The major concern for any enterprise is improved performance. For this purpose, business intelligence gathering systems were being used by companies in the 21st century. The identification of the gaps will be done by post-mortem data analysis with the help of order fulfilment cycles. In many organizations, technologies such as artificial intelligence (AI) and machine learning can revolutionize the main aspects of operations, while facilitating redefining new and existing consumer experience. The advancement becomes the urgent need due to hike in the production, potential loss of revenues, lack of customer service and transportation cost.

Our objective is to estimate the unknown compositional input from its output response through an unknown system after estimating the inverse of the original system with a training set. The proposed methods using artificial neural networks (ANNs) can compete with the optimal bounds for linear systems, where convex optimization theory applies, and demonstrate promising results for nonlinear system inversions. We performed extensive experiments by designing numerous different types of nonlinear systems. Compositional data is used in many fields because the data in population ratios or fractions is easy to interpret. However, when the compositional data cannot be produced by simple scaling or normalization with the whole population size from the raw data or measurements, the process to produce such compositional outputs may not be straightforward. Here, we consider noisy outputs as our observations from an unknown linear or nonlinear system with the corresponding compositional variable inputs of interest. The pairs of input and outputs will be used as a training set for artificial neural networks (ANN) modeling to estimate the inverse of the unknown system. This trained inverse system can predict the unknown compositional input, given the output measurement coming from the original system with the input. As our approach is based on ANNs, we do not directly estimate the forward observation model, as in the classical inversion theory, but the inverse of the original system. The measurements, the outputs from the original system with the compositional inputs, are then the input of our estimated inverse system, which will predict the original compositional inputs. Se Un Park is with Schlumberger, Houston, TX 77077, USA. Rather, we directly apply non-negativity and scaling layers in the proposed ANNs. We considered both linear observation models and several types of nonlinear models. For the linear cases, where we can theoretically analyze the optimal performance bounds, we demostrated with our experiments that the performance of ANNs for the inversion of the linear model outputs can compete with the optimal bounds. For the nonlinear systems, where convex optimization methods are not well suited for these general cases, we could still present promising results compared to the error levels in the linear models and leave the comparitive analysis with other feasible optimization methods for our future work. O BSERVATIONM ODELS We first define a compositional vector and then present a general observation model. Then, we will formulate more specific observation models. An example of a compositional data or vector includes population ratos, concentration of chemicals in the air, numerous survey statistics in percentage. We define the compositional vector m to be constrained such that its components are nonnegative and sum to unity.

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment.

The American Physical Society (APS) has recognized a summer intern at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) for producing an outstanding research poster at the world-wide APS Division of Plasma Physics (DPP) gathering last October. The student, Marco Miller, a senior at Columbia University majoring in applied physics, used machine learning to accelerate a leading PPPL computer code known as XGC as a participant in the DOE's Summer Undergraduate Laboratory Internship (SULI) program in 2019. The modifications, which will enable the XGC code to calculate more quickly, could help expand the physics included in detailed simulations of the plasma that fuels fusion reactions. The poster, prepared under the mentorship of PPPL physicist Michael Churchill, showed how Miller used machine learning techniques in his research and was presented at the APS-DPP conference in Fort Lauderdale, Florida. "It felt great to get the award," Miller said.

Algae, that green scum often seen on the surface of ponds, and credited with harmful ocean algal blooms that kill ocean life might just hold an important key to addressing climate change. Algae, much like trees, uses carbon dioxide to conduct photosynthesis, sequestering CO2 as it grows. Hypergiant, an AI products and solutions company, is harnessing this unique power of algae in its latest technology, the EOS bio-reactor which uses AI to optimize algae growth and carbon sequestration. Its bio-reactor is built to hook up to HVAC systems found in large industrial buildings, skyscrapers and apartment buildings which are some of the biggest contributors to global warming from the CO2 emitted through their energy usage and air conditioning systems. The science is clear that we must not only cut our carbon emissions as a means to stop the irreversible harm of climate change and limit global warming but that we also need to take carbon out of the atmosphere to stay within the stated target 1.5 C of the Paris Climate Agreement.

Artificial intelligence routinely produces startling achievements, but those advances require staggering amounts of computing power and electricity. Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months. The effort may have consumed about 2.8 gigawatt-hours of electricity, estimates Evan Sparks, CEO of Determined AI, a startup that provides software to help companies manage AI projects. A spokesperson for OpenAI questioned the calculation, noting that it makes several assumptions. But OpenAI declined to disclose further details of the project or offer an estimate of the electricity it consumed.

In this paper, a MIMO simulated annealing SA based Q learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional P controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfaces, system modeling and controller designing can be extremely challenging. The mathematical modeling for the robot is presented in this paper, and a simulator is designed based on this model. The basic Q learning methods are based pure exploitation and the epsilon-greedy methods, which help exploration, can harm the controller performance after learning completion by exploring nonoptimal actions. The simulated annealing based Q learning method tackles this drawback by decreasing the exploration rate when the learning increases. The simulation and experimental results are provided to evaluate the effectiveness of the proposed controller.

--This paper presents a novel deep learning architecture for short term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. T ogether with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks. Due to increasing utilization of renewables controlling the demand flexibility is becoming crucial part of the stabilization of smart grids. In this setting individual buildings are becoming key resources since buildings consume 32% of global final energy use [1].

GreenWaves Technologies, a fabless semiconductor vendor focused on ultra-low power edge-based AI processing, recently announced a new member of its GAP IoT application processor family, the GAP9. This latest member combines architectural enhancements using Global Foundries 22-nm FDX process to deliver a peak cluster memory bandwidth of 41.6 Gbytes/s and up to 50 GOPS combined compute power at an overall power consumption of 50 mW. GAP9 lets OEMs embed machine learning and signal processing capabilities into battery-powered or energy-harvesting devices.

Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months. The effort may have consumed about 2.8 gigawatt-hours of electricity, estimates Evan Sparks, CEO of Determined AI, a startup that provides software to help companies manage AI projects. A spokesperson for OpenAI questioned the calculation, noting that it makes several assumptions. But OpenAI declined to disclose further details of the project or offer an estimate of the electricity it consumed.