ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Variational single image interpolation with time-varying regularization

Single image interpolation has wide applications in digital photography and image display. Most single image interpolation approaches achieve state-of-the-art performance at the expense of very high computation time. While efficient alternatives exist, they do not reach the same level of image quality. In this paper, we propose an image interpolation method offering both high computational efficiency and high interpolation quality. We exploit a newly-developed variational framework with time-varying regularization, i.e., the parameters of the regularization are allowed to change with time, making it different to conventional variational problems with time-independent regularization parameters. These time-varying parameters are learned from training samples. We train the model parameters for the problem of single image interpolation. Experiments show that the trained models lead to promising quality of the interpolated images in terms of quantitative measurements (e.g., PSNR and SSIM), compared with the state-of-the-art approaches. Meanwhile, high computational efficiency is obtained. (C) 2017 Elsevier B.V. All rights reserved.
Profiling and occupational health risk assessment study on coal ashes in terms of polycyclic aromatic hydrocarbons (PAHs)

Profiling and cancer risk assessment on the polycyclic aromatic hydrocarbons (PAHs) content of coal ashes produced by the major coal combustion plants from the eastern coalfield region in India was conducted. Thirteen PAHs were detected on coal ashes collected from ash deposition sites of major thermal power plants and the profiling of the PAHs was done. Benzo[a]pyrene equivalents (BaPeq) for individual PAHs were calculated and applied to the probabilistic assessment model from US EPA (1989). Monte Carlo simulations were conducted to assess the risk of inhabitants exposed to PAHs through the dust of the coal ash deposition site. In fly ash, the range of total amount of carcinogenic PAHs was from 3.50 to 6.72 µg g-1 and for the bottom ash, the range was 8.49 to 14.91 µg g-1. Bottom ashes were loaded with ample amounts of 5- and 6-ring carcinogenic PAHs, whereas fly ashes were dominated by medium molecular weight PAHs. The simulated mean cancer risks from fly ashes were 2.187 E-06 for children and 3.749 E-06 for adults. For the case of bottom ash, the mean risks were 1.248 E-05 and 2.173 E-05 respectively for children and adults. Among all the three exposure routes, dermal contact was the major and caused 81% of the total cancer risk. The most sensitive parameters were exposure duration and relative skin adherence factor for soil, which contributed the most to total variation. The 90% risks calculated from the bottom ashes (2.617 E-05 for children and 4.803 E-05 for adults) are marginally above the acceptable limit (>1.000 E-06) according to US EPA. In this study, a comprehensive risk assessment on carcinogenic PAHs present in coal ashes was done for the first time that may be helpful to develop potential strategies against occupational cancer risk.
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
Coordination Switch Drives Selective C-S Bond Formation by the Non-Heme Sulfoxide Synthases

The non-heme iron ergothioneine synthase (EgtB) is a sulfoxide synthase that catalyzes oxidative C-S bond formation in the synthesis of ergothioneine, which plays roles against oxidative stress in cells. Despite extensive experimental and computational studies of the catalytic mechanisms of EgtB, the root causes for the selective C-S bond formation remain elusive. Using quantum mechanics/molecular mechanics (QM/MM) calculations, we show herein that a coordination switch of the sulfoxide intermediate is involved in the catalysis of the non-heme iron EgtB. This coordination switch from the S to the O atom is driven by the S/π electrostatic interactions, which efficiently promotes the observed stereoselective C-S bond formation while bypassing cysteine dioxygenation. The present mechanism is in agreement with all available experimental data, including regioselectivity, stereoselectivity and KIE results. This match underscores the critical role of coordination switching in the catalysis of non-heme enzymes.
Toward Large-Pixel Number High-Speed Imaging Exploiting Time and Space Sparsity

In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain, but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.
Today and tomorrow of analyses' methodology of electric power system reliability

