What are some reasons that make the UK’s trade in services a surplus?

A quick look at the historical background of the UK indicates that capital accumulation started during the wars of conquest and colonization with maritime trade having a significant impact on cargo insurance and financial services. Soon enough, London had become the financial center of the world and has held the tradition into modern days but now with serious competition looming in the near future as a result of Brexit.

According to the Office for National Statistics’ (ONS) Pink Book, details the UK’s balance of payments, showing which countries the UK traded within 2016 and it reports a deficit in goods but a surplus in services

The importance of UK financial services trade with the EU

London is regarded as the world’s leading financial center, but the uncertainty caused by the UK’s recent decision to leave the EU could have major implications on whether it can maintain its competitive position in financial services. Major multinationals, such as Prudential Insurance, have begun to deliberate about moving operations to other centers in Europe in order to maintain access to the EU’s Single Market. A Research team here in London has been looking at the UK’s trade with EU markets to assess how Brexit will disrupt financial services.

In terms of export volume, the UK exports more goods than it does services. Germany is the UK’s largest trade partner for goods. Other major goods exporters to the UK are the Netherlands and France. ONS states that approximately 44% of the UK’s total exports both in goods and services went to the EU in 2014. However, the UK operates a significant trade deficit with the EU in terms of goods value meaning that the UK imports more goods from the EU than it sells. By contrast, the UK has a surplus in its services trade with the EU. Total services exports to the EU countries where Germany, France and the Netherlands as the primary markets for UK services exports.

Financial services (FS) and insurance make up the largest proportion of the UK’s services trade, accounting for 26% of services exports to the EU. The UK also has a number of important trading partners globally. The US imports insurance services from the UK, nearly the same as the total amount of financial services exports to the EU. Other major recipients of UK FS and insurance exports include Japan and Australia.

Taiwan and Indian markets also imported large sums of FS in 2014. Even though, there was a general decline in FS and insurance exports from the UK in 2014, economies such as South Africa, Singapore, Hong Kong and Saudi Arabia have remained major trading partners of financial services.

Who knows about imports/exports to Qatar?

Alfonso Llanes, Master Degree in International Development

Answered just now

One of the best sources of information is The World Bank in its WITS site:

https://wits.worldbank.org/Count...

Qatar trade statistics

Exports, Imports, Products, Tariffs, GDP and related Development Indicator.

Country ISO3 : QAT Country Code : 634 Income Group : High income Lending Category : Region : Middle East & North Africa Currency Unit : Qatari riyal WTO Member : Yes

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Why do we use the derivatives of activation functions in a neural network?

Derivatives represent a slope on a curve, they can be used to find maxima and minima of functions, when the slope, is zero. Also, the derivative measures the steepness of the graph of a function at some particular point on the graph. In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be represented by the binary set (1) "ON" or (0) "OFF", depending on the input.

When constructing Artificial Neural Network (ANN) models, one of the key considerations is selecting an activation functions for hidden and output layers that are differentiable. This is because calculating the backpropagation error is used to determine ANN parameter updates that require the gradient of the activation function for updating the layer. The most commonly-used activation functions used in ANNs are the identity function, the logistic sigmoid function, and the hyperbolic tangent function

This activation function simply maps the pre-activation to it and can output values that range from positive infinity to negative infinity. But why use an identity activation function? It turns out that the identity activation function is very useful. For instance, some of the traditional methods for forecasting include linear and nonlinear regression, ARMA and ARIMA time series forecasting, logistic regression, principal component analysis, discriminant analysis, and cluster analysis. These methods require statistical analyst to filter through tens or even hundreds of variables to determine which ones might be appropriate to use in one of these classical statistical techniques.

Theoretically any differential function can be used as an activation function, however, the identity and sigmoid functions are the two most commonly applied. The identity activation function, also referred to as linear activation, is a flow through mapping of least squares linear regression in algebra as h(xl) = xl.

A multi-layer network that has a nonlinear activation functions amongst the hidden units and an output layer that uses the identity activation function implements a powerful form of nonlinear regression. Specifically, the network can predict continuous target values using a linear combination of signals that arise from one or more layers of nonlinear transformations of the input.

The Logistic Sigmoid Activation Function Non-linear Activation Function

The Nonlinear Activation Functions are the most used activation functions. Non linearity helps to make the graph a binary classification problems (i.e. outputs values that range (0, 1), thus, the logistic sigmoid.

Therefore, it is especially useful for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice for it is a differentiable function. That means, we can find the slope of the sigmoid curve at any two points by use of the derivative.

The logistic sigmoid is inspired somewhat on biological neurons and can be interpreted as the probability of an artificial neuron “firing” given its inputs. Moreover, the logistic sigmoid can also be derived as the maximum likelihood solution for logistic regression in statistics. Calculating the derivative of the logistic sigmoid function makes use of the quotient rule and a clever trick that both adds and subtracts a one from the numerator:

Deriving the Sigmoid Derivative for Neural Networks

Training a neural network refers to finding values for every cell in the weight matrices such that the squared differences between the observed and predicted data are minimized. In practice, the individual weights comprising the two weight matrices are adjusted by iteration and their initial values are often set randomly.

