Answer:
Invasion of Kuwait was largely motivated by its desire to take control over the latter's vast oil reserves.
Explanation:
Answer:Id
Explanation:
Id is part our personality system that is more demanding and want to fulfill our desires at any cost. It has no sense of considering other people's needs or how they will feel when we do certain things , it only considers us and no one else. It can't wait, it requires that we fulfil our desires right there and then. This is why a four year old is impatient to wait for his turn to play with toys but he would rather grab them because the id tells him not to wait for his gratification but take it now.
Just like in any other country in the world. Senegal used to be a French colony which is why there are still a lot of French traces in this African country, especially when it comes to food. People in Senegal have a lot of things they can do - explore the beautiful nature that surrounds this country, the sea, the animals, etc. Also there are some modern buildings so not all is traditional. Additionally, prostitution in Senegal is legal, so this is also one of the ways to have fun in Senegal.
Answer:
Have each member rate all the ideas and use the one most people like best.
Explanation:
Given the situation provided by the exercise, the option that covers the most bases is to have each member rate the ideas that the team has at that moment and pick the one that has the most votes. This way, the frustration can be avoided and Kaleel should not be offended if his idea isn't picked given that each member has the same right to vote for what they believe is the best idea to solve the problem given.
dzmitry bahdanau, kyunghyun cho, and yoshua bengio. 2014. neural machine translation by jointly learning to align and
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.
The models proposed as of late for brain machine interpretation frequently have a place with a group of encoder-decoders and comprises of an encoder that encodes a source sentence into a fixed-length vector from which a decoder creates an interpretation.
In this paper, we guess that the utilization of a fixed-length vector is a bottleneck in working on the exhibition of this essential encoder-decoder engineering, and propose to broaden this by permitting a model to naturally (delicate )look for parts of a source sentence that are pertinent to anticipating an objective word, without shaping these parts as a hard section unequivocally.
With this new methodology, we accomplish an interpretation execution equivalent to the current cutting edge state put together framework with respect to the undertaking of English-to-French interpretation. Moreover, subjective examination uncovers that the (delicate )arrangements found by the model concur well with our instinct.
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