Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary temporally, or by re-fitting a model with a temporally-invariant linear operator at each time point in a sliding window. Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic. Here, we tackle these two issues by using a deep convolution neural network (CNN) in a hierarchical statistical IDE framework, where the CNN is designed to extract process dynamics from the process' most recent behaviour. Once the CNN is fitted, probabilistic forecasting can be done extremely quickly online using an ensemble Kalman filter with no requirement for repeated parameter estimation. We conduct an experiment where we train the model using 13 years of daily sea-surface temperature data in the North Atlantic Ocean. Forecasts are seen to be accurate and calibrated. A key advantage of our approach is that the CNN provides a global prior model for the dynamics that is realistic, interpretable, and computationally efficient. We show the versatility of the approach by successfully producing 10-minute nowcasts of weather radar reflectivities in Sydney using the same model that was trained on daily sea-surface temperature data in the North Atlantic Ocean.

This paper presents a new clustering algorithm for space-time data based on the concepts of topological data analysis and in particular, persistent homology. Employing persistent homology - a flexible mathematical tool from algebraic topology used to extract topological information from data - in unsupervised learning is an uncommon and a novel approach. A notable aspect of this methodology consists in analyzing data at multiple resolutions which allows to distinguish true features from noise based on the extent of their persistence. We evaluate the performance of our algorithm on synthetic data and compare it to other well-known clustering algorithms such as K-means, hierarchical clustering and DBSCAN. We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.

LOS ALAMOS, N.M., Oct. 22, 2019--A probabilistic artificial intelligence computer model developed at Los Alamos National Laboratory provided the most accurate state, national and regional forecasts of the flu in 2018, beating 23 other teams in the Centers for Disease Control and Prevention's FluSight Challenge. The CDC announced the results last week. "Accurately forecasting diseases is similar to weather forecasting in that you need to feed computer models large amounts of data so they can'learn' trends," said Dave Osthus, a statistician at Los Alamos and developer of the computer model, Dante. "But it's very different because disease spread depends on daily choices humans make in their behavior--such as travel, hand-washing, riding public transportation, interacting with the healthcare system, among other things. Those are very difficult to predict."

The Mayflower Autonomous Ship (MAS) will begin its journey on 6 September 2020 and cross the Atlantic Ocean, from Plymouth to Plymouth. Like its namesake in 1620, MAS will rely to some extent on favourable weather to complete its crossing as it will be powered by state-of-the-art hybrid propulsion system, utilizing wind, solar, state-of-the-art batteries, and a diesel generator. MAS will carry three research pods containing myriad sensors that scientists will utilize to conduct persistent, ground-breaking research in meteorology, oceanography, climatology, biology, marine pollution and conservation, and autonomous navigation. MAS is being coordinated through a partnership lead by ProMare, a non-profit charity established to promote marine research and exploration throughout the world. The research pods will be coordinated by Plymouth University, a world-leading centre of excellence for marine and maritime education, research and innovation.

An autonomous boat under developments could be the first ship to cross the Atlantic that is able to navigate around ships and other hazards by itself. The Mayflower Autonomous Ship (MAS) is an autonomous vessel due to depart from Plymouth in England on the fourth centenary of the original Mayflower voyage, on 6 September 2020, with its destination Plymouth, USA. The project was put together by marine research and exploration company ProMare in an effort to expand the scope of marine research. The boat will carry three research pods equipped with scientific instruments to measure various phenomena such as ocean plastics, mammal behaviour or sea level changes. IBM has now joined the initiative, and it will supply technical support for all navigation operations. The Mayflower Autonomous Ship (MAS) is an unmanned vessel set to depart from Plymouth in England on the fourth centenary of the original Mayflower voyage.

A fully autonomous ship called the "Mayflower" will make its voyage across the Atlantic Ocean next September, to mark the 400-year anniversary of the trip of the first Mayflower, which was very much not autonomous. It's a stark way to drive home just how much technology has advanced in the last four centuries, but also a key demonstration of autonomous seafaring technology, put together by marine research and exploration organization Promare and powered by IBM technology. The autonomous Mayflower will be decked out with solar panels, as well as diesel and wind turbines to provide it with its propulsion power, as it attempts the 3,220-mile journey from Plymouth in England, to Plymouth in Massachusetts in the U.S. The trip, if successful, will be among the first for full-size seafaring vessels navigating the Atlantic on their own, which Promare is hoping will open the doors to other research-focused applications of autonomous seagoing ships. To support that use case, it'll have research pods on board while it makes its trip. Three to be specific, developed by academics and researchers at the University of Plymouth, who will aim to run experiments in areas including maritime cybersecurity, sea mammal monitoring and even addressing the challenges of ocean-borne microplastics.

Every summer there is a mass exodus from New York City towards the white beach at Jones Beach State Park. Here, looking out over the Atlantic Ocean, you can sunbathe, catch a concert or play a game of mini-golf. And get away from the bustle of the city. But you have to get there first. And there's something odd about the route you take. The flyovers over the Southern State Parkway that leads to Jones Beach are low.

Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horizontally sparse, knowledge of the ocean interior thermal structure. The objective of this paper is to develop a methodology to combine these complementary ocean observing systems measurements to obtain a three-dimensional time series of ocean temperatures with high horizontal and vertical resolution. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics in the North Atlantic region may be decomposed into a mixture of dynamical modes, characterized by different local regressions between Sea Surface Temperature (SST), Sea Level Anomalies (SLA) and Vertical Temperature fields. Ultimately we propose a Latent-class regression method to improve prediction of vertical ocean temperature.

In July 1958, the U.S. Office of Naval Research unveiled a remarkable invention. An IBM 704 – a 5-ton computer the size of a room – was fed a series of punch cards. After 50 trials, the computer taught itself to distinguish cards marked on the left from cards marked on the right. It was a demonstration of the "perceptron" – "the first machine which is capable of having an original idea," according to its creator, Frank Rosenblatt '50, Ph.D. '56. At the time, Rosenblatt – who later became an associate professor of neurobiology and behavior in Cornell's Division of Biological Sciences – was a research psychologist and project engineer at the Cornell Aeronautical Laboratory in Buffalo, New York.

Robotics, as a discipline has been around for a long time. The increasing demand for automation, however, has seen interest in robots grow exponentially. Statista estimates that the market for Robo-advisors will be worth $225 billion in 2020. It is just one segment of the field of robotics. The increasing prominence of AI as a framework for more efficient robots has revolutionized the whole industry.