Southern united states The language stocks and shares lower as investors unpack high-tech/monetary dilemmas

SHOTLIST
1. Wide of Korea Exchange with computer screen
2. Close-up of screen reading KOSPI
3. Medium of screen
4. Close-up of screen
5. Close-up, tilt-down of graph
6. Medium of screen
7. Close-up of moving screen
8. SOUNDBITE: (Korean) Lee Rommel, Manager of Retail Research Department of Goodmoring Shinhan Securities:
„There’s a point of view that US economic stagnation could cause greater South Korea’s economic slump from now on, so there’s great possibility that market will fall for the next year and a half.“
9. Medium of KOSPI (written in Korean) screen
10. Medium of employees
11. Wide of office in Korea Exchange
12. Various of screens
STORYLINE:
South Korean stocks opened lower on Friday amid lingering global financial instability sparked by the collapse of US investment bank Lehman Brothers Holdings Incorporated.
The benchmark Korea Composite Stock Price Index (KOSPI) fell 3.32 points, or 0.22 percent, to 1,498.31 in the first 15 minutes of trading, according to Yonhap news agency.
On Thursday, South Korean markets rose on reports of a 700 (b) billion US dollar plan to bail out financial companies, but the deal broke down on Thursday night amid a revolt by the Republicans.
The conservatives are now proposing an alternative plan to rescue shaky financial institutions – a package of tax breaks and a new government-sponsored insurance programme for mortgage-backed securities.
Speaking in Seoul, Lee Rommel, a manager at Goodmoring Shinhan Securities, said the financial slowdown could be here to stay.
„There’s a point of view that US economic stagnation could cause greater South Korea’s economic slump from now on, so there’s great possibility that market will fall for the next year and a half,“ he said.

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I Sold Astounding Sony Stocks and shares Now & Owned…For what reason?

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We Put $10,000 Right now & We’re Relaying Our Store Collection (Discuss. 2 through Surplus Income)

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TensorTraffic that in fact traffic prophecy by using equipment getting to know that Pawel Gora

Description
Together with an international group of scientists, I am developing TensorFlow-based tool, TensorTraffic, for predicting traffic simulation outcomes, such as delays / total waiting times on a red signal, as a function of traffic control settings. We achieved great results, which may find applications in transport planning, real-time traffic management and many other fields.

Abstract
Together with an international group of scientists, I am developing TensorFlow-based tool, TensorTraffic, for predicting traffic simulation outcomes, such as delays / total waiting times on a red signal, as a function of traffic control settings. We achieved great results, which may find applications in transport planning, real-time traffic management and many other fields.

First, I will talk about a goal of the research and problem we would like to solve, which is: approximating traffic simulation outcomes fast and with an acceptable accuracy. The motivation behind this goal is to have a meta-model of traffic, which can allow to effectively evaluate quality of a large number of settings (e.g., traffic control strategies or road infrastructure settings).

Then, I will explain our approach and experiemnts. We built a dataset consisting of almost 120 000 traffic simulation scenarios, using Traffic Simulation Framework software, each case corresponded to a different traffic signal control strategy, simulations computed the total waiting time of all cars on a red signal in a given region of Warsaw (Stara Ochota district, 15 crossroads with traffic signals). Then, we built meta-models based on machine learning, in order to approximate outcomes of traffic simulations faster and with an acceptable accuracy. First, we obtained very good accuracy of predictions using neural networks (average error 1.18%), later we even improved these results using XGBoost (average error lower than 1%). What is important, results of approximations can be obtained a few orders of magnitude faster than by running traffic simulations.

Finally, I will tell about conclusions and possible directions of a future research. I will show how achieved results may be applied in real-time traffic management and transport planning, which may lead to significant reduction of the required time and cost of traffic engineering works, as well as better results in terms of traffic efficiency. Thus, this may lead to developing AI-based tools able to support traffic engineers in their work. An interesting conclusion is that similar approach may be used in many other cases, in which it is required to run a large number of simulations, e.g., weather forecast, medicine, bioinformatics. There may be also important theoretical conclusions, related to automata theory and computational complexity.

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