86,510 research outputs found

    Compressed Sensing Inspired Neural Decoder for Undersampled MRI with Self-Assessment

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    An important problem in magnetic resonance imaging (MRI) is the long time lapse required to acquire a fully sampled, high resolution scan. To speed up acquisition, Compressed Sensing (CS) has been used and recently coupled with Neural Networks (NN). In the latter setting, commonly CS has been split into two different problems: i) design of the encoder, or selection of the undersampling pattern, and ii) design of the decoder. A significant progress was recently introduced by a solution (called LOUPE) where encoding and decoding are simultaneously addressed. Here we propose an improvement of this model, called 'regularized-LOUPE' (r-LOUPE), which add measurement constraint into the picture, resulting in a ×8 speed-up in the MRI acquisition time. A further benefit of our methodology is that measurement constraint can be leveraged to implement a self-assessment tool able to predict the reconstruction error and to identify possible out-layers

    A Deep Learning Method for Optimal Undersampling Patterns and Image Recovery for MRI Exploiting Losses and Projections

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    Compressed Sensing was recently proposed to reduce the long acquisition time of Magnetic Resonance Imaging by undersampling the signal frequency content and then algorithmically reconstructing the original image. We propose a way to significantly improve the above method by exploiting a deep neural network to tackle both problems of frequency sub-sampling and image reconstruction simultaneously, thanks to the introduction of a new loss function to drive the training and the addition of a post-processing non-neural stage. Furthermore, we highlight how some of the quantities along the processing chain can be used as a proxy of the quality of the recovered image, thus allowing a self-assessment of the whole technique. All improvements hinge on the possibility of identifying constraints to which the final image must obey and suitably enforce them. The effectiveness of our approach is tested on real-world MRI acquisitions from the fastMRI public database and achieves an appreciable improvement in Peak Signal-to-Noise Ratio with respect to the original CS-based proposal with speed-up factors 4 and 8

    Training Binary Layers by Self-Shrinking of Sigmoid Slope: Application to Fast MRI Acquisition

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    Deep Neural Networks (DNN) have become popular and widespread because they combine computational power and flexibility, but they may present critical hyper-parameters that need to be tuned before the model can be trained. Recently, the use of trainable binary masks in the field of Magnetic Resonance Imaging (MRI) acquisition brought new state-of-the-art results, but with the disadvantage of introducing a bulky hyper-parameter, which tuning is usually time-consuming. We present a novel callback-based method that is applied during training and turns the tuning problem into a triviality, also bringing non-negligible performance improvements. We test our method on the fastMRI dataset

    Structured Pruning in Deep Neural Networks with Trainable Probability Masks

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    The current trend of over-parameterized Deep Neural Networks makes the deployment on resource constrained systems challenging. To deal with this, optimization techniques, such as network pruning, can be adopted. We propose a novel pruning technique based on trainable probability masks that, when binarized, select the elements of the network to prune. Our method features i) an automatic selections of the elements to prune by jointly training the binary masks with the model, ii) the capability of controlling the pruning level through hyper-parameters of a novel regularization term. We assess the effectiveness of our method by employing it in the structured pruning of the fully connected layers of shallow and deep neural networks where it outperforms the magnitude-based pruning approache

    Variations on the Author

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    “Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship

    [Newspaper Clipping: Author Claims Evidence of Second JFK Assassin #1]

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    Newspaper article titled "Author Claims Evidence of Second JFK Assassin." The article states that author Richard J. Whalen concluded "that there is circumstantial evidence to support the theory of a second assassin in the shooting of President John F. Kennedy.

    Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation

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    The desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data. In particular, name disambiguation is required to allow accurate publication lists, citation counts and impact measures to be determined. This paper describes a graph-based approach to author disambiguation on large-scale citation networks. Using self-citation, co-authorship and document source analyses, AKTiveAuthor clusters papers, achieving precision of 0.997 and recall of 0.818 over a test group of eight surname clusters

    John F. Kennedy telegram to Roosevelt

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    Jersey Homesteads (later the Borough of Roosevelt) was established in the 1930s as an agro-industrial cooperative community. It was established specifically for urban Jewish garment workers, many of whom had emigrated from Europe. President John F. Kennedy sent a telegram to the citizens of Roosevelt, New Jersey, apologizing for not being able to attend the memorial dedication in honor of former President Franklin Delano Roosevelt. (Jersey Homesteads became Roosevelt in 1945 in honor of the president.) President Kennedy expressed his gratitude to the people of Roosevelt for constructing the memorial, and commented that it will serve as a constant reminder of Roosevelt's good works

    Logarithmic variance profiles and the corresponding f-1 spectra of temperature fluctuations in turbulent Rayleigh-Bénard convection

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    We report experimental results for the temperature variance 2(z) and the corresponding frequency spectra P(f) in turbulent Rayleigh-Bénard convection (RBC) in a cylindrical sample of aspect ratioT= D/L = 1:00 (D = 1:12 m is the diameter and L = 1:12 m the height). The measurements were conducted in the Rayleigh-number range 1011 < Ra < 1:35 1014 and Pr ' 0:8. For Ra = 1:35x1014, 2(z) could be described well by a logarithmic dependence on the vertical position z in a range of z 1 < z < z 2 with z 1 ' 70 and z 2 = 0:1L. Here L=(2Nu) is the thickness of a thin thermal sublayer adjacent to the horizontal plate where the heat flux (denoted by the Nusselt number Nu) is carried mostly by thermal diffusion. In the log layer, we found that the temperature spectra had a significant frequency range over which P(f) f with close to 1. As Ra decreased, increased so that the log layer became thinner. At Ra = 2:05 1011, z 2 < z 1 and therefore there was no range for a log layer. Correspondingly, the temperature spectrum near the horizontal plate did not have the f1 scaling form either

    Maine author Franklin F. Gould recalls his first glimpse of the outside world

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    Maine author Franklin F. Gould recalls his first glimpse of the outside world as he relates how, as a young farm boy in the late 1800\u27s, he drove his father\u27s horses on an errand to an icebound river
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