The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles. Meanwhile, the model encodes the historical trajectory information of all the vehicles in the scene. Experiments are carried out on the public NGSIM US-101 and I-80 Dataset and the prediction performance is measured by the Root Mean Square Error (RMSE). The quantitative and qualitative experimental results show that our model significantly improves the trajectory prediction accuracy, by up to 50.00%, compared to existing models.

At the offices of startup Vicarious in Union City, where the San Francisco Bay Area's sprawl abuts rolling hills, 10 robot arms tirelessly place travel-sized beauty products into bins on a conveyor belt. Each gray arm ends in a suction-cup-tipped finger that makes a high-pitched whine as it plucks items such as antiperspirant or hand lotion from crowded boxes. Google Cloud today announced the beta launch of Cloud AI Platform Pipelines, a new enterprise-grade service that is meant to give developers a single tool to deploy their machine learning pipelines, together with tools for monitoring and auditing them. "When you're just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward," Google notes in today's announcement. "But when you need to start paying attention to the other pieces required to make an ML workflow sustainable and scalable, things become more complex."

The COVID-19 Open Research Dataset (CORD-19), a repository of more than 29,000 scholarly articles on the coronavirus family from around the world, is being released today for free. The data set is the result of work by Microsoft Research, the Allen Institute for AI, the National Library of Medicine at the National Institutes of Health (NIH), the White House Office of Science and Technology (OSTP), and others. It includes machine-readable research from more than 13,000 scholarly articles. The aim is to empower the medical and machine learning research communities to mine text data for insights that can help fight COVID-19. "The White House worked with the National Academies of Science, Engineering, and Medicine and the World Health Organization to identify dozens of high-priority scientific questions related to COVID-19 to inform the call to action," White House CTO Michael Kratsios said today in a teleconference call.

A Data Science Technology Company helping enterprises harness their data and build AI-driven innovative solutions. Are you sure you want to view these Tweets? This #MachineLearning use case provides an in-depth analysis of a Transit system in San Francisco Bay Area. These insights will help the organization to smoothly plan and evaluate its services. If your #ATMs are down, what are the chances of your customers switching to your competitors?

Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.

Players of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.

Researchers in Norway are testing how salmon in a commercial fish farm might react to being regularly monitored by an underwater robots. While fish farms are typically uneventful environments, they still require oversight to ensure the captive fish are safe and healthy, a task most commercial fish farms assign to a human diver. Maarja Kruusmaa and a team of researchers at the Norwegian University of Science and Technology wanted to test how fish would respond to being watched over by robots instead of people. 'The happier the fish are, the healthier the fish are, the better they eat, the better they grow, the less parasites they have and the less they get sick,' Kruusmaa told New Scientist. The team used two different underwater robots to test whether the fish would react differently based on the size and propulsion method.

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This paper presents a new approach for dimension reduction of data observed in a sphere. Several dimension reduction techniques have recently developed for the analysis of non-Euclidean data. As a pioneer work, Hauberg (2016) attempted to implement principal curves on Riemannian manifolds. However, this approach uses approximations to deal with data on Riemannian manifolds, which causes distorted results. In this study, we propose a new approach to construct principal curves on a sphere by a projection of the data onto a continuous curve. Our approach lies in the same line of Hastie and Stuetzle (1989) that proposed principal curves for Euclidean space data. We further investigate the stationarity of the proposed principal curves that satisfy the self-consistency on a sphere. Results from real data analysis with earthquake data and simulation examples demonstrate the promising empirical properties of the proposed approach.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.