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1. Persistence Atlas for Critical Point Variability in Ensembles

   https://doi.org/10.1109/TVCG.2018.2864432  

   Favelier, Guillaume; Faraj, Noura; Summa, Brian; Tierny, Julien (January 2019, IEEE Transactions on Visualization and Computer Graphics)
2. Imposing Motion Variability for Ergonomic Human-Robot Collaboration

   https://doi.org/10.1080/24725838.2024.2329114  

   Zolotas, Mark; Luo, Rui; Bazzi, Salah; Saha, Dipanjan; Mabulu, Katiso; Kloeckl, Kristian; Padır, Taşkın (April 2024, IISE Transactions on Occupational Ergonomics and Human Factors)
3. Neuronal Variability as a Proxy for Network State

   https://doi.org/10.1016/j.tins.2018.02.003  

   Nogueira, Ramon; Lawrie, Sofía; Moreno-Bote, Rubén (April 2018, Trends in Neurosciences)
4. Taming Performance Variability

   Maricq, A; Duplyakin, D; Jimenez, I; Maltzahn, C; Stutsman, R; Ricci, R (October 2018, Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18))

   The performance of compute hardware varies: software run repeatedly on the same server (or a different server with supposedly identical parts) can produce performance results that differ with each execution. This variation has important effects on the reproducibility of systems research and ability to quantitatively compare the performance of different systems. It also has implications for commercial computing, where agreements are often made conditioned on meeting specific performance targets. Over a period of 10 months, we conducted a large-scale study capturing nearly 900,000 data points from 835 servers. We examine this data from two perspectives: that of a service provider wishing to offer a consistent environment, and that of a systems researcher who must understand how variability impacts experimental results. From this examination, we draw a number of lessons about the types and magnitudes of performance variability and the effects on confidence in experiment results. We also create a statistical model that can be used to understand how representative an individual server is of the general population. The full dataset and our analysis tools are publicly available, and we have built a system to interactively explore the data and make recommendations for experiment parameters based on statistical analysis of historical data. 

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5. Spectral variability of a sample of extreme variability quasars and implications for the Mg ii broad-line region

   https://doi.org/10.1093/mnras/staa645  

   Yang, Qian; Shen, Yue; Chen, Yu-Ching; Liu, Xin; Annis, James; Avila, Santiago; Bertin, Emmanuel; Brooks, David; Buckley-Geer, Elizabeth; Carnero Rosell, Aurelio; et al (April 2020, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT We present new Gemini/GMOS optical spectroscopy of 16 extreme variability quasars (EVQs) that dimmed by more than 1.5 mag in the g band between the Sloan Digital Sky Survey (SDSS) and the Dark Energy Survey epochs (separated by a few years in the quasar rest frame). These EVQs are selected from quasars in the SDSS Stripe 82 region, covering a redshift range of 0.5 < z < 2.1. Nearly half of these EVQs brightened significantly (by more than 0.5 mag in the g band) in a few years after reaching their previous faintest state, and some EVQs showed rapid (non-blazar) variations of greater than 1–2 mag on time-scales of only months. To increase sample statistics, we use a supplemental sample of 33 EVQs with multi-epoch spectra from SDSS that cover the broad Mg ii λ2798 line. Leveraging on the large dynamic range in continuum variability between the multi-epoch spectra, we explore the associated variations in the broad Mg ii line, whose variability properties have not been well studied before. The broad Mg ii flux varies in the same direction as the continuum flux, albeit with a smaller amplitude, which indicates at least some portion of Mg ii is reverberating to continuum changes. However, the full width at half-maximum (FWHM) of Mg ii does not vary accordingly as continuum changes for most objects in the sample, in contrast to the case of the broad Balmer lines. Using the width of broad Mg ii to estimate the black hole mass with single epoch spectra therefore introduces a luminosity-dependent bias. 

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