Margin Trading - Cryptocurrency Dictionary

BW.com A letter to all users on the occasion of the 2nd anniversary

Dear BWer
BW is 2 years old
Thank you for being with us
Remember how the first deal on BW happened?
I think it all comes from the purest trust.
Looking forward to new journey with you
Let’s ride the waves and sail for a Long voyage together
Thanks to all users and partners, thanks to all industry colleagues, because of your support and company, BW can start in the difficult bear market, has the power to move forward in the turbulent market, and will be more confident and calm. At the same time, thanks to this great era, we have given the BW and BWer the opportunity to become a member of the large maritime fleet in the blue ocean of blockchain.
In August 2018, BW.com Thanks to your support, two years of painstaking work have composed the movement of red and green of the Kline; two years of hard work witnessed the ups and downs of the coin circle.
Over the past two years, as the world’s leading digital currency trading platform, coin.com has provided safe and reliable digital currency trading services for more than 1 million users in more than 40 countries and regions for 7 * 24 hours. It has continued and stable currency and legal currency transactions, innovated and reliable interest-free Margin trading, perpetual contracts, and token launchpad, mineral pool financial management, moon landing plan, all are the fulfillment of the commitment to investors after the company’s day and night discussion and test; every activity is a gift that we have carefully prepared and given to users.
For two years, it is a journey of thousands of miles. In BW’s dictionary, it is bit, the smallest unit in the information world. The two years of day and night are too long, even if there are thousands of bitterness, it is only a small step;
BW has a grand ideal and strives to become a benchmark in the industry. Its full desire for protection means that it is responsible for every user’s assets. The spark of heroism and idealism always twinkles in the heart. However, reality is not perfect. The two-year-old BW is Simba who is not strong enough to become the lion king. Over the past two years, it has given you a lot of joy, but also some disappointment. Even so, you still have your day and night. I have to say that BW is a lucky lion.
Because of you, BW will be more powerful.
Thank you for being with us
Let’s ride the waves and sail for a Long voyage together
Participate in the activity and share the story between you and BW
You will have the opportunity to receive BW annual gift package and participate in BW annual user award ceremony
Link or scan the code:https://forms.gle/mZZnHjXJ9mMbURcN9
Yours sincerefully :),
BW.com
submitted by bwexchange to u/bwexchange [link] [comments]

Sweden’s Famously Stealthy Submarine Is Now Even Quieter

Go Sweden! Thanks for that job done!
What's the difference inSweden and Switzerland. Switzerland has an economy more like The United States. They complain over there that the Franc is overvalued and they are not paid enough to live. Sweden has progressive taxation and income distribution and has a more stabler economy because of it. Stockholm is the Capital of Sweden since 1523 A.D. It shares the Scandinavian Peninsula with Norway. Coming up is the Difference between Norway and Normandy.
Here's a map of Sweden and Norway:

Location of Sweden Map from Encyclopedia Britannica Online.
Encyclopedia Britannica States," The country has a 1,000-year-long continuous history as a sovereign state, but its territorial expanse changed often until 1809. Today it is a constitutional monarchy with a well-established parliamentary democracy that dates from 1917. Swedish society is ethnically and religiously very homogeneous, although recent immigration has created some social diversity. Historically, Sweden rose from backwardness and poverty into a highly developed postindustrial society and advanced welfare state with a standard of living and life expectancy that rank among the highest in the world. "
Here are the Facts of Sweden according to Merriam-Webster:
Official Name: Konungariket Sverige (Kingdom of Sweden)
Form Of Government: constitutional monarchy with one legislative house (Riksdag, or Parliament [349])
Head Of State: King: Carl XVI Gustaf
Head Of Government: Prime Minister: Stefan Löfven
Capital: Stockholm
Official Language: Swedish
Official Religion: none
Monetary Unit: Swedish krona (SEK)
Currency Exchange Rate:
1 USD equals 9.879 Swedish krona
Population:
(2019 est.) 10,284,000
Population Rank:
(2018) 89
Population Projection 2030:
11,261,000
Total Area (Sq Mi)**172,750
Total Area (Sq Km)**447,420
Density: Persons Per Sq Mi(2018) 64.7
Density: Persons Per Sq Km(2018) 25
Urban-Rural Population:
Urban: (2018) 87.4%
Rural: (2018) 12.6%
Life Expectancy At Birth:
Male: (2017) 80.7 years
Female: (2017) 84.1 years
Literacy: Percentage Of Population Age 15 And Over:
Male: 100%
Female: (2008) 100%
GNI (U.S.$ ’000,000)
(2017) 529,460
GNI Per Capita (U.S.$)
(2017) 52,590
Here's what The CIA World FactBook has to say about Sweden: (Here are some highlights):
Sweden’s small, open, and competitive economy has been thriving and Sweden has achieved an enviable standard of living with its combination of free-market capitalism and extensive welfare benefits. Sweden remains outside the euro zone largely out of concern that joining the European Economic and Monetary Union would diminish the country’s sovereignty over its welfare system.
Timber, hydropower, and iron ore constitute the resource base of a manufacturing economy that relies heavily on foreign trade. Exports, including engines and other machines, motor vehicles, and telecommunications equipment, account for more than 44% of GDP. Sweden enjoys a current account surplus of about 5% of GDP, which is one of the highest margins in Europe.
GDP grew an estimated 3.3% in 2016 and 2017 driven largely by investment in the construction sector. Swedish economists expect economic growth to ease slightly in the coming years as this investment subsides. Global economic growth boosted exports of Swedish manufactures further, helping drive domestic economic growth in 2017. The Central Bank is keeping an eye on deflationary pressures and bank observers expect it to maintain an expansionary monetary policy in 2018. Swedish prices and wages have grown only slightly over the past few years, helping to support the country’s competitiveness.
In the short and medium term, Sweden’s economic challenges include providing affordable housing and successfully integrating migrants into the labor market.
Agriculture - products:
This entry is an ordered listing of major crops and products starting with the most important.
barley, wheat, sugar beets; meat, milk
Industries:
This entry provides a rank ordering of industries starting with the largest by value of annual output.
iron and steel, precision equipment (bearings, radio and telephone parts, armaments), wood pulp and paper products, processed foods, motor vehicles
Unemployment rate:This entry contains the percent of the labor force that is without jobs. Substantial underemployment might be noted.
6.7% (2017 est.)7% (2016 est.)country comparison to the world: 101
Population below poverty line:National estimates of the percentage of the population falling below the poverty line are based on surveys of sub-groups, with the results weighted by the number of people in each group. Definitions of poverty vary considerably among nations. For example, rich nations generally employ more generous standards of poverty than poor nations.
15% (2014 est.)
Household income or consumption by percentage share:Data on household income or consumption come from household surveys, the results adjusted for household size. Nations use different standards and procedures in collecting and adjusting the data. Surveys based on income will normally show a more unequal distribution than surveys based on consumption. The quality of surveys is improving with time, yet caution is still necessary in making inter-country comparisons.
lowest 10%: 3.4%highest 10%: 24% (2012)
Budget:This entry includes revenues, expenditures, and capital expenditures. These figures are calculated on an exchange rate basis, i.e., not in purchasing power parity (PPP) terms.
revenues: 271.2 billion (2017 est.)expenditures: 264.4 billion (2017 est.)
Taxes and other revenues:This entry records total taxes and other revenues received by the national government during the time period indicated, expressed as a percent of GDP. Taxes include personal and corporate income taxes, value added taxes, excise taxes, and tariffs. Other revenues include social contributions - such as payments for social security and hospital insurance - grants, and net revenues from public enterprises. Normalizing the data, by dividing total revenues by GDP, enables easy comparisons acr . . . more
50.6% (of GDP) (2017 est.)

Now for Switzerland:

Map of Switzerland
It's rights next to France and Austria and is the size of Half of Scotland according to Encyclopedia Britannica online; This map is from their page.
According to Merriam-Webster:
Dialing code: +41
Population: 8.57 million (2019)
Currency: Swiss franc
Swit·​zer·​land | \ ˈswit-sər-lənd \variants: or French Suisse \ ˈswʸēs \ or German Schweiz \ ˈshvīts \ or Italian Svizzera \ ˈzvēt-​tsā-​rä \ or Latin Helvetia \ hel-​ˈvē-​sh(ē-​)ə \

Definition of Switzerland

landlocked country (a federal republic) in western Europe in the Alps; capital Bern area 15,937 square miles (41,277 square kilometers), population 8,293,000
see also SWISS entry 1 sense 1
Britannica states: " For many outsiders, Switzerland also evokes a prosperous if rather staid and unexciting society, an image that is now dated. Switzerland remains wealthy and orderly, but its mountain-walled valleys are far more likely to echo the music of a local rock band than a yodel or an alphorn. Most Swiss live in towns and cities, not in the idyllic rural landscapes that captivated the world through Johanna Spyri’s Heidi (1880–81), the country’s best-known literary work. Switzerland’s cities have emerged as international centres of industry and commerce connected to the larger world, a very different tenor from Switzerland’s isolated, more inward-looking past. As a consequence of its remarkably long-lived stability and carefully guarded neutrality, Switzerland—Geneva, in particular—has been selected as headquarters for a wide array of governmental and nongovernmental organizations, including many associated with the United Nations (UN)—an organization the Swiss resisted joining until the early 21st century. "
According to CIA"S World Factbook. Switzerland's Economy is as such:
Switzerland, a country that espouses neutrality, is a prosperous and modern market economy with low unemployment, a highly skilled labor force, and a per capita GDP among the highest in the world. Switzerland's economy benefits from a highly developed service sector, led by financial services, and a manufacturing industry that specializes in high-technology, knowledge-based production. Its economic and political stability, transparent legal system, exceptional infrastructure, efficient capital markets, and low corporate tax rates also make Switzerland one of the world's most competitive economies.
The Swiss have brought their economic practices largely into conformity with the EU's to gain access to the Union’s Single Market and enhance the country’s international competitiveness. Some trade protectionism remains, however, particularly for its small agricultural sector. The fate of the Swiss economy is tightly linked to that of its neighbors in the euro zone, which purchases half of Swiss exports. The global financial crisis of 2008 and resulting economic downturn in 2009 stalled demand for Swiss exports and put Switzerland into a recession. During this period, the Swiss National Bank (SNB) implemented a zero-interest rate policy to boost the economy, as well as to prevent appreciation of the franc, and Switzerland's economy began to recover in 2010.
The sovereign debt crises unfolding in neighboring euro-zone countries, however, coupled with economic instability in Russia and other Eastern European economies drove up demand for the Swiss franc by investors seeking a safehaven currency. In January 2015, the SNB abandoned the Swiss franc’s peg to the euro, roiling global currency markets and making active SNB intervention a necessary hallmark of present-day Swiss monetary policy. The independent SNB has upheld its zero interest rate policy and conducted major market interventions to prevent further appreciation of the Swiss franc, but parliamentarians have urged it to do more to weaken the currency. The franc's strength has made Swiss exports less competitive and weakened the country's growth outlook; GDP growth fell below 2% per year from 2011 through 2017.
In recent years, Switzerland has responded to increasing pressure from neighboring countries and trading partners to reform its banking secrecy laws, by agreeing to conform to OECD regulations on administrative assistance in tax matters, including tax evasion. The Swiss Government has also renegotiated its double taxation agreements with numerous countries, including the US, to incorporate OECD standards.
GDP (purchasing power parity)
$523.1 billion (2017 est.)
$514.5 billion (2016 est.)
$506.5 billion (2015 est.)
note: data are in 2017 dollars
GDP (official exchange rate)
$679 billion (2017 est.)
GDP - per capita (PPP):
$62,100 (2017 est.)
$61,800 (2016 est.)
$61,500 (2015 est.)
note: data are in 2017 dollars
Gross national saving:
33.8% of GDP (2017 est.)
32.3% of GDP (2016 est.)
33.9% of GDP (2015 est.)
GDP - composition, by end use: 53.7% (2017 est.)
government consumption: 12% (2017 est.)
investment in fixed capital: 24.5% (2017 est.)
investment in inventories: -1.4% (2017 est.)
exports of goods and services: 65.1% (2017 est.)
imports of goods and services: -54% (2017 est.)
GDP - composition, by sector of origin:
agriculture: 0.7% (2017 est.)
industry: 25.6% (2017 est.)
services: 73.7% (2017 est.)
Agriculture - products: grains, fruits, vegetables; meat, eggs, dairy products
Industries: machinery, chemicals, watches, textiles, precision instruments, tourism, banking, insurance, pharmaceuticals
Industrial production growth rate: 3.4% (2017 est.)
country comparison to the world:92
Labor force**:**5.159 million (2017 est.)
country comparison to the world:81
Labor force - by occupation:
agriculture: 3.3%
industry: 19.8%
services: 76.9% (2015)
Unemployment rate:
3.2% (2017 est.)
3.3% (2016 est.)
country comparison to the world: 40
Population below poverty line:
6.6% (2014 est.)
Household income or consumption by percentage share:
lowest 10%: 7.5%
highest 10%: 19% (2007)
Budget:
revenues: 242.1 billion (2017 est.)
expenditures: 234.4 billion (2017 est.)
note: includes federal, cantonal, and municipal budgets
Taxes and other revenues:
35.7% (of GDP) (2017 est.)
country comparison to the world: 60
Budget surplus (+) or deficit (-):
1.1% (of GDP) (2017 est.)
country comparison to the world: 33
Public debt:
41.8% of GDP (2017 est.)
41.8% of GDP (2016 est.)
note: general government gross debt; gross debt consists of all liabilities that require payment or payments of interest and/or principal by the debtor to the creditor at a date or dates in the future; includes debt liabilities in the form of Special Drawing Rights (SDRs), currency and deposits, debt securities, loans, insurance, pensions and standardized guarantee schemes, and other accounts payable; all liabilities in the GFSM (Government Financial Systems Manual) 2001 system are debt, except for equity and investment fund shares and financial derivatives and employee stock options
country comparison to the world: 119
Fiscal year:
Inflation rate (consumer prices):
0.5% (2017 est.)
-0.4% (2016 est.)
country comparison to the world: 30
Current account balance:
$66.55 billion (2017 est.)$63.16 billion (2016 est.)
country comparison to the world: 7
Exports:
$313.5 billion (2017 est.)
$318.1 billion (2016 est.)
note: trade data exclude trade with Switzerland
country comparison to the world: 17
Exports - partners:
Germany 15.2%
US 12.3%
China 8.2
%India 6.7%
France 5.7%
UK 5.7%
Hong Kong 5.4%
Italy 5.3%
(2017)
Exports - commodities:
machinery, chemicals, metals, watches, agricultural products
Imports:
$264.5 billion (2017 est.)
$266.3 billion (2016 est.)
country comparison to the world: 18
Imports - commodities:
machinery, chemicals, vehicles, metals; agricultural products, textiles
Imports - partners:
Germany 20.9%, US 7.9%
Italy 7.6%, UK 7.3%
France 6.8%
China 5%
(2017)
Reserves of foreign exchange and gold:
$811.2 billion (31 December 2017 est.)
$679.3 billion (31 December 2016 est.)
country comparison to the world: 3
Debt - external:.
$1.664 trillion (31 March 2016 est.)
$1.663 trillion (31 March 2015 est.)
country comparison to the world: 12
Exchange rates:
Swiss francs (CHF) per US dollar -0.9875 (2017 est.)
0.9852 (2016 est.)0.9852 (2015 est.)
0.9627 (2014 est.)
0.9152 (2013 est.)
And their Military is such as CIA states:
Military expenditures**:This entry gives spending on defense programs for the most recent year available as a percent of gross domestic product (GDP); the GDP is calculated on an exchange rate basis, i.e., not in terms of purchasing power parity (PPP). For countries with no military forces, this figure can include expenditures on public security and police.
0.68% of GDP (2018)0.68% of GDP (2017)0.68% of GDP (2016)0.66% of GDP (2015)0.66% of GDP (2014)country comparison to the world: 138
Military and security forces**:This entry lists the military and security forces subordinate to defense ministries or the equivalent (typically ground, naval, air, and marine forces), as well as those belonging to interior ministries or the equivalent (typically gendarmeries, bordecoast guards, paramilitary police, and other internal security forces).
Swiss Armed Forces: Land Forces, Swiss Air Force (Schweizer Luftwaffe) (2019)
Military service age and obligation**:This entry gives the required ages for voluntary or conscript military service and the length of service obligation.
18-30 years of age generally for male compulsory military service; 18 years of age for voluntary male and female military service; every Swiss male has to serve at least 245 days in the armed forces; conscripts receive 18 weeks of mandatory training, followed by six 19-day intermittent recalls for training during the next 10 years (2019)
Refugees and internally displaced persons:
refugees (country of origin):
34,072 (Eritrea)
16,565 (Syria)
12,282 (Afghanistan)
5,744 (Sri Lanka) (2018)
stateless persons:
49 (2018)
Illicit drugs
a major international financial center vulnerable to the layering and integration stages of money laundering; despite significant legislation and reporting requirements, secrecy rules persist and nonresidents are permitted to conduct business through offshore entities and various intermediaries; transit country for and consumer of South American cocaine, Southwest Asian heroin, and Western European synthetics; domestic cannabis cultivation and limited ecstasy production.
Here's an article about an International Dispute with the European Union (EU): https://www.express.co.uk/news/world/1283471/eu-news-switzerland-rejected-membership-bloc-twice-spt
When looking to solve problems with countries, look at their economy and study it.



