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1. Cycle-by-cycle analysis of neural oscillations

   https://doi.org/10.1152/jn.00273.2019  

   Cole, Scott; Voytek, Bradley (July 2019, Journal of Neurophysiology)

   Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. This has successfully uncovered numerous links between oscillations and cognition or disease. However, neural data are nonsinusoidal, and these nonsinusoidal features are increasingly linked to a variety of behavioral and cognitive states, pathophysiology, and underlying neuronal circuit properties. Here, we present a new analysis framework-one that is complementary to existing Fourier- and Hilbert-transform based approaches-that quantifies oscillatory features in the time domain, on a cycle-by-cycle basis. We have released this cycle-by-cycle analysis suite as bycycle, a fully documented, open-source Python package with detailed tutorials and troubleshooting cases. This approach performs tests to assess whether an oscillation is present at any given moment and, if so, quantifies each oscillatory cycle by its amplitude, period, and waveform symmetry, the latter of which is missed using conventional approaches. In a series of simulated event-related studies, we show how conventional Fourier- and Hilbert-transform approaches can conflate event-related changes in oscillation burst duration as increased oscillatory amplitude and as a change in the oscillation frequency, even though those features were unchanged in simulation. Our approach avoids these errors. Further, we validate this approach in simulation and against experimental recordings of patients with Parkinson's disease, who are known to have nonsinusoidal beta (12-30 Hz) oscillations. 

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2. Incremental Cycle Bases for Cycle-Based Pose Graph Optimization

   https://doi.org/10.1109/LRA.2023.3236580  

   Forsgren, Brendon; Brink, Kevin; Ganesh, Prashant; McLain, Timothy W. (February 2023, IEEE Robotics and Automation Letters)
3. Business-Cycle Anatomy

   https://doi.org/10.1257/aer.20181174  

   Angeletos, George-Marios; Collard, Fabrice; Dellas, Harris (October 2020, American Economic Review)

   null (Ed.)

   We propose a new strategy for dissecting the macroeconomic time series, provide a template for the business-cycle propagation mechanism that best describes the data, and use its properties to appraise models of both the parsimonious and the medium-scale variety. Our findings support the existence of a main business-cycle driver but rule out the following candidates for this role: technology or other shocks that map to TFP movements; news about future productivity; and inflationary demand shocks of the textbook type. Models aimed at accommodating demand-driven cycles without a strict reliance on nominal rigidity appear promising. (JEL C22, E10, E32) 

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4. Cycle-to-cycle Variation Enabled Energy Efficient Privacy Preserving Technology in ANN

   https://doi.org/10.1109/SOCC49529.2020.9524794  

   Fu, Jingyan; Liao, Zhiheng; Wang, Jinhui (September 2020, 33rd IEEE International SoC Conference (SoCC’20))
5. The Ocean Carbon Cycle

   https://doi.org/10.1146/annurev-environ-120920-111307  

   DeVries, Tim (October 2022, Annual Review of Environment and Resources)

   The ocean holds vast quantities of carbon that it continually exchanges with the atmosphere through the air-sea interface. Because of its enormous size and relatively rapid exchange of carbon with the atmosphere, the ocean controls atmospheric CO 2 concentration and thereby Earth's climate on timescales of tens to thousands of years. This review examines the basic functions of the ocean's carbon cycle, demonstrating that the ocean carbon inventory is determined primarily by the mass of the ocean, by the chemical speciation of CO 2 in seawater, and by the action of the solubility and biological pumps that draw carbon into the ocean's deeper layers, where it can be sequestered for decades to millennia. The ocean also plays a critical role in moderating the impacts of climate change by absorbing an amount of carbon equivalent to about 25% of anthropogenic CO 2 emissions over the past several decades. However, this also leads to ocean acidification and reduces the chemical buffering capacity of the ocean and its future ability to take up CO 2 . This review closes with a look at the uncertain future of the ocean carbon cycle and the scientific challenges that this uncertainty brings. 

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