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1. Interactive Demonstrations and Hands-On Use of thenet.science Cyberinfrastructure for Network Science Chairs’ Welcome and Tutorial Summary

   https://doi.org/10.1145/3462741.3466679  

   Barrow, Golda; Kuhlman, Chris J.; Machi, Dustin; Ravi, S. S. (June 2021, 13th ACM Web Science Conference 2021)

   null (Ed.)

   Networks are readily identifiable in many aspects of society: cellular telephone networks and social networks are two common examples. Networks are studied within many academic disciplines. Consequently, a large body of (open-source) software is being produced to perform computations on networks. A cyberinfrastructure for network science, called net.science, is being built to provide a computational platform and resource for both producers and consumers of networks and software tools. This tutorial is a hands-on demonstration of some of net.science’s features. 

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2. Science DMZ Networks: How Different Are They Really?

   Mutter, E; Shannigrahi, S (October 2024, 2024 IEEE 50th Conference on Local Computer Networks (LCN))

   The Science Demilitarized Zone (Science DMZ) is a network environment optimized for scientific applications. The Science DMZ model provides a reference set of network design patterns, tuned hosts and protocol stacks dedicated to large data transfers and streamlined security postures that significantly improve data transfer performance, accelerating scientific collaboration and discovery. Over the past decade, many universities and organizations have adopted this model for their research computing. Despite becoming increasingly popular, there is a lack of quantitative studies comparing such a specialized network to conventional production networks regarding network characteristics and data transfer performance. But does a Science DMZ exhibit significantly different behavior than a general-purpose campus network? Does it improve application performance compared a to general-purpose network? Through a two-year-long quantitative network measurement study, we find that a Science DMZ exhibits lower latency, higher throughput, and lower jitter behaviors. We also see several non-intuitive results. For example, a DMZ may take a longer route to external destinations and experience higher latency than the campus network. While the DMZ model benefits researchers, the benefits are not automatic, careful network tuning based on specific use cases is required to realize the full potential of Science DMZs. 

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3. Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

   https://doi.org/10.1002/cpe.6080  

   Vekaria, K.; Calyam, P.; Sivarathri, S.; Wang, S.; Zhang, Y.; Pandey, A.; Chen, C.; Xu, D.; Joshi, T.; Nair, S.S. (January 2020, Wiley Concurrency and Computation: Practice and Experience)

   Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). 

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4. Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

   https://doi.org/10.1115/1.4064449  

   Faroughi, Salah A; Pawar, Nikhil M; Fernandes, Célio; Raissi, Maziar; Das, Subasish; Kalantari, Nima K; Kourosh_Mahjour, Seyed (April 2024, Journal of Computing and Information Science in Engineering)

   Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers. 

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5. Enabling rich data sharing for Science Gateways via the SeedMeLab platform

   https://doi.org/10.17605/OSF.IO/WV3BF  

   Chourasia, Amit; Nadeau, David; Luo, Jiaping; Chen, Tony; Miller, Mark; Brookes, Emre H (September 2019, Proceedings of Gateways 2019)

   Abstract Science Gateways provide an easily accessible and powerful computing environment for researchers. These are built around a set of software tools that are frequently and heavily used by large number of researchers in specific domains. Science Gateways have been catering to a growing need of researchers for easy to use computational tools, however their usage model is typically single user-centric. As scientific research becomes ever more team oriented, the need driven by user-demand to support integrated collaborative capabilities in Science Gateways is natural progression. Ability to share data/results with others in an integrated manner is an important and frequently requested capability. In this article we will describe and discuss our work to provide a rich environment for data organization and data sharing by integrating the SeedMeLab (formerly SeedMe2) platform with two Science Gateways: CIPRES and GenApp. With this integration we also demonstrate SeedMeLab’s extensible features and how Science Gateways may incorporate and realize FAIR data principles in practice and transform into community data hubs. 

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