ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Nearest-Neighbor based Metric Functions for indoor scene recognition

Indoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classification method that can handle such difficulties of the problem domain by employing a metric function based on the Nearest-Neighbor classification procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. (C) 2011 Elsevier Inc. All rights reserved.
Segmentation of farmlands in aerial images by deep learning framework with feature fusion and context aggregation modules

Automated segmentation of farmland patterns in high resolution aerial images is very crucial for smart farming. Recently, deep learning techniques have achieved tremendous success in various semantic segmentation tasks, however, little efforts have been made in farmland semantic segmentation in high resolution aerial images. Farmland semantic segmentation in aerial images is a challenging task due to large variation in scales and shapes of agriculture patterns. Furthermore, different agriculture patterns share similar visual features that usually result in mis-classifications of pixels. To efficiently tackle these problems, we propose a deep learning framework that captures scene context and aggregate multi-scale information from different convolutional blocks. Generally, the framework consists of two main modules:(1) feature fusion module and (2) global contextual module. Feature fusion module combines the feature maps of different convolutional blocks to capture wide variation in object scales, while global contextual module aggregate rich contextual information from different regions of the image by employing pyramid pooling module. We gauge the performance of proposed framework on challenging benchmarks dataset, Agriculture-vision and also compare our results with various state-of-the-art methods. From experiment results, we demonstrate that the proposed framework achieves best performance in identifying various complex agriculture patterns and supersedes state-of-the-art methods.
A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information

Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model ((MNCM)-N-1) to remove the noncentral chi-distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed (MNCM)-N-1-NSS-LSS-PR method with various versions of (MNCM)-N-1-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed (MNCM)-N-1-NSS-LSS-PR method was robust to the number of diffusion directions.
A Fast Method for High-Resolution Voiced/Unvoiced Detection and Glottal Closure/Opening Instant Estimation of Speech

We propose a fast speech analysis method which simultaneously performs high-resolution voiced/unvoiced detection (VUD) and accurate estimation of glottal closure and glottal opening instants (GCIs and GOIs, respectively). The proposed algorithm exploits the structure of the glottal flow derivative in order to estimate GCIs and GOIs only in voiced speech using simple time-domain criteria. We compare our method with well-known GCI/GOI methods, namely, the dynamic programming projected phase-slope algorithm (DYPSA), the yet another GCI/GOI algorithm (YAGA) and the speech event detection using the residual excitation and a mean-based signal (SEDREAMS). Furthermore, we examine the performance of the aforementioned methods when combined with state-of-the-art VUD algorithms, namely, the robust algorithm for pitch tracking (RAPT) and the summation of residual harmonics (SRH). Experiments conducted on the APLAWD and SAM databases show that the proposed algorithm outperforms the state-of-the-art combinations of VUD and GCI/GOI algorithms with respect to almost all evaluation criteria for clean speech. Experiments on speech contaminated with several noise types (white Gaussian, babble, and car-interior) are also presented and discussed. The proposed algorithm outperforms the state-of-the-art combinations in most evaluation criteria for signal-to-noise ratio greater than 10 dB.
A Randomized, Double-Blind, Prospective Study to Evaluate the Effect of Oral Pregabalin in Upper Limb Surgeries Under Brachial Plexus Block

Context The oral pregabalin administration preoperatively has been reported to reduce acute postoperative pain and prolong the duration of anesthesia produced by single-injection peripheral nerve blockade. Aim To study the effect of single dose pregabalin on duration of brachial plexus block Settings and design Prospective, randomised, double blind, comparative study Material and methods Patients were divided into two groups (groups A and B), with each group having 50 patients. In group A, the patient received a pregabalin capsule of 300 mg orally two hours before surgery with a sip of water. Group B received a placebo (vitamin B complex capsule) orally two hours before surgery. Brachial plexus block was performed, and data was collected. Statistical analysis Data analysis was done using SPSS version 21.0 statistical analysis software. Demographic data and clinical variables were compared using the student's t-test, chi-square test, and Mann-Whitney U test. Results The requirement of the first dose of analgesia was significantly earlier in group B as compared to group A (4:56±0:20 vs. 8:01±0:30 hours). Group B patients, as compared to group A patients, had significantly higher levels of pain after two hours of surgery (0.32±0.47 vs. 0.00±0.00) and at four hours of surgery (2.42±0.50 vs. 0.34±0.59). Conclusions Oral pregabalin prolongs analgesia from brachial plexus block without significant effect on the motor block. In addition, premedication with oral pregabalin increases the sensory block of brachial plexus block.
The Role of Landscape Art in Cultural and National Identity: Chinese and European Comparisons

