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The properties of word embeddings are certainly interesting, but can we do anything useful with them? Besides predicting silly things, like whether a 5-gram is ‘valid’?

W and F learn to perform task A. Later, G can learn to perform B based on W .

We learned the word embedding in order to do well on a simple task, but based on the nice properties we’ve observed in word embeddings, you may suspect that they could be generally useful in NLP tasks. In fact, word representations like these are extremely important:

The use of word representations… has become a key “secret sauce” for the success of many NLP systems in recent years, across tasks including named entity recognition, part-of-speech tagging, parsing, and semantic role labeling. ( Luong et al. (2013) )

This general tactic – learning a good representation on a task A and then using it on a task B – is one of the major tricks in the Deep Learning toolbox. It goes by different names depending on the details: pretraining, transfer learning, and multi-task learning. One of the great strengths of this approach is that it allows the representation to learn from more than one kind of data.

There’s a counterpart to this trick. Instead of learning a way to represent one kind of data and using it to perform multiple kinds of tasks, we can learn a way to map multiple kinds of data into a single representation!

One nice example of this is a bilingual word-embedding, produced in Socher et al. (2013a) . We can learn to embed words from two different languages in a single, shared space. In this case, we learn to embed English and Mandarin Chinese words in the same space.

We train two word embeddings, W e n and W z h in a manner similar to how we did above. However, we know that certain English words and Chinese words have similar meanings. So, we optimize for an additional property: words that we know are close translations should be close together.

Of course, we observe that the words we knew had similar meanings end up close together. Since we optimized for that, it’s not surprising. More interesting is that words we didn’t know were translations end up close together.

In light of our previous experiences with word embeddings, this may not seem too surprising. Word embeddings pull similar words together, so if an English and Chinese word we know to mean similar things are near each other, their synonyms will also end up near each other. We also know that things like gender differences tend to end up being represented with a constant difference vector. It seems like forcing enough points to line up should force these difference vectors to be the same in both the English and Chinese embeddings. A result of this would be that if we know that two male versions of words translate to each other, we should also get the female words to translate to each other.

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Reading: Abnormal Tether Price Moves on Kraken Leave Analysts Puzzled

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| Jun 30, 2018 | 15:00

Austin Kennedy | Jun 30, 2018 | 15:00

Massive trade orders on Kraken fail to sway the price of Tether any further than modest ones, leaving researchers and analysts puzzled.

“USD-backed” Tether (USDT) is making headlines yet again for the dubious activity surrounding the token. A report from Bloomberg today details evidence that Tether markets on the US-based Kraken exchange are possibly being manipulated by the practice of wash trading —in which market-makers fill their own orders to control the price of an asset.

The study cited data between May 1 and June 22 of 56,000 different USDT trades on Kraken and found repetitive orders of unlikely similarity. Curiously specific trades, such as one for 13,076.389 Tethers, were among the most frequently ordered amounts on Kraken during the evaluated time period, leading researchers to conclude that the orders were produced by “automated trading programs.”

While there is nothing illegal or even abnormal about the use of trading bots, Tether’s resistance to price fluctuations under massive buying pressure begs the question as to whether the bots are being employed for purposes of price manipulation.

Tether’s abnormal price action is illustrated in the graphics below, which compare the price movements of Bitcoin and Tether during a specific timeframe.

R has three basic indexing operators, with syntax displayed by the following examples

For vectors and matrices the [[ forms are rarely used, although they have some slight semantic differences from the [ form (e.g. it drops any names or dimnames attribute, and that partial matching is used for character indices). When indexing multi-dimensional structures with a single index, x[[i]] or x[i] will return the i th sequential element of x .

For lists, one generally uses [[ to select any single element, whereas [ returns a list of the selected elements.

The [[ form allows only a single element to be selected using integer or character indices, whereas [ allows indexing by vectors. Note though that for a list or other recursive object, the index can be a vector and each element of the vector is applied in turn to the list, the selected component, the selected component of that component, and so on. The result is still a single element.

The form using $ applies to recursive objects such as lists and pairlists. It allows only a literal character string or a symbol as the index. That is, the index is not computable: for cases where you need to evaluate an expression to find the index, use x[[expr]] . Applying $ to a non-recursive object is an error.

Next: Indexing matrices and arrays , Previous: Indexing , Up: Indexing [ Contents ][ Index ]

R allows some powerful constructions using vectors as indices. We shall discuss indexing of simple vectors first. For simplicity, assume that the expression is x[i] . Then the following possibilities exist according to the type of i .

Integer

If i is positive and exceeds length(x) then the corresponding selection is NA . Negative out of bounds values for i are silently disregarded since R version 2.6.0, S compatibly, as they mean to drop non-existing elements and that is an empty operation (“no-op”).

A special case is the zero index, which has null effects: x[0] is an empty vector and otherwise including zeros among positive or negative indices has the same effect as if they were omitted.

Other numeric Logical Character Factor

Indexing with a missing (i.e. NA ) value gives an NA result. This rule applies also to the case of logical indexing, i.e. the elements of x that have an NA selector in i get included in the result, but their value will be NA .

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