Dr. Noa Ruschin Rimini, Founder & CEO Decentralization of energy supply and deregulation of energy markets, coinciding with exponential increases in the data available from energy users, have created a pressing need for software to understand energy consumers and manage energy resources. Underlying each of those requirements, are challenges that cannot be met by the analytical approaches that have been employed in the past. Predictive Analytics that forecast behavior and faults of individual meters and connected devices meet these challenges with a product suite built on a core set of patented Machine Learning algorithms. "Grid4C's edge lies in the ability to squeeze the greatest value from existing, ubiquitous data sources, non-intrusively, without needing to wait for new sensors to reach mass adoption" "Machine Learning provides a window into homes and businesses using smart meter data that facilitate granular, accurate predictions, enabling more reliable operation of the grid and integration of renewable resources and energy storage,'' explains Dr. Noa Ruschin- Rimini, Founder and CEO, Grid4C. Over the years, the company has been able to solve some of the industry's greatest challenges, outperforming the competition in industry benchmarks.

We live in interesting times. Clean energy-fueled electric race cars are giving their Formula 1 counterparts a run for their money. Robots no longer just take commands, they can even be taught to conduct a symphony orchestra in 14 short hours. The journey has started well in such technologies like electric mobility and artificial intelligence (AI). But how will a country of more than a billion people reap the inclusive benefits of these tools and address relevant issues in deploying them?

The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose a nonparametric algorithm that is based on the idea of clustering point triplets instead of the original points. We define an appropriate distance measure on point triplets and then apply a single-link hierarchical clustering on the triplets. Compared to parametric approaches like RANSAC or the Hough transform, the new algorithm has the advantage of potentially finding trajectories even of shapes that are not known beforehand. This feature is particularly important in low-energy nuclear physics experiments with AT operating inside a magnetic field. The algorithm has been validated using data from experiments performed with the Active Target Time Projection Chamber (AT-TPC) at the National Superconducting Cyclotron Laboratory (NSCL).The results demonstrate the capability of the algorithm to identify and isolate particle tracks that describe non-analytical trajectories. For curved tracks, the vertex detection recall was 86% and the precision 94%. For straight tracks, the vertex detection recall was 96% and the precision 98%. In the case of a test set containing only straight linear tracks, the algorithm performed better than an iterative Hough transform. Keywords: Time Projection Chambers, Active Target, Pattern Recognition, Clustering 1. Introduction One of the present aims of modern low-energy nuclear physics is to provide a more complete understanding about the behavior of subatomic matter under large isospin (i.e.

Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local control rules are subpar, the approach of customized control rules designed in a quasi-static fashion features as a golden middle. Departing from affine control rules, this work puts forth non-linear inverter control policies. Drawing analogies to multi-task learning, reactive control is posed as a kernel-based regression task. Leveraging a linearized grid model and given anticipated data scenarios, inverter rules are jointly designed at the feeder level to minimize a convex combination of voltage deviations and ohmic losses via a linearly-constrained quadratic program. Numerical tests using real-world data on a benchmark feeder demonstrate that nonlinear control rules driven also by a few non-local readings can attain near-optimal performance.

Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany. In total the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.

We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency $\epsilon$, an extra observation is gathered by the agent for free. We prove that, no matter how small $\epsilon$ is the agent can ensure a regret uniformly bounded in time. More precisely, we construct an algorithm with a regret smaller than $\sum_i \frac{\log(1/\epsilon)}{\Delta_i}$, up to multiplicative constant and loglog terms. We also prove a matching lower-bound, stating that no reasonable algorithm can outperform this quantity.

How it works: Control hot water and heating from your smartphone. How it works: A lightbulb you can dim, or turn on and off, using your phone. How it works: A smart speaker you control by speaking to Alexa, an intelligent'virtual assistant'. Functions include answering questions and playing music. How it works: Be alerted on your phone when someone rings your doorbell, and speak to that person via a video link, regardless of where you are.

Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the system development, and evaluate the significance of the deviation. We begin with discussions of data reduction, symbolic data representation, and the anomaly detection in temporal discrete sequences. Time-series representation methods are also discussed and used in this paper to discretize raw data into sequences of system states. Markov Chains and stationary state distributions are continuously generated from temporal sequences to represent snapshots of the system dynamics in different time frames. We use generalized Jensen-Shannon Divergence as the measure to monitor changes of the stationary symbol probability distributions and evaluate the significance of system deviations. We prove that the proposed approach is able to detect deviations of the systems we monitor and assess the deviation significance in probabilistic manner.

The first 100 of Baidu's "Level 4" self driving buses have rolled off the production lines, said Robin Li, chief executive of China's largest search engine operator on Wednesday. The self-driving buses, which can seat up to 14 people, were co-developed by Baidu, which is transforming itself into an artificial intelligence (AI) company, and bus maker King Long United Automotive Industry Co. Level 4 operations means that the vehicles can take over all driving in certain conditions. With no steering wheel and high automation, the buses will be put into use in cities including Beijing, Xiongan, Shenzhen and Tokyo, Li said at the Baidu AI Developer forum being held in Beijing. "They will help with shuttle services around nuclear power stations and senior communities in Japan," for example, said Li. Baidu will partner with SB Drive, a subsidiary of SoftBank Group, to export the self-driving buses to Japan. Autonomous vehicles are a key part of the Nasdaq-listed Chinese company's future as it seeks to reshape itself into a major player in artificial intelligence, in line with China's national strategy to develop excellence in the field.

Unsurprisingly, it has been China – not Europe – that has proposed, with little success, forming a common front against Donald Trump's trade tantrums. Even Washington's bullying cannot awaken European policymakers from their slumber – or, as seems more likely, their moderately lubricated afternoon nap. Hardly a week passes without a new alarming announcement that Beijing has managed to outmanoeuvre Brussels in yet another domain. Last week brought three such developments. First, China Merchants Group, a state-owned company, joined forces with SPF Group and Centricus – asset managers based in Beijing and London respectively – to form a $15bn fund to compete with SoftBank's $100bn Vision Fund, launched to invest in the most promising technology firms worldwide.