submitted by jeskalana to u/jeskalana [link] [comments]

Why do you guys claim Bernie Sanders is a communist? Are you that ignorant of political theory, economics and history?

Steven Crowder has repeatedly stated that Bernie Sanders is a communist. Oxford English Dictionary defines communism as "a political theory derived from Karl Marx, advocating class war and leading to a society in which all property is publicly owned and each person works and is paid according to their abilities and needs." Clearly, this does not apply to a Senator who believes in a capitalist system that simply has more regulation, taxation and publicly funded institutions. It therefore makes no sense that Crowder calls him a communist. So why does he?
Because Sanders supports Medicare-For-All? Every single first world country in existence other than the US has state-guaranteed healthcare for all of its citizens. We are the odd ones out, and when people in the UK find out that an ambulance ride in the US costs about $2500 - not $25 - their jaws drop. I assume you guys would not attempt to argue that the US is the only developed nation that is not communist. So how is Bernie a communist?
Because he wants to raise taxes on the rich to make the top marginal bracket 54%? Under the administrations of Truman and Eisenhower, the top marginal tax rate was 91%. Conservatives sometimes try to claim JFK as one of their own because "he cut taxes," but what they fail to mention is that he cut the top rate from 91% to 77%. They also supported universal healthcare, as Sanders pointed out in a recent ad. Were all three of those presidents communists? How did that fly during the cold war? And again I ask, is Europe communist?
Because he wants to raise the minimum wage to $15 an hour? If the minimum wage had kept up with both inflation and living costs since 1968, it would currently be about $22. Not everyone is a spoiled rich kid like Ben Shapiro and has a Dad who can pay their way through UCLA and Harvard, some people actually need those jobs to support themselves and their loved ones.
Because he wants tuition-free public colleges and universities, and more affordable private ones? Again, I will differ to my comment about not everyone being a spoiled rich kid like Ben Shapiro and has a Dad who can pay their way through UCLA and Harvard.
Because there are some old, out-of-context clips where he somewhat praises certain things Fidel Castro did? Well Trump not only praises Kim Jong Un, but meets with him and talks about how he "fell in love" with him. So clearly Trump is a communist by this standard as well. Additionally, it is a known fact that he kept a book of Hitler's speeches (Trump reading is the only remarkable part of this story), so clearly he must be a Nazi too. This logic is apt for smearing anybody.
Because he wants to regulate Wall Street? Well firstly, add President Roosevelt to Crowder's list of communist US presidents, and then remember that Goldman Sachs and Lehman Brothers conned the working people of this country out of billions of dollars, then trillions via the Wall Street bailout that corporate democrat Obama gave them at tax-payer expense. You guys complain that virtue-signalling Hollywood celebrities are the "coastal elites" (because I guess millionaire farmers and oil tycoons in the midwest are the real Americans) but while they are annoying they are inconsequential; Wall Street executives are the ACTUAL coastal elites, who prey on working people and influence policies to deregulate them and bail them out, also funding think tanks to propagate Crowder's autistic free market ideology.
Now, since in the spirit of your idols you guys will probably dodge and deflect from the question while trying to sound smart, I am not asking whether or not you agree with Bernie's positions. I am well versed in your autistic free market arguments. Rather, I am asking why you would categorize them as 'communist' without any basis for doing so? Admittedly, right-wing corporate democrats like Biden, Obama and Hillary - who support free trade, solidifying Bush's tax cuts and Mitt Romney's healthcare system - were tarred and feathered as 'socialists' by the TEA Party mouth-breathers, so it seems like you guys are consistent in smearing anyone to the left of John Bolton as some sort of radical leftist. However, it may be worth reconsidering this angle, because once you argue against someone who is not an ignorant teenager like yourselves, this argument will crumble and fail. 2020 will be a landslide if Bernie is the nominee and all he has to oppose him is "muh communism" retardation.
Since I know how much you guys care about free speech, I trust that you will not ban me or remove this.
submitted by AmNatDemSoc to LouderWithCrowder [link] [comments]

[OC] Identifying the NBAs greatest over and under achieving teams.

With the NBA season indefinitely on hold and having already watched Tiger King 7 times through, I recently found myself more hungry for NBA content than a tiger for Saff's left arm. I knew I hit rock bottom when I found myself on NBA_draft arguing about the marginal value of late first round prospects. Was this going to be my life for the next few months, never-ending pointless discussions about floors and ceilings that are really just projections of how dope we thought someone's youtube highlight video was? With my sanity on the brink of a full collapse, something needed to be done.   Armed with only my laptop and the sports reference API, I decided to determine the greatest over and under achieving teams the NBA has ever seen. Why you ask, because I'm fucking bored and reading about how some B-tier Euro prospect's ceiling is Porzingis with shades of Magic Johnson makes my brain cells commit seppuku.  

Methods

First, I needed to define what it means to over or under achieve. Per the Cambridge dictionary:
Overachieve (Verb):to do more than is expected or be more successful than others Underachieve (Verb): to do less well than you could or should
Ideally I would be able to compare a team's expected performance prior to its season to the team's actual performance. Unfortunately, this type of analysis isn't feasible for a number of reasons. Instead, I decided to use in season statistics to generate a prediction for each team's performance in terms of regular season wins, then compare this to actual wins. As such an overachieving team would have more wins than predicted and an underachieving team would have less.   Next, I needed to create a model for predicting wins based on a team's in season statistics. While I had many advanced statistics to choose from in building my model, I wanted to keep things simple. Thankfully there is one simple statistic that is an excellent predictor of team success, Average Margin of Victory (MOV). It doesn't take a genius to figure that scoring more points than your opponents is usually going to lead to a winning season.   Using data on every team from 1980 to 2020, I fit a simple linear regression of MOV to a team's win rate. This lead to the following simple formula win_rate = 0.032 * MOV + 0.5 which is visualized below. This regression has an R2 = 0.941 and when used to predict total wins has a mean error of 2.4 wins. Not bad for something so simple!   Regression Plot  

Results

Using the model discussed above I generated a prediction of win rate for every team then picked out the teams who's performance deviated most from the prediction. By this measure the NBA's most overachieving team, coming in at 10 wins over their prediction, was ... the 1992-93 Dallas Mavericks???   One of the worst teams in NBA history, the 1992-93 Mavericks floundered through a disaster of a season to finish with a paltry 11 wins. This was a team that lost 42 games by over 15 points, lost 26 games by over 20 points, and lost one game by 58 points! How could a team so bad actually be considered to have overachieved? By posting a historically bad and record setting MOV of -15.2 points of course. The only other team coming remotely close being the 2011-12 Charlotte Hornets with a MOV of -13.9 points. The 1992-93 Mavericks were so bad that my simple model predicted them to win only 1 game, literally breaking my analysis.  

Methods Pt. II, the Re-Regressioning

This edge case highlighted a major shortcoming of my simple linear regression, which is that using a linear model to predict win rate is inherently flawed because a win rate can never be less than 0 or greater than 1. Had the 1993 Mavericks posted a slightly worse MOV they would have been predicted to win negative games! This meant my previous analysis would tend to select historically bad teams as overachievers and historically great teams as underachievers.   First and foremost, I needed a way to fix my model. Leaving the simple green pastures of linear regression, I ventured into the dark world of machine learning to seek answers. If only there were some type of blood sacrifice, demonic liturgy, or generalized linear model that could be used to twist the edges of my regression and constrain it's output. It turns out the Satanic ritual I was searching for exists and is known as logistic regression.   Rebuilding my model using logistic regression I was able to generate the following prediction curve with an R2 = 0.944 and mean error on wins of 2.3. Although this doesn't appear to be much improvement at all, the model does a much better job accounting for very good and very bad teams as can be seen in the plot linked below.   Logistic Regression Plot  

Results Pt. II

With new and improved predictions in hand I re-ran the analysis. To obtain more compelling results, I filtered overachievers to only teams that had at least 4 playoff wins and filtered under-achievers to only teams had positive margin of victory and less than 4 playoff wins. Now let's get to the good stuff.   NBA's Greatest Overachiever award goes to...

The 1986-87 Golden State Warriors

honorable mention: 1994-95 Lakers, 2017-18 Cavs   Led by Joe Barry Carroll, Sleepy Floyd, and Chris Mullin, the 1986-87 Warriors posted a rather poor -2.3 MOV. While this would typically lead to a disappointing season, the 1986-87 Warriors scraped together 42 wins, well above their predicted 34 wins and good for 5th place in the Western Conference. The team managed to do this by posting a 63% win rate in two possession games, while also managing to lose 30 games by 10 or more points. In the playoffs, the Warriors took out the 4th seed Utah Jazz in 5 games (3-2) before losing to the Showtime Lakers, a team widely considered one of the best of all time, in 5 games (1-4).   NBA's Greatest Underachiever award goes to...

The 2013-14 Minnesota Timberwolves

honorable mention: 1997-98 Pistons, 2007-08 Raptors
Led by Kevin Love and featuring Nikola Pekovic, Kevin Martin, and a young Ricky Rubio, the 2013-2014 Minnesota Timberwolves posted a very respectable MOV of 2.7 points. While this should have been good for 49 wins and a likely playoff spot in a loaded Western Conference, the Timberwolves only managed 40 wins and were left out of the playoff picture in a distant 10th place. The team managed to post such an odd season by winning 26 games by 10 or more points while only winning 14 games by less than 10 points. Further, in games that were decided by less than 10 points the Timberwolves managed only a 14-24 record. To pile on the disappointment, Kevin Love was traded in the offseason and the team proceeded to win only 16 games in 2014-15.   There you have it. I hope you nephews enjoyed the read. Remember, if you ever find yourself on NBA_draft arguing about the ceiling of some B-tier Euro prospect, you should probably reevaluate your life.
Github link to my work.
submitted by SpamCamel to nba [link] [comments]

i don't like doing this but here we are

this should be 10 thousand words
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submitted by Gourmet_Salad to OneWordBan [link] [comments]

I cannot find a conservative subreddit that won't ban me, so here is the brief essay I wrote against smears from Ben Shapiro and other conservatives that Bernie Sanders is a "communist."

