Notes

Autoantibody users delineate distinctive subsets associated with scleromyositis.
The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset. AZD2171 datasheet To further analyze the performance of low-light restoration methods, we also propose the L3F-wild dataset that contains LF captured late at night with almost zero lux values. No ground truth is available in this dataset. To perform well on the L3F-wild dataset, any method must adapt to the light level of the captured scene. To do this we use a pre-processing block that makes L3Fnet robust to various degrees of low-light conditions. Lastly, we show that L3Fnet can also be used for low-light enhancement of single-frame images, despite it being engineered for LF data. We do so by converting the single-frame DSLR image into a form suitable to L3Fnet, which we call as pseudo-LF. Our code and dataset is available for download at https//mohitlamba94.github.io/L3Fnet/.Scene text recognition, the final step of the scene text reading system, has made impressive progress based on deep neural networks. However, existing recognition methods devote to dealing with the geometrically regular or irregular scene text. They are limited to the semantically arbitrary-orientation scene text. Meanwhile, previous scene text recognizers usually learn the single-scale feature representations for various-scale characters, which cannot model effective contexts for different characters. In this paper, we propose a novel scale-adaptive orientation attention network for arbitrary-orientation scene text recognition, which consists of a dynamic log-polar transformer and a sequence recognition network. Specifically, the dynamic log-polar transformer learns the log-polar origin to adaptively convert the arbitrary rotations and scales of scene texts into the shifts in the log-polar space, which is helpful to generate the rotation-aware and scale-aware visual representation. Next, the sequence recognition network is an encoder-decoder model, which incorporates a novel character-level receptive field attention module to encode more valid contexts for various-scale characters. The whole architecture can be trained in an end-to-end manner, only requiring the word image and its corresponding ground-truth text. Extensive experiments on several public datasets have demonstrated the effectiveness and superiority of our proposed method.We consider lossy compression of a broad class of bilevel images that satisfy the smoothness criterion, namely, images in which the black and white regions are separated by smooth or piecewise smooth boundaries, and especially lossy compression of complex bilevel images in this class. We propose a new hierarchical compression approach that extends the previously proposed fixed-grid lossy cutset coding (LCC) technique by adapting the grid size to local image detail. LCC was claimed to have the best rate-distortion performance of any lossy compression technique in the given image class, but cannot take advantage of detail variations across an image. The key advantages of the hierarchical LCC (HLCC) is that, by adapting to local detail, it provides constant quality controlled by a single parameter (distortion threshold), independent of image content, and better overall visual quality and rate-distortion performance, over a wider range of bitrates. We also introduce several other enhancements of LCC that improve reconstruction accuracy and perceptual quality. These include the use of multiple connection bits that provide structural information by specifying which black (or white) runs on the boundary of a block must be connected, a boundary presmoothing step, stricter connectivity constraints, and more elaborate probability estimation for arithmetic coding. We also propose a progressive variation that refines the image reconstruction as more bits are transmitted, with very small additional overhead. Experimental results with a wide variety of, and especially complex, bilevel images in the given class confirm that the proposed techniques provide substantially better visual quality and rate-distortion performance than existing lossy bilevel compression techniques, at bitrates lower than lossless compression with the JBIG or JBIG2 standards.The acoustic output characterization of medical ultrasonic equipment requires regular calibration of the hydrophones used to ensure the reliability of measurements. Such hydrophone calibration is offered as a service by several institutions. Various calibration techniques using a variety of ultrasonic excitation pressure waveforms comprising different pressure amplitude ranges and frequency compositions as well as different reference measurement systems have been proposed and applied over the past decades. Currently, four different setups for hydrophone calibration are available at the Physikalisch-Technische Bundesanstalt (PTB). This internal comparison study addresses the consistency of all four methods, including direct primary calibration and substitution calibration using reference hydrophones. The methods apply single-frequency tonebursts and swept tonebursts in the kPa amplitude range of quasi-linear acoustics as well as impulse excitation including nonlinear propagation. In recent years, a new primary calibration setup using a high-frequency vibrometer has been implemented at PTB, enabling the characterization of hydrophone frequency responses in modulus and phase and extending the upper frequency limit to up to 100 MHz. For the comparison in the frequency range from 0.5 MHz to 60 MHz, two passive membrane hydrophones with well-known characteristics gained from many years of measurements were used. Another membrane hydrophone with a nominal diameter of 0.2 mm and an integrated preamplifier was applied to address the frequency range up to 100 MHz. The results obtained with the different setups showed good agreement with average root-mean-square (rms) deviations of 3% (primary calibrations, 1-60 MHz) and 4% (1-100 MHz). The consistency of the implementations was thus verified in this comparison.Multi-element transmit arrays with low peak 10 g specific absorption rate (SAR) and high SAR efficiency (defined as ( [Formula see text]SAR [Formula see text] are essential for ultra-high field (UHF) magnetic resonance imaging (MRI) applications. Recently, the adaptation of dipole antennas used as MRI coil elements in multi-channel arrays has provided the community with a technological solution capable of producing uniform images and low SAR efficiency at these high field strengths. However, human head-sized arrays consisting of dipole elements have a practical limitation to the number of channels that can be used due to radiofrequency (RF) coupling between the antenna elements, as well as, the coaxial cables necessary to connect them. Here we suggest an asymmetric sleeve antenna as an alternative to the dipole antenna. When used in an array as MRI coil elements, the asymmetric sleeve antenna can generate reduced peak 10 g SAR and improved SAR efficiency. To demonstrate the advantages of an array consisting of our suggested design, we compared various performance metrics produced by 16-channel arrays of asymmetric sleeve antennas and dipole antennas with the same dimensions.
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