Resistive switching devices, important for emerging memory and neuromorphic applications, face significant challenges related to the control of delicate filamentary states in the oxide material. As a device switches, its rapid conductivity change is involved in a positive feedback process that would lead to runaway destruction of the cell without current, voltage, or energy limitation. Typically, cells are directly patterned on MOS transistors to limit the current, but this approach is very restrictive as the necessary integration limits the materials available as well as the fabrication cycle time. In this article, we propose an external circuit to cycle resistive memory cells, capturing the full transfer curves while driving the cells in a way that suppresses runaway transitions. Using this circuit, we demonstrate the acquisition of 105 I, V loops per second without using on-wafer current limiting transistors. This setup brings voltage sweeping measurements to a relevant timescale for applications and enables many new experimental possibilities for device evaluation in a statistical context.

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The gender gap is a well-known problem in academia and, despite its gradual narrowing, recent estimations indicate that it will persist for decades. Short-term descriptive studies suggest that this gap may have actually worsened during the months of confinement following the start of the COVID-19 pandemic in 2020. In this work, we evaluate the impact of the COVID-19 lockdown on female and male academics’ research productivity using preprint drop-off data. We examine a total of 307,902 unique research articles deposited in 5 major preprint repositories during the period between January and May each year from 2017 to 2020. We find that the proportion of female authors in online repositories steadily increased over time; however, the trend reversed during the confinement and gender parity worsened in two respects. First, the proportion of male authors in preprints increased significantly during lockdown. Second, the proportion of male authors in COVID-19-related articles was significantly higher than that of women. Overall, our results imply that the gender gap in academia suffered an approximately 1-year setback during the strict lockdown months of 2020, and COVID-related research areas suffered an additional 1.5-year setback.

Functional magnetic resonance imaging (fMRI) is a non‐invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting‐state, that is, resting‐state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high‐resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high‐dimensional functional data relying on depth‐based notions that enable computationally efficient 2‐dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.

By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for large-scale simulations of computational architectures based on emerging devices is to accurately capture device response, hysteresis, noise, and the covariance structure in the temporal domain as well as between the different device parameters. We address this challenge with a high throughput generative model for synaptic arrays that is based on a recently available type of electrical measurement data for resistive memory cells. We map this real-world data onto a vector autoregressive stochastic process to accurately reproduce the device parameters and their cross-correlation structure. While closely matching the measured data, our model is still very fast; we provide parallelized implementations for both CPUs and GPUs and demonstrate array sizes above one billion cells and throughputs exceeding one hundred million weight updates per second, above the pixel rate of a 30 frames/s 4K video stream.