Another Fortune 500 Company to Conduct Pilot Evaluation of OneSoft--s Machine Learning Platform


Edmonton, Alberta, Feb. 07, 2018 (GLOBE NEWSWIRE) -- OneSoft Solutions Inc. (the --Company-- or --OneSoft--) (TSX-V:OSS, OTC:OSSIF)--is pleased to announce that its wholly owned subsidiary, OneBridge Solutions, Inc. (--OneBridge--), has entered into a Pilot Program agreement with another U.S.-based, Fortune 500 natural gas, oil and petrochemical company (the --Client--). The Client, whose operations include natural gas gathering, treating, processing, transportation and storage, primarily in the United States, will evaluate OneBridge--s Cognitive Integrity ManagementTM (--CIM--) SaaS solution.

Funding of $5.5m announced for machine learning for geothermal work


University of Southern California (Los Angeles, CA): Developing novel data-driven predictive models for integration into real-time fault detection and diagnosis, and integrate those models by using predictive control algorithms to improve the efficiency of energy production operations in a geothermal power plant. The project will develop deep dynamic neural networks for fault prediction and predictive process control workflows to improve the efficiency of geothermal operations. Upflow Limited (Taupo, New Zealand): Making available multiple decades of closely-guarded production data from one of the world's longest operating geothermal fields, and combining it with the archives from the largest geothermal company operating in the U.S. Models developed from this massive data store will enable the creation of a prediction/recommendation engine that will help operators improve plant availability. Colorado School of Mines (Golden, CO): Applying new machine learning techniques to analyze remote-sensing images, with the goal of developing a process to identify the presence of blind geothermal resources based on surface characteristics. Colorado School of Mines will develop a methodology to automatically label data from hyperspectral images of Brady's Hot Springs, Desert Rock, and the Salton Sea.

Multimodal deep learning for short-term stock volatility prediction Machine Learning

Stock market volatility forecasting is a task relevant to assessing market risk. We investigate the interaction between news and prices for the one-day-ahead volatility prediction using state-of-the-art deep learning approaches. The proposed models are trained either end-to-end or using sentence encoders transfered from other tasks. We evaluate a broad range of stock market sectors, namely Consumer Staples, Energy, Utilities, Heathcare, and Financials. Our experimental results show that adding news improves the volatility forecasting as compared to the mainstream models that rely only on price data. In particular, our model outperforms the widely-recognized GARCH(1,1) model for all sectors in terms of coefficient of determination $R^2$, $MSE$ and $MAE$, achieving the best performance when training from both news and price data.

Stocks tick higher in a quiet start; Apple is a bright spot

Los Angeles Times

U.S. stocks were slightly higher Wednesday morning as utility companies climbed. Energy companies were trading lower as the price of oil continued to slip. Stocks are at their lowest levels in two months after large losses in two of the last three days. The Dow Jones industrial average advanced 31 points, or 0.2%, to 18,097 as of 10:05 a.m. The Standard & Poor's 500 index rose 5 points, or 0.2%, to 2,132.