Ben Shapiro has repeatedly stated that Bernie Sanders is a communist. Oxford English Dictionary defines communism as "a political theory derived from Karl Marx, advocating class war and leading to a society in which all property is publicly owned and each person works and is paid according to their abilities and needs." Clearly, this does not apply to a Senator who believes in a capitalist system that simply has more regulation, taxation and publicly funded institutions. It therefore makes no sense that Ben Shapiro calls him a communist. So why does he?
Because Sanders supports Medicare-For-All? Every single first world country in existence other than the US has state-guaranteed healthcare for all of its citizens. We are the odd ones out, and when people in the UK find out that an ambulance ride in the US costs about $2500 - not $25 - their jaws drop.
Because he wants to raise taxes on the rich to make the top marginal bracket 54%? Under the administrations of Truman and Eisenhower, the top marginal tax rate was 91%. Conservatives sometimes try to claim JFK as one of their own because "he cut taxes," but what they fail to mention is that he cut the top rate from 91% to 77%. They also supported universal healthcare, as Sanders pointed out in a recent ad. Were all three of those presidents communists? How did that fly during the cold war?
Because he wants to raise the minimum wage to $15 an hour? If the minimum wage had kept up with both inflation and living costs since 1968, it would currently be about $22. Not everyone is a spoiled rich kid like Ben Shapiro and has a Dad who can pay their way through UCLA and Harvard, some people actually need those jobs to support themselves and their loved ones.
Because he wants tuition-free public colleges and universities, and more affordable private ones? Again, I will differ to my comment about not everyone being a spoiled rich kid like Ben Shapiro and has a Dad who can pay their way through UCLA and Harvard.
Because there are some old, out-of-context clips where he somewhat praises certain things Fidel Castro did? Well Trump not only praises Kim Jong Un, but meets with him and talks about how he "fell in love" with him. So clearly Trump is a communist by this standard as well. Additionally, it is a known fact that he kept a book of Hitler's speeches (Trump reading is the only remarkable part of this story), so clearly he must be a Nazi too. This logic is apt for smearing anybody.
Because he wants to regulate Wall Street? Well firstly, add President Roosevelt to Ben Shapiro's list of communist US presidents, and then remember that Goldman Sachs and Lehman Brothers conned the working people of this country out of billions of dollars, then trillions via the Wall Street bailout that corporate democrat Obama gave them at tax-payer expense.
Now, since in the spirit of your idol you guys will probably dodge and deflect from the question while trying to sound smart, I am not asking whether or not you agree with Sanders's positions. I am well versed in your autistic free-market arguments. Rather, I am asking why you would categorize them as 'communist' without any basis for doing so? Admittedly, right-wing corporate democrats like Obama and Hillary Clinton - who support free trade, solidifying Bush's tax cuts and Mitt Romney's healthcare system - were tarred and feathered as 'socialists' by the TEA Party mouth-breathers, so it seems like you guys are consistent in smearing anyone to the left of John Bolton as some sort of radical leftist. However, it may be worth reconsidering this angle, because once you argue against someone who is not an ignorant teenager like yourselves, this argument will crumble and fail.
submitted by AmNatDemSoc to seculartalk [link] [comments]

