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1. Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models?

   https://doi.org/10.32470/CCN.2019.1175-0  

   Lindsay, Grace; Moskovitz, Theodore; Yang, Guangyu Robert; Miller, Kenneth (September 2019, 2019 Conference on Cognitive Computational Neuroscience)

   null (Ed.)

   Many details are known about microcircuitry in visual cortices. For example, neurons have supralinear activation functions, they're either excitatory (E) or inhibitory (I), connection strengths fall off with distance, and the output cells of an area are excitatory. This circuitry is important as it's believed to support core functions such as normalization and surround suppression. Yet, multi-area models of the visual processing stream don't usually include these details. Here, we introduce known-features of recurrent processing into the architecture of a convolutional neural network and observe how connectivity and activity change as a result. We find that certain E-I differences found in data emerge in the models, though the details depend on which architectural elements are included. We also compare the representations learned by these models to data, and perform analyses on the learned weight structures to assess the nature of the neural interactions. 

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2. Generating realistic scaled complex networks

   https://doi.org/10.1007/s41109-017-0054-z  

   Staudt, Christian L.; Hamann, Michael; Gutfraind, Alexander; Safro, Ilya; Meyerhenke, Henning (December 2017, Applied Network Science)
3. Generating Realistic Synthetic Population Datasets

   https://doi.org/10.1145/3182383  

   Wu, Hao; Ning, Yue; Chakraborty, Prithwish; Vreeken, Jilles; Tatti, Nikolaj; Ramakrishnan, Naren (July 2018, ACM Transactions on Knowledge Discovery from Data)
4. Revisiting Membership Inference Under Realistic Assumptions

   https://doi.org/10.2478/popets-2021-0031  

   Jayaraman, Bargav; Wang, Lingxiao; Knipmeyer, Katherine; Gu, Quanquan; Evans, David (January 2021, Proceedings on Privacy Enhancing Technologies)

   Abstract We study membership inference in settings where assumptions commonly used in previous research are relaxed. First, we consider cases where only a small fraction of the candidate pool targeted by the adversary are members and develop a PPV-based metric suitable for this setting. This skewed prior setting is more realistic than the balanced prior setting typically considered. Second, we consider adversaries that select inference thresholds according to their attack goals, such as identifying as many members as possible with a given false positive tolerance. We develop a threshold selection designed for achieving particular attack goals. Since previous inference attacks fail in imbalanced prior settings, we develop new inference attacks based on the intuition that inputs corresponding to training set members will be near a local minimum in the loss function. An attack that combines this with thresholds on the per-instance loss can achieve high PPV even in settings where other attacks are ineffective. 

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5. Ensembles of realistic power distribution networks

   https://doi.org/10.1073/pnas.2205772119  

   Meyur, Rounak; Vullikanti, Anil; Swarup, Samarth; Mortveit, Henning S.; Centeno, Virgilio; Phadke, Arun; Poor, H. Vincent; Marathe, Madhav V. (October 2022, Proceedings of the National Academy of Sciences)

   The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks. 

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