8,469 research outputs found
Digital earth:yesterday, today, and tomorrow
The concept of Digital Earth (DE) was formalized by Al Gore in 1998. At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary. Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society. This creates new opportunities and challenges for the realization of DE. ‘What is DE today?’, ‘What could DE be in the future?’, and ‘What is needed to make DE a reality?’. To answer these questions it is necessary to examine DE considering all the technological, scientific, social, and economic aspects, but also bearing in mind the principles that inspired its formulation. By understanding the lessons learned from the past, it becomes possible to identify the remaining scientific and technological challenges, and the actions needed to achieve the ultimate goal of a ‘Digital Earth for all’. This article reviews the evolution of the DE vision and its multiple definitions, illustrates what has been achieved so far, explains the impact of digital transformation, illustrates the new vision, and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.</p
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies
Emergence and causality are two fundamental concepts for understanding
complex systems. They are interconnected. On one hand, emergence refers to the
phenomenon where macroscopic properties cannot be solely attributed to the
cause of individual properties. On the other hand, causality can exhibit
emergence, meaning that new causal laws may arise as we increase the level of
abstraction. Causal emergence theory aims to bridge these two concepts and even
employs measures of causality to quantify emergence. This paper provides a
comprehensive review of recent advancements in quantitative theories and
applications of causal emergence. Two key problems are addressed: quantifying
causal emergence and identifying it in data. Addressing the latter requires the
use of machine learning techniques, thus establishing a connection between
causal emergence and artificial intelligence. We highlighted that the
architectures used for identifying causal emergence are shared by causal
representation learning, causal model abstraction, and world model-based
reinforcement learning. Consequently, progress in any of these areas can
benefit the others. Potential applications and future perspectives are also
discussed in the final section of the review.Comment: 57 pages, 17 figures, 1 tabl
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
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Occupational Cultures as a Challenge to Technological Innovation
This paper explains conflict over technological process innovation in cultural terms, drawing primarily on a case study of electric power distribution and strategies to automate its operation
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