[D] ICML 2019 Machine Learning Talks

Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms
Presented by Jeff Clune, Joel Lehman and Kenneth Stanley
https://www.facebook.com/icml.imls/videos/481758745967365/
Never-Ending Learning
Presented by Tom Mitchell and Partha Talukdar.
https://www.facebook.com/icml.imls/videos/350412952342021/ https://www.facebook.com/icml.imls/videos/1083330081864839/
A Primer on PAC-Bayesian Learning
Presented by Benjamin Guedj and John Shawe-Taylor
https://www.facebook.com/icml.imls/videos/318683639013879/
Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning
Presented by Chelsea Finn and Sergey Levine
https://www.facebook.com/icml.imls/videos/400619163874853/ https://www.facebook.com/icml.imls/videos/2970931166257998/
Active Learning: From Theory to Practice
Presented by Robert Nowak and Steve Hanneke
https://www.facebook.com/icml.imls/videos/662482727539899/
Neural Approaches to Conversational AI
Presented by Michel Galley and Jianfeng Gao
https://www.facebook.com/icml.imls/videos/2375117292730871/
A Tutorial on Attention in Deep Learning
Presented by Alex Smola and Aston Zhang
https://www.facebook.com/icml.imls/videos/382464939283864/ https://www.facebook.com/icml.imls/videos/889237771440064/
Active Hypothesis Testing: An Information Theoretic (re)View
Presented by Tara Javidi
https://www.facebook.com/icml.imls/videos/478549476247044/
Algorithm configuration: learning in the space of algorithm designs
Presented by Kevin Leyton-Brown and Frank Hutter
https://www.facebook.com/icml.imls/videos/2044426569187107/
"The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century" invited talk by John M. Abowd
Best Paper Awards: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
https://www.facebook.com/icml.imls/videos/446476306189465/
Session on Deep Learning Algorithms
• SelectiveNet: A Deep Neural Network with an Integrated Reject Option
• Manifold Mixup: Better Representations by Interpolating Hidden States
• Processing Megapixel Images with Deep Attention-Sampling Models
• TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
• Online Meta-Learning
• Training Neural Networks with Local Error Signals
• GMNN: Graph Markov Neural Networks
• Self-Attention Graph Pooling
• Combating Label Noise in Deep Learning using Abstention
• LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
https://www.facebook.com/icml.imls/videos/336722770596090/
Session on Deep Reinforcement Learning
• ELF OpenGo: an analysis and open reimplementation of AlphaZero
• Making Deep Q-learning methods robust to time discretization
• Nonlinear Distributional Gradient Temporal-Difference Learning
• Composing Entropic Policies using Divergence Correction
• TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
• Multi-Agent Adversarial Inverse Reinforcement Learning
• Policy Consolidation for Continual Reinforcement Learning
• Off-Policy Deep Reinforcement Learning without Exploration
• Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
• Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
https://www.facebook.com/icml.imls/videos/1577337105730518/
Session on Adversarial Examples
• Adversarial Attacks on Node Embeddings via Graph Poisoning
• First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
• On Certifying Non-Uniform Bounds against Adversarial Attacks
• Improving Adversarial Robustness via Promoting Ensemble Diversity
• Adversarial camera stickers: A physical camera-based attack on deep learning systems
• Adversarial examples from computational constraints
• POPQORN: Quantifying Robustness of Recurrent Neural Networks
• Using Pre-Training Can Improve Model Robustness and Uncertainty
• Generalized No Free Lunch Theorem for Adversarial Robustness
• PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
https://www.facebook.com/icml.imls/videos/689280291532883/
Session on Generative Adversarial Networks
• Self-Attention Generative Adversarial Networks
• Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
• High-Fidelity Image Generation With Fewer Labels
• Revisiting precision recall definition for generative modeling
• Wasserstein of Wasserstein Loss for Learning Generative Models
• Flat Metric Minimization with Applications in Generative Modeling
• Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
• Non-Parametric Priors For Generative Adversarial Networks
• Lipschitz Generative Adversarial Nets
• HexaGAN: Generative Adversarial Nets for Real World Classification
https://www.facebook.com/icml.imls/videos/713631379054038/
Session on Deep Reinforcement Learning
• An Investigation of Model-Free Planning
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
• Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
• Collaborative Evolutionary Reinforcement Learning
• EMI: Exploration with Mutual Information
• Imitation Learning from Imperfect Demonstration
• Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
• Dynamic Weights in Multi-Objective Deep Reinforcement Learning
• Fingerprint Policy Optimisation for Robust Reinforcement Learning
https://www.facebook.com/icml.imls/videos/298536957693171/
Session on Deep Learning Theory
• On Learning Invariant Representations for Domain Adaptation
• Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
• Adversarial Generation of Time-Frequency Features with application in audio synthesis
• On the Universality of Invariant Networks
• Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
• Gauge Equivariant Convolutional Networks and the Icosahedral CNN
• Feature-Critic Networks for Heterogeneous Domain Generalization
• Learning to Convolve: A Generalized Weight-Tying Approach
• On Dropout and Nuclear Norm Regularization
• Gradient Descent Finds Global Minima of Deep Neural Networks
https://www.facebook.com/icml.imls/videos/2339557826311186/
Session on Deep Learning Architectures
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
• BayesNAS: A Bayesian Approach for Neural Architecture Search
• Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
• Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
• Graph U-Nets
• SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
• Area Attention
• The Evolved Transformer
• Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
• Stochastic Deep Networks
https://www.facebook.com/icml.imls/videos/3253466301345987/
Session on Deep Learning Optimization
• An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
• Differentiable Linearized ADMM
• Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
• A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
• The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
• AdaGrad stepsizes: sharp convergence over nonconvex landscapes
• Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
• SWALP : Stochastic Weight Averaging in Low Precision Training
• Efficient optimization of loops and limits with randomized telescoping sums
• Self-similar Epochs: Value in arrangement
https://www.facebook.com/icml.imls/videos/874988016194584/
Session on Large Scale Learning and Systems
• Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
• Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
• Compressing Gradient Optimizers via Count-Sketches
• Scalable Fair Clustering
• Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
• Fault Tolerance in Iterative-Convergent Machine Learning
• Static Automatic Batching In TensorFlow
• Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
• Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
• DL2: Training and Querying Neural Networks with Logic
https://www.facebook.com/icml.imls/videos/2250364101882755/
"Machine Learning for Robots To Think Fast" invited talk by Aude Billard
Test of time Award Online dictionary learning for Sparse Coding
https://www.facebook.com/icml.imls/videos/2368059266588651/
Session on Deep Generative Models
• Sum-of-Squares Polynomial Flow
• FloWaveNet : A Generative Flow for Raw Audio
• Are Generative Classifiers More Robust to Adversarial Attacks?
• A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
• Disentangling Disentanglement in Variational Autoencoders
• EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
• A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
• Emerging Convolutions for Generative Normalizing Flows
• A Large-Scale Study on Regularization and Normalization in GANs
• Variational Annealing of GANs: A Langevin Perspective
https://www.facebook.com/icml.imls/videos/325725335009518/ https://www.facebook.com/icml.imls/videos/518469445360005/
Session on Deep Reinforcement Learning
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
• Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
• Imitating Latent Policies from Observation
• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
• Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
• Structured agents for physical construction
• Learning Novel Policies For Tasks
• Taming MAML: Efficient unbiased meta-reinforcement learning
• Self-Supervised Exploration via Disagreement
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://www.facebook.com/icml.imls/videos/355035025132741/
Session on Adversarial Examples
• Theoretically Principled Trade-off between Robustness and Accuracy
• The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
• ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
• Certified Adversarial Robustness via Randomized Smoothing
• Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
• Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
• Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
• Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
• NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
• Simple Black-box Adversarial Attacks
https://www.facebook.com/icml.imls/videos/607431793098200/
Session on Deep Learning Architectures
• Invertible Residual Networks
• NAS-Bench-101: Towards Reproducible Neural Architecture Search
• Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
• LegoNet: Efficient Convolutional Neural Networks with Lego Filters
• Sorting Out Lipschitz Function Approximation
• Graph Element Networks: adaptive, structured computation and memory
• Training CNNs with Selective Allocation of Channels
• Equivariant Transformer Networks
• Overcoming Multi-model Forgetting
• Bayesian Nonparametric Federated Learning of Neural Networks
https://www.facebook.com/icml.imls/videos/552835701913736/
Session on Deep Reinforcement Learning
• The Natural Language of Actions
• Control Regularization for Reduced Variance Reinforcement Learning
• On the Generalization Gap in Reparameterizable Reinforcement Learning
• Trajectory-Based Off-Policy Deep Reinforcement Learning
• A Deep Reinforcement Learning Perspective on Internet Congestion Control
• Model-Based Active Exploration
• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
• Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
• A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
• Remember and Forget for Experience Replay
https://www.facebook.com/icml.imls/videos/674476986298614/
Session on Causality
• Causal Identification under Markov Equivalence: Completeness Results
• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
• Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
• Classifying Treatment Responders Under Causal Effect Monotonicity
• Learning Models from Data with Measurement Error: Tackling Underreporting
• Adjustment Criteria for Generalizing Experimental Findings
• Conditional Independence in Testing Bayesian Networks
• Sensitivity Analysis of Linear Structural Causal Models
• More Efficient Off-Policy Evaluation through Regularized Targeted Learning
• Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
https://www.facebook.com/icml.imls/videos/2188227091246504/
Session on Representation Learning
• Adversarially Learned Representations for Information Obfuscation and Inference
• Adaptive Neural Trees
• Connectivity-Optimized Representation Learning via Persistent Homology
• Minimal Achievable Sufficient Statistic Learning
• Learning to Route in Similarity Graphs
• Invariant-Equivariant Representation Learning for Multi-Class Data
• Infinite Mixture Prototypes for Few-shot Learning
• MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
• Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
https://www.facebook.com/icml.imls/videos/307375446865883/
Session on Generative Models
• Tensor Variable Elimination for Plated Factor Graphs
• Predicate Exchange: Inference with Declarative Knowledge
• Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
• Hierarchical Decompositional Mixtures of Variational Autoencoders
• Finding Mixed Nash Equilibria of Generative Adversarial Networks
• CompILE: Compositional Imitation Learning and Execution
• Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
• Deep Generative Learning via Variational Gradient Flow
• Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
• Learning Neurosymbolic Generative Models via Program Synthesis
https://www.facebook.com/icml.imls/videos/457663645035961/
Session on Deep Learning Algorithms
• How does Disagreement Help Generalization against Label Corruption?
• EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
• Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
• Deep Compressed Sensing
• Differentiable Dynamic Normalization for Learning Deep Representation
• Toward Understanding the Importance of Noise in Training Neural Networks
• Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
• Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
• Understanding the Impact of Entropy on Policy Optimization
• Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
https://www.facebook.com/icml.imls/videos/600823507067800/
Session on Deep Generative Models
• State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
• Variational Laplace Autoencoders
• Latent Normalizing Flows for Discrete Sequences
• Multi-objective training of Generative Adversarial Networks with multiple discriminators
• Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
• Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
• Graphite: Iterative Generative Modeling of Graphs
• Hybrid Models with Deep and Invertible Features
• MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
• On Scalable and Efficient Computation of Large Scale Optimal Transport
https://www.facebook.com/icml.imls/videos/1269891676506524/
Session on Reinforcement Learning
• Batch Policy Learning under Constraints
• Quantifying Generalization in Reinforcement Learning
• Learning Latent Dynamics for Planning from Pixels
• Projections for Approximate Policy Iteration Algorithms
• Learning Structured Decision Problems with Unawareness
• Calibrated Model-Based Deep Reinforcement Learning
• Reinforcement Learning in Configurable Continuous Environments
• Target-Based Temporal-Difference Learning
• Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
• Finding Options that Minimize Planning Time
https://www.facebook.com/icml.imls/videos/2547484245262588/
Session on Interpretability
• Neural Network Attributions: A Causal Perspective
• Towards a Deep and Unified Understanding of Deep Neural Models in NLP
• Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
• Functional Transparency for Structured Data: a Game-Theoretic Approach
• Exploring interpretable LSTM neural networks over multi-variable data
• TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
• Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute
• State-Regularized Recurrent Neural Networks
• Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
• On the Connection Between Adversarial Robustness and Saliency Map Interpretability
https://www.facebook.com/icml.imls/videos/460378531393374/
Session on Deep Learning
• Understanding and correcting pathologies in the training of learned optimizers
• Demystifying Dropout
• Ladder Capsule Network
• Unreproducible Research is Reproducible
• Geometric Scattering for Graph Data Analysis
• Robust Inference via Generative Classifiers for Handling Noisy Labels
• LIT: Learned Intermediate Representation Training for Model Compression
• Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
• What is the Effect of Importance Weighting in Deep Learning?
• Similarity of Neural Network Representations Revisited
https://www.facebook.com/icml.imls/videos/308727963404001/
Session on Deep Sequence Models
• Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
• Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
• Meta-Learning Neural Bloom Filters
• CoT: Cooperative Training for Generative Modeling of Discrete Data
• Non-Monotonic Sequential Text Generation
• Insertion Transformer: Flexible Sequence Generation via Insertion Operations
• Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
• Trainable Decoding of Sets of Sequences for Neural Sequence Models
• Learning to Generalize from Sparse and Underspecified Rewards
• Efficient Training of BERT by Progressively Stacking
https://www.facebook.com/icml.imls/videos/895968107420746/
Session on Deep Learning Theory
• Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
• On the Spectral Bias of Neural Networks
• Recursive Sketches for Modular Deep Learning
• Zero-Shot Knowledge Distillation in Deep Networks
• A Convergence Theory for Deep Learning via Over-Parameterization
• A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
• Approximation and non-parametric estimation of ResNet-type convolutional neural networks
• Global Convergence of Block Coordinate Descent in Deep Learning
• Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
• On the Limitations of Representing Functions on Sets
https://www.facebook.com/icml.imls/videos/606052416553010/
"What 4 Year Olds Can Do and AI Can't (yet)"
invited talk by Alison Gopnik
Best Paper Awards: Rates of Convergence for Sparse Variational Gaussian Process Regression
https://www.facebook.com/icml.imls/videos/680801775700033/
Session on Representation Learning
• Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
• Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
• Multi-Object Representation Learning with Iterative Variational Inference
• Cross-Domain 3D Equivariant Image Embeddings
• Loss Landscapes of Regularized Linear Autoencoders
• Hyperbolic Disk Embeddings for Directed Acyclic Graphs
• LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
• Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
• Lorentzian Distance Learning for Hyperbolic Representations
https://www.facebook.com/icml.imls/videos/321425055451434/
Session on Bandits and Multiagent Learning
• Decentralized Exploration in Multi-Armed Bandits
• Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
• Exploiting structure of uncertainty for efficient matroid semi-bandits
• PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
• Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
• TarMAC: Targeted Multi-Agent Communication
• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
• Actor-Attention-Critic for Multi-Agent Reinforcement Learning
• Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
https://www.facebook.com/icml.imls/videos/444326646299556/
Session on Bayesian Deep Learning
• Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
• Nonparametric Bayesian Deep Networks with Local Competition
• Good Initializations of Variational Bayes for Deep Models
• Dropout as a Structured Shrinkage Prior
• ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
• On Variational Bounds of Mutual Information
• Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
• Hierarchical Importance Weighted Autoencoders
• Faster Attend-Infer-Repeat with Tractable Probabilistic Models
• Understanding Priors in Bayesian Neural Networks at the Unit Level
https://www.facebook.com/icml.imls/videos/2202320806483370/
Workshop on Generative Modeling and Model-Based Reasoning for Robotics and AI
"Self Supervised Learning" invited talk by Yann LeCun
"Mental Simulation, Imagination, and Model-Based Deep RL" invited talk by Jessica B. Hamrick
• Bayesian Inference to Identify the Cause of Human Errors
• Data-Efficient Model-Based RL through Unsupervised Discovery and Curiosity-Driven Exploration
• A Top-Down Bottom-Up Approach to Learning Hierarchical Physics Models for Manipulation
• Discovering, Predicting, and Planning with Objects
• FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
• Generalized Hidden Parameter MDPs for Model-based Meta-reinforcement Learning
• HEDGE: Hierarchical Event-Driven Generation
• Improved COnditional VRNNs for Video Prediction
• Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
• Learning Feedback Linearization by MF RL
• "Learning High Level Representations from Continous Experience"
• Deep Knowledge-Based Agents
https://www.facebook.com/icml.imls/videos/394896141118878/ https://www.facebook.com/icml.imls/videos/2084133498380491/
Workshop on Uncertainty and Robustness in Deep Learning
https://www.facebook.com/icml.imls/videos/892421577776699/
Workshop on Understanding and Improving Generalizing in Deep Learning
Daniel Roy - Progress on Nonvacuous Generalization Bounds
Chelsea Finn - Training for Generalization
Spotlight Talk - A Meta-Analysis of Overfitting in Machine Learning
Spotlight Talk - Uniform Convergence may be unable to explain generalization in deep learning
https://www.facebook.com/icml.imls/videos/834773703576296/
Workshop on Understanding and Improving generalization in Deep Learning
Sham Kakade - Prediction, Learning and Memory
Mikhail Belkin - A Hard Look at Generalization and its Theories
Spotlight Talk - Towards Task and Architecture-Indipendent Generalization Gap Predictors
Spotlight Talk - Data-Dependent Sample Complexity of Deep Neural Networks Via Lipschitz Augmentation
https://www.facebook.com/icml.imls/videos/2543954589165286/
Workshop on Generative Modeling and Model-Based Reasoning for Robotics and AI
"What should be Learned?" Invited talk by Stefan Schaal
• When to Trust Your Model: Model-Based Policy Optimization
• Model Based Planning with Energy Based Models
• A Perspective on Objects and Systematic Generalization in Model-Based RL
https://www.facebook.com/icml.imls/videos/1286528018196347/
Workshop Session
Keynote by Kilian Weinberger: On Calibration and Fairness
• Why ReLU networks yield high-confidence predictions far away from training data and how to mitigate the problem
• Detecting Extrapolation with Influence Functions
• How Can We Be So Dense? The Robustness of Highly Sparse Representations
Keynote by Suchi Saria: Safety Challenges with Black-Box Predictors and Novel Learning Approaches for Failure Proofing
https://www.facebook.com/icml.imls/videos/474831503062000/
Workshop on Understanding and Improving generalization in Deep Learning
Invited Speaker: Aleksander Mądry "Are All Features Created Equal?" Invited Speaker: Jason Lee "On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization" Spotlight Talk: "Towards Large Scale Structure of the Loss Landscape of Neural Networks" Spotlight Talk: "Zero-Shot Learning from scratch: leveraging local compositional representations"
https://www.facebook.com/icml.imls/videos/365029137702011/
Workshop Session
• Subspace Inference for Bayesian Deep Learning
• Quality of Uncertainty Quantification for Bayesian Neural Network Inference
• ‘In-Between’ Uncertainty in Bayesian Neural Networks
Keynote by Dawn Song: Adversarial Machine Learning: Challenges, Lessons, and Future Directions
https://www.facebook.com/icml.imls/videos/320132412242165/
Workshop on Generative Modeling and Model-Based Reasoning for Robotics and AI
Value Focused Models, Invited Talk by David Silver
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Model-based Policy Gradients with Entropy Exploration through Sampling
Model-based Reinforcement Learning for Atari
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Physics-as-Inverse-Graphics: Joint Unsupervised Learning of Objects and Physics from Video
Planning to Explore Visual Environments without Rewards
PRECOG: PrEdiction Conditioned On Goals in Visual Multi-Agent settings
Regularizing Trajectory Optimization with Denoising Autoencoders
Towards Jumpy Planning
Variational Temporal Abstraction
Visual Planning with Semi-Supervised Stochastic Action Representations
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
Online Learning and Planning without Prior Knowledge
https://www.facebook.com/icml.imls/videos/2366831430268790/
Workshop on Generative Modeling and Model-Based Reasoning for robotics and AI
"Online Learning for Adaptive Robotic Systems" - Byron Boots
"An inference perspective on model-based reinforcement learning"
"Reducing Noise in GAN Training with Variance Reduced Extragradient"
"Complexity without Losing Generality: The Role of Supervision and Composition" - Chelsea Finn
"Self-supervised Learning for Exploration & Representation" - Abhinav Gupta
Panel Discussion
https://www.facebook.com/icml.imls/videos/449245405622423/
Workshop on Understanding and Improving generalization in Deep Learning
Panel Discussion (Moderator: Nati Srebro)
"Overparameterization without Overfitting: Jacobian-based Generalization Guarantees for Neural Networks"
"How Learning Rate and Delay Affect Minima Selection in Asynchronous Training of Neural Networks: Toward Closing the Generalization Gap"
https://www.facebook.com/icml.imls/videos/854556684898913/
Workshop on Self-Supervised Learning
"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" - Jacob Devlin
"Play as Self-Supervised Learning" - Alison Gopnik
"Learning Latent Plans from Play" - Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet
"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty" - Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song
https://www.facebook.com/icml.imls/videos/2479161722147572/
Workshop on Identify and Understanding Deep Learnign Phenomena
"Optimization’s Untold Gift to Learning: Implicit Regularization" - Nati Srebro
"Bad Global Minima Exist and SGD Can Reach Them "
"Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask"
"Are all layers created equal? -- Studies on how neural networks represent functions" - Chiyuan Zhang
https://www.facebook.com/icml.imls/videos/450413519084800/
Workshop on Exploration in Reinforcement Learning
"Exploration: The Final Frontier" - Doina Precup
"Overcoming Exploration with Play" - Corey Lynch
"Optimistic Exploration with Pessimistic Initialisation" - Tabish Rashid
"Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration" - Nicolai Dorka
"Generative Exploration and Exploitation" (Missing)
"The Journey is the Reward: Unsupervised Learning of Influential Trajectories" - Jonathan Binas
https://www.facebook.com/icml.imls/videos/2236060723167801/
Workshop on Exploration in Reinforcement Learning
"Sampling and exploration for control of physical systems" - Emo Todorov
"Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment" - Adrien Taiga
"Simple Reget Minimzation for Contextual Bandits" - Aniket Deshmukh
"Some Explorations of Exploration in Reinforcement Learning" - Pieter Abbeel
https://www.facebook.com/icml.imls/videos/2265408103721327/
Workshop Session
• Line attractor dynamics in recurrent networks for sentiment classification
• Do deep neural networks learn shallow learnable examples first?
• Crowdsourcing Deep Learning Phenomena
https://www.facebook.com/icml.imls/videos/855147788189057/
"Agents that Set Measurable Goals for Themselves" - Chelsea Finn
https://www.facebook.com/icml.imls/videos/315467659393385/
Workshop Session
"Reverse engineering neuroscience and cognitive science principles" - Aude Oliva
"On Understanding the Hardness of Samples in Neural Networks"
"On the Convex Behavior of Deep Neural Networks in Relation to the Layers' Width"
"Intriguing phenomena in training and generalization dynamics of deep networks" - Andrew Saxe
https://www.facebook.com/icml.imls/videos/2353033231653025/
Workshop session on Self-Supervised Learning
"Self Supervised Learning" - Yann LeCun
"Revisiting Self-Supervised Visual Representation Learning" - Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer
"Data-Efficient Image Recognition with Contrastive Predictive Coding" - Olivier J. Henaff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord
https://www.facebook.com/icml.imls/videos/378993762742156/
Workshop Session on Explroation in Reinforcemnt Learning
"Exploration... in a dangerous world" - Raia Hadsell
Lightning Talks:
"Curious iLQR: Resolving Uncertainty in Model-based RL" - Sarah Bechtle
"An Empirical and Conceptual Categorization of Value-based Exploration Methods" - Niko Yasui
"Skew-Fit: State-Covering Self-Supervised Reinforcement Learning" - Vitchyr H. Pong
"Optimistic Proximal Policy Optimization" - Takahisa Imagawa
"Exploration with Unreliable Intrinsic reward in Multi-Agent reinforcement Learning" - Tabish Rashid
"Parameterized Exploration" - Lili Wu
"Efficient Exploration in Side-scrolling VIdeo Games with Trajectory Replay" - I-Huan Chiang
"Hypothesis Driven Exploration for Deep Reinforcement Learning" - Caleb Chuck
"Epistemic Risk-Sensitive Reinforcemnt Learning" - Hannes Eriksson
"Near-optimal Optimistic Reinforcement Learning using Empriical Bernstein Inequalities" - Aristide Tossou
"Improved Tree Search for Automatic Program Synthesis" - Lior Wolf
"MuleX: Disentangling Exploration and Exploitation in Deep Reinforcement Learning" - Olivier Teboul
https://www.facebook.com/icml.imls/videos/2324338441219681/
Workshop Session on Explroation in Reinforcemnt Learning
"Adapting Behaviour via Intrinsic Rewards to Learn Predictions" - Martha White
Panel Discussion: Martha White, Jeff Clune, Pulkit Agrawal, and Pieter Abbeel. Moderated by Doina Precup
https://www.facebook.com/icml.imls/videos/1094687407344868/
Workshop Session
"Stratagies for mitigating social bias in deep learning systems" - Olga Russakovsky Panel Discussion: Kevin Murphy, Nati Srebro, Aude Oliva, Andrew Saxe, Olga Russakovsky Moderator: Ali Rahimi
https://www.facebook.com/icml.imls/videos/2374820496098856/
Workshop Session on Self-Supervised Learning
"Self-Supervised learning from videos (with sound)" - Andrew Zisserman
"SuperSizing+Empowering Self-Supervised Learning" - Abhinav Gupta
"The Revolution Will Not Be Supervised!" - Alexei Efros
https://www.facebook.com/icml.imls/videos/2030095370631729/
Workshop Session
"The Deep Unknown: on Open-set and Adversarial Examples in Deep Learning" - Terrance Boult
Panel Discussion (moderated by Tom Dietterich)
https://www.facebook.com/icml.imls/videos/2436992626360413/
I thought I would put together a list of the machine learning talks from ICML 2019 since I found they were kind of difficult to look through on facebook, and I figured I would share it here. There may be some minor errors in the listing also. I believe they are mostly available on the ICML website too, but I was just looking through the livestreams: https://icml.cc/Conferences/2019/Videos . I already posted some of these over on /reinforcementlearning as well.
submitted by goolulusaurs to MachineLearning [link] [comments]

The Anunnaki , The Vampire and the Structure of Dissent

by Marcus LiBrizzi
from Reconstruction Website

The vampire, an archetypal figure who pops up in many myths from around the world, is most familiar to Western audiences in the form Bram Stoker’s Dracula and Anne Rice’s Lestat - aristocratic bloodsucking immortals of unholy origin. In more paranoid circles, vampires have been re-imagined as a race of alien beings called the Anunnaki, who have traveled from beyond to control and colonize the planet Earth (in fact, they’ve been in control for quite a while now). Looking at the conspiracy theories of underground celebrity David Icke, Marcus LiBrizzi offers his own theory about the meaning of these horrific beings for a world caught in the grip of a grand economic reorganization. Linking these myths to the realities of transnational capital and the Network Society, LiBrizzi is able to craft his own compelling narrative about the horrors of the New World Order.

