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Free, publicly-accessible full text available January 1, 2023
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Abstract Galaxy clusters identified via the Sunyaev-Zel’dovich effect (SZ) are a key ingredient in multi-wavelength cluster cosmology. We present and compare three methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding, a Convolutional Neural Networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuningmore »
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Simultaneous human activities, such as the Super Bowl game, would cause certain impacts on frequency fluctuations in power systems. With the help of FNET/GridEye measurements, this paper aims to give comprehensive analyses on the frequency fluctuations during Super Bowl LIV held on Feb. 2, 2020, so as to better understand several phenomena caused by simultaneous activities which will help system operations and controls. First, recent developments of the FNET/GridEye are briefly introduced. Second, the frequency fluctuations of the Eastern Interconnection (EI), western electricity coordinating council (WECC), and electric reliability council of Texas (ERCOT) power systems during Super Bowl LIV aremore »
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Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. However, current datasets for neuronal nuclei usually contain volumes smaller than 0.01 cubic mm with fewer than 500 instances per volume, unable to reveal the complexity in large brain regions and restrict the investigation of neuronal structures. In this paper, we have pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 cubic mm electron microscopy (EM) volume containing nearly the entire zebra sh brainmore »
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Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due to the complex morphology, an accurate reconstruction of cortical axons has become a major challenge. Worse still, there is no publicly available large-scale EM dataset from the cortex that provides dense ground truth segmentation for axons, making it difficult to develop and evaluate large-scale axon reconstruction methods. To address this, we introduce the AxonEM dataset, which consists of two 30x30x30 cubic mm EM image volumes from the human and mouse cortex, respectively. We thoroughly proofread overmore »