EcoFactor is one of several startups with a cloud computing platform to manage and analyze data from smart thermostats and other home energy devices. But it also specializes in using that data to monitor and predict performance problems and impending failures of the air conditioners keeping houses cool. That kind of technology could have a lot of value to the companies that make heating, air conditioning and ventilation equipment -- enough to make it worth owning. On Tuesday, HVAC giant Trane announced it has acquired EcoFactor's energy analytics software for an undisclosed sum. Trane, a brand of Ingersoll Rand, plans to integrate EcoFactor's "unique artificial intelligence (AI) capabilities for energy efficiency and HVAC fault detection" into its existing Nexia home automation line.

KOCHI: Don't be surprized if you receive personalized offers based on your vehicle profile, the next time you pull into an Indian Oil petrol pump. Neuroplex, a startup that emerged from Future Technologies Lab an initiative by Kerala Startup Mission has signed an agreement with Indian Oil Corporation (IOC) to use artificial intelligence (AI) to qualitatively improve customer interaction and safety in their petrol pumps across the country. Their signature product'Eyes Age' uses deep learning to empower video surveillance with AI capabilities. It creates visual relationships between objects in the video which allows for a logical analysis of video data. "The system can identify customers by analysing the number plate of the vehicle and give them unique offers," said Keerthi, a developer with Neuroplex.

According to Thomas Edison, g, for example a fragrance composed of 700 g of "ingredient "Genius is one percent inspiration and 99 percent A" and 300 g of "ingredient B". A fragrance can have desired perspiration" is an example. In many situations, properties related to aromatics (e.g., the type of smell), researchers and inventors already have a variety popularity (e.g., frequent patterns of ingredient combinations, of data and manage to create something new or combinations that should be avoided), and appropriateness by using it, but the key problem is how to select for certain use cases (e.g., combinations for perfumes, shampoos, and combine knowledge. In this paper, we propose or hand soaps). Perfumers who create new fragrances a new Markov chain Monte Carlo (MCMC) algorithm seek to develop various fragrances with desired properties. It to generate composition ratios, nonnegativeinteger-valued is also possible that perfumers are willing to accept certain vectors with two properties: (i) the fragrances lacking some desired properties, because they can sum of the elements of each vector is constant, and still draw inspiration from such fragrances. Thus, it is interesting (ii) only a small number of elements is nonzero.

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.

During the space race of the 50s, 60s, and 70s, there was a reason so many monkeys and chimpanzees were shot up into space. Getting into space is relatively easy. One particular difficulty involves atmospheric re-entry. If a spacecraft's speed and angle is too steep, the deceleration forces will generate too much heat. If the angle is too shallow, other unpleasant things can happen.

During the space race of the 50s, 60s, and 70s, there was a reason so many monkeys and chimpanzees were shot up into space. Getting into space is relatively easy. One particular difficulty involves atmospheric re-entry. If a spacecraft's speed and angle is too steep, the deceleration forces will generate too much heat. Contrary to popular opinion that the spaceship will "bounce off the atmosphere like a flat stone skipping off the water surface of a pond," what typically happens is the craft doesn't lose enough velocity in the dense atmosphere, may miss its mark, continue on its orbit, or be exposed to heat flux for much longer periods of time.

In a well-worn cliché, data is often referred to as "the new oil". The analogy is limited, but it does have some truth to it as data -- like oil -- is the defining resource for a new industrial age. Likewise, data seems set to be dominated by a small number of massive global players. For organisations hoping to become pioneers in artificial intelligence (AI) and data analytics, scale confers significant competitive advantages. Bigger companies will be better placed to build the bigger data sets that enable more sophisticated analysis to be performed more quickly.

The model, Global Automated Target Recognition (GATR), runs in the cloud, using Maxar Technologies' Geospatial Big Data platform (GBDX) to access Maxar's 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms. Fast GPUs enable GATR to scan a large area very quickly, while deep learning methods automate object recognition and reduce the need for extensive algorithm training. The tool teaches itself what the identifying characteristics of an object area or target, for example, learning how to distinguish between a cargo plane and a military transport jet. The system then scales quickly to scan large areas, such as entire countries. GATR uses common deep learning techniques found in the commercial sector and can identify airplanes, ships,, buildings, seaports, etc. "There's more commercial satellite data than ever available today, and up until now, identifying objects has been a largely manual process," says Maria Demaree, vice president and general manager of Lockheed Martin Space Mission Solutions.

Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the surrogate is minimum (exploitation of the surrogate) and looking for regions of the feasible space that have not yet been visited and that may potentially contain better values of the objective function (exploration of the feasible space). This paper proposes a new global optimization algorithm that uses a combination of inverse distance weighting (IDW) and radial basis functions (RBF) to construct the acquisition function. Rather arbitrary constraints that are simple to evaluate can be easily taken into account by the approach. Compared to Bayesian optimization, the proposed algorithm is computationally lighter and, as we show in a set of benchmark global optimization and hyperparameter tuning problems, it has a very similar (and sometimes superior) performance. MATLAB and Python implementations of the proposed approach are available at http://cse.lab.imtlucca.it/~bemporad/idwgopt