The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is a weighted graph that is constructed from a sample-taxa count matrix, and can be used to model co-occurrences and/or interactions of the constituent members of a microbial community. The nodes in this graph represent microbial taxa and the edges represent pairwise associations amongst these taxa. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network.

COVID-19, a low oil price and an industry facing increased environmental scrutiny has resulted in a turbulent 2020 for the oil and gas sector. As many North Sea fields reach maturity, stakeholders will be carefully considering their options including decommissioning and diversifying the energy mix. The National Decommissioning Centre (NDC) (a partnership between the University of Aberdeen, the Oil & Gas Technology Centre (OGTC), and industry) has said that efficient late-life management and decommissioning of assets is a "societal and economic necessity". Emerging tech and artificial intelligence (AI) can help achieve this. However, the contribution AI and new technology could have on decommissioning cannot be considered in isolation.

Floods are one of the major climate-related disasters, leading to substantial economic loss and social safety issue. However, the confidence in predicting changes in fluvial floods remains low due to limited evidence and complex causes of regional climate change. The recent development in machine learning techniques has the potential to improve traditional hydrological models by using monitoring data. Although Recurrent Neural Networks (RNN) perform remarkably with multivariate time series data, these models are blinded to the underlying mechanisms represented in a process-based model for flood prediction. While both process-based models and deep learning networks have their strength, understanding the fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial to improve the prediction accuracy of flood occurrence. This paper demonstrates a neural network architecture (HydroDeep) that couples a process-based hydro-ecological model with a combination of Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to build a hybrid baseline model. HydroDeep outperforms the performance of both the independent networks by 4.8% and 31.8% respectively in Nash-Sutcliffe efficiency. A trained HydroDeep can transfer its knowledge and can learn the Geo-spatiotemporal features of any new region in minimal training iterations.

A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task based on a resolution method for partial differential equations: the separation of variables. This inspiration allows us to introduce a dynamical interpretation of spatiotemporal disentanglement. It induces a principled model based on learning disentangled spatial and temporal representations of a phenomenon to accurately predict future observations. We experimentally demonstrate the performance and broad applicability of our method against prior state-of-the-art models on physical and synthetic video datasets. The interest of the machine learning community in physical phenomena has substantially grown for the last few years (Shi et al., 2015; Long et al., 2018; Greydanus et al., 2019). In particular, an increasing amount of works studies the challenging problem of modeling the evolution of dynamical systems, with applications in sensible domains like climate or health science, making the understanding of physical phenomena a key challenge in machine learning. To this end, the community has successfully leveraged the formalism of dynamical systems and their associated differential formulation as powerful tools to specifically design efficient prediction models. In this work, we aim at studying this prediction problem with a principled and general approach, through the prism of Partial Differential Equations (PDEs), with a focus on learning spatiotemporal disentangled representations. Prediction via spatiotemporal disentanglement was first studied in video prediction works, in order to separate static and dynamic information (Denton & Birodkar, 2017) for prediction and interpretability purposes. Existing models are particularly complex, involving either adversarial losses or variational inference.

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Combinatorial problems which have been proven to be NP-hard are faced in Higher Education Institutions and researches have extensively investigated some of the well-known combinatorial problems such as the timetabling and student project allocation problems. However, NP-hard problems faced in Higher Education Institutions are not only confined to these categories of combinatorial problems. The majority of NP-hard problems faced in institutions involve grouping students and/or resources, albeit with each problem having its own unique set of constraints. Thus, it can be argued that techniques to solve NP-hard problems in Higher Education Institutions can be transferred across the different problem categories. As no method is guaranteed to outperform all others in all problems, it is necessary to investigate heuristic techniques for solving lesser-known problems in order to guide stakeholders or software developers to the most appropriate algorithm for each unique class of NP-hard problems faced in Higher Education Institutions. To this end, this study described an optimization problem faced in a real university that involved grouping students for the presentation of semester results. Ordering based heuristics, genetic algorithm and the ant colony optimization algorithm implemented in Python programming language were used to find feasible solutions to this problem, with the ant colony optimization algorithm performing better or equal in 75% of the test instances and the genetic algorithm producing better or equal results in 38% of the test instances.

Artificial intelligence, machine learning and unmanned systems are enabling surface and undersea activities even while COVID-19 hampers the ability to put humans on ships, maritime leaders said during a webinar on Sept. 17. Retired Rear Adm. Tim Gallaudet, deputy administrator of the National Oceanic and Atmospheric Administration and the former Oceanographer of the Navy, said COVID has put ship deployments on hold for months, but the agency has leveraged autonomous systems to keep the work going. For instance, NOAA sent Sail Drones to Alaska to perform a critical fishery survey and for coastal mapping. "We were able to map in pretty shallow areas that would have been hazardous for ships," Gallaudet said in the webinar, hosted by the Marine Technology Society's Washington section and the company Oceaneering. NOAA was also able to use underwater gliders to measure water temperatures, which helped accurately predict the track of Hurricane Laura.

Knowledge of the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed, interventional actions. The inference of these factors without supervision given time series data remains an open challenge. Here, we focus on spatio-temporal processes, including wave propagations and weather dynamics, and assume that universal causes (e.g. physics) apply throughout space and time. We apply a novel DIstributed, Spatio-Temporal graph Artificial Neural network Architecture, DISTANA, which learns a generative model in such domains. DISTANA requires fewer parameters, and yields more accurate predictions than temporal convolutional neural networks and other related approaches on a 2D circular wave prediction task. We show that DISTANA, when combined with a retrospective latent state inference principle called active tuning, can reliably derive hidden local causal factors. In a current weather prediction benchmark, DISTANA infers our planet's land-sea mask solely by observing temperature dynamics and uses the self inferred information to improve its own prediction of temperature. We are convinced that the retrospective inference of latent states in generative RNN architectures will play an essential role in future research on causal inference and explainable systems.

A high-tech ship is marking the 400th anniversary of the sailing of the Mayflower -- the ship that carried a group of European settlers to North America. The famous trip prepared the way for England's colonization of what Europeans in 1620 called the New World. The anniversary was marked this week in Plymouth, England – the starting point for the historic Mayflower crossing of the Atlantic Ocean. Local officials gathered with sea travelers and scientists for the launch of the new ship. It is called the Mayflower Autonomous Ship.

New drone video captured the mess Hurricane Sally left behind on Alabama's coastline after tearing through early Wednesday morning with 105 mph winds, torrential rainfall and a powerful storm surge. The storm tore apart buildings, hurled boats and debris around, and dumped as much as 30 inches of rain in southern Alabama and the Florida Panhandle before downgrading to a tropical storm Wednesday. It continued to weaken to a post-tropical depression Thursday as it moved northeast. The wall of a residential high-rise building was sheared completely off in the storm, leaving multiple levels exposed, the video shows. Sally tore through bedrooms and furniture, leaving dresser drawers open and bedding unraveled, the video shows.