The question then becomes how should the weights be adjusted--i.e., in which direction +/- and by what value? And, that's where the derivative comes in. A large value for the derivative will result in a large adjustment to the corresponding weight. This makes sense because if the derivative is large that means one is far from a minimum. Weights are adjusted in the direction of steepest descent surface defined by the total error observed versus predicted by squaring the error.

After the error on each pattern is computed by subtracting the actual value of the output vector from the value predicted by the NN during that iteration, each weight in the weight matrices is adjusted in proportion to the calculated error gradient. Because the error calculation begins at the end of the NN and proceeds to the front, it is called back-propagation.

At this point, the process is complete. The simple technique that has actually been used is to derive the quotient and product rules in calculus: adding and subtracting the same thing, which changes nothing, to create a more useful representation.

Can the Trans-Pacific Partnership be renegotiated after Japan has already ratified it?

The Trans-Pacific Partnership (TPP) has now changed to the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) after the US withdrew. As it currently stands, the trade agreement is between Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore and Vietnam. The agreement, originally signed on 4 February 2016, is being renegotiated after the United States withdrew

According to recent reports “The TPP began as an expansion of the Trans-Pacific Strategic Economic Partnership Agreement (TPSEP or P4) signed by Brunei Darussalam, Chile, New Zealand, and Singapore in 2005. Beginning in 2008, additional countries joined the discussion for a broader agreement: Australia, Canada, Japan, Malaysia, Mexico, Peru, the United States, and Vietnam, bringing the total number of countries participating in the negotiations to twelve. In 2017, the United States withdrew from the agreement. The other 11 TPP countries agreed in May 2017 to revive the deal without US participation.”

The original TPP contained measures to lower both non-tariff and tariff barriers to trade, and establish an investor-state dispute settlement (ISDS) mechanism. Separately, an analysis using an unconventional model by two Tufts University economists found the agreement would adversely affect the signatories. Also, many observers of the negotiations have argued that the trade deal would have functioned in a geopolitical context by reducing the signatory member’s dependence on Chinese trade and bring the signatories closer to the United States.

Twelve countries participated in negotiations for the TPP: the four parties to the 2005 Trans-Pacific Strategic Economic Partnership Agreement and eight additional countries. As it stands, the agreement will enter into force after ratification by all signatories, but if the agreement is not ratified by all before 4 February 2018, it will enter into force after ratification by at least 6 states which together have a GDP of more than 85% of the GDP of all signatories.

The largest economy in the Pacific Rim not yet a member of TPP is China. According to the Brookings Institution in 2013, the most fundamental challenge for the TPP project regarding China was that "it may not constitute a powerful enough enticement to propel China to sign on to these new standards on trade and investment. China so far has reacted by accelerating its own trade initiatives in Asia."

Academic followers of the agreement report that Japan's main competition in the region is China, and the two nations have opposite views on how the South-east Asia's economy should develop. Japan tried to take control by establishing the Asian Monetary Fund (AMF), which the U.S blocked. In 2011 however, Japan managed to establish a cooperative agreement with China and Korea called the "PRC–Japan–Republic of Korea Free trade agreement", also known as the CJK FTA, which did not include the U.S. The consensus is that Japan's intention was that they could use the People's Republic of China issue in order to gain U.S support for backing the TPP, and shift the negotiations toward Japan's agendas.

What is a good cryptocurrency project that's trying to change the world of supply and demand?

Alfonso Llanes, studied at Florida International University

Answered 1h ago

Bitcoin is being used to supplant national currencies as trade across borders takes place without a bank or a factoring organization that guaranties that a transaction is conducted efficiently and with honesty. There are however, many obstacles to surpass before bitcoin or cryptocurrency can replace paper money, money orders, letters of credit and so on for conducting business and international commerce.

A project launched by Rahakott, is a cryptocurrency wallet that allows anyone to send or receive digital currencies and to monitor their balance.

Bitcoin or “digital currencies do not exist in physical form and are not stored in a single place. All that actually exist are transaction records in a block-chain. Therefore, when Bitcoin or other cryptocurrencies are transferred, the ownership rights to a certain amount are simply rewritten to your wallet’s address. However, to take control of that currency, the private key for your wallet has to match the public address to which the currency has been allocated. If they match, a withdrawal happens at one wallet and a deposit at the other. And all of this is just a record in the block-chain.”

The key issue is registration. It is on this step that some users simply cannot make sense of the system and go on to look elsewhere. In a bitcoin system the wallet is well-protected from hacking by two-factor authentication. In addition, users that conduct business using cryptocurrency can set up subaccounts for their clients and generate requests for payment.

The wallets are algorithm driven, meaning that a new address is generated for each transaction, which increases user anonymity. The existence of an API allows for the wallet’s functionality to be integrated into external web services and software.

The next step is to integrate currency exchanges, so that users can choose the best exchange rates when converting to other cryptocurrencies and fiat currencies (P2P exchange).

