Models in the Cloud: Exploring Next Generation Environmental Software Systems

There is growing interest in the application of the latest trends in computing and data science methods to improve environmental science. However we found the penetration of best practice from computing domains such as software engineering and cloud computing into supporting every day environmental science to be poor. We take from this work a real need to re-evaluate the complexity of software tools and bring these to the right level of abstraction for environmental scientists to be able to leverage the latest developments in computing. In the Models in the Cloud project, we look at the role of model driven engineering, software frameworks and cloud computing in achieving this abstraction. As a case study we deployed a complex weather model to the cloud and developed a collaborative notebook interface for orchestrating the deployment and analysis of results. We navigate relatively poor support for complex high performance computing in the cloud to develop abstractions from complexity in cloud deployment and model configuration. We found great potential in cloud computing to transform science by enabling models to leverage elastic, flexible computing infrastructure and support new ways to deliver collaborative and open science

Exploring the Use of Virtual Worlds as a Scientific Research Platform: The Meta-Institute for Computational Astrophysics (MICA)

We describe the Meta-Institute for Computational Astrophysics (MICA), the first professional scientific organization based exclusively in virtual worlds (VWs). The goals of MICA are to explore the utility of the emerging VR and VWs technologies for scientific and scholarly work in general, and to facilitate and accelerate their adoption by the scientific research community. MICA itself is an experiment in academic and scientific practices enabled by the immersive VR technologies. We describe the current and planned activities and research directions of MICA, and offer some thoughts as to what the future developments in this arena may be.Comment: 15 pages, to appear in the refereed proceedings of "Facets of Virtual Environments" (FaVE 2009), eds. F. Lehmann-Grube, J. Sablating, et al., ICST Lecture Notes Ser., Berlin: Springer Verlag (2009); version with full resolution color figures is available at http://www.mica-vw.org/wiki/index.php/Publication

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Post-industrial robotics: the new tendency of digital fabrication for exploring responsive forms and materials through performance

The contribution proposes the experimental results of research on robotics manufacturing issues for the realization of informed architectural organisms on a 1:1 scale. The pavilions Fusta Robotics and Digital Urban Orchard and the technological system In.Flux represent the results of tests in which material, environmental and structural performance inform the computational process and the consequent materialization. The two pavilions, both wooden, constitute the physical implementation of different functional programs realised through a collaboration with industrial partners. Fusta Robotics is the result of a collaboration between industry and universities for the tectonic experimentation derived from the use of local non-engineered material. Digital Urban Orchard is the formal expression of a complex functional program arising from the relationship amongst form (shape), function and context for a new concept of socialization space and food production within the agenda at the self-sufficiency in Barcelona. Finally, through the In.Flux prototype, we investigated the relationship among formal generation, structural analysis and robotic manufacturing for the realization of concrete free-form structures. The analysis of the prototypes opens the debate on the role of IT in the post-digital era when the design process manifest through the control and management of the flow of information affecting the digital computation and fabrication and the material behaviour. The resulting theoretical assumption considers the architectural form as the result of a diagram of forces where the achievement of the performance is the driving parameter for the formal geometric exploration. The continuous variation resulting therefrom is informed by performance parameters that define a new aesthetic which represents together the manifestation of objectively measurable performance parameters and the power of the tool through which the form is generated

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28