ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Image Retrieval Using a Deep Attention-Based Hash

Image retrieval is becoming more and more important due to the rapid increase of the number of images on the web. To improve the efficiency of computing the similarity of images, hashing has moved into the focus of research. This paper proposes a Deep Attention-based Hash (DAH) retrieval model, which combines an attention module and a convolutional neural network to obtain hash codes with strong representability. Our DAH has the following features: The Hamming distance between the hash codes generated by similar images is small and the Hamming distance of hash codes of dissimilar images has a larger constant value. The quantitative loss from Euclidean distance to Hamming distance is minimized. DAH has a high image retrieval precision: We thoroughly compare it with ten state-of-the-art approaches on the CIFAR-10 dataset. The results show that the Mean Average Precision (MAP) of DAH reaches more than 92% in terms of 12, 24, 36 and 48 bit hash codes on CIFAR-10, which is better than what the state-of- art methods used for comparison can deliver.
Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

Radar can obtain high spatial resolution at large operating distances, and its operation is largely independent from weather and lighting conditions. This makes it an important instrument in an ever-increasing number of applications requiring robust and reliable sensing of the environment. Due to the advancements in semiconductor technology and digital computation power, radar can obtain increasingly better position estimation accuracy, detection sensitivity, and target resolution. High-resolution imaging is an example of a rapidly advancing field of radar technology. Inverse synthetic aperture radar (ISAR) is an imaging technique that uses the motion of a target to obtain a high-resolution radar image of it. This paper presents a comprehensive review of past work and the state of the art in ISAR imaging systems, algorithms, and applications. Starting from the early historical developments of simple monostatic range-Doppler imaging, we present the developments up to the current advanced spatially distributed imaging modalities and reconstruction algorithms. The list of references includes the most influential ISAR publications in the open literature up to early 2021.
Characterization of the novel KIR3DL1*01507 allele identified in a Chinese Han individual

The novel KIR3DL1*01507 allele differs from the closest allele KIR3DL1*01502 by a single synonymous mutation.
Genetic Algorithms for Multi-objective Optimization: Application to a Multi-reservoir System in the Chi River Basin, Thailand

The multi-objective genetic algorithm is applied to determine the optimal operation of a multi-reservoir system in the Chi River Basin, Thailand. Two competing objective functions are considered; dam release and dam storage. The predicted values for the release and storage needed are mostly lower than in current established management practice.
Keypoint detection by wave propagation

We propose to rely on the wave equation for the detection of repeatable keypoints invariant up to image scale and rotation and robust to viewpoint variations, blur, and lighting changes. The algorithm exploits the properties of local spatial-temporal extrema of the evolution of image intensities under the wave propagation to highlight salient symmetries at different scales. Although the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to poorly textured objects, which are hardly dealt with by current pipelines based on local invariant features. The impact on the overall algorithm of different numerical wave simulation schemes and their parameters is discussed, and a pyramidal approximation to speed-up the simulation is proposed and validated. Experiments on publicly available datasets show that the proposed algorithm offers state-of-the-art repeatability on a broad set of different images while detecting regions that can be distinctively described and robustly matched. (c) 2021 SPIE and IS&T
Information-Optimum LDPC Decoders Based on the Information Bottleneck Method

The Information Bottleneck method is a powerful and generic tool from the field of machine learning. It compresses an observation to a quantized variable while attempting to preserve the mutual information shared with a relevant random variable. This paper describes a new application of the Information Bottleneck method in communications. It explains in detail, how the Information Bottleneck method can be applied to construct discrete message passing decoders for regular low-density parity-check codes. The obtained decoders process only unsigned integers and use only simple lookup tables as node operations. As a consequence, the decoders can be implemented using only unsigned integer arithmetic which makes them significantly simpler and faster than the state-of-the-art decoders which process real valued log-likelihood ratios. Anyway, included results show that the considered discrete message passing decoders perform surprisingly close to optimum message passing decoders and even outperform state-of-the-art decoders, such as the min-sum decoder. We aim to take the reader on a journey from the theoretical idea of the Information Bottleneck method to a complete design framework for the considered discrete decoders. Several included figures and examples illustrate the decoder construction process and its analysis.
Treated Waste Water Comsumption Microlysimetric Quantification in Poinsettia (Euphorbia pulcherrima Willd. ex Klotzsch)

