Last Saturday, the nonprofit Ocean Cleanup dispatched a device to help clean up litter in the Pacific Ocean. NPR's Michel Martin talks with Boyan Slat, the young CEO who came up with the idea.

Next year, Oklahoma City residents will be able to have their groceries delivered to them by an autonomous vehicle. Udelv announced this week that a new partnership will bring its self-driving delivery vehicles to the city's largest local chain of grocery stores, which includes supermarkets such as Uptown Grocery, Buy For Less, Buy For Less Super Mercado and Smart Saver. Ten vehicles are scheduled to be delivered to the stores by the end of June 2019. Udelv made its first delivery with the vehicles in California this January, and since then, it has completed more than 700 deliveries in partnership with a handful of merchants in the San Francisco Bay Area. The company and its Oklahoma City parter Esperanza Real Estate Investments will work with city authorities ahead of the vehicles' deployment and Oklahoma's Secretary of Transportation, Mike Patterson, said in a statement that the state has a regulatory group in place focusing on the use of autonomous delivery vehicle technology.

The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestation as control inputs to a building. Furthermore, the occupants of a building typically lack the independent motivation necessary to contribute to and play a key role in the control of smart building infrastructure. Moreover, true human actions and their integration with sensing/actuation platforms remains unknown to the decision maker tasked with improving operational efficiency. By modeling user interaction as a sequential discrete game between non-cooperative players, we introduce a gamification approach for supporting user engagement and integration in a human-centric cyber-physical system. We propose the design and implementation of a large-scale network game with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms. A benchmark utility learning framework that employs robust estimations for classical discrete choice models provided for the derived high dimensional imbalanced data. To improve forecasting performance, we extend the benchmark utility learning scheme by leveraging Deep Learning end-to-end training with Deep bi-directional Recurrent Neural Networks. We apply the proposed methods to high dimensional data from a social game experiment designed to encourage energy efficient behavior among smart building occupants in Nanyang Technological University (NTU) residential housing. Using occupant-retrieved actions for resources such as lighting and A/C, we simulate the game defined by the estimated utility functions.

TOKYO (WASHINGTON POST) - Japan is working to develop technology that will fully utilise artificial intelligence (AI) to detect suspicious vessels, according to sources. Aimed at strengthening maritime surveillance capabilities in waters around Japan, the envisioned technology is projected to be used for such purposes as monitoring North Korean ship-to-ship cargo transfers in international waters, the sources said. The government aims to start testing the AI-based technology in fiscal year 2021 using vessels of the Self-Defence Forces. The system will analyse information automatically transmitted by radio from the Automatic Identification System on board many ships. The AI will learn an enormous amount of information on the location and speed of ships, making it possible to automatically detect abnormalities such as ships navigating far away from ordinary routes or in the opposite direction. The Self-Defence Forces will identify suspicious ships by comparing the AI-collected data with information gathered by warning radar, and will dispatch destroyers and patrol aircraft for warning and surveillance activities.

The El Niño/La Niña pattern in the Pacific Ocean is notorious for its long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as "teleconnections," and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Chicago, University of Wisconsin-Madison and the University of California-Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate. Researchers will apply data science methods such as machine learning, network analysis and predictive modeling to the growing flood of climate data. "There are fundamental challenges pervasive in data science that are epitomized in the climate science setting, making this collaboration a nice opportunity for advances on a number of fronts," said Rebecca Willett, professor of computer science and statistics at UChicago.

Hurricane Harvey, shown in 2017. A new data project hopes to sniff out weather patterns. The El Nino and La Nina patterns in the Pacific Ocean are notorious for their long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as teleconnections, and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Wisconsin–Madison, the University of Chicago, and the University of California, Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate.

The evaluation of very large amounts of data is becoming increasingly relevant in ocean research. Diving robots or autonomous underwater vehicles that carry out measurements independently in the deep sea can now record large quantities of high-resolution images. To evaluate these images scientifically in a sustainable manner, a number of prerequisites have to be fulfilled in data acquisition, curation and data management. "Over the past three years, we have developed a standardized workflow that makes it possible to scientifically evaluate large amounts of image data systematically and sustainably," explains Dr. Timm Schoening from the Deep Sea Monitoring working group headed by Prof. Dr. Jens Greinert at GEOMAR. The ABYSS autonomous underwater vehicle was equipped with a new digital camera system to study the ecosystem around manganese nodules in the Pacific Ocean. With the data collected in this way, the workflow was designed and tested for the first time.

Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to help generate the outputs, but it fails to capture the temporal patterns across multiple time steps. In this paper, we propose to use a set of filters to extract time-invariant temporal patterns, which is similar to transforming time series data into its "frequency domain". Then we proposed a novel attention mechanism to select relevant time series, and use its "frequency domain" information for forecasting. We applied the proposed model on several real-world tasks and achieved the state-of-the-art performance in all of them with only one exception. We also show that to some degree the learned filters play the role of bases in discrete Fourier transform.

A 600-meter plastic-sweeper set to head to the Pacific Ocean to clean up the notorious floating Great Garbage Patch is finally ready for launch, its makers have revealed. The gigantic'pac man' system consists of a 600-meter-long floating tube that sits at the surface of the water, with a tapered 3-meter-deep skirt attached below to catch plastic waste. It harnesses the power of wind and surface waves to autonomously sweep through the area, gathering up plastic waste as it goes. The gigantic'pac man' system consists of a 600-meter-long floating tube that sits at the surface of the water, with a tapered 3-meter-deep skirt attached below to catch plastic waste'On September 8, we will launch the world's first ocean cleanup system from our assembly yard in Alameda, through the San Francisco Bay, toward the infamous Great Pacific Garbage Patch,' organisers revealed. The team has spent six months building the contraption.

Fisher discriminant analysis (FDA) is a widely used method for classification and dimensionality reduction. When the number of predictor variables greatly exceeds the number of observations, one of the alternatives for conventional FDA is regularized Fisher discriminant analysis (RFDA). In this paper, we present a simple, iterative, sketching-based algorithm for RFDA that comes with provable accuracy guarantees when compared to the conventional approach. Our analysis builds upon two simple structural results that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized linear algebra. We analyze the behavior of RFDA when the ridge leverage and the standard leverage scores are used to select predictor variables and we prove that accurate approximations can be achieved by a sample whose size depends on the effective degrees of freedom of the RFDA problem. Our results yield significant improvements over existing approaches and our empirical evaluations support our theoretical analyses.