178 research outputs found

    Photoacoustic Tomography Of Water In Phantom And Biological Tissue

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    Chinas grain production: a decade of consecutive growth or stagnation?

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    pre-printSome progressive writers have argued that while China's agricultural privatization achieved short-term gains, it did so by undermining longterm production facilities such as the infrastructure and public services built in the socialist era.1 Environmental scholars have questioned the sustainability of the Chinese agriculture. In a report published in 1995, Lester R. Brown raised the question: "Who will feed China?" He argued that the Chinese population's changing diet, shrinking cropland, stagnating productivity, and environmental constraints would lead to a widening gap between China's food supply and demand, a gap the world's leading grain exporters would not be able to fill.

    Photoacoustic tomography of water in biological tissue

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    As an emerging imaging technique that combines high optical contrast and ultrasonic detection, photoacoustic tomography (PAT) has been widely used to image optically absorptive objects in both human and animal tissues. PAT overcomes the depth limitation of other high-resolution optical imaging methods, and it is also free from speckle artifacts. To our knowledge, water has never been imaged by PAT in biological tissue. Here, for the first time, we experimentally imaged water in both tissue phantoms and biological tissues using a near infrared (NIR) light source. The differences among photoacoustic images of water with different concentrations indicate that laser-based PAT can usefully detect and image water content in tissue

    Photoacoustic speckles: boundary dependence and experimental validation

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    Photoacoustic tomography (PAT) suppresses speckles by prominent boundary buildups. We theoretically study the dependence of PAT speckles on the boundary roughness, which is quantified by the root-mean-squared (RMS) value and the correlation length of the height. The speckle visibility and the correlation coefficient between the reconstructed and actual boundaries are quantified as a function of the boundary roughness. The statistics of PAT speckles is studied experimentally

    A Monte-Carlo-Based Network Method for Source Positioning in Bioluminescence Tomography

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    We present an approach based on the improved Levenberg Marquardt (LM) algorithm of backpropagation (BP) neural network to estimate the light source position in bioluminescent imaging. For solving the forward problem, the table-based random sampling algorithm (TBRS), a fast Monte Carlo simulation method we developed before, is employed here. Result shows that BP is an effective method to position the light source

    How Does Information Bottleneck Help Deep Learning?

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    Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while task-relevant information is maximized. However, a rigorous argument for justifying why it is desirable to control information bottlenecks has been elusive. In this paper, we provide the first rigorous learning theory for justifying the benefit of information bottleneck in deep learning by mathematically relating information bottleneck to generalization errors. Our theory proves that controlling information bottleneck is one way to control generalization errors in deep learning, although it is not the only or necessary way. We investigate the merit of our new mathematical findings with experiments across a range of architectures and learning settings. In many cases, generalization errors are shown to correlate with the degree of information bottleneck: i.e., the amount of the unnecessary information at hidden layers. This paper provides a theoretical foundation for current and future methods through the lens of information bottleneck. Our new generalization bounds scale with the degree of information bottleneck, unlike the previous bounds that scale with the number of parameters, VC dimension, Rademacher complexity, stability or robustness. Our code is publicly available at: https://github.com/xu-ji/information-bottleneckComment: Accepted at ICML 2023. Code is available at https://github.com/xu-ji/information-bottlenec

    Multi-objective Transmission Planning Paper

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    2008-2009 > Academic research: refereed > Refereed conference pape
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