We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.

Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and sub-grid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate timescales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system. We also find that in general those models which produce skillful forecasts are also associated with the best climate simulations.

The "Planet Earth Report" connects you to headline news on the science, technology, discoveries, people and events changing our planet and the future of the human species. We have a new global tally of the insect apocalypse. "Scary Known Unknown" –A Vast Hidden Asteroid Population Close to Sun Elizabeth Warren wants to ban the US from using nuclear weapons first –This 12-word bill could change how we use nuclear weapons. Bill Gates tweeted out a chart and sparked a huge debate about global poverty–Has global poverty declined dramatically? Intelligent Machines –Trump has a plan to keep America first in artificial intelligence.

Boaty McBoatface may be better known for its name than for its oceangoing prowess. But the autonomous underwater vehicle and darling of the internet is headed to greater things: embarking on the longest journey of an AUV by far, with an uninterrupted, roughly 2,000-mile crossing of the Arctic Ocean. The submersible robot got its moniker when it became the consolation prize in a 2016 publicity stunt. The United Kingdom's Natural Environmental Research Council had created an online poll to name the country's new polar research ship. The public picked "Boaty McBoatface" (suggested by a BBC radio announcer), but the British government nixed the idea and named the ship after naturalist David Attenborough.

Antarctic scientists seeking to locate the wreck of Sir Ernest Shackleton's lost ship, the Endurance, have arrived at the search site. The team broke through thick pack ice on Sunday to reach the vessel's last known position in the Weddell Sea. Robotic submersibles will now spend the next few days scouring the ocean floor for the maritime icon. Shackleton and his crew had to abandon Endurance in 1915 when it was crushed by sea ice and sank in 3,000m of water. Their escape across the frozen floes on foot and in lifeboats is an extraordinary story that has resonated down through the years - and makes the wooden polar yacht perhaps the most sought-after of all undiscovered wrecks.

A team of scientists will for the first time search the wreck of polar explorer Sir Ernest Shackleton's doomed ship that was crushed in ice more than 100 years ago. Scientists on board the SA Agulhas II will leave for the Weddell Sea in Antarctica on New Year's day and head towards the Larsen C ice shelf. The team want to find and search Shackleton's lost Endurance vessel, which sank in 1915, with robotic submarines and drones. As part of one of the most ambitious polar expeditions in recent years, the scientists will also try and discover why a trillion tonne iceberg the size of Northumberland broke off the ice shelf and floated 28 miles (45km) last year. The team of experts, technicians and researchers are travelling to the region to study what pressures the shelf is under and what life survives in the extreme conditions.

The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth System Model development could play a key role in reducing climate prediction uncertainty in the coming decade.

A robotic system, which draws from both nautical and biological designs, has been developed by engineers from the Massachusetts Institute of Technology (MIT). Their innovative robotic glider can skim along the water's surface. The research team say their robotic device rides the wind like an albatross while also surfing the waves like a sailboat. In high wind conditions the robot is designed to stay aloft, much like its avian counterpart, whereas in calmer winds, the robot has a keel it can dip into the water allowing it to ride in the manner of a highly efficient sailboat. The robotic system is relatively lightweight, weighing about 6 pounds, and can cover a given distance using one-third as much wind as an albatross and traveling 10 times faster than a typical sailboat.