The depiction of landscape in art has played a major role in the creation of cultural identities in both China and Europe. Landscape depiction has a history of over 1000 years in China, whilst in Europe its evolution has been more recent. Landscape art (shan shui) has remained a constant feature of Chinese culture and has changed little in style and purpose since the Song dynasty. In Europe, landscape depictions have been significant in the modern determination of cultural and national identities and have served to educate consumers about their country. Consideration is given here to Holland, England, Norway, Finland and China, demonstrating how landscape depictions served to support a certain definition of Chinese culture but have played little political role there, whilst in Europe landscape art has been produced in a variety of contexts, including providing support for nationalism and the determination of national identity.
Cone-beam reprojection using projection-matrices

This paper addresses reprojection of three-dimensional (3-D) reconstructions obtained from cone-beam scans using a C-arm imaging equipment assisted by a pose-determining system. The emphasis is on reprojecting without decomposing the estimated projection matrix (P-matrix) associated with a pose. Both voxel- and ray-driven methods are considered. The voxel-driven reprojector follows the algorithm for backprojection using a P-matrix. The ray-driven reprojector is derived by extracting from the P-matrix the equation of the line joining a detector-pixel and the X-ray source position. This reprojector can be modified to a ray-driven backprojector. When the geometry is specified explicitly in terms of the physical parameters of the imaging system, the projection matrices can be constructed. The resulting "projection-matrix method" is advantageous, especially when the scanning trajectory is irregular. The algorithms presented are useful in iterative methods of image reconstruction and enhancement procedures, apart from their well-known role in visualization and volume rendering. Reprojections of 3-D patient data compare favorably with the original X-ray projections obtained from a prototype C-arm system. The algorithms for reprojection can be modified to compute perspective maximum intensity projection.
Hardware-efficient color correlation-adaptive demosaicing with multifiltering

Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image that has a color filter array (CFA) pattern. The color correlation-based guided filters, such as minimized-Laplacian residual interpolation (MLRI), are known as the state-of-the-art demosaicing techniques. However, in the conventional guided filter-based techniques, the artifacts are generated in areas with low color correlation. Furthermore, a large number of line memories are required in the hardware implementation of a conventional guided filter-based technique because of the large effective field size. To overcome these two problems, we propose a color correlation-adaptive demosaicing algorithm that selectively applies a specific intracolor demosaicing to regions with low color correlation. We also propose an algorithm structure that reduces the effective field size in the vertical direction to reduce the number of line memories, while maintaining the image quality performance during the hardware implementation. The experimental results show that the proposed scheme can reduce the line memory to one-third while showing marginal performance degradation compared to the state-of-the-art MLRI weighted framework in terms of the color peak signal-to-noise ratio for the IMAX datasets. (C) 2019 SPIE and IS&T
VMD-DMD coupled data-driven approach for visual saliency in noisy images

Human visual system is endowed with an innate capability of distinguishing the salient regions of an image. It do so even in the presence of noise and other natural disturbances. Conventional8 computational saliency models in the literature assume that the input images are clean, though an explicit treatment of noise is missing. In this paper, we propose a coupled data-driven approach for estimating saliency map for a noisy input using Variational Mode Decomposition (VMD) and Dynamic Mode Decomposition(DMD. Variational Mode Decomposition (VMD) is a well received technique explored for denoising in the literature. VMD modes with high entropy (randomness) are removed and the residual modes are employed to generate a scalar valued saliency map. The proposed method is compared against seven state-of-the-art methods over a wide range of noise strengths. The submitted approach furnished comparable results with respect to state-of-the art methods for clean and noisy images in terms of various benchmark performance measures.
Characterization of Arachidonate 5S-Lipoxygenase from Danio rerio with High Activity for the Production of 5S- and 7S-Hydroxy Polyunsaturated Fatty Acids