In an instrumented greenhouse a poinsettia culture was implemented in Jiutepec, Morelos, consumption of treated wastewater (ART) were measured with suction microlysimeters polyethylene pots 15 cm in diameter with similar granulometry substrates N, V and A, where N = plant leaf-litter/tezontle/coconut fiber in proportion 60: 20: 20, V = plant leaf-litter/tezontle/agrolita/coconut fiber (86:7:3.5:3.5) A = coconut fiber/plant leaf-litter/agrolita (40:40:20) and water consumption through microlysimeters balance with substrate N (BN) were measured, too; 11 of 22 variables had significant differences. BN has the highest values and V the lowest ones. N is equal to A, for all variables. The bracts have differences in area, fresh weight and dry weight, without differences in L*, C* and h. The leaves have no differences in area neither chlorophyll content, and they have differences in fresh and dry weight. Fresh and dry root weight without significant differences. The A substrate showed better agronomic results that N and V substrates. ART consumption of poinsettia can be affected by plant type and irrigation method. In the week after transplantation 2 (after punch), 8, 9 and 10 (start of pigmentation) there are differences between treatments and coincides with an ART decreased consumption period. The suction microlysimetric is a tool to quantify the consumption of ART on poinsettia.
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network

Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image quality of low-dose CT (LDCT) to reduce the cumulative radiation exposure of patients undergoing routine screening exams. The resurgence of deep learning has yielded a new approach for noise reduction by training a deep multi-layer convolutional neural networks (CNN) to map the low-dose to normal-dose CT images. However, CNN-based methods heavily rely on convolutional kernels, which use fixed-sizefilters to process one local neighborhood within the receptive field at a time. As a result, they are not efficient at retrieving structural information across large regions. In this paper, we propose a novel 3D self-attention convolutional neural network for the LDCT denoising problem. Our 3D self-attention module leverages the 3D volume of CT images to capture a wide range of spatial information both within CT slices and between CT slices. With the help of the 3D self-attention module, CNNs are able to leverage pixels with stronger relationships regardless of their distance and achieve better denoising results. In addition, we propose a self-supervised learning scheme to train a domain-specific autoencoder as the perceptual loss function. We combine these two methods and demonstrate their effectiveness on both CNN-based neural networks and WGAN-based neural networks with comprehensive experiments. Tested on the AAPM-Mayo Clinic Low Dose CT Grand Challenge data set, our experiments demonstrate that self-attention (SA) module and autoencoder (AE) perceptual loss function can efficiently enhance traditional CNNs and can achieve comparable or better results than the state-of-the-art methods.
Adaptive Model Update Strategy for Correlation Filter Trackers

Model update is an important module in target trackers. It plays an important role in adaptive tracking. Many researches have proven that different model update strategies should be adopted, when tracking in different scenes, especially in occlusion and deformation. Though many strategies have been proposed in recent years, few of them make high improvement and good combination on trackers. In this paper, we first proved there is a close relationship between the tracking scenes and the response maps. Then, we proposed an adaptive model update strategy for calculating model update rate based on the response map. Many experiments have been done to compare the proposed model update strategy with some state-of-the-art strategies, and the results have shown that the proposed model update strategy outperforms the best model update strategy by 7 on the test of Kernel Correlation Filter tracker. Furthermore, the proposed model update strategy was evaluated on some state-of-the-art correlation filter trackers. Results have shown the proposed strategy was well integrated into many trackers, and improved the tracking accuracy effectively.
Molecular Screening of Clinical Multidrug-Resistant Gram-Negative Bacteria Shows Endemicity of Carbapenemases, Coexistence of Multiple Carbapenemases, and Rarity of mcr in South Africa

Background: Extensive use of carbapenems to treat multidrug-resistant (MDR) Gram-negative bacteria (GNB) facilitates the wide dissemination of carbapenemase-producing carbapenem-resistant GNB. Colistin was reintroduced into clinical settings to manage these GNB infections. However, there is currently an increase in the dissemination of mobile colistin resistance (mcr)-producing colistin-resistant GNB isolates in clinical settings. The epidemiology of carbapenemases and mcr in Pretoria was evaluated. Methods: Clinical MDR GNB were collected and screened for carbapenemases and mcr using polymerase chain reaction (PCR); their antibiotic susceptibility profiles were elucidated using the Vitek® 2 automated system (Biomerieux, France) and microbroth dilution (for colistin). Results and Discussion: A total of 306 isolates were collected; a majority of these were Klebsiella pneumoniae (n = 208) and were collected from males (n = 158). The isolates were retrieved from a variety of infection sites, including urine, blood cultures, and rectal swabs. The Vitek 2 system found that these isolates were largely resistant to β-lactams, where 217 (70.9%) had reduced susceptibility to at least one carbapenem (ertapenem, meropenem, or imipenem), and 81 isolates (26.5%) were resistant to colistin. PCR screening identified 201 (65.7%) isolates harboring carbapenemase genes consisting of blaOXA-48 (170, 84.2%), blaNDM (31, 15.4%), blaIMP (5, 2%), blaKPC (4, 1%), and blaVIM (5, 2%). Furthermore, 14 blaOXA-48-producing isolates were coharboring blaVIM (2), blaNDM (9), blaKPC (1), and blaIMP (2) genes. Only one isolate harbored the mobile colistin resistance (mcr)-1 gene, and this is the first report of an mcr-1-producing Acinetobacter baumannii isolate in South Africa. Conclusion: There is high endemicity of carbapenemase genes and a low prevalence of mcr genes in GNB, particularly in K. pneumoniae, in health care facilities in Pretoria and surrounding regions of South Africa. Significance: Health care facilities in Pretoria are becoming breeding grounds for MDR infections that threaten public health. Careful use of carbapenems and other antibiotics is necessary to prevent further escalation and outbreak of these MDR strains that can claim several lives.
Wave Energy Assessment and Performance Estimation of State of the Art Wave Energy Converters in Italian Hotspots