In this paper the review of the state-of-art of electric power system reliability analyses methodology as well as the new trends are given. The definition and the relationships between electric power system reliability and electric energy security and quality of electricity supply are presented. The available models, methods and computer tools are discussed. (Today and tomorrow of analyses' methodology of electric power system reliability).
Stochastic Computing Improves the Timing-Error Tolerance and Latency of Turbo Decoders: Design Guidelines and Tradeoffs

Stochastic computing has been recently proposed for the hardware implementation of both low-density parity-check (LDPC) decoders and turbo decoders, which facilitate near-optimal error correction capabilities in wireless communication applications. Previous contributions have demonstrated that stochastic LDPC decoders offer an attractive tradeoff between their error correction capabilities, hardware performance, and timing-error tolerance. Motivated by this, we propose a pair of stochastic turbo decoder (S Ill) designs having significantly enhanced timing-error tolerance and significantly reduced processing latency. Moreover, we characterize the tradeoffs between chip area, energy efficiency, latency, throughput, and error correction capabilities of both the timing-error-tolerant S ID and of the reduced-latency S ID. We demonstrate that our proposed timing-error-tolerant S ID operated at 1.20 V, with a clock period of 2.2 ns and in the presence of a three-standard deviation power supply variation of 7%, exhibits an unimpaired performance, compared with the state-of-the-art S ID, operated at 1.20 V and 4 ns and with no power supply variations. This corresponds processing throughput improvement by a factor of 2.42 and energy consumption reduction by a factor of 4. Finally, we demonstrate that our proposed reduced-latency S ID has a processing latency that is an order of magnitude lower than that of the state-of-the-art STD. This is despite reducing the chip area by a factor of 4, increasing the processing throughput by a factor of 65, while consuming only 0.005 times the energy of the state-of-the-art S ID, when using binary phase-shift keying for communication over an additive white Gaussian noise channel having E-b/N-0 = 3 dB.
The Internet of Audio Things: State of the Art, Vision, and Challenges

The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis, and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this article, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss the challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.
Variational EM method for blur estimation using the spike-and-slab image prior

Most state-of-the-art blind image deconvolution methods rely on the Bayesian paradigm to model the deblurring problem and estimate both the blur kernel and latent image. It is customary to model the image in the filter space, where it is supposed to be sparse, and utilize convenient priors to account for this sparsity. In this paper, we propose the use of the spike-and-slab prior together with an efficient variational Expectation Maximization (EM) inference scheme to estimate the blur in the image. The spike-and-slab prior, which constitutes the gold standard in sparse machine learning, selectively shrinks irrelevant variables while mildly regularizing the relevant ones. The proposed variational Expectation Maximization algorithm is more efficient than usual Markov Chain Monte Carlo (MCMC) inference and, also, proves to be more accurate than the standard mean-field variational approximation. Additionally, all the prior model parameters are estimated by the proposed scheme. After blur estimation, a non blind restoration method is used to obtain the actual estimation of the sharp image. We investigate the behavior of the prior in the experimental section together with a series of experiments with synthetically generated and real blurred images that validate the method's performance in comparison with state-of-the-art blind deconvolution techniques. (C) 2019 Elsevier Inc. All rights reserved.
Benign acute childhood myositis following human Influenza B virus infection - A case series

Influenza virus primarily affects ciliated cells of respiratory epithelium. Humans do not have innate immunity for these viruses and are vulnerable to get attacked. Benign acute childhood myositis usually occurs at the early convalescent phase of a influenza viral illness when fever, cough, myalgia, nasal discharge are the initial presentation.
Weld defect classification in radiographic images using unified deep neural network with multi-level features

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including weld defect classification. However, there is still a large room for improving the classification performance over the generic DNN models. In this paper, a unified deep neural network with multi-level features is proposed for weld defect classification. Firstly, we define 11 weld defect features as inputs of our proposed classification model. Not limited to geometric and intensity features, 4 features based on the intensity contrast between weld defect and its background are proposed in this paper. Secondly, we construct a novel deep learning framework: a unified deep neural network, where multi-level features of each hidden layer are fused by the last hidden layer to predict the type of weld defect comprehensively. In addition, we investigate pre-training and fine-turning strategies to get better generalization performance with small dataset. Comparing with other classification methods like SVM and generic DNN model, our framework takes full advantage of multi-level features extracted from each hidden layer, an outstanding performance is shown where the classification accuracy is improved by 3.18% and 4.33% on the test dataset, to reach 91.36%.
BitCluster: Fine-Grained Weight Quantization for Load-Balanced Bit-Serial Neural Network Accelerators