A recombinant putative lipoxygenase (LOX) from Danio rerio (zebrafish), ALOX3c protein with 6-histidine tag, was purified using affinity chromatography, with a specific activity of 17.2 U mg-1 for arachidonic acid (AA). The molecular mass of the native ALOX3c was 156 kDa composed of a 78-kDa dimer by gel-filtration chromatography. The product obtained from the conversion of AA was identified as 5S-hydroxyeicosatetraenoic acid (5S-HETE) by HPLC and LC-MS/MS analyses. The specific activity and catalytic efficiency of the LOX from D. rerio for polyunsaturated fatty acids (PUFAs) followed the order AA (17.2 U mg-1, 1.96 s-1 μM-1) > docosahexaenoic acid (DHA, 13.6 U mg-1, 0.91 s-1 μM-1) > eicosapentaenoic acid (EPA, 10.5 U mg-1, 0.65 s-1 μM-1) and these values for AA were the highest among the 5S-LOXs reported to date. Based on identified products and substrate specificity, the enzyme is an AA 5S-LOX. The enzyme exhibited the maximal activity at pH 8.0 and 20 °C with 0.1 mM Zn2+ in the presence of 10 mM cysteine. Under these reaction conditions, 6.88 U mL-1 D. rerio 5S-LOX converted 1.0 mM of AA, EPA, and DHA to 0.91 mM 5S-HETE, 0.72 mM 5S-hydroxyeicosapentaenoic acid (5S-HEPE), and 0.68 mM 7S-hydroxydocosahexaenoic acid (7S-HDHA) in 25, 30, and 25 min, corresponding to molar conversion rates of 91, 72, and 68% and productivities of 2.18, 1.44, and 1.63 mM h-1, respectively. To the best of our knowledge, this study is the first to describe the bioconversion into 5S-HETE, 5S-HEPE, and 7S-HDHA for the application of biotechnological production.
The complete mitochondrial genome of the Pundamilia nyererei (Perciformes, Cichlidae)

Pundamilia nyererei (Perciformes, Cichlidae) is a member of Cichlid fishes that lives in the Great Lakes of East Africa. Fishes of the Cichlidae family can adapt spectacular trophic radiations and provide good potential examples of vertebrate adaptive radiations. Here, we firstly assembled the complete mitochondrial genome (mitogenome) of Pundamilia nyererei. The mitgenome was 16 761 bp in length, including 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes and 1 putative control region. Most of these protein-coding genes started with a traditional ATG codon except for COX1, which initiated with an infrequent start codon GTG instead, and terminated with the mitochondrial stop codon (TAA/AGG/AGA) or a single T base. The mitogenome structural organization is identical to other Cichlid fish. The overall GC content is 45.25%, which is lower than the AT content. According to these new determined mitogenome sequences and 10 other species under the same family or order, we have constructed the species phylogenetic tree to verify the accuracy of newly assembled mitogenome sequences. We accept that by taking the advantage of full mitogenome, we can address taxonomic issue and study the related evolutionary events. Our current data are going to provide important resources for the research of Cichlid fishes mitochondrial evolution and energy metabolism.
Multi-Dimensional Flow-Preserving Compressed Sensing (MuFloCoS) for Time-Resolved Velocity-Encoded Phase Contrast MRI

4-D time-resolved velocity-encoded phase-contrast MRI (4-D PCI) is a fully non-invasive technique to assess hemodynamics in vivo with a broad range of potential applications in multiple cardiovascular diseases. It is capable of providing quantitative flow values and anatomical information simultaneously. The long acquisition time, however, still inhibits its wider clinical use. Acceleration is achieved at present using parallel MRI (pMRI) techniques which can lead to substantial loss of image quality for higher acceleration factors. Both the high-dimensionality and the significant degree of spatio-temporal correlation in 4-D PCI render it ideally suited for recently proposed compressed sensing (CS) techniques. We propose the Multi-Dimensional Flow-preserving Compressed Sensing (MuFloCoS) method to exploit these properties. A multi-dimensional iterative reconstruction is combined with an interleaved sampling pattern (I-VT), an adaptive masked and weighted temporal regularization (TMW) and fully automatically obtained vessel-masks. The performance of the novel method was analyzed concerning image quality, feasibility of acceleration factors up to 15, quantitative flow values and diagnostic accuracy in phantom experiments and an in vivo carotid study with 18 volunteers. Comparison with iterative state-of-the-art methods revealed significant improvements using the new method, the temporal normalized root mean square error of the peak velocity was reduced by 45.32% for the novel MuFloCoS method with acceleration factor 9. The method was furthermore applied to two patient cases with diagnosed high-grade stenosis of the ICA, which confirmed the performance of MuFloCoS to produce valuable results in the presence of pathological findings in 56 s instead of over 8 min (full sampling).
Survey on GAN-based face hallucination with its model development

