Sometime next April, a 50-foot-long autonomous ship will shake loose the digital bonds of its human controllers, scan the horizon with radar, and set a course westward across the Atlantic. The Mayflower Autonomous Ship won't be taking commands from a human captain like the first Mayflower did during its crossing back in 1620. Instead it will get orders from an "AI captain" built by programmers at IBM. The Mayflower's computing system processes data from 30 onboard sensors and six cameras to help the ship sail across the ocean, obey shipping rules (like how to pass other ships at sea), and control electrical and mechanical systems like the engine and rudder. There won't be anyone on board if something goes wrong, although it does have to send a daily report to a human operator back in the UK.

A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large number of sites. The problem of tackling both issues simultaneously, which we refer to as the zero-inflated multi-target regression problem, has not been addressed by previous methods in statistics and machine learning. In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem. To this end, we first model the joint distribution of multiple response variables as a multivariate probit model and then couple the positive outcomes with a multivariate log-normal distribution. By penalizing the difference between the two distributions' covariance matrices, a link between both distributions is established. The whole model is cast as an end-to-end learning framework and we provide an efficient learning algorithm for our model that can be fully implemented on GPUs. We show that our model outperforms the existing state-of-the-art baselines on two challenging real-world species distribution datasets concerning bird and fish populations.

The tragedy happened to the AirFrance 447 more than 10 years ago, in 2009. The flight took off in Rio de Janeiro and was planned to land in Paris. It suddenly disappeared in the middle of the Atlantic ocean without any warning. Immediately, rescuers reached the zone and what they found were just some wreckage and corpse. All 228 people onboard died in the crash.

Singular Value Decomposition (SVD) plays a pivotal role in exploratory data analysis. However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset. Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting. However, these projection methods come at the cost of approximation errors in both top singular values and vectors. Furthermore, in order to bound the approximation error, an over-sampled projection is required, often much larger in dimension than the desired rank. This latter consideration can still be memory intensive when the data dimension is large or extraneous when the desired rank approximation is close to the full rank. We present a two stage neural optimization approach as an alternative to conventional and randomized SVD techniques, where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size. The proposed scheme reads data samples in a streaming setting with the network minimization problem converging to a low rank approximation with high precision. Our architecture is fully interpretable where all the network outputs and weights have a specific meaning. We evaluate our results on various performance metrics against state of the art streaming methods. We also present numerical experiments for Singular and Eigen value decomposition on real data at various scales to show the memory efficiency of our proposed approach.

Despite being a poor and unequal country, Costa Rica has managed to close the gap in access to technology for its citizens, and it is now leading the way in the region. The country started the process of admission for the Organization for Economic Cooperation and Development (OECD) several years ago with reforms on laws, the creation of policies and the use of Computer Technologies to improve education, information access, financial markets, competitiveness, and a more open government. In May 2020, Costa Rica became the first Central American or Caribbean country invited to become an OECD member. The OECD has almost 60 years of existence, and its members are many of the world's more developed countries that work together to shape policies that foster prosperity, equality, opportunity, and well-being for their citizens. Costa Rica will become the 38th member, the fourth of Latin America.

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of our directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of our proposed algorithm, we evaluate it as a mode clustering method on both simulated and real-world datasets.

In the latest episode of our podcast, Machine Learning that Works, I had a great pleasure to talk to Gabriel Preda, a Lead Data Scientist at Endava and a Kaggle Grandmaster. For those of you who want to see the full interview, here is the video version. If, on the other hand, you prefer to read, I prepared a summary as well. It's not a faithful transcript of our conversation, but a structured and rephrased version of the interview, that includes the key points and observations. Without further ado, let's meet Gabriel Preda! I work for Endava, which is a software service company, and our projects are actually our clients' projects.

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.