<1> The latest incarnation of the vampire - in the conspiracy theories of David Icke - reveals the critical, revolutionary heart of the vampire legend. Discourse on the vampire appears above all to provide a structure of dissent, a metaphorical means of representing and soliciting critiques of the social order. The Anunnaki form of the vampire - in its immersion in the constellation of contemporary conspiracy theories, in its reflection on global capitalism, and in its blurring of historical and fictional narratives - has moved this structure of dissent from the cloak of darkness to the light of day. <2> Considered by some to be the reigning conspiracy theorist in the US, David Icke (who is British) formulates his theories of a worldwide, age-old conspiracy around an extraterrestrial race of beings called the Anunnaki. Self-styled the "most controversial author and speaker in the world," David Icke has been subject to much ridicule but has nonetheless become an industry, publishing eleven books, producing video and audiotapes, embarking on a worldwide lecture circuit, and creating a website that allegedly attracts 10,000 visitors a day (Canadian Par. 13).
A former soccer player from a working-class family, Icke became a household name in the UK as a national sports and news reporter for the BBC and as the spokesperson for the Green Party ("About" Par. 7-8). Starting a full-time writing career in the early 1990s, Icke began with New Age inspired works like Truth Vibrations (1991), which combines accounts of his self transformation with psychically-imparted warnings on the imminent destruction of the earth, from there moving towards conventional conspiracy theories, and finally, beginning with his 1999 book The Biggest Secret, focusing his conspiracy theories around the Anunnaki and their nefarious involvement in human history. <3> The Anunnaki, whose name is Sumerian, meaning "those who from Heaven to Earth Came" (Icke 5), refer to a reptilian race that originated from the legendary planet known as Nibiru (Planet X), or the place of the crossing, which has a 3,600 year elliptical orbit that takes it between Jupiter and Mars and then out into space (5). For the past 450,000 years, according to Icke, the Anunnaki have been ruling earth in different guises and from different dimensions. Through genetic engineering, the Anunnaki have manipulated the evolution of humans as a slave race. "The Anunnaki created bloodlines to rule humanity on their behalf," he writes, "and these … are the families still in control of the world to this day" (9). The interbreeding of the rich and powerful (primarily, for Icke, the European aristocracy and the Eastern Establishment of the US) is not done for reasons of snobbery but rather, "to hold a genetic structure that gives them certain abilities, especially the ability to ’shape-shift’ and manifest in other forms" (9). Working with these crossbreeds are full-blooded Anunnaki, some physically present on earth, others influencing individuals and events psychically from what Icke calls "the lower fourth dimension" (25). Forming a "Brotherhood" or secret society network, the Anunnaki have effectively "hijacked the planet" (46). <4> The recurring motif in the discourse on the Anunnaki is vampirism. In fact, so strong is this component in their depiction that it’s safe to say that Icke’s work represents one of the most recent developments in the discourse of the vampire. "While vampire beliefs are varied," writes James Craig Holte, "certain elements of the vampire myth are consistent. The most important are the inability to experience death, the importance of blood, and the sexual connection between vampire and victim" (246). Other structural similarities between the traditional vampire and the Anunnaki include shape-shifting, hypnotism, and links to secret societies. After establishing the Anunnaki as a manifestation of the vampire, we’ll unpack the implications of this figure, using the tools of a Marxist critical practice. <5> The Anunnaki, like traditional vampires, enjoy eternal or extenuated life spans. Icke claims that, "the fourth dimensional reptilians wear their human bodies like a genetic overcoat and when one body dies the same reptilian ’moves house’ to another body and continues the Agenda into another generation" (46). One type of creature Icke describes is a reptilian "inside" a human physical body; "it seems that … the Anunnaki need to occupy a very reptilian dominated genetic stream to do this, hence certain bloodlines always end up in the positions of power. Other less pure crossbreed human-reptilians are those bodies which are possessed by a reptilian consciousness from the fourth dimension and these are people who psychics see as essentially human, but ’overshadowed’ by a reptilian" (46). Crossbreeding to infuse reptilian genetics into human bloodlines, the Anunnaki gain the means to defy death, as we conceive it. <6> In respect to blood drinking, Icke is very clear: The Anunnaki drink blood, which they need in order to exist in this dimension and hold a human form (288). Embedded in this need lies another parallel between the Anunnaki and the figure of the vampire - the power to shape-shift (from reptilian to human form for the Anunnaki, and usually from vampire form to that of bat or even mist for the traditional vampire). But the Anunnaki also feed off fear, aggression, and other negative emotions. Thus, while blood is needed as a vital life force, the Anunnaki are also addicted to "adrenalchrome," a hormone released in the human body during periods of extreme terror (290, 331). Rather than sucking the blood directly from the necks of their victims, the Anunnaki apparently slash the throats of their victims from left to right and consume the blood out of goblets (303). Icke claims that the origin of the vampire stories are the blood drinking and "energy sucking" rituals of the Anunnaki (26). "In India," he writes, "it was called soma and in Greece it was ambrosia, some researchers suggest. This was said to be the nectar of the gods and it was - the reptilian gods who are genetic blood drinkers" (288). <7> In the sexual connection between slayer and victim, the Anunnaki also share another similarity with the traditional vampire. However, depictions of the Anunnaki by Icke contain none of the erotic allure and seductiveness that distinguish many vampire texts. Instead, the sexual bond between the Anunnaki and their victims is characterized by violence - rape, murder, and Satanic ritual. "Satanism at its core is about the manipulation and theft of another person’s energy and consciousness," writes Icke, who states that "sex is so common in Satanic ritual because at the moment of orgasm, the body explodes with energy which the Satanists and the reptiles can capture and absorb" (295). For Icke, of course, the demons honored or appeased by satanic sex rituals are none other than the reptilian Anunnaki (34). Sex is also a fundamental tool of the Anunnaki mind control program and, more prosaically, it figures prominently as a means of blackmail. The picture that emerges is one involving vast networks of sexual abuse and ritual murder - graphic accounts of satanic practices at the playgrounds for world leaders, such as the Bohemian Grove, a 2,700 acre compound north of San Francisco - mass graves for victims drained of their blood and libidinal energies - and the cultivation of sexual crimes to create an energy field that nourishes these rapacious ETs. <8> There are other shared traits between the traditional vampire and the Anunnaki, for example, the role of secret societies. One of Icke’s chief contributions to the discourse on the vampire lies in his immersion of this figure into a vast web of clandestine organizations, from ancient mystery schools and cults like the Brotherhood of the Snake to the Knights Templar and the Masonic Order, from global entities like the UN, the Trilateral Commission, and the Council on Foreign Relations to drug cartels, satanic churches, and the Black Nobility. A keystone in this architecture of conspiracy is the Order of Draco, which conjures up the most famous of all vampires - Count Dracula - and underscores his demonic, draconian, and reptilian associations. "According to Laurence Gardner, the name Dracula means ’Son of Dracul’ and was inspired by Prince Vlad III of Transylvania-Wallachia, a chancellor of the Court of the Dragon in the 15th century. This prince’s father was called Dracul within the Court" (56). In their network of secret societies, of which the Order of Draco is but a single manifestation, the Anunnaki highlight the conspiratorial dimension of all vampires. Finally, the Anunnaki share with the traditional vampire the capacity to hypnotize: Icke writes that reptilian bloodlines, "have the ability to produce an extremely powerful hypnotic stare, just like a snake hypnotizing its prey and this is the origin of giving someone the ’evil eye’" (42). <9> Icke’s paradigm displays more than the vitality, persistence, and adaptive qualities of the vampire legend. His theories reveal the dissident energies contained already in the vampire legacy. <10> To begin with, Icke’s work represents a major fusion of the vampire cult and the field of conspiracy theories. Richard Hofstadter, in his famous essay "The Paranoid Style in American Politics" (1963) claims that the, "distinguishing thing about the paranoid style is not that its exponents see conspiracies or plots here and there in history, but that they regard a ’vast’ … conspiracy as the motive force in historical events. History is a conspiracy" (29). Conspiracies, even when they’re not construed as vast, over-arching plots, however, have an internal, integrative logic. In other words, there is a momentum in conspiracy theories to pull in all other theories, and finally to arrive at a state in which everything is connected. Part of Icke’s popularity lies in his ability to integrate most contemporary American conspiracy theories into one over-arching framework. Situated squarely in the center of this design is the ancient figure of the vampire. Thus, the vampire (or, more specifically, the Anunnaki Vampire) has colonized the field of conspiracy theories - government-sponsored alien cover-ups, the New World Order, suspicious deaths, the secret government, suppressed research, the intrigues of the CIA, and the list goes on indefinitely. <11> From a Marxist perspective, of course, this development is more than just a formal or aesthetic innovation, for many of the conspiracy theories now circulating in the cultural medium of the US contain, at their core, critical, dissenting, and rebellious points of view (encompassing both extreme right and left) that are articulated in opposition to the social, political, and cultural status quo. While Hofstadter claims that the US has no monopoly on conspiracism, other scholars like Peter Knight hold that conspiracy theories hold an indispensable place in American ideology formation, and that current, "conspiracy theories can be read in part as panicked responses to the increasing multiculturalism and globalization of the present" (5). Revolutionary or reactionary, however, these theories are inimical to the governing elite and represent a tradition of oppositional practice. As Knight puts it, "conspiracy theory has become the lingua franca of a countercultural opposition that encompasses a vast spectrum of political thinking from the committed to the casual" (6-7). <12> An initial difficulty in seeing the vampire as a symbol of the ruling class - capitalist or otherwise - lies in the diverse variations taken on by vampires in different places and times. As Brian Frost puts it, "the vampire is a polymorphic phenomenon with a host of disparate guises to its credit" (1). Among the various legendary "guises" of the vampire inventoried by Frost are spirit vampires, astral vampires, psychic vampires, animal vampires, and real-life vampires who are, "sadistic criminals … urged on by a physical craving for blood" (15). Complicating the picture is the fact that Bram Stoker’s character of Count Dracula, who for many encapsulates the aristocratic ethos of the vampire, "lacks precisely what makes a man ’noble’: servants. Dracula stoops to driving the carriage, cooking the meals, making the beds, cleaning the castle" (Moretti 90). Furthermore, in some of the earliest European vampire legends, the undead feed off the living members of their own families (Murgoci 18), which at first glance mitigates the social-class dynamic often conjured up in the image of aristocratic vampires draining the lifeblood of their locals. <13> There is, nevertheless, a critical and even radical dimension to the figure of the vampire, who, as a parasite, circulates as a political metaphor. The word vampire has from the start been used in oppositional literature as a symbol of an exploiting class, government, industry, or institution. A decade, "after the introduction of the word ’vampire’ in an English publication in 1732, (an account of the investigation of Arnold Paul in Serbia) … a serious utilization of the vampire as a political metaphor occurred in Observations on the Revolution of 1688 (… published in 1741)"
which identified foreign investors as "’Vampires of the Publick’" (Melton 538).
Only "a few years later, in 1764, Voltaire, in his Philosophical Dictionary," refers to "vampires" as "’stock-jobbers, brokers, and men of business who sucked the blood of the people in broad daylight’" (538). <14> But it was Marx who first suggested that the vampire can be interpreted as a metaphor of capitalism and who also implied a method for this interpretation. In volume one of Capital (1867), he writes that, "capital is dead labour, which, vampire-like, lives only by sucking living labour, and lives the more, the more labour it sucks" (342). Extrapolating on this analogy, Franco Moretti provides a reading of Bram Stoker’s Dracula, writing, "If the vampire is a metaphor for capital, then Stoker’s vampire, who is of 1897, must be the capital of 1897" (92). Accordingly, Moretti sees Count Dracula as the expression or figure of monopoly capitalism, which, to the 19th century bourgeoisie, could not be recognized as an emerging force but only as a relic of the past displaced into the present (93). Whether or not one agrees with Moretti’s reading of the Count, it is his method that’s of most value. As Rob Latham pus it, "Moretti stresses that, while the vampire is a perfect general image for the basic mechanism of capitalist development, individual vampire texts illuminate specifically the historical phases of capitalism in which they are produced" (129). <15> Applying Moretti’s method, we can perceive the Anunnaki as metaphorical of the unique forms capitalism has taken by the 21st century. Certainly, Anunnaki vampires embody the market for genetic engineering as well as space exploration. These dimensions, in fact, are projected back into the origins of Anunnaki control over earth and its resources: travel from another planet, interdimensional traffic, and a crossbreeding agenda coterminous with the evolution of the human race. Anunnaki vampires also control finance, which was undergoing a tremendous transformation and development during the time when Icke was writing that, of all the spheres of Anunnaki domination, "the most important … in terms of control, is banking" (207).
Electronic banking, credit, and the demediation of stock exchange through on-line trading are some of the key elements in the recent development of the finance industry (Castells 152-53). But we can go deeper than this kind of analysis, and discover in the discourse on the Anunnaki examples of remarkable changes, not in select markets, but rather in the very structure of the economy. <16> In this, more significant sense, the Anunnaki are linked to present-day capitalism through their association with global control. Icke consistently depicts these alien bloodsuckers as monopolizing world leadership positions in government, finance, religion, and the media. In this sense, Anunnaki vampires represent a demonized expression of the unique form capitalism has taken during the very period in which Icke’s theories were formulated, published, and popularized. The late 1990s issued in - for the first time in history - a global economy, defined by Manuel Castells as, "an economy whose core components have the institutional, organizational, and technological capacity to work as a unit in real time, or in chosen time, on a planetary scale" (102). Thus, "this is a new brand of capitalism, technologically, organizationally, and institutionally distinct" (160-61). <17> The forces spearheading this change derive in part from key industries, notably information technology - centering on the Internet - finance, and biotechnology (Castells 161). Other contributing factors in the formation of the global economy are government policies that restructured capitalism through laws deregulating and liberalizing economic activity (148). The global economy has, of course, catapulted the scale of capitalism; "for the first time in history the whole planet is capitalist or dependent on its connection to global capitalist networks" (160-61). However, as Castells points out, the global economy, "is not a planetary economy … because it does not embrace all economic processes in the planet, it does not include all territories, and it does not include all people in its workings, although it does affect directly or indirectly the livelihood of all humankind" (132). Thus the global economy is significant, not only for it inclusivity, but also for its significant and shifting exclusions, marginalizations, and hidden bypasses fraught through its great grid or network of power relations. <18> Anunnaki vampires are perfectly suited to, and a perfect representation of, a global economy in the scope of their engagement and their profile in emergent industries, but there are other ways as well. This is because their secret agenda has always already been the creation of a one-world government - a New World Order - bypassing nations and creating a system or web from which there is no escape. The New World Order figures prominently in conspiracy theories and in literature such as Aldous Huxley’s Brave New World (1932) and George Orwell’s Nineteen Eighty-Four (1949). But during the millenium and start of the 21st century, demonstrations against globalism have been on the rise, responding to rapid developments in transnationalism.
Another aspect of the Anunnaki relevant here is their multicultural image. The Anunnaki have been written retroactively into all mythological systems, making them true transnationals. For example, they people the pages of the Indian Vedas, Babylonian myths, as well as the books of the Bible, and they are at the heart of ancient snake-worshipping cults worldwide. Moreover, they are literally seeded into the human genome through the Anunnaki engineering of the race, interbreeding alien genetics into all peoples, symbolized, for example in Genesis, as the saliva Jehovah mixes with clay to form the first man. <19> Not surprisingly, Anunnaki narratives have a lot to say in terms of the location, construction, and commodification of the self. Unlike traditional vampires who feed solely off a victim’s blood or soul, the Anunnaki thrive off of negative energies such as fear and aggression. These ETs drain individuals of their sense of wellbeing through the manipulation and absorption of libidinal energies and - ultimately - the theft of consciousness and agency. On the one hand, the location of the self that the Anunnaki attack seems closely linked to consumerist notions. For example, New Age self-actualization products as well as the market for energy drinks - even caffeine-enhanced water - not to mention designer drugs - are only a few of the new industries catering profitably to the very malady Icke derives from Anunnaki domination.
And, of course, Icke’s works themselves represent a (profitable) venture in a multi-million dollar market for conspiracy theories in American popular culture. On the other hand, discourse on the Anunnaki is not necessarily complicit with the capitalist system that produces such effects. A current line of cultural theory, "has alleged that the modalities of consumer culture - and the forms of subjectivity they enable - do not necessarily integrate seamlessly into the capitalist society which has mobilized them but may instead be potentially subversive of its purposes" (Latham 132). The consumption of Icke’s works - in fact, the growing market for conspiracism in the US - would seem to be a case in point here, disseminating and perpetuating an oppositional worldview, a "hermeneutics of suspicion," while contributing to the accumulation of capital. <20> Another revealing dimension of Anunnaki vampires lies in their collective depiction; unlike many accounts of the vampire, Icke’s theories do not revolve around distinct Anunnaki individuals but rather focuses on them as a class or group; in this sense the Anunnaki do not convey the same individualistic focus so often encountered in vampire narratives. Even Anunnaki forms of consciousness are best described as a "groupthink" mentality. On this, Icke writes that, "the reptilians seek … to influence everyone by stimulating the behavioral patterns of the reptile region of the brain: hierarchical thinking aggression conflict division lack of compassion a need for ritual" (46) Symbolic of contemporary capitalism, this collective depiction of the Anunnaki reflects the rise of networks, and their decentering development, which have instrumentally caused - and are themselves produced by - the new global economy. The network supersedes the individual as the subject of the vampire narrative. Here Castells, speaking on the network society of global economics, is instructive: "For the first time in history, the basic unit of economic organization is not a subject, be it individual (such as the entrepreneur …) or collective (such as the capitalist class, the corporation, the state)" (214). Instead, "the unit is the network, made up of a variety of subjects and organizations, relentlessly modified as networks adapt" (214). <21> In their networked, post-subjective form of the vampire, the Anunnaki are metaphorical of the precise trajectory assumed by contemporary capitalism. Network is the same term Icke uses to describe the reptilian base of operations today, writing, "after thousands of years of evolution, the reptilian network is now a vast and often unfathomable web of interconnecting secret societies, banks, businesses, political parties, security agencies, media owners, and so on" (259). Discourse on the Anunnaki vampire is in step with broader trends in American conspiracy theories, themselves responses to ideological crises associated with post-modernism and the growth of a network society. Writing on conspiracy theories in the postwar US, Timothy Melley points out that, "the term ’conspiracy’ rarely signifies a small, secret plot any more. Instead, it frequently refers to the workings of a large organization, technology, or system, a powerful and obscure entity so dispersed that it is the very antithesis of the traditional conspiracy" (59). Melley argues that conspiracy theories in the US have historically been an ideological means of validating individualism. And this new, impersonal breed of conspiracism reflects anxiety over the loss of individuality and agency and stands as both "an acknowledgment, and rejection, of postmodern subjectivity" (65).
<22> Perhaps most revealing of all is the dissolution of the boundary between fantasy and reality - the presentation of the vampire as an historical agent rather than a fictional character. Deeply ironic and radical, this slippage of fact and fantasy drives the vampire legacy much closer to its critical core. If the traditional vampire articulates dissent, it also distorts the representation of real relations, which are displaced into the realm of the imaginary. In the form of the Anunnaki, however, vampires have infiltrated the field of conspiracy theories, spilling from the page onto the pavement, as it were. Moving from metaphor to a kind of mimesis of the grotesque, the vampire legacy shape-shifts - its implicit charge evolving into an explosive critique.
Works Cited
"About David Icke, the Man, His Philosophy, and His Work." N.d. Online. Internet. 3 January 2003. Available http.//davidicke.com/icke/index1a.html
Canadian Association for Free Expression. David Icke’s Telling the Truth Archives: Conspiracies, CoverUps, Truths, Facts, Oddities, Research: "Dante’s Infernal Guide to Human Rights and Wrongs."
Castells, Manuel. The Rise of the Network Society. 2nd ed. Vol. 1. Oxford: Blackwell, 2000.
Frost, Brian J. The Monster with a Thousand Faces: Guises of the Vampire in Myth and Literature. Bowling Green, OH: Bowling Green State U Popular P, 1989.
Hofstadter, Richard. "The Paranoid Style in American Politics." In The Paranoid Style in American Politics and Other Essays. 1963. Cambridge: Harvard UP, 1996.
Holte, James Craig. "The Vampire." Malcolm South, ed. Mythical and Fabulous Creatures: A Source Book and Research Guide. New York: Greenwood, 1987. 243-64.
Icke, David. The Biggest Secret: The Book That Will Change the World. Scottsdale, AZ: Bridge of Love, 1999.
Knight, Peter. "Introduction: A Nation of Conspiracy Theorists." In Conspiracy Nation: The Politics of Paranoia in Postwar America. Ed. Peter Knight. New York: New York UP, 2002. 1-17.
Latham, Rob. "Consuming Youth: The Lost Boys Cruise Mallworld." Blood Read: The Vampire as Metaphor in Contemporary Culture. Joan Gordon and Veronica Hollinger, eds. Philadelphia: U of Pennsylvania P, 1997. 129-47.
Marx, Karl. Capital. Vol. 1. 1867. Harmondworth, UK: Penguin, 1976.
Melley, Timothy. "Agency Panic and the Culture of Conspiracy." In Conspiracy Nation: The Politics of Paranoia in Postwar America. Ed. Peter Knight. New York: New York UP, 2002. 57-81.
Melton, J. Gordon. The Vampire Book: The Encyclopedia of the Undead. Detroit: Visible Ink, 1999.
Moretti, Franco. "The Dialectic of Fear." Signs Taken for Wonders: Essays in the Sociology of Literary Forms. 1983. New York: Verso, 1997. 83-108.
Murgoci, Agnes. "The Vampire in Roumania." Alan Dundes, ed. The Vampire: A Casebook. Madison: U of Wisconsin P, 1998. 12-34.
submitted by CuteBananaMuffin to conspiracy [link] [comments]