Face hallucination aims to produce a high-resolution face image from an input low-resolution face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure of the face image is complex and sensitive, obtaining a super-resolved face image is more difficult than generic image super-resolution. Recently, with great success in the high-level face recognition task, deep learning methods, especially generative adversarial networks (GANs), have also been applied to the low-level vision task - face hallucination. This work is to provide a model evolvement survey on GAN-based face hallucination. The principles of image resolution degradation and GAN-based learning are presented firstly. Then, a comprehensive review of the state-of-art GAN-based face hallucination methods is provided. Finally, the comparisons of these GAN-based face hallucination methods and the discussions of the related issues for future research direction are also provided.
Improving the performance of pedestrian detectors using convolutional learning

We present new achievements on the use of deep convolutional neural networks (CNN) in the problem of pedestrian detection (PD). In this paper, we aim to address the following questions: (i) Given non-deep state-of-the-art pedestrian detectors (e.g. ACF, LDCF), is it possible to improve their top performances?; (ii) is it possible to apply a pre-trained deep model to these detectors to boost their performances in the PD context? In this paper, we address the aforementioned questions by cascading CNN models (pre-trained on Imagenet) with state-of-the-art non-deep pedestrian detectors. Furthermore, we also show that this strategy is extensible to different segmentation maps (e.g. RGB, gradient, LUV) computed from the same pedestrian bounding box (i.e. the proposal). We demonstrate that the proposed approach is able to boost the detection performance of state-of-the-art non-deep pedestrian detectors. We apply the proposed methodology to address the pedestrian detection problem on the publicly available datasets INRIA and Caltech. (C) 2016 Elsevier Ltd. All rights reserved.
Application of Adaptive Resonance Theory neural networks to monitor solar hot water systems and detect existing or developing faults

Reliability is the Achilles' heel of domestic solar hot water (SHW) systems, which otherwise offer a cost-effective way of reducing energy consumption and related emissions. Using a solar hot water system reliability testbed developed for this purpose, novel neural-network-based monitoring and fault detection methods were developed. It is argued that these methods could easily be incorporated in control or supervisory software, thereby allowing rapid detection and correction of faults. This would in turn prevent further damage, and ensure continued energy savings. In particular, the Adaptive Resonance Theory (ART) class of neural networks was used to detect and classify anomalies. Compared with other network types, ART networks are fast, efficient learners and retain memory while learning new patterns. Various ART networks were trained using simulation, and tested in the field using the testbed. The results show that simulation-based training is representative of real-life operating conditions, and that faults are correctly detected in the field. Using this technology, it will be possible to improve the reliability of SHW systems with little or no additional sensing equipment compared to typical installations. (c) 2012 Elsevier Ltd. All rights reserved.
Protective roles of tRNA-derived small RNA tRF-Ile-AAT-019 in pathological progression of psoriasis

Psoriasis is a chronic recurrent inflammatory skin disease that is characterized by abnormal proliferation and differentiation of keratinocytes (KCs), angiogenesis and skin inflammation. Transfer RNA fragments (tRFs) are tRNA-derived small RNAs (tsRNAs), which possess regulatory functions in many diseases. Their potential roles in the pathological development of psoriasis have not been established. We first identified differentially expressed (DE) tRFs from psoriatic skin lesions using small RNA sequencing, and collected additional clinical samples for validation. Then, we investigated the function and mechanism of target tRFs in vitro. As a result of our investigation: we identified 234 DE transcripts in psoriatic skin lesions compared with normal controls. Further functional analysis showed the downregulation of tRF-Ile-AAT-019 in psoriatic lesions plays a critical role in pathogenesis since it could target 3'UTR of the serine protease serpin protein E1 (SERPINE1) gene. We next demonstrated that tRF-Ile-AAT-019 could suppress SERPINE1, thus leading to decreased expressions of vascular endothelial growth factor but increased expressions of keratinocytes (KCs) differentiation markers including Keratin1 and Involucrin. In conclusion, tRF-Ile-AAT-019 plays a protective role in the pathological progression of psoriasis via targeting SERPINE1, resulting in regulation of KCs differentiation and vascular proliferation biomarkers and providing a potential novel targeting pathway for the disease treatment.
300-mm Monolithic Silicon Photonics Foundry Technology

A competitive 300-mm silicon photonics foundry technology has been developed by GLOBALFOUNDRIES for general availability, which takes advantage of advanced CMOS process technology and provides a manufacturing scale. A state-of-the-art process design kit offers a codesign environment with access to a comprehensive photonics device library along with a mono-lithically integrated SOI CMOS device library. Advances in automated wafer-level optical test enable statistical photonic device characterization for development, photonic modeling, and manufacturing controls. The key challenges and solutions in developing a manufacturable photonic technology with state-of-the-art performance are described, as well as a roadmap for next generation high-performance monolithic silicon photonics are outlined.