Convolutional neural network (CNN) has demonstrated great success in pattern recognition scenarios at the cost of nearly billions of parameters and consequent convolution operations. Various dedicated hardware designs are proposed to accelerate the CNN computation in more energy-efficient manners. Especially, the bit-serial accelerator (BSA) is one of the most effective approaches on resource-limited platforms by eliminating zero-bit computations. However, the irregular distribution and varying number of effectual (nonzero) bits in weights significantly cause hardware underutilization, impeding further performance improvement of state-of-the-art BSAs. To address this issue, BitCluster, a hardware-friendly quantization method, is proposed to make each weight with the identical number of effectual bits for load-balanced computation. Considering distinct sensitivities to weight precision in different neural layers, layer-level BitCluster is proposed to design further for fine-grained weight quantization. It systematically determines the layerwise quantization configurations, which significantly improve the overall performance with <1% accuracy loss. BitCluster is comprehensively evaluated on a BitCluster-compatible BSA design by taking six mainstream CNN models as benchmarks. The experimental results show that the BitCluster-based BSA achieves 1.6x higher hardware utilization and 3.4x speedup on average than state-of-the-art BSAs, with 5x better energy efficiency on average.
Towards Ultrasound Everywhere: A Portable 3D Digital Back-End Capable of Zone and Compound Imaging

Ultrasound imaging is a ubiquitous diagnostic technique, but does not fit the requirements of the telemedicine approach, because it relies on the real-time manipulation and image recognition skills of a trained expert, called sonographer. Sonographers are only available in hospitals and clinics, negating or at least delaying access to ultrasound scans in many locales-rural areas, developing countries-as well as in medical rescue operations. Telesonography would require an advanced imager that supports three-dimensional (3-D) acquisition; this would allow untrained operators to acquire broad scans and upload them remotely for diagnosis. Such advanced imagers do exist, but do not meet several other requirements for telesonography, such as being portable, inexpensive, and sufficiently low power to enable battery operation. In thiswork, we present our prototype of the first portable 3-D digital ultrasound back-end system. The prototype is implemented in a single midrange Xilinx field programmable gate array (FPGA), for an estimated power consumption of 5 W. The device supports up to 1024 input channels, which is state of the art and could be scaled further, and supports multiple image reconstruction modes. We evaluate the resource utilization of the FPGA and provide various quality metrics to ascertain the output image quality.
Fuzzy-Coded Space-Frequency Quantization for SAR Data Compression

In this letter, we propose a new image coding technique, which is a combination of space frequency quantization and a context-based modeling using fuzzy logic. The compression results showed that the proposed coder outperforms the state-of-the-art coders in the rate-distortion sense for compression of processed synthetic aperture radar amplitude data.
Steven-Johnson Syndrome: A Rare but Serious Adverse Event of Nivolumab Use in a Patient With Metastatic Gastric Adenocarcinoma

Nivolumab is a humanized monoclonal anti-programmed cell death receptor-1 (PD-1) antibody that has been authorized for use in the treatment of advanced malignancies. Cutaneous reactions are the most common immune-related adverse events reported with anti-PD-1 agents, and they range broadly from mild localized reactions to rarely severe or life-threatening systemic dermatoses. The occurrence of Steven-Johnson syndrome (SJS) or toxic epidermal necrolysis (TEN) with nivolumab use is an exceedingly rare phenomenon that was only documented in a handful of cases in the current literature, but it deserves careful attention as SJS/TEN may be associated with fatal outcomes. We present a case of nivolumab-induced SJS/TEN in a middle-aged female patient with metastatic gastric adenocarcinoma that was successfully treated with immunosuppressive therapy and supportive care. Prompt recognition of SJS/TEN with discontinuation of nivolumab is warranted when SJS/TEN is suspected clinically. Multidisciplinary management in a specialized burn unit is the key to improving outcomes of SJS/TEN.
Local Information Assisted Attention-Free Decoder for Audio Captioning

Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions. To address this issue, we propose a method using the pretrained audio neural networks (PANNs) as the encoder and local information assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of our method is in the proposal of the LocalAFT decoder, which allows local information within an audio signal to be captured while retaining the global information. This enables the events of different duration, including short duration, to be captured for more precise caption generation. Experiments show that our method outperforms the state-of-the-art methods in Task 6 of the DCASE 2021 Challenge with the standard attention-based decoder for caption generation.
Associations of genome-wide cell-free DNA fragmentation profiles with blood biochemical and hematological parameters in healthy individuals

Cell-free DNA (cfDNA), as a non-invasive approach, has been introduced in a wide range of applications, including cancer diagnosis/ monitoring, prenatal testing, and transplantation monitoring. Yet, studies of cfDNA fragmentomics in physiological conditions are lacking. In this study, we aim to explore the correlation of fragmentation patterns of cfDNA with blood biochemical and hematological parameters in healthy individuals. We addressed the impact of physiological variables and abnormal blood biochemical and hematological parameters on cfDNA fragment size distribution. We also figured and validated that hematological inflammation markers, including leukocyte, lymphocyte, neutrophil, and platelet distribution width as well as aspartate transaminase levels were significantly correlated with the genome-wide cfDNA fragmentation pattern. Our findings suggest that cfDNA fragmentation profiles were associated with physiological parameters related to cardiovascular risk factors, inflammatory response and hepatocyte injury, which may provide insights for further research on the potential role of cfDNA fragmentation in diagnosis and monitor of several disease.
Differentiation therapy: Unlocking phenotypic plasticity of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. The current treatment of HCC mainly includes surgery, chemotherapy, and liver transplantation. HCC differentiation therapy aims to restore tumor cells' normal liver characteristics and unlock their phenotypic plasticity. Understanding the molecular and signaling pathways that control HCC differentiation can help identify new targets for inducing differentiation and provide ideas for drug design. Downregulation of liver enriched transcription factors, imbalanced signal pathway, and dysregulated microRNA play essential roles in regulating the differentiation state of HCC. Restoring normal expression levels of these molecules could induce the tumor cells to differentiate into hepatocyte-like cells (HLCs) and suppress the malignant tumor phenotype. The strategies for inducing HLCs from induced pluripotent stem cells, fibroblasts, and other somatic cells provide a reference for the induced differentiation of liver cancer. The differentiation therapy is expected to be a promising and effective treatment for HCC.
Dual-Bit-Wise Stochastic Decoding for Polar Codes

codes, being the first class of codes with provable capacity-achieving property, have aroused extensive attention. The belief propagation (BP) algorithm, which is one of the popular decoding approaches for polar codes, possesses inherent high parallelism but also high computational complexity. With low complexity and high fault tolerance, stochastic computing has been well studied and applied to BP decoding for polar codes. However, existing stochastic BP decoders suffer from high decoding latency. In this paper, firstly, a novel dual-bit-wise iterative message update method is proposed for stochastic BP, reducing the decoding latency by more than half. Based on the dual-bit-wise decoding, more special nodes in the factor graph are investigated and then fully exploited to pursue lower decoding complexity than prior arts. Moreover, a well-optimized architecture is developed for the proposed stochastic decoder, where the random number generator (RNG), tracking forecast memory (TFM), and hard decision unit are intelligently designed for reduced hardware cost. Experimental results demonstrate that the presented decoder can exhibit 61.7% lower decoding latency than the state-of-the-art works. Besides, the throughput and hardware efficiency can be improved by nearly 2.8x and 2.7x, respectively.