There are several entrepreneurs, IT specialist, block-chain enthusiast who believe that modern technology will help build new economic relationships in the world nonetheless, the jury is still out trying to resolve many of the legal questions raised by transactions with cryptocurrencies and anonymity which opens the door for money laundering.

Is TTIP a normal trade agreement?

Alfonso Llanes, Master Degree in International Development

Answered just now

The Transatlantic Trade and Investment Partnership (TTIP) is a proposed trade agreement and the subject of an ongoing series of negotiations between the EU and US directed at creating the world’s largest free trade zone in the north Atlantic and in the history of the world’s commerce.

This agreement would be the biggest ever in the history of international trade; it could boost the size of the EU economy by €120bn ($100bn) representing 0.5% of GDP and the US economy by 0.4% of GDP. However, this giant agreement is as usual more about corporations than citizens.

It is foreseen to create several million jobs export-dependent where consumers would enjoy cheaper products and services. “The average European household of four would be around €500 a year better off as a result of wage increases and price reductions, according to the study commissioned from the Centre for Economic Policy Research in 2013.”

The envisioned plan is to reduced tariff barriers to zero and other non-tariff barriers by 25-50%. The sectors that would benefit most include industries based on metal products, processed food and chemicals, and more than not the automotive industry.

The main objective of TTIP is to reduce regulatory barriers to trade, in areas going from food safety law to environmental rules and banking regulations. The argument against it is that it will white-wash important EU regulations. One big issue has been food safety as a major stumbling block in the negotiations as both sides prepared for the latest 15th round held in New York October 3 – October 7, 2016.

There are still significant work to resolve differences in several important areas of the negotiations, such as “how to treat the most sensitive tariff lines on both sides; how to expand market access in key services sectors; how to reconcile differences on sanitary and phytosanitary measures; how to encourage the recognition of qualifications to facilitate licensing of experienced professionals; how to improve access to each other’s government procurement markets; how to address standards and conformity assessment procedures in ways that yield greater openness, transparency, and convergence, reduce redundant and burdensome conformity assessment procedures, and enhance cooperation; how best to achieve our shared objective of providing strong investor protection while preserving the right of governments to regulate, including with respect to dispute resolution mechanisms; how to reflect our shared commitment to including strong and effective disciplines on labor and environmental protection; how to structure commitments on data flows that will reinforce the essential electronic commerce and digital infrastructure of our economic relationship while respecting legitimate concerns about protecting privacy; how best to promote transparent, open, and secure energy markets; and how to reconcile differences in our approaches to trademarks, generic names, and geographical indications. On these and other challenging issues, our work over the past three years brought greater clarity to our differences and enabled us to explore avenues for reconciling them.”

A big concern reported by The Guardian Newspaper is whether standards will drop. For example, the EU bans cosmetics tested on animals but the US does not. Another question is what happens if EU countries want some protection, for instance Italy for its Parma ham, and the UK for its pork pies.

Leading the controversies is the Investor State Dispute Settlement (ISDS) provision. ISDS provisions have been included in many trade deals since the 1980s, to inspire overseas investment in poorer countries. It means private investors can ask a tribunal of international arbitrators to judge if a government has treated them unfairly – and be compensated as a result.

Over the past decade some big, mainly American companies, such as big tobacco i.e. Philip Morris, have used ISDS to claim rights to profits. The provision allows in theory that private investors can sue governments for the loss of future profits due to decisions made by national parliaments which of course raises the question of national sovereignty.

How do I learn computer architecture for deep learning?

Deep learning is a:

“Field of study that gives computers the ability to learn without being explicitly programmed”

Arthur Samuel, 1959

Machine learning, and specifically deep learning, has been and continues to be an extremely disruptive technology in many fields of computer science. The success of deep learning techniques is based on solving extremely difficult selective grouping and regression problems which has resulted in their prompting acceptance by academics for solving complex problems.

The emergence of deep learning is generally accredited to a cycle of mastery and understanding of complexity whereby fundamental developments in training deeper models were assisted by the accessibility to enormous data sets and high-performance computer hardware.

Deep learning algorithms continue to evolve at a speedy pace. Early on, frameworks were subjugated to the availability of matrix multiplication libraries. However, more finely tuned algorithms have been developed over decades as research has continued with the introduction of newer and more sophisticated kinds of algorithms.

The ensuing enlightenment was the need to go beyond generic matrix multiplication and therefore, complex networks came along which resulted in even more innovative algorithms. Many of these algorithms are designed manually using assembly language. Nevertheless, low-level tweaks can lead to amazing performance enhancements. For some operations such as group stability performance can increase many times over when compared to non-optimized solution.

Here are some examples of resources for Academic and non-academic applications:

School of Engineering and Applied Sciences Harvard University

http://caffe.berkeleyvision.org/

Tutorial introduction to deep learning architecture and networks

http://www.rle.mit.edu/eems/wp-c...

MICRO Tutorial (2016) MIT

http://eyeriss.mit.edu/tutorial.... Joel Emer, Vivienne Sze, Yu-Hsin Chen

Google’s Tensor Flow models are coarse-grained dataflow graphs Basic building block is an “operation” https://www.tensorflow.org/insta...