This paper presents an assessment of offshore wave energy potential at the scale of the whole Mediterranean Sea. The offshore wave data were propagated, by means of numerical modeling, toward four Italian coastal areas, namely stretches of coast of Tuscany, Liguria, Sardinia and Sicily. For each area, the wave power and the monthly, seasonal and annual variability at water depths of 50 m and 15 m were analyzed and hotspots were located. The results show strong variability of the wave energy potential from point to point of the same area thus highlighting the need for spatially detailed analysis. The higher values of wave energy potential are located in the hotspots of Sardinia and Sicily, at 11.4 kW/m and 9.1 kW/m, respectively. The Tuscany and the Liguria hotspots are characterized, respectively, by 4.7 kW/m and 2.0 kW/m. In order to point out which state of the art WEC is best suited for the Italian areas, the performances of six different state of the art Wave Energy Converters (WECs) were evaluated. Finally, a comparison of the performances of each WEC in the selected Italian sites and in some European (EU) oceanic sites was conducted. The energy potential in the most energetic EU oceanic site, among those here investigated, is up to 38-times greater than the potentials in the studied Italian areas but the power output, of the best WEC technology, is no more than nine times greater.
Robust Chalcogenophene Viologens as Anolytes for Long-Life Aqueous Organic Redox Flow Batteries with High Battery Voltage

A series of chalcogenophene viologens ([(NPr)2FV]Cl4, [(NPr)2TV]Cl4, and [(NPr)2SeV]Cl4) as anolytes for neutral aqueous organic redox flow batteries (AORFBs) via a combination of chalcogenophenes (furan, thiophene, and selenophene) and viologens are reported. The chalcogenophene viologens showed narrow HOMO-LUMO energy gap, high solubility, and stable electrochemical properties. Compared with the parent [(NPr)2V]Cl4, the introduction of π-conjugated chalcogenophene groups reduced the redox potential and enhanced the stability of their free radical state, which endowed the chalcogenophene viologens/FcNCl-based AORFBs with a higher theoretical battery voltage of 1.20 V and enhanced stability for one-electron storage. In particular, the [(NPr)2FV]Cl4/FcNCl-based AORFB exhibited excellent long-cycle stability for 3000 cycles with 0.0006% capacity decay per cycle for one-electron storage and 300 cycles with 0.06% capacity decay per cycle for two-electron storage at a charge voltage of 1.9 V (1.42 V theoretical battery voltage). This work provided a new strategy for regulating the voltage and improving the performance of neutral AORFBs.
Sinkhorn Adversarial Attack and Defense

Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the l(p) space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance. Unlike previous approaches, we use an unbalanced optimal transport formulation which is naturally suited for images. We first compute an adversarial sample using a gradient step and then project the resultant image into Wasserstein ball with respect to original sample. The attack introduces perturbation in the form of pixel mass distribution which is guided by a cost metric. Elaborate experiments on MNIST, Fashion-MNIST, CIFAR-10 and Tiny ImageNet demonstrate a sharp decrease in the performance of state-of-art classifiers. We also perform experiments with adversarially trained classifiers and show that our system achieves superior performance in terms of adversarial defense against several state-of-art attacks. Our code and pre-trained models are available at https://bit.ly/2SQBR4E.
RootsGLOH2: embedding RootSIFT 'square rooting' in sGLOH2

This study introduces an extension of the sGLOH2 local image descriptor inspired by RootSIFT 'square rooting' as a way to indirectly alter the matching distance used to compare the descriptor vectors. The extended descriptor, named RootsGLOH2, achieved the best results in terms of matching accuracy and robustness among the latest state-of-the-art non-deep descriptors in recent evaluation contests dealing with both planar and non-planar scenes. RootsGLOH2 also achieves a matching accuracy very close to that obtained by the best deep descriptors to date. Beside confirming that 'square rooting' has beneficial effects on sGLOH2 as it happens on SIFT, experimental evidence shows that classical norm-based distances, such as the Euclidean and Manhattan distances, only provide suboptimal solutions to the problem of local image descriptor matching. This suggests matching distance design as a topic to investigate further in the near future.
Plenoptic Face Presentation Attack Detection

