We conduct a post hoc analysis of solar flare predictions made by a Long Short Term Memory (LSTM) model employing data in the form of Space-weather HMI Active Region Patches (SHARP) parameters. These data are distinguished in that the parameters are calculated from data in proximity to the magnetic polarity inversion line where the flares originate. We train the the LSTM model for binary classification to provide a prediction score for the probability of M/X class flares to occur in next hour. We then develop a dimension-reduction technique to reduce the dimensions of SHARP parameter (LSTM inputs) and demonstrate the different patterns of SHARP parameters corresponding to the transition from low to high prediction score. Our work shows that a subset of SHARP parameters contain the key signals that strong solar flare eruptions are imminent. The dynamics of these parameters have a highly uniform trajectory for many events whose LSTM prediction scores for M/X class flares transition from very low to very high. The results suggest that there exist a few threshold values of a subset of SHARP parameters when surpassed could indicate a high probability of strong flare eruption. Our method has distilled the knowledge of solar flare eruption learnt by deep learning model and provides a more interpretable approximation where more physics related insights could be derived.

Delivering presents to every child around the world in a single evening is an exhausting task, with only one man fit for the job - Father Christmas. But his outdated techniques seem more antiquated now than ever before. MailOnline spoke to a forward-thinking industry expert who offered Father Christmas some helpful advice to make his arduous task more efficient. Dr Carl Diver, academic lead at Manchester Metropolitan University in industry 4.0, said a hydrogen-powered sleigh, AI algorithms and elf-assisting robots could help. As well as streamlining production and making the manufacturing and delivery process more efficient, Dr Diver thinks the old methods would benefit from a sprucing up to make things easier, more cost-effective and better for the environment.

Fifteen years after a magnitude 9.1 earthquake and tsunami struck off the coast of Indonesia, killing more than 200,000 people in over a dozen countries, geologists are still working to understand the complex fault systems that run through Earth's crust. While major faults are easy for geologists to spot, these large features are connected to other, smaller faults and fractures in the rock. Identifying these smaller faults is painstaking, requiring weeks to study individual slices from a 3D image. Researchers at the University of Texas at Austin are shaking up the process with deep learning models that identify geologic fault systems from 3D seismic images, saving scientists time and resources. The developers used NVIDIA GPUs and synthetic data to train neural networks that spot small, subtle faults typically missed by human interpreters.

Capitalism's global success has lifted billions of people out of grinding poverty while spurring incredible technological advancements. However, this progress has not been without cost. Abundant energy from fossil fuels, global supply chains and middle-class lifestyles fueled by advancements such as on-demand electronic commerce have created complex and widespread challenges including climate change and a lack of economic inclusion. We've reached a major inflection point and the way forward – toward a more sustainable and inclusive future – requires a significant shift in mindset and behaviour at individual, institutional and industry levels. Venture capital can play an outsized role in addressing the economic, environmental, social and technological challenges we face today by returning to its roots of industrial transformation.

This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs). Approximate multipliers have significantly better performance in terms of speed, power, and area compared to exact multipliers. However, approximate multipliers have an inaccuracy which is defined in terms of the Mean Relative Error (MRE). To assess the applicability of approximate multipliers in enhancing CNN training performance, a simulation for the impact of approximate multipliers error on CNN training is presented. The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy. Additionally, the paper proposes a hybrid training method which mitigates this negative impact on the accuracy. Using the proposed hybrid method, the training can start using approximate multipliers then switches to exact multipliers for the last few epochs. Using this method, the performance benefits of approximate multipliers in terms of speed, power, and area can be attained for a large portion of the training stage. On the other hand, the negative impact on the accuracy is diminished by using the exact multipliers for the last epochs of training.

A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper. The models were trained using an open-source dataset that contains 24 ultrasound sensors readings and the corresponding direction for each sample. This dataset was captured using SCITOS G5 mobile robot by placing the sensors on the robot waist. In addition to the full format with 24 sensors per record, the dataset has two simplified formats with 4 and 2 input sensor readings per record. Several control models were proposed previously for this dataset using all three dataset formats. In this paper, two primary research contributions are presented. First, presenting machine learning models with accuracies higher than all previously proposed models for this dataset using all three formats. A perfect solution for the 4 and 2 inputs sensors formats is presented using Decision Tree Classifier by achieving a mean accuracy of 100%. On the other hand, a mean accuracy of 99.82% was achieves using the 24 sensor inputs by employing the Gradient Boost Classifier. Second, presenting a comparative study on the performance of different machine learning and deep learning algorithms on this dataset. Therefore, providing an overall insight on the performance of these algorithms for similar sensor fusion problems. All the models in this paper were evaluated using Monte-Carlo cross-validation.

Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019]. While this is extremely useful for helping define the appropriate social norms of AI, we believe that it is equally important to discuss both the potential and risks of ML and to inspire the community to use ML for beneficial objectives. In the present article, which is specifically aimed at ML practitioners, we thus focus more on the latter, carrying out an overview of existing high-level ethical frameworks and guidelines, but above all proposing both conceptual and practical principles and guidelines for ML research and deployment, insisting on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.

Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift, and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

Transport and Logistics – Optimal route planning and delivery information powers more than just resource management Case Study – Distributors which include global retailers, eCommerce companies, and rideshare applications work to make sure all major players on their platforms efficiently operate whether you're predicting loads for third party logistics or you have your own fleet to manage. In general, resource and route planning for businesses and consumers works better when you power it with ML. Food and Agriculture – An active prevention method saves companies millions of dollars upstream and downstream. John Deere is famous for proactively visiting farmers by detecting exactly which part in the tractor needs maintenance or replacement. Case Study – A few of our customers in this industry use IoT sensors and other field data collection devices combined with hyperledger technology to keep livestock healthy.