parameters the model has the more complex problems the model can learn. This means that the classical models were constructed initially to handle a more complex environment like the stock market. However, this isn't exactly a fair comparison, as the classical model parameters were all connected linearly where the quantum models are connected continuously via the continuous variable quantum gates. This does begin to show the potential power of quantum computing as our quantum models were still able to learn how to trade on the stock market with a limited set of model parameters (60 for quantum vs 257,000 for classical). This is shown in Figures 13-16 where the rewards are increasing after each training epoch which shows learning is taking place. The quantum models training have a similar shape to the classical training in
We introduced a meta-learning approach to learn to trade on the stock market via both classical and quantum computing. Our approach has multiple benefits. With our method, we can learn to trade a new portfolio with similar but different stocks with much less training time required. This means that firms who would like to update their portfolios with new stocks can do so much faster with this new method, which gets their algorithms trading on the market with minimal downtime, increasing their stock trading efficiency. Lastly, we have implemented a new way to use the meta-learning algorithms MAML and CAVIA on a quantum computer with our Q-MAML and Q-CAVIA algorithms. While our results were limited due to a lack of computation power to simulate a large quantum computer, we show that quantum neural networks that contain the beamsplitter gate are superior than those that do not with 1.48% greater ROI, and that our quantum models are still able to achieve meta-learning on the stock market with comparable training performance to the classical models while having only 60 model parameters vs the classical models which have over 250,000 learning parameters. Therefore, Q-MAML and Q-CAVIA opens the door for more powerful computation when run on a real quantum computer. To the best of our knowledge, we are the first to explore using meta-learning and quantum meta- learning techniques to enhance algorithmic stock trading. In summary:
· We are the first to explore using meta-learning to improve stock trading. We have found that meta-learning can be used to dramatically reduce the time it takes to learn a new stock portfolio, from 2500 training epochs to 20, while maintaining comparable trading performance within 0.1% of the market value.
· Our novel quantum meta-learning algorithms Q-MAML and Q-CAVIA are able to learn to trade stock portfolios, even with limited computation power and training parameters, and that the quantum algorithms that have the beamsplitter gate achieve 1.48% higher ROI than those without. At the time of writing, Q-MAML and Q-CAVIA are the first quantum meta-learning algorithms.
We believe that meta-learning is an important step in creating truly intelligent learning agents that can generalize previous learning to new tasks. Furthermore, enabling these agents to train on a quantum computer enables more possibilities in their learning with an increase in computational power. Our research has taken a first step in exploring the practical possibilities of meta-learning and quantum meta-learning to trade on the stock market. Future research can explore the possibilities of our quantum meta-reinforcement algorithms in new practical areas.
The authors declare no conflicts of interest regarding the publication of this paper.
Sorensen, E. and Hu, W. (2020) Practical Meta-Reinforce- ment Learning of Evolutionary Strategy with Quantum Neural Networks for Stock Trading. Journal of Quantum Information Sci- ence, 10, 43-71. https://doi.org/10.4236/jqis.2020.103005
Here is a list of notations so that you can read through equations in the paper more easily.


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FAYETTEVILLE, N.C. (WNCN) – President Donald Trump has postponed his Thursday evening rally in Fayetteville over windy conditions.
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The event was scheduled for 6:30 p.m. the Fayetteville Regional Airport. Details on Monday's event were not immediately available.
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The first wall is up......
The first wall is up in the new gallery and we couldn't resist hanging a picture on it straight away (much to the delight of the builders who had to move the scaffolding out of the way!).
It takes a big wall to make an Undine XL look small, we are definitely going to need some even bigger paintings!
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Canada's Drag Race winner Priyanka is more than just Toronto's most famous queen
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#4525 by Obi-Wan Knievel Wed Aug 13, 2008 11:54 pm
As far as internet scams go, I think this is the oldest one in the book. This may in fact be the letter that got this kind of thing known as the "Nigerian Letter scam" so many years ago.
from Mr G. Jimeta <ajsesma3>
reply-to [email protected]
date 13 August 2008 11:48
subject Your urgent assistance required.
Dear Friend,
I greet you most respectfully.
First,I am obliged to solicit for your assistance,almost beg for my existence,for not having the means to transfer and secure this funds tralked about here.I know this is a bit of an unusual reason to e-mail you but I need your help.I am assuring you that my contacting you as a private person is without any intention to violate your integrity; but solely due to the nature of this business as a special deal.Permit me to say also that I got your contact in confidence through the email database list purchased by my firm for resourseful individual based in overseas.
I am Alhaji Gamso Jimeta,the special assistant to the secretary of the
petroleum trust development fund-(PTDF)based here in the Independent Republic of Nigeria.
