ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Asymmetric hashing based on generative adversarial network

In the era of big data, social media, large-scale video, image, and text data is produced every day. The approximate nearest neighbor (ANN) search has drawn significant attention for content-based image retrieval applications to ensure retrieval quality and computational efficiency. Hashing has become a cutting-edge technology for image retrieval and big data applications due to its low-storage and high-computational efficiency. Hashing algorithms are useful for mapping images into short binary codes and generating a similar binary code for similar data points from the database. Many supervised/unsupervised hashing methods have been deployed for retrieving the query points from the database images, and many recently developed methods can achieve a higher accuracy regarding image retrieval performance. However, the current state-of-the-art algorithms can only improve binary code hashing, and the retrieval performance of binary representation is not good. To overcome this issue, we propose an asymmetric learning method that generates the hash codes. This work proposes a novel asymmetric learning-based generative adversarial network (AGAN) for image retrieval, which integrates the feature learning with hashing to an end-to-end learning framework. Moreover, to equip with the binary representation of image retrieval; we propose three loss functions, i.e., encoder loss, generator loss, and discriminator loss, which significantly improve retrieval performance. The extensive experiments show that our proposed method outperformed several state-of-the-art methods.
Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifierand/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D high-resolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-the-art landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in amulti-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.
A survey on indoor RGB-D semantic segmentation: from hand-crafted features to deep convolutional neural networks

Semantic segmentation is one of the most important tasks in the field of computer vision. It is the main step towards scene understanding. With the advent of RGB-Depth sensors, such as Microsoft Kinect, nowadays RGB-Depth images are easily available. This has changed the landscape of some tasks such as semantic segmentation. As the depth images are independent of illumination, the combination of depth and RGB images can improve the quality of semantic labeling. The related research has been divided into two main categories, based on the usage of hand-crafted features and deep learning. Although the state-of-the-art results are mainly achieved by deep learning methods, traditional methods have also been at the center of attention for some years and lots of valuable work have been done in that category. As the field of semantic segmentation is very broad, in this survey, a comprehensive analysis has been carried out on RGB-Depth semantic segmentation methods, their challenges and contributions, available RGB-Depth datasets, metrics of evaluation, state-of-the-art results, and promising directions of the field.
Nucleic Acid Delivery to the Vascular Endothelium

This Review examines the state-of-the-art in the delivery of nucleic acid therapies that are directed to the vascular endothelium. First, we review the most important homeostatic functions and properties of the vascular endothelium and summarize the nucleic acid tools that are currently available for gene therapy and nucleic acid delivery. Second, we consider the opportunities available with the endothelium as a therapeutic target and the experimental models that exist to evaluate the potential of those opportunities. Finally, we review the progress to date from investigations that are directly targeting the vascular endothelium: for vascular disease, for peri-transplant therapy, for angiogenic therapies, for pulmonary endothelial disease, and for the blood-brain barrier, ending with a summary of the future outlook in this field.
A Comparison of Three Uniquely Different State of the Art and Two Classical Multiobjective Optimization Algorithms as Applied to Electromagnetics

This paper compares three modern and two classical multiobjective optimizers (MOOs) as applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers on simple test functions has been studied exhaustively. In contrast, the algorithms here are tested on practical applications, where the function evaluations are computationally expensive, making the convergence rate a crucial factor. The examples considered include the optimization of a narrowband slot antenna, a mushroom-type electromagnetic bandgap structure, and an ultrawideband Vivaldi antenna. Another popular topic in the literature is in comparing classical MOOs on electromagnetics problems. The modern optimizers chosen in this paper are state of the art and each has a distinct design philosophy. This paper introduces two unique MOOs to the electromagnetics community: BORG, an auto-adaptive genetic algorithm and the Multi-Objective Covariance Matrix Adaptation Evolutionary Strategy (MO-CMA-ES), an extension of the popular single-objective CMA-ES. These algorithms are compared to the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), a Chebysheff scalarization algorithm, and two classical MOOs. This paper will study the behavior of these algorithms on problems in electromagnetics with a limited number of function evaluations using five distinct metrics and will provide useful guidelines and recommended optimizer settings.
Biomedical Magnetic Induction Tomography: An Inhomogeneous Green's Function Approach

Magnetic induction tomography aims to reconstruct the passive electric properties of an object by measuring its scattered magnetic field. Current state-of-the-art numerical techniques are based on differential formulations such as the finite element method. A formulation based on volume integral equations has not yet been applied to its biomedical field and could improve the reconstruction speed by reducing the number of unknowns. This paper investigates salient characteristics of the approach and offers a solution based on inhomogeneous Green's functions.
Robust action recognition using local motion and group sparsity

Recognizing actions in a video is a critical step for making many vision-based applications possible and has attracted much attention recently. However, action recognition in a video is a challenging task due to wide variations within an action, camera motion, cluttered background, and occlusions, to name a few. While dense sampling based approaches are currently achieving the state-of-the-art performance in action recognition, they do not perform well for many realistic video sequences since, by considering every motion found in a video equally, the discriminative power of these approaches is often reduced due to clutter motions, such as background changes and camera motions. In this paper, we robustly identify local motions of interest in an unsupervised manner by taking advantage of group sparsity. In order to robustly classify action types, we emphasize local motion by combining local motion descriptors and full motion descriptors and apply group sparsity to the emphasized motion features using the multiple kernel method. In experiments, we show that different types of actions can be well recognized using a small number of selected local motion descriptors and the proposed algorithm achieves the state-of-the-art performance on popular benchmark datasets, outperforming existing methods. We also demonstrate that the group sparse representation with the multiple kernel method can dramatically improve the action recognition performance. (C) 2013 Elsevier Ltd. All rights reserved.
Assessment of HDAC Inhibitor-Induced Endoplasmic Reticulum (ER) Stress

The endoplasmic reticulum (ER) is a multifunctional cell organelle which is important for the folding and processing of proteins. Different endogenous and exogenous factors can disturb the ER homeostasis, causing ER stress and activating the unfolded protein response (UPR) to remove misfolded proteins and aggregates. ER stress and the UPR are associated with several human diseases, such as diabetes, Alzheimer's or Parkinson's disease, and cancer. Histone deacetylase inhibitors (HDACi) are used to treat cancer and were shown to induce ER stress/to modulate the UPR, although the exact mechanism is not fully understood and needs further research. Several approaches to monitoring ER stress exist. Here we describe methods including qPCR, Western blot, transmission electron microscopy, and fluorescence microscopy to analyze changes in mