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result(s) for
"Cable television broadcasting industry"
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Newsom, DeSantis debate on 'Hannity'
2023
California Gov. Gavin Newsom (D) and Florida Gov. Ron DeSantis (R) appeared on Fox News's \"Hannity\" for a debate on Nov. 30.
Streaming Video
Fully hardware-implemented memristor convolutional neural network
2020
Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks
. However, convolutional neural networks (CNNs)-one of the most important models for image recognition
-have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices
. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST
image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing.
Journal Article
McConnell decries Fox News depiction of Jan. 6 tapes
2023
Senate Minority Leader Mitch McConnell (R-Ky.) on March 7 criticized Fox News host Tucker Carlson’s depiction of the Jan. 6, 2021, Capitol attack, and noted his support for U.S. Capitol Police Chief Tom Manger.
Streaming Video
Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction
by
Royer, Loic A
,
Belthangady, Chinmay
in
Biological products
,
Cable television broadcasting industry
,
Deep Learning
2019
Deep learning is becoming an increasingly important tool for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration and super-resolution imaging, and discuss how the latest deep learning research could be applied to other image reconstruction tasks. Despite its successes, deep learning also poses substantial challenges and has limits. We discuss key questions, including how to obtain training data, whether discovery of unknown structures is possible, and the danger of inferring unsubstantiated image details.
Journal Article
An empirical survey of data augmentation for time series classification with neural networks
by
Iwana, Brian Kenji
,
Uchida, Seiichi
in
Analysis
,
Archives & records
,
Artificial neural networks
2021
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.
Journal Article
DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning
by
Nehme, Elias
,
Alalouf, Onit
,
Ferdman, Boris
in
Biological activity
,
Biological Phenomena
,
Cable television broadcasting industry
2020
An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy.
Journal Article
Stirewalt explains ‘red mirage' from 2020 election night
2022
Chris Stirewalt, a former political editor for Fox News, explained “the red mirage” on June 13 during the Jan. 6 hearing.
Streaming Video
Deep learning for multi-year ENSO forecasts
by
Kim, Jeong-Hwan
,
Ham, Yoo-Geun
,
Luo, Jing-Jia
in
Analysis
,
Artificial neural networks
,
Atmosphere
2019
Variations in the El Niño/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts
. Robust, long-lead forecasts would therefore be valuable for managing policy responses. But despite decades of effort, forecasting ENSO events at lead times of more than one year remains problematic
. Here we show that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observation data, we use transfer learning to train a convolutional neural network (CNN) first on historical simulations
and subsequently on reanalysis from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models. A heat map analysis indicates that the CNN model predicts ENSO events using physically reasonable precursors. The CNN model is thus a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms.
Journal Article