Sometime ago, to be precise by mid-last year,contracts were awarded to a conglomerate of multinational and indigenous companies by my committee on behalf of PDTF. In the general running of things, this contracts payments was over-invoiced to the tune of USD15.6M in total.This was done deliberately in a deal by members of my committee who we were assigned to share an office that also afforded a measure of privacy to get this deal arranged.We want to benefit from the project so that we will not retire to a life of penury after putting in many years in the civil service and where offoicially we have been working as gaggle of struggling individuals without any adoption programme for workers from our government so far; now and in the foreground ahead.
Right off the bat,we now desire to transfer the money,which is presently lying in a suspense account of PDTF at the Central Bank of Nigeria (CBN) into an overseas account which we expect you to provide for a commission.And fundamentally,however,we want this money to be transferred to the foreign account which you are going to provide bearing in mind that our civil service code of conduct laws forbids us as civil servants from owning foreign company or running foreign account.Though all expenses regarding the account opening if not already in existence shall be borne by you and reimbursable as already stated above on conclusion of business.
And for the present,at least,we have agreed that the following funds will be shared thus after it has been paid into your account;
a) 20% of the money will go to you for acting as the beneficiary of the fund.
b) 10% will be set aside as an abstract projection for reimbursement to both parties for incidental expenses that may be incurred in the course of the transaction.
c) 70% to us the government officials (which we wish to commence a joint business in conjunction with you when we come for sharing).
For this matter, I would require;
a) Name and address of Company/ Beneficiary.
b)Your personal Telephone and Fax numbers .
The above informations would be used to make formal application;as a matter of procedure to the government remittance institution for the release of this funds and onward transfer to your bank nominated account. It does not matter whether or not you or your company does contract projects of the nature described here.The assumption is that you won a major contract and subcontracted it out to other companies.After all and more often than not,it is a known fact globally that big trading companies or individuals of unrelated field win major contacts and subcontracts to more specialized firms for execution of such contracts.This known principle in contract business is the cardinal element of support on which we rest our backs here to pursue the actualization of this mutually beneficial transaction without any hassles.
More importantly,we have strong and reliable connections and contacts at the Central Bank of Nigeria (CBN) and the Federal Ministry of Finance and we have no doubts at all that this money will be released and transferred if we get the necessary foreign partners to assist us in this deal. Therefore,when the business is successfully concluded we shall through the same connections withdraw all documents used in the execution of this deal from all the concerned government ministries for 100% security.
By the by; and out of an abundance of caution,I will be counting on your discretion to keep this matter close to your chest alone.This is to enable us achieve our targets.I do not mean to enforce attitude from you but our present sedentary positions of still working in the civil service demands that we must remain sticklers for laid down etiquettes/law of the civil service which prohibits us from engaging in business of this nature.I crave your indulgence here on this issue of secrecy so that we do not incite things to riot that will in turn cause a leak to our career.
On all sides,and should you think we can have an agreeable and productive time together with real interest on the subject line above,kindly send me a mail through my confidential email address--([email protected])-- so that I can brief you further with the blue print details of the master plan to ensure a successful transfer of this fund to your account to our collective benefit.
I wait in anticipation of your co-operation to assisting me in this responsibility and even as I do not seek to buy my way into the alcove of your mind irrespective of the fact that you are already on a secured location (overseas) and maybe also in a good position to help me.
I will greatly appreciate your timely response back to me.I look forward to it.
Alhaji Gamso Jimeta.
Got a suspicious e-mail? Run it by The Scam Checklist to see if it's real.


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Paramedics and fire crews responding to heavy fire at business in Mississauga
by Jonah Shinuda on November 14, 2020
Crews are attending to a fire in Mississauga this afternoon.
Police say a call came in at 3:16 p.m. for reports of heavy black smoke coming from a commercial building in the area of Dundas and Hurontario St.
Paramedics and Mississauga Fire are on scene.
Roads are closed in the area and residents are advised to take alternate routes.
More details to come.
UPDATE: Police say the fire, which broke out in the kitchen of a restaurant, has been extinguished.
No injuries were reported.
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14 Gauge 9/16 Gold Tone Ice Skate Snowman Christmas Nipple Ring Set
The snowmen on this 14 gauge nipple jewelry are certainly enjoying the frosty weather! It's made with 14mm gold tone PVD over 316L surgical grade stainless steel straight barbells. Each end features a snowman charm dressed in red and green winter wear. They're even sporting little hats for a touch of added pizazz! Add some pizazz of your own to your nipple piercings with these sporty holiday snowmen! Sold as a set of two.
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