The vulnerability of current face recognition systems to presentation attacks significantly limits their application in biometrics. Herein, we present a passive presentation attack detection method based on a complete plenoptic imaging system which can derive the complete plenoptic function of light rays using a single detector. Moreover, we constructed a multi-dimensional face database with 50 subjects and seven different types of presentation attacks. We experimentally demonstrated that our approach outperforms the state-of-the-art methods on all types of presentation attacks.
Physical and Sedentary Activities in Association with Reproductive Outcomes among Couples Seeking Infertility Treatment: A Prospective Cohort Study

Background: The aim of this study was to investigate the association of physical activity (PA) with assisted reproductive technology (ART) treatment and pregnancy outcomes among couples seeking infertility treatment. Methods: This prospective cohort study was carried out among 128 infertile individuals (64 couples), entering the infertility clinic for ART procedures. Baseline PA (before entering any treatment) was assessed using accelerometry for both women and men. For every couple the infertility treatment outcomes were recorded. Results: The couples that required invasive ART procedures such as in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) spent less time in vigorous PA (-73 min/week per couple, woman + man) than those couples who became spontaneously pregnant after entering the study (p = 0.001). We observed no significant associations between the time spent in physical activities and positive pregnancy test or live birth. Conclusions: Our results do not support a positive nor negative relation between the time the couples spent in physical activities and the chances of getting pregnant or having a baby among patients seeking infertility treatment. However, couples undergoing invasive ART procedures did less vigorous PA than couples that became spontaneously pregnant, suggesting that PA may interfere with their reproductive health.
Patterns of Population Structure and Introgression Among Recently Differentiated Drosophila melanogaster Populations

Despite a century of genetic analysis, the evolutionary processes that have generated the patterns of exceptional genetic and phenotypic variation in the model organism Drosophila melanogaster remains poorly understood. In particular, how genetic variation is partitioned within its putative ancestral range in Southern Africa remains unresolved. Here, we study patterns of population genetic structure, admixture, and the spatial structuring of candidate incompatibility alleles across a global sample, including 223 new accessions, predominantly from remote regions in Southern Africa. We identify nine major ancestries, six that primarily occur in Africa and one that has not been previously described. We find evidence for both contemporary and historical admixture between ancestries, with admixture rates varying both within and between continents. For example, while previous work has highlighted an admixture zone between broadly defined African and European ancestries in the Caribbean and southeastern USA, we identify West African ancestry as the most likely African contributor. Moreover, loci showing the strongest signal of introgression between West Africa and the Caribbean/southeastern USA include several genes relating to neurological development and male courtship behavior, in line with previous work showing shared mating behaviors between these regions. Finally, while we hypothesized that potential incompatibility loci may contribute to population genetic structure across the range of D. melanogaster; these loci are, on average, not highly differentiated between ancestries. This work contributes to our understanding of the evolutionary history of a key model system, and provides insight into the partitioning of diversity across its range.
Assessing SIRT7 Activity In Vivo and In Vitro in Response to DNA Damage

The class III histone deacetylase (HDACs) also known as sirtuins (SIRTs 1-7) are ubiquitously expressed, but SIRT7 mainly resides as nucleolar protein. In this chapter a couple of methods are described that are used to detect modulation of SIRT7 in response to DNA damage. SIRT7 is localized in the nucleoli and binds to the chromatin after DNA damage. Therefore, a protocol was optimized by our lab for chromatin fractionation. By this method, the movement of SIRT7 can be detected from the soluble part (cytosol+nucleoplasm) to the solid part (chromatin) of the cell. Change of SIRT7 expression levels, in different cells or after different treatment, can be detected by isolating whole-cell lysate followed by Western blotting. For analyzing binding of SIRT7 to other substrates, we have also optimized manual immunoprecipitation assays by using 1% NP40 buffer. This protocol is very helpful to pull down SIRT7 and associated proteins by using a single buffer. SIRT7 is a deacetylase, and its deacetylation activity can be checked both inside the cell by in vivo deacetylation assay and outside the cell by in vitro deacetylation assays. Recently it was also discovered that SIRT7 has desuccinylase activity which can be detected by histone desuccinylation assay. This chapter provides the methodology of SIRT7 detection in the whole cell lysate, binding of SIRT7 to the chromatin and other proteins for performing deacetylation and desuccinylation activity.