Idk if this is allowed, but...

  1. troop
  2. shelf
  3. insure
  4. stall
  5. admiration
  6. aid
  7. suburb
  8. inappropriate
  9. owe
  10. extort
  11. lose
  12. assumption
  13. aunt
  14. spell
  15. crack
  16. bird
  17. trainer
  18. rub
  19. section
  20. arrange
  21. enhance
  22. faith
  23. reasonable
  24. unlawful
  25. excavate
  26. calm
  27. fat
  28. heavy
  29. resource
  30. cower
  31. tax
  32. exempt
  33. valid
  34. unlike
  35. board
  36. fog
  37. joke
  38. casualty
  39. zero
  40. anniversary
  41. variable
  42. cat
  43. stunning
  44. level
  45. navy
  46. privilege
  47. capital
  48. due
  49. dare
  50. fund
  51. enfix
  52. mile
  53. exploit
  54. flock
  55. situation
  56. poor
  57. photocopy
  58. water
  59. ally
  60. hypnothize
  61. activity
  62. rate
  63. spit
  64. widen
  65. help
  66. blade
  67. expertise
  68. card
  69. beginning
  70. brag
  71. shift
  72. marketing
  73. story
  74. slab
  75. exceed
  76. consumer
  77. approval
  78. rumor
  79. youth
  80. inject
  81. count
  82. neighbour
  83. closed
  84. discover
  85. forecast
  86. consultation
  87. latest
  88. pardon
  89. honor
  90. plot
  91. separate
  92. pot
  93. burst
  94. candidate
  95. freshman
  96. work
  97. approach
  98. class
  99. withdraw
  100. seminar
  101. demonstrate
  102. unique
  103. excess
  104. reveal
  105. paragraph
  106. thank
  107. technology
  108. dismissal
  109. divorce
  110. brand
  111. lecture
  112. agency
  113. chemistry
  114. suite
  115. left
  116. range
  117. right wing
  118. addition
  119. lunch
  120. weakness
  121. steak
  122. evening
  123. sale
  124. integrity
  125. handicap
  126. hero
  127. twitch
  128. denial
  129. flower
  130. shell
  131. hot
  132. chord
  133. attention
  134. sacred
  135. generate
  136. mislead
  137. few
  138. shop
  139. morsel
  140. useful
  141. voucher
  142. shoot
  143. ban
  144. bush
  145. favourite
  146. athlete
  147. discourage
  148. fitness
  149. soul
  150. dull
  151. avant-garde
  152. agile
  153. invasion
  154. quit
  155. residence
  156. factor
  157. pattern
  158. species
  159. angle
  160. prisoner
  161. acquisition
  162. chaos
  163. battlefield
  164. god
  165. rubbish
  166. enjoy
  167. public
  168. native
  169. deputy
  170. button
  171. disaster
  172. embrace
  173. compete
  174. note
  175. index
  176. quality
  177. print
  178. put
  179. intention
  180. cassette
  181. picture
  182. regret
  183. dribble
  184. sock
  185. decoration
  186. mail
  187. dish
  188. trouser
  189. happen
  190. strict
  191. switch
  192. surgeon
  193. retailer
  194. prefer
  195. jacket
  196. accompany
  197. mystery
  198. multimedia
  199. neglect
  200. marriage
  201. aware
  202. assume
  203. medieval
  204. fool
  205. sculpture
  206. singer
  207. ideology
  208. makeup
  209. forbid
  210. pound
  211. publish
  212. tray
  213. interactive
  214. coal
  215. gradient
  216. expectation
  217. alarm
  218. diamond
  219. refer
  220. whip
  221. devote
  222. advantage
  223. communist
  224. amuse
  225. plan
  226. plagiarize
  227. month
  228. percent
  229. obese
  230. colon
  231. kitchen
  232. conclusion
  233. constitutional
  234. indirect
  235. career
  236. debut
  237. notebook
  238. mayor
  239. chance
  240. Koran
  241. rider
  242. bin
  243. quota
  244. cord
  245. precision
  246. sulphur
  247. prize
  248. powder
  249. breathe
  250. emphasis
  251. squash
  252. stretch
  253. root
  254. thinker
  255. inquiry
  256. live
  257. flourish
  258. disability
  259. circumstance
  260. temperature
  261. rest
  262. age
  263. memorandum
  264. cluster
  265. opposition
  266. gap
  267. borrow
  268. bullet
  269. injury
  270. moving
  271. panel
  272. atmosphere
  273. dip
  274. paint
  275. attraction
  276. childish
  277. giant
  278. rocket
  279. set
  280. leave
  281. demonstrator
  282. east
  283. orientation
  284. marsh
  285. solo
  286. appeal
  287. chimney
  288. funny
  289. well
  290. confront
  291. morning
  292. band
  293. screen
  294. square
  295. voter
  296. wage
  297. celebration
  298. day
  299. break down
  300. half
  301. annual
  302. slap
  303. page
  304. soprano
  305. contradiction
  306. gaffe
  307. ladder
  308. frown
  309. classify
  310. stir
  311. staircase
  312. gate
  313. paradox
  314. continuation
  315. rain
  316. braid
  317. secular
  318. broadcast
  319. producer
  320. pour
  321. grounds
  322. golf
  323. center
  324. improvement
  325. appoint
  326. warning
  327. dragon
  328. management
  329. architect
  330. diagram
  331. transparent
  332. rugby
  333. coverage
  334. straw
  335. like
  336. twin
  337. sea
  338. temporary
  339. outlook
  340. folklore
  341. rotten
  342. pasture
  343. traction
  344. psychology
  345. vein
  346. mirror
  347. heat
  348. knowledge
  349. goat
  350. proclaim
  351. withdrawal
  352. chauvinist
  353. background
  354. fossil
  355. behavior
  356. shout
  357. rhythm
  358. authorise
  359. fall
  360. security
  361. fascinate
  362. splurge
  363. option
  364. fist
  365. banana
  366. training
  367. hypothesize
  368. radiation sickness
  369. hostile
  370. pastel
  371. mainstream
  372. constraint
  373. bake
  374. defend
  375. season
  376. triangle
  377. herd
  378. see
  379. reference
  380. cow
  381. grace
  382. clique
  383. porter
  384. site
  385. product
  386. canvas
  387. pier
  388. pig
  389. cooperative
  390. disclose
  391. machinery
  392. slot
  393. random
  394. kick
  395. great
  396. spread
  397. knot
  398. momentum
  399. electron
  400. shock
  401. crew
  402. sport
  403. sound
  404. introduction
  405. lane
  406. disappoint
  407. beach
  408. opponent
  409. fireplace
  410. animal
  411. oven
  412. bracket
  413. horn
  414. cup
  415. Mars
  416. branch
  417. cathedral
  418. job
  419. champagne
  420. justify
  421. nationalist
  422. year
  423. dinner
  424. interference
  425. technique
  426. cool
  427. lift
  428. trial
  429. cover
  430. valley
  431. election
  432. director
  433. fuel
  434. comedy
  435. retired
  436. respect
  437. biography
  438. mercy
  439. correction
  440. toll
  441. declaration
  442. integration
  443. dream
  444. sign
  445. infection
  446. revoke
  447. deficit
  448. weight
  449. contain
  450. take
  451. settle
  452. projection
  453. price
  454. overall
  455. thesis
  456. explicit
  457. lodge
  458. compose
  459. cute
  460. narrow
  461. labour
  462. confine
  463. proposal
  464. feminist
  465. arrogant
  466. bean
  467. tooth
  468. polite
  469. incentive
  470. cook
  471. highlight
  472. trunk
  473. suggest
  474. teach
  475. entertain
  476. mind
  477. brown
  478. biology
  479. grandmother
  480. monster
  481. dump
  482. man
  483. credit
  484. vacuum
  485. socialist
  486. needle
  487. spirit
  488. baby
  489. network
  490. departure
  491. miracle
  492. opera
  493. resort
  494. funeral
  495. protect
  496. bank
  497. record
  498. real
  499. preparation
  500. student
  501. capture
  502. boot
  503. gloom
  504. crouch
  505. method
  506. portrait
  507. correspond
  508. suntan
  509. start
  510. chart
  511. coup
  512. grip
  513. riot
  514. affair
  515. president
  516. rear
  517. advertising
  518. unity
  519. cheek
  520. thrust
  521. channel
  522. number
  523. route
  524. reverse
  525. literacy
  526. craft
  527. orthodox
  528. provoke
  529. assault
  530. abnormal
  531. dry
  532. productive
  533. vague
  534. parameter
  535. football
  536. grateful
  537. contrast
  538. beam
  539. testify
  540. dog
  541. slippery
  542. loyalty
  543. outline
  544. trade
  545. conviction
  546. lie
  547. infect
  548. profound
  549. relief
  550. negative
  551. landscape
  552. contempt
  553. tropical
  554. bang
  555. criminal
  556. elapse
  557. hope
  558. yard
  559. rich
  560. television
  561. joystick
  562. medal
  563. myth
  564. crackpot
  565. elbow
  566. visible
  567. hunting
  568. essay
  569. fox
  570. woman
  571. joint
  572. recognize
  573. haircut
  574. bow
  575. conference
  576. deport
  577. barrel
  578. episode
  579. miserable
  580. sell
  581. gas pedal
  582. arrangement
  583. utter
  584. learn
  585. gold
  586. integrated
  587. representative
  588. pin
  589. sister
  590. descent
  591. stereotype
  592. banner
  593. consensus
  594. replacement
  595. account
  596. scene
  597. serious
  598. sandwich
  599. depend
  600. defendant
  601. population
  602. secretary
  603. heart
  604. spontaneous
  605. embarrassment
  606. implication
  607. flat
  608. subject
  609. hallway
  610. throne
  611. lineage
  612. theft
  613. feedback
  614. infinite
  615. score
  616. install
  617. lonely
  618. tin
  619. illness
  620. theorist
  621. abridge
  622. filter
  623. headquarters
  624. parade
  625. audience
  626. official
  627. spoil
  628. rib
  629. familiar
  630. indulge
  631. injection
  632. vessel
  633. screw
  634. hurt
  635. mug
  636. back
  637. log
  638. west
  639. assembly
  640. applied
  641. tie
  642. jury
  643. storage
  644. mechanical
  645. rent
  646. bend
  647. lily
  648. system
  649. hold
  650. contract
  651. unfortunate
  652. matrix
  653. home
  654. response
  655. eyebrow
  656. confuse
  657. aluminium
  658. artificial
  659. result
  660. permanent
  661. ensure
  662. collection
  663. settlement
  664. content
  665. glue
  666. market
  667. explain
  668. worm
  669. cigarette
  670. allocation
  671. brush
  672. calculation
  673. empire
  674. sweater
  675. camera
  676. answer
  677. harbor
  678. sun
  679. sip
  680. fastidious
  681. fight
  682. misplace
  683. passion
  684. cut
  685. grow
  686. treaty
  687. qualification
  688. clear
  689. pleasure
  690. bulb
  691. pony
  692. failure
  693. entertainment
  694. able
  695. physics
  696. brink
  697. concern
  698. rice
  699. ride
  700. conceive
  701. gradual
  702. achieve
  703. earthflax
  704. responsible
  705. intelligence
  706. glow
  707. ministry
  708. duck
  709. professor
  710. deer
  711. beef
  712. child
  713. microphone
  714. judge
  715. abortion
  716. pocket
  717. look
  718. manual
  719. exclude
  720. mathematics
  721. stream
  722. civilization
  723. characteristic
  724. appreciate
  725. listen
  726. exercise
  727. explosion
  728. bedroom
  729. sausage
  730. write
  731. pay
  732. shine
  733. different
  734. consideration
  735. ambition
  736. program
  737. ballot
  738. wife
  739. interface
  740. make
  741. orchestra
  742. frozen
  743. fine
  744. lifestyle
  745. bet
  746. reservoir
  747. sweet
  748. move
  749. fragrant
  750. trance
  751. reproduction
  752. tease
  753. develop
  754. breeze
  755. basic
  756. minimum
  757. crutch
  758. depart
  759. privacy
  760. continuous
  761. flu
  762. opinion
  763. salad
  764. restrain
  765. muggy
  766. cage
  767. quarrel
  768. motorist
  769. acceptance
  770. wardrobe
  771. pill
  772. approve
  773. depressed
  774. rebel
  775. notice
  776. orgy
  777. unpleasant
  778. have
  779. rotate
  780. tough
  781. gossip
  782. ring
  783. software
  784. pioneer
  785. wire
  786. increase
  787. presidency
  788. intensify
  789. pop
  790. width
  791. wheat
  792. diplomat
  793. launch
  794. realism
  795. collar
  796. courage
  797. rotation
  798. guide
  799. scratch
  800. love
  801. definition
  802. low
  803. nose
  804. portion
  805. claim
  806. partnership
  807. automatic
  808. building
  809. bathtub
  810. good
  811. expansion
  812. peak
  813. normal
  814. displace
  815. serve
  816. potential
  817. publication
  818. foundation
  819. contemporary
  820. star
  821. feeling
  822. bundle
  823. shallow
  824. monarch
  825. rehearsal
  826. carry
  827. upset
  828. recovery
  829. motorcycle
  830. adviser
  831. profile
  832. institution
  833. snarl
  834. south
  835. fit
  836. language
  837. assessment
  838. defeat
  839. seal
  840. halt
  841. fruit
  842. unaware
  843. wash
  844. deliver
  845. clarify
  846. migration
  847. berry
  848. history
  849. meadow
  850. just
  851. texture
  852. guideline
  853. layer
  854. light
  855. lease
  856. nonremittal
  857. penetrate
  858. think
  859. visual
  860. remunerate
  861. style
  862. dedicate
  863. ankle
  864. remain
  865. variant
  866. ritual
  867. respectable
  868. favorable
  869. liberty
  870. outlet
  871. design
  872. Sunday
  873. preoccupation
  874. bus
  875. edition
  876. bloody
  877. offense
  878. carrot
  879. march
  880. contraction
  881. cabin
  882. dangerous
  883. blind
  884. waiter
  885. ethnic
  886. sweat
  887. musical
  888. swing
  889. lace
  890. parallel
  891. computing
  892. notorious
  893. compound
  894. cake
  895. absolute
  896. original
  897. agent
  898. banish
  899. beer
  900. fever
  901. evaluate
  902. ostracize
  903. prescription
  904. sugar
  905. lead
  906. bring
  907. mixture
  908. revive
  909. hard
  910. classroom
  911. salt
  912. excuse
  913. fly
  914. clerk
  915. follow
  916. floor
  917. cave
  918. throat
  919. railcar
  920. owner
  921. store
  922. quotation
  923. related
  924. node
  925. steep
  926. depression
  927. spite
  928. trench
  929. compromise
  930. laser
  931. bitch
  932. bathroom
  933. tune
  934. top
  935. positive
  936. sequence
  937. grave
  938. thoughtful
  939. bury
  940. expenditure
  941. skilled
  942. crusade
  943. tired
  944. tycoon
  945. tent
  946. frame
  947. wall
  948. birthday
  949. feast
  950. concession
  951. flour
  952. journal
  953. safety
  954. fate
  955. chocolate
  956. scheme
  957. present
  958. embark
  959. manage
  960. operation
  961. flavor
  962. tank
  963. activate
  964. candle
  965. dance
  966. reflect
  967. tactic
  968. huge
  969. investigation
  970. arena
  971. size
  972. leadership
  973. return
  974. rough
  975. destruction
  976. examination
  977. relationship
  978. viable
  979. color-blind
  980. element
  981. offend
  982. poem
  983. railroad
  984. crisis
  985. maze
  986. debt
  987. blame
  988. context
  989. oak
  990. kid
  991. deprivation
  992. minute
  993. critic
  994. solve
  995. weed
  996. customer
  997. gasp
  998. operational
  999. expression
  1000. traffic
  1001. blast
  1002. incapable
  1003. academy
  1004. literature
  1005. refund
  1006. blank
  1007. qualified
  1008. enter
  1009. shelter
  1010. delete
  1011. escape
  1012. sex
  1013. perfume
  1014. report
  1015. bubble
  1016. change
  1017. noble
  1018. lamp
  1019. location
  1020. recruit
  1021. pile
  1022. perception
  1023. freighter
  1024. omission
  1025. flag
  1026. attract
  1027. relative
  1028. service
  1029. persist
  1030. quest
  1031. information
  1032. pavement
  1033. tight
  1034. mother
  1035. radiation
  1036. theater
