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1. Open Data and Open Source GIS

   https://doi.org/10.1016/B978-0-12-409548-9.09592-0  

   Ye, Xinyue (January 2018, Comprehensive Geographic Information Systems)
2. Open markets

   https://doi.org/10.1111/mafi.12294  

   Karatzas, Ioannis; Kim, Donghan (October 2020, Mathematical Finance)

   Abstract An open market is a subset of a larger equity market, composed of a certain fixed number of top‐capitalization stocks. Though the number of stocks in the open market is fixed, their composition changes over time, as each company's rank by market capitalization fluctuates. When one is allowed to invest also in a money market, an open market resembles the entire “closed” equity market in the sense that the market viability (lack of arbitrage) is equivalent to the existence of a numéraire portfolio (which cannot be outperformed). When access to the money market is prohibited, the class of portfolios shrinks significantly in open markets; in such a setting, we discuss the Capital Asset Pricing Model, how to construct functionally generated portfolios, and the concept of universal portfolio. 

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3. Open knots

   https://doi.org/https://doi.org/10.1201/9781138298217  

   Dorier, Julien; Goundaroulis, Dimos; Rawdon, Eric J; Stasiak, Andrzej (January 2021, Encyclopedia of knot theory)

   Adams, Colin; Flapan, Erica; Henrich, Allison; Kauffman, Louis H; Ludwing, Lewis D; Nelson, Sam (Ed.)

   The goal of this chapter is to describe different techniques used to measure knotting in open curves. Note that there is no "agreed upon" definition for describing knotting in open curves. As a result, we describe the context motivating each definition and then describe some advantages and disadvantages of the different approaches. 

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4. Open-ti: open traffic intelligence with augmented language model

   https://doi.org/10.1007/s13042-024-02190-8  

   Da, Longchao; Liou, Kuanru; Chen, Tiejin; Zhou, Xuesong; Luo, Xiangyong; Yang, Yezhou; Wei, Hua (May 2024, International Journal of Machine Learning and Cybernetics)
5. Open Compass: Accelerating the Adoption of AI in Open Research

   https://doi.org/10.1145/3332186.3332253  

   Buitrago, Paola A.; Nystrom, Nicholas A. (October 2019, PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning))

   Artificial intelligence (AI) has immense potential spanning research and industry. AI applications abound and are expanding rapidly, yet the methods, performance, and understanding of AI are in their infancy. Researchers face vexing issues such as how to improve performance, transferability, reliability, comprehensibility, and how better to train AI models with only limited data. Future progress depends on advances in hardware accelerators, software frameworks, system and architectures, and creating cross-cutting expertise between scientific and AI domains. Open Compass is an exploratory research project to conduct academic pilot studies on an advanced engineering testbed for artificial intelligence, the Compass Lab, culminating in the development and publication of best practices for the benefit of the broad scientific community. Open Compass includes the development of an ontology to describe the complex range of existing and emerging AI hardware technologies and the identification of benchmark problems that represent different challenges in training deep learning models. These benchmarks are then used to execute experiments in alternative advanced hardware solution architectures. Here we present the methodology of Open Compass and some preliminary results on analyzing the effects of different GPU types, memory, and topologies for popular deep learning models applicable to image processing. 

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