To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.

Current storytelling systems focus more ongenerating stories with coherent plots regard-less of the narration style, which is impor-tant for controllable text generation. There-fore, we propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context. To tacklethe problem, we propose a novel generationmodel that first plans the stylized keywordsand then generates the whole story with theguidance of the keywords. Besides, we pro-pose two automatic metrics to evaluate theconsistency between the generated story andthe specified style. Experiments demonstratesthat our model can controllably generateemo-tion-driven orevent-driven stories based onthe ROCStories dataset (Mostafazadeh et al.,2016). Our study presents insights for stylizedstory generation in further research.

In the race to harness the power of cloud computing, and further develop artificial intelligence, academics have a new concern: falling behind a fast-moving tech industry. In the US, 22 higher education institutions, including Stanford and Carnegie Mellon, have signed up to a National Research Cloud initiative seeking access to the computational power they need to keep up. It is one of several cloud projects being called for by academics globally, and is being explored by the US Congress, given the potential of the technology to deliver breakthroughs in healthcare and climate change. Under the US proposal, authored by Fei-Fei Li and John Etchemendy from the Stanford Institute for Human-Centered Artificial Intelligence, a national cloud platform would enable more academic and industry researchers to work at the leading edge of AI, and help train a new generation of experts. Li and Etchemendy's NRC proposal cautions about declining government funding for basic and foundational research and highlights the US's history of federally funding research into innovations -- from gene sequencing to the internet itself.

Complimentary Project Preview - This is an ongoing global program. Preview our research program before you make a purchase decision. We are offering a complimentary access to qualified executives driving strategy, business development, sales & marketing, and product management roles at featured companies. Previews provide deep insider access to business trends; competitive brands; domain expert profiles; and market data templates and much more. You may also build your own bespoke report using our MarketGlass Platform which offers thousands of data bytes without an obligation to purchase our report.

As the influence and use of artificial intelligence (AI) have grown As the influence and use of artificial intelligence (AI) have grown and its transformative potential has become more apparent, many and its transformative potential has become more apparent [32, 54], questions have been raised regarding the economic, political, social, many questions have been raised regarding the economic, political, and ethical implications of its use. Public opinion plays an important social, and ethical implications of its use [27]. The development role in these discussions, influencing product adoption, commercial and application of AI increasingly features in media, academic, development, research funding, and regulation. In this paper we industrial, regulatory, and public discussions [18, 23, 28], with active present results of an in-depth survey of public opinion of artificial debate on wide-ranging issues such as the impact of automation intelligence conducted with 10,005 respondents spanning eight on the future of work [8, 50, 52], the interaction of AI with human countries and six continents. We report widespread perception rights issues such as privacy and discrimination [1, 4, 10, 16], the that AI will have significant impact on society, accompanied by ethics of autonomous weapons [53, 59], and the development and strong support for the responsible development and use of AI, and availability of dual-use technologies such as synthetic media that also characterize the public's sentiment towards AI with four key may be used for either benevolent or nefarious purposes [48].

Modern machine learning techniques based on artificial neural networks (ANNs) are implemented through algebraic manipulations of vectors, matrices, and tensors in high-dimensional spaces. While ANNs have an impressive ability to process data to find patterns, they do not typically model high-level cognition. Furthermore, ANNs are usually models of only a single task. Otherwise, when an ANN is trained to learn a series of tasks, catastrophic interference occurs, with each new task causing the ANN to forget all previously learned tasks [8, 21, 22]. On the other hand, symbolic cognitive architectures, such as the widely used ACT-R [1, 31], can capture the complexities of high-level cognition but scale poorly to the naturalistic, non-symbolic data of sensory perception, e.g., images, or to big data sets necessary for modelling learning over a lifetime, e.g., corpora with hundreds of millions of words.

As has been argued in [Rod91] and [FOP92b], there is a growing demand for information systems, not only allowing for changes of their information base, but also for modifications in their underlying structure (conceptual schema and specification of dynamic aspects). In case of snapshot databases, structure modifications will lead to costly data conversions and reprogramming. The intention of an evolving information system ([FOP92a], [OPF94]) is to be able to handle updates of all components of the so-called application model, containing the information structure, the constraints on this structure, the population conforming to this structure and the possible operations. The theory of such systems should, however, be independent of whatever modelling technique is used to describe the application model. In this paper, we discuss a general theory for the evolution of application models. However, only conceptual aspects are considered, focus is on what evolution is, rather than on how to implement evolution in a database manegement system. In [PW93], an informal introduction to this theory is provided.

In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences. Building and maintaining public trust in AI has been identified as the key to successful and sustainable innovation. This chapter discusses the challenges related to operationalizing ethical AI principles and presents an integrated view that covers high-level ethical AI principles, the general notion of trust/trustworthiness, and product/process support in the context of responsible AI, which helps improve both trust and trustworthiness of AI for a wider set of stakeholders.

Convex clustering is an attractive clustering algorithm with favorable properties such as efficiency and optimality owing to its convex formulation. It is thought to generalize both k-means clustering and agglomerative clustering. However, it is not known whether convex clustering preserves desirable properties of these algorithms. A common expectation is that convex clustering may learn difficult cluster types such as non-convex ones. Current understanding of convex clustering is limited to only consistency results on well-separated clusters. We show new understanding of its solutions. We prove that convex clustering can only learn convex clusters. We then show that the clusters have disjoint bounding balls with significant gaps. We further characterize the solutions, regularization hyperparameters, inclusterable cases and consistency.

The graph represents a network of 3,600 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 14 May 2021 at 12:08 UTC. The requested start date was Friday, 14 May 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 5-day, 18-hour, 4-minute period from Saturday, 08 May 2021 at 05:55 UTC to Friday, 14 May 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.