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Deep Learning Identifies Wet Road Hazards From Sound Input (thestack.com)
An anonymous reader writes: Researches have used recurrent neural network architecture to develop an audio-interpretation system that can understand how wet a road is, using techniques more commonly employed in speech recognition and music analysis. Every year 384,032 persons are injured and 4,789 persons killed through wet roads, and it's a problem that also threatens to hamper the usefulness of self-driving cars, which are likely to either become dangerous or prohibitively cautious in the absence of good information about the safety of road surfaces.
the new Thatcher government in Britain timberland Homme and in part a desire to increase the attractiveness of London as an International financial centre, by replicating its fully liberal status. The US strongly supported the early growth of the Euro-dollar market. This was important due to the dominant presence of US banks and corporations in the market. Although, it had power, the US government did not prevent these banks and firms from operating in the market. This approach had two roots: First, the US banks and US multinational corporations demanded the freedom to operate offshore to compensate for the limitations on their freedom One problem encountered throughout my research was that the initial emergence of the market was not very clear, due to the fact that statistics relating towards the total size of the Euro-dollar market were not collected by the BIS until However, it would be safe to acknowledge that the size of the market grew significantly from the late 1950s to the early 1960s. Nevertheless, the following offers an account In 1964, the US passed the Interest Equalisation Tax to discourage foreign borrowers from raising money in the US market. The Foreign Credit Restraint Program of 1965, limited American bank loans to foreign borrowers.