Fuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style. In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between the input factors and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70% of the categories associated to the fuel-factors are similar to the previous literature. With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12% and 15% in a large fleet (more than 1000 vehicles).

This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set of 15 benchmark problems, the relevant source code, and a performance indicator, designed for comparative studies and competitions in large-scale dynamic optimization. Although its primary purpose is to provide a coherent basis for running competitions, its generality allows the interested reader to use this document as a guide to design customized problem instances to investigate issues beyond the scope of the presented benchmark suite. To this end, we explain the modular structure of the GMPB and how its constituents can be assembled to form problem instances with a variety of controllable characteristics ranging from unimodal to highly multimodal, symmetric to highly asymmetric, smooth to highly irregular, and various degrees of variable interaction and ill-conditioning.

Detecting abrupt changes in temporal behavior patterns is of interest in many industrial and security applications. Abrupt changes are often local and observable primarily through a well-aligned sensing action (e.g., a camera with a narrow field-of-view). Due to resource constraints, continuous monitoring of all of the sensors is impractical. We propose the bandit quickest changepoint detection framework as a means of balancing sensing cost with detection delay. In this framework, sensing actions (or sensors) are sequentially chosen, and only measurements corresponding to chosen actions are observed. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We then propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions. We derive expected delay bounds for the proposed scheme and show that these bounds match our information-theoretic lower bounds at low false alarm rates, establishing optimality of the proposed method. We then perform a number of experiments on synthetic and real datasets demonstrating the efficacy of our proposed method.

The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., data are not observed over equally-spaced time intervals. In multi-dimensional time series analysis, a task is to conduct evolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures. Many existing methods can only process time series with regular time steps while time series are unevenly sampled in many situations such as missing data. In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced. We demonstrate that this method can not only interpolate data at any time step for the evolutionary subspace clustering task, but also achieve higher accuracy than other state-of-the-art evolutionary subspace clustering methods. Both synthetic and real-world data are used to illustrate the efficacy of our proposed method.

Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields. A promising direction is continuous optimization for score-based methods, which efficiently learn the causal graph in a data-driven manner. However, to date, those methods require constrained optimization to enforce acyclicity or lack convergence guarantees. In this paper, we present ENCO, an efficient structure learning method for directed, acyclic causal graphs leveraging observational and interventional data. ENCO formulates the graph search as an optimization of independent edge likelihoods, with the edge orientation being modeled as a separate parameter. Consequently, we can provide convergence guarantees of ENCO under mild conditions without constraining the score function with respect to acyclicity. In experiments, we show that ENCO can efficiently recover graphs with hundreds of nodes, an order of magnitude larger than what was previously possible, while handling deterministic variables and latent confounders.

Single Australians looking for sex or love will soon stand out from the competition if they've been vaccinated against Covid-19. Dating app Bumble announced it will roll out a new feature this week enabling users in Australia and New Zealand to add a "vaccination badge" to their profile if they've received a Covid-19 jab. Competitor Tinder says it looks forward to "making [vaccine] badges available soon" in Australia too. A Bumble representative says it will not independently verify the vaccination status of those who claim the badge, meaning the system will rely on user honesty. The dating app says it decided to launch the feature after a recent survey revealed a "45% increase in users asking potential dates if they had the vaccine or have Covid symptoms". It's not just in Australia that dating apps are encouraging users to boast about their vaccination status.

From smart infrastructure grids to bot-authored news reports, algorithms and artificial intelligence capabilities are routinely working behind the scenes in various aspects of our day-to-day lives. COVID-19 only accelerated the adoption of automation across industries and Gartner pegged "smarter, responsible [and] scalable AI" as one of its top 2021 data and analytics tech trends. In this roundup, we've highlighted some of the ways AI is transforming everything from animal conversation efforts to matchmaking in the digital age. The agtech company AppHarvest is using a number of transformative practices to reimagine farming in the 21st century, including AI. The company is tapping computer vision and AI to help its robo-harvester, Virgo, pick ripe produce right from the vine.

Anyone with any doubts about the interest in AI and its use across enterprise technologies only needs to look at the example of the Intelligent Document Processing (IDP) market and the kind of verticals that are investing in it to quash those doubts. According to the Everest Group's recently published report, Intelligent Document Processing (IDP) State of the Market Report 2021 (purchase required) the market for this segment alone is estimated at $700-750 million in 2020 and expected to grow at a rate of 55-65% over the next year. Cost impact is now the key driver for intelligent document processing adoption, closely followed by improving operational efficiency and productivity. These solutions blend AI technologies to efficiently process all types of documents and feed the output into downstream applications. Optical character recognition (OCR), computer vision, machine learning (ML) and deep learning models, and natural language processing (NLP) are the key core technologies powering IDP capabilities.

Artificial intelligence is a fifth more accurate at predicting whether individuals are likely to become long-term recipients of benefits. A new method of predicting welfare dependency, developed by Dr. Dario Sansone from the University of Exeter Business School and Dr. Anna Zhu from RMIT University, could save governments billions in welfare costs as well as help them make earlier interventions to prevent long-term economic disadvantage and social exclusion. Their study found that machine learning algorithms, which improve through several iterations and use of big data, are 22% more accurate at predicting the proportion of time individuals are on income support than the standard early warning systems. The researchers were able to apply the off-the-shelf algorithms to the entire population of people enrolled in the Australian social security system between 2014 and 2018. This included demographic and socio-economic data of anyone who received a welfare payment from Australia's social security system Centrelink, whether on the grounds of unemployment, disability, having children, or being a carer, a student or of pensionable age.

We consider the task of feature selection for reconstruction which consists in choosing a small subset of features from which whole data instances can be reconstructed. This is of particular importance in several contexts involving for example costly physical measurements, sensor placement or information compression. To break the intrinsic combinatorial nature of this problem, we formulate the task as optimizing a binary mask distribution enabling an accurate reconstruction. We then face two main challenges. One concerns differentiability issues due to the binary distribution. The second one corresponds to the elimination of redundant information by selecting variables in a correlated fashion which requires modeling the covariance of the binary distribution. We address both issues by introducing a relaxation of the problem via a novel reparameterization of the logitNormal distribution. We demonstrate that the proposed method provides an effective exploration scheme and leads to efficient feature selection for reconstruction through evaluation on several high dimensional image benchmarks. We show that the method leverages the intrinsic geometry of the data, facilitating reconstruction.