  1037. economy
  1038. offer
  1039. honest
  1040. sunrise
  1041. pledge
  1042. go
  1043. bind
  1044. burn
  1045. absorb
  1046. autonomy
  1047. restoration
  1048. row
  1049. dose
  1050. doubt
  1051. observer
  1052. city
  1053. grandfather
  1054. gesture
  1055. snack
  1056. composer
  1057. administration
  1058. complain
  1059. build
  1060. tick
  1061. tumour
  1062. invite
  1063. pneumonia
  1064. battle
  1065. discriminate
  1066. rally
  1067. smooth
  1068. constitution
  1069. economist
  1070. jewel
  1071. fuss
  1072. find
  1073. compensation
  1074. strength
  1075. roof
  1076. prosecute
  1077. employ
  1078. retirement
  1079. demonstration
  1080. pupil
  1081. uncle
  1082. precedent
  1083. magnitude
  1084. graze
  1085. culture
  1086. equinox
  1087. professional
  1088. similar
  1089. perforate
  1090. printer
  1091. rainbow
  1092. model
  1093. immune
  1094. fare
  1095. still
  1096. damage
  1097. stitch
  1098. kettle
  1099. equip
  1100. please
  1101. mention
  1102. laborer
  1103. meet
  1104. environment
  1105. mourning
  1106. theory
  1107. cottage
  1108. custody
  1109. concrete
  1110. crime
  1111. ecstasy
  1112. philosophy
  1113. dive
  1114. double
  1115. photography
  1116. player
  1117. redundancy
  1118. treat
  1119. air
  1120. census
  1121. obstacle
  1122. word
  1123. flatware
  1124. suitcase
  1125. skeleton
  1126. hen
  1127. elect
  1128. pigeon
  1129. threshold
  1130. bloodshed
  1131. jockey
  1132. ferry
  1133. prevent
  1134. asset
  1135. speed
  1136. achievement
  1137. plane
  1138. bacon
  1139. pepper
  1140. ball
  1141. mean
  1142. partner
  1143. ignorance
  1144. guard
  1145. shaft
  1146. domestic
  1147. prevalence
  1148. tire
  1149. resolution
  1150. variation
  1151. kidney
  1152. safe
  1153. threat
  1154. coat
  1155. slump
  1156. incongruous
  1157. tournament
  1158. decorative
  1159. young
  1160. swallow
  1161. torture
  1162. jet
  1163. exhibition
  1164. assignment
  1165. try
  1166. mobile
  1167. despair
  1168. stroll
  1169. folk
  1170. archive
  1171. belief
  1172. private
  1173. minister
  1174. brain
  1175. hour
  1176. add
  1177. video
  1178. stand
  1179. revolution
  1180. park
  1181. requirement
  1182. trolley
  1183. inspire
  1184. sword
  1185. clock
  1186. vegetable
  1187. sodium
  1188. offspring
  1189. council
  1190. mutation
  1191. circle
  1192. extent
  1193. butterfly
  1194. correspondence
  1195. height
  1196. form
  1197. lot
  1198. watch
  1199. outside
  1200. dimension
  1201. grain
  1202. slant
  1203. systematic
  1204. value
  1205. color
  1206. corpse
  1207. guilt
  1208. say
  1209. empirical
  1210. strip
  1211. arrow
  1212. update
  1213. combination
  1214. quarter
  1215. understanding
  1216. buffet
  1217. deal
  1218. deadly
  1219. exchange
  1220. past
  1221. arise
  1222. ballet
  1223. grind
  1224. insistence
  1225. fresh
  1226. item
  1227. likely
  1228. base
  1229. slice
  1230. trip
  1231. patient
  1232. stay
  1233. window
  1234. meal
  1235. amputate
  1236. movie
  1237. staff
  1238. impact
  1239. fragment
  1240. warn
  1241. feel
  1242. skin
  1243. dressing
  1244. cell phone
  1245. radio
  1246. exploration
  1247. hill
  1248. nun
  1249. combine
  1250. tempt
  1251. nominate
  1252. angel
  1253. forge
  1254. wear out
  1255. groan
  1256. slam
  1257. ghostwriter
  1258. margin
  1259. genetic
  1260. scenario
  1261. carriage
  1262. sacrifice
  1263. quaint
  1264. criticism
  1265. road
  1266. copy
  1267. endorse
  1268. bee
  1269. disposition
  1270. accessible
  1271. hang
  1272. embox
  1273. press
  1274. lack
  1275. difficulty
  1276. conglomerate
  1277. patent
  1278. fantasy
  1279. pumpkin
  1280. provincial
  1281. substitute
  1282. dairy
  1283. proportion
  1284. duty
  1285. absence
  1286. tip
  1287. dirty
  1288. appear
  1289. graduate
  1290. overcharge
  1291. conservation
  1292. castle
  1293. rage
  1294. drift
  1295. fraud
  1296. create
  1297. looting
  1298. compliance
  1299. silver
  1300. tablet
  1301. egg
  1302. essential
  1303. buttocks
  1304. muscle
  1305. realize
  1306. grudge
  1307. mosquito
  1308. federation
  1309. crossing
  1310. driver
  1311. reason
  1312. heel
  1313. aspect
  1314. raw
  1315. jump
  1316. date
  1317. cream
  1318. appendix
  1319. wriggle
  1320. request
  1321. hook
  1322. shy
  1323. isolation
  1324. innocent
  1325. harm
  1326. nerve
  1327. piano
  1328. project
  1329. display
  1330. remedy
  1331. father
  1332. engagement
  1333. object
  1334. sow
  1335. anxiety
  1336. get
  1337. snow
  1338. corner
  1339. craftsman
  1340. apology
  1341. unfair
  1342. tissue
  1343. pawn
  1344. food
  1345. girlfriend
  1346. tone
  1347. behave
  1348. restless
  1349. tumble
  1350. consider
  1351. modest
  1352. specimen
  1353. copper
  1354. snap
  1355. concentration
  1356. premature
  1357. sticky
  1358. echo
  1359. appetite
  1360. sustain
  1361. imagine
  1362. bread
  1363. snail
  1364. eternal
  1365. thirsty
  1366. silk
  1367. courtship
  1368. soup
  1369. speaker
  1370. earthwax
  1371. tape
  1372. tear
  1373. main
  1374. exception
  1375. sharp
  1376. addicted
  1377. vain
  1378. satellite
  1379. cinema
  1380. straight
  1381. confession
  1382. parking
  1383. cultivate
  1384. crosswalk
  1385. bottom
  1386. linger
  1387. edge
  1388. investment
  1389. ghost
  1390. deep
  1391. pat
  1392. habitat
  1393. money
  1394. recycle
  1395. crown
  1396. norm
  1397. high
  1398. impulse
  1399. conventional
  1400. afford
  1401. reduce
  1402. participate
  1403. breakdown
  1404. laboratory
  1405. multiply
  1406. glasses
  1407. single
  1408. jam
  1409. helpless
  1410. liberal
  1411. cart
  1412. AIDS
  1413. negotiation
  1414. race
  1415. guerrilla
  1416. absent
  1417. pest
  1418. advance
  1419. genuine
  1420. eavesdrop
  1421. remark
  1422. wreck
  1423. leftovers
  1424. ruin
  1425. wander
  1426. average
  1427. economic
  1428. polish
  1429. treasurer
  1430. door
  1431. authority
  1432. feature
  1433. share
  1434. imposter
  1435. terminal
  1436. construct
  1437. irony
  1438. want
  1439. compartment
  1440. pillow
  1441. green
  1442. wrap
  1443. chain
  1444. resist
  1445. fool around
  1446. soldier
  1447. sight
  1448. efflux
  1449. bounce
  1450. reproduce
  1451. concentrate
  1452. constant
  1453. snatch
  1454. analysis
  1455. assertive
  1456. passage
  1457. pure
  1458. verdict
  1459. execute
  1460. pride
  1461. conscience
  1462. rescue
  1463. recommend
  1464. measure
  1465. decrease
  1466. deficiency
  1467. onion
  1468. healthy
  1469. taxi
  1470. scholar
  1471. car
  1472. clean
  1473. earwax
  1474. lawyer
  1475. charter
  1476. distort
  1477. vegetation
  1478. trivial
  1479. dictionary
  1480. survival
  1481. fur
  1482. computer
  1483. differ
  1484. drum
  1485. comfortable
  1486. squeeze
  1487. corn
  1488. X-ray
  1489. governor
  1490. relax
  1491. swear
  1492. gallery
  1493. promote
  1494. chalk
  1495. government
  1496. lend
  1497. solid
  1498. ice cream
  1499. emergency
  1500. drawing
  1501. talented
  1502. heir
  1503. robot
  1504. rock
  1505. crude
  1506. hut
  1507. figure
  1508. crowd
  1509. bulletin
  1510. eagle
  1511. hiccup
  1512. attic
  1513. locate
  1514. disappointment
  1515. ash
  1516. diplomatic
  1517. scrap
  1518. articulate
  1519. club
  1520. forum
  1521. protection
  1522. helmet
  1523. constituency
  1524. regular
  1525. mood
  1526. password
  1527. yearn
  1528. bottle
  1529. gas
  1530. gene
  1531. cereal
  1532. hear
  1533. dress
  1534. banquet
  1535. pack
  1536. wording
  1537. comprehensive
  1538. affect
  1539. novel
  1540. overview
  1541. default
  1542. prayer
  1543. express
  1544. necklace
  1545. repetition
  1546. tasty
  1547. inflate
  1548. swipe
  1549. coffee
  1550. perceive
  1551. peanut
  1552. clothes
  1553. research
  1554. confidence
  1555. deny
  1556. morale
  1557. license
  1558. vision
  1559. houseplant
  1560. unanimous
  1561. tiptoe
  1562. directory
  1563. cousin
  1564. hand
  1565. merit
  1566. game
  1567. girl
  1568. belly
  1569. heaven
  1570. satisfied
  1571. overwhelm
  1572. pie
  1573. deprive
  1574. tolerate
  1575. disagreement
  1576. payment
  1577. last
  1578. petty
  1579. coach
  1580. lover
  1581. horse
  1582. wolf
  1583. paper
  1584. package
  1585. ordinary
  1586. beard
  1587. jurisdiction
  1588. exemption
  1589. practical
  1590. garage
  1591. revival
  1592. plain
  1593. virus
  1594. noise
  1595. volcano
  1596. nightmare
  1597. judicial
  1598. neck
  1599. supply
  1600. bald
  1601. physical
  1602. magnetic
  1603. enemy
  1604. linen
  1605. baseball
  1606. rabbit
  1607. shed
  1608. boom
  1609. econobox
  1610. digital
  1611. slave
  1612. snake
  1613. inflation
  1614. amber
  1615. simplicity
  1616. tidy
  1617. sanctuary
  1618. salvation
  1619. rob
  1620. jelly
  1621. expose
  1622. school
  1623. whole
  1624. debate
  1625. junior
  1626. researcher
  1627. skip
  1628. forward
  1629. hay
  1630. shake
  1631. electronics
  1632. spend
  1633. undermine
  1634. memory
  1635. college
  1636. river
  1637. hell
  1638. distortion
  1639. timber
  1640. excavation
  1641. fun
  1642. apathy
  1643. helicopter
  1644. frank
  1645. century
  1646. offset
  1647. anger
  1648. clash
  1649. brick
  1650. decade
  1651. reckless
  1652. transition
  1653. racism
  1654. feminine
  1655. pit
  1656. vertical
  1657. crop
  1658. application
  1659. soar
  1660. wine
  1661. consumption
  1662. horror
  1663. liability
  1664. salesperson
  1665. horseshoe
  1666. image
  1667. tense
  1668. impound
  1669. deteriorate
  1670. influence
  1671. difficult
  1672. distant
  1673. turn
  1674. damn
  1675. brave
  1676. accept
  1677. eaux
  1678. calorie
  1679. equal
  1680. power
  1681. arm
  1682. guitar
  1683. relate
  1684. soil
  1685. waterfall
  1686. origin
  1687. citizen
  1688. basis
  1689. feed
  1690. penny
  1691. possibility
  1692. provision
  1693. strikebreaker
  1694. retire
  1695. harvest
  1696. carbon
  1697. sink
  1698. keep
  1699. team
  1700. haunt
  1701. dollar
  1702. coerce
  1703. hemisphere
  1704. veteran
  1705. plant
  1706. surprise
  1707. loan
  1708. ancestor
  1709. passive
  1710. mole
  1711. view
  1712. friendly
  1713. selection
  1714. suffering
  1715. shot
  1716. enlarge
  1717. harsh
  1718. desire
  1719. revenge
  1720. cancel
  1721. foreigner
  1722. fashion
  1723. agreement
  1724. cash
  1725. handy
  1726. possible
  1727. detail
  1728. TRUE
  1729. cotton
  1730. truck
  1731. white
  1732. magazine
  1733. punch
  1734. inhibition
  1735. coincide
  1736. ex
  1737. acquit
  1738. global
  1739. chew
  1740. foot
  1741. cope
  1742. witness
  1743. abundant
  1744. miner
  1745. convert
  1746. decide
  1747. diet
  1748. disagree
  1749. reject
  1750. occasion
  1751. unit
  1752. field
  1753. effect
  1754. pity
  1755. division
  1756. dine
  1757. demand
  1758. attractive
  1759. party
  1760. advocate
  1761. creation
  1762. alive
  1763. stamp
  1764. frequency
  1765. tender
  1766. predict
  1767. curtain
  1768. studio
  1769. shower
  1770. seem
  1771. statement
  1772. cherry
  1773. galaxy
  1774. stadium
  1775. notion
  1776. fax
  1777. mutual
  1778. impress
  1779. satisfaction
  1780. smell
  1781. fling
  1782. restaurant
  1783. budget
  1784. trap
  1785. danger
  1786. hurl
  1787. spy
  1788. dependence
  1789. national
  1790. museum
  1791. plaintiff
  1792. even
  1793. hole
  1794. harmony
  1795. matter
  1796. agony
  1797. major
  1798. mold
  1799. file
  1800. accident
  1801. artist
  1802. attack
  1803. penalty
  1804. guest
  1805. subway
  1806. mature
  1807. spray
  1808. symptom
  1809. certain
  1810. slip
  1811. established
  1812. revolutionary
  1813. reaction
  1814. innovation
  1815. cell
  1816. patrol
  1817. negligence
  1818. runner
  1819. undertake
  1820. coin
  1821. meat
  1822. plaster
  1823. strong
  1824. list
  1825. extract
  1826. speech
  1827. disorder
  1828. shoulder
  1829. herb
  1830. border
  1831. question
  1832. finger
  1833. estimate
  1834. dismiss
  1835. obscure
  1836. sit
  1837. fluctuation
  1838. siege
  1839. challenge
  1840. win
  1841. harmful
  1842. indication
  1843. formal
  1844. poetry
  1845. rehabilitation
  1846. topple
  1847. captain
  1848. preference
  1849. carpet
  1850. evolution
  1851. chicken
  1852. ideal
  1853. ethics
  1854. business
  1855. veil
  1856. mosaic
  1857. disgrace
  1858. smash
  1859. hospitality
  1860. breakfast
  1861. hypothesis
  1862. cause
  1863. seat
  1864. scale
  1865. copyright
  1866. worth
  1867. instal
  1868. doll
  1869. intermediate
  1870. fisherman
  1871. clinic
  1872. determine
  1873. fear
  1874. employee
  1875. save
  1876. priority
  1877. fair
  1878. country
  1879. instrument
  1880. resident
  1881. hotdog
  1882. freeze
  1883. rack
  1884. mud
  1885. pierce
  1886. pool
  1887. welcome
  1888. duke
  1889. threaten
  1890. material
  1891. purpose
  1892. flow
  1893. insight
  1894. indoor
  1895. lick
  1896. pleasant
  1897. nut
  1898. translate
  1899. stop
  1900. knee
  1901. executrix
  1902. throw
  1903. mistreat
  1904. organize
  1905. length
  1906. attitude
  1907. benefit
  1908. gravity
  1909. chorus
  1910. dividend
  1911. virgin
  1912. seize
  1913. lump
  1914. domination
  1915. concept
  1916. prospect
  1917. article
  1918. accent
  1919. test
  1920. state
  1921. bold
  1922. accountant
  1923. sector
  1924. falsify
  1925. care
  1926. elaborate
  1927. command
  1928. body
  1929. charity
  1930. fire
  1931. circulate
  1932. dead
  1933. rush
  1934. burial
  1935. mushroom
  1936. island
  1937. queue
  1938. proud
  1939. stumble
  1940. vigorous
  1941. legislature
  1942. symbol
  1943. tragedy
  1944. elite
  1945. term
  1946. brake
  1947. incident
  1948. safari
  1949. convulsion
  1950. sweep
  1951. meeting
  1952. cheque
  1953. mask
  1954. lamb
  1955. degree
  1956. budge
  1957. miss
  1958. willpower
  1959. bridge
  1960. countryside
  1961. strain
  1962. insert
  1963. motivation
  1964. reactor
  1965. credit card
  1966. reward
  1967. smile
  1968. worry
  1969. salmon
  1970. extension
  1971. slow
  1972. thaw
  1973. science
  1974. experience
  1975. favor
  1976. stock
  1977. glory
  1978. menu
  1979. thick
  1980. pray
  1981. deserve
  1982. hilarious
  1983. first-hand
  1984. murder
  1985. superintendent
  1986. improve
  1987. distinct
  1988. sleep
  1989. chapter
  1990. limited
  1991. week
  1992. character
  1993. communication
  1994. franchise
  1995. cooperation
  1996. issue
  1997. problem
  1998. highway
  1999. posture
  2000. mine
submitted by Trap_17 to OneWordBan [link] [comments]

Margin Trading 101: How It Works - YouTube Margin Trading A quick guide to margin trading on Poloniex Trading 101: What is a Margin Account? - YouTube What is Margin Trading?  Fidelity - YouTube

Margin trading definition - What does Margin trading mean? The use of borrowed money to buy securities with the expectation of magnifying profits. Margin trading can lead greater returns, but is also very risky. Margin trading is the practice of buying investments on margin. This is accomplished through borrowing money from your broker in order to buy stocks. Another way of understanding margin trading is taking out a loan from your broker to buy greater amounts of stock shares. Margin trading generally requires a margin account. Define margin trading. margin trading synonyms, margin trading pronunciation, margin trading translation, English dictionary definition of margin trading. Investing in securities using borrowed money, often using the securities themselves as security against the debt. DEFINITION of Margin Margin is collateral that the holder of a financial instrument has to deposit with a counterparty to cover some or all of the credit risk the holder poses for the counterparty. WHAT IT IS IN ESSENCE Margin trading describes a way of investing where you use margin. Margin increases your investment power. Margin is a borrowed asset (stocks or cryptocurrencies), usually from the financial services company you are investing with, that allows you to make larger investments in the hopes of making even more money.

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Margin Trading 101: How It Works - YouTube

--~-- What is Margin is a question many retail investors ask along with what is a margin account and what is margin trading. Today I am going to tell you wha... Loading... Rating is available when the video has been rented. This feature is not available right now. Please try again later. Published on May 22, 2019. There’s no quicker way for a trader to ... What is margin trading? What is a margin? What is the difference between a cash account and a margin account? In episode #34 of Real World Finance we dive de... An investor who wants to take a position in a stock but doesn't have enough funds can use borrowed funds to purchase the asset. This is called a leveraged position, and the investor is said to be ... FREE eBook: "How to Day Trade" Download Now: http://webinar.warriortrading.com/signup In this video, presented by Lightspeed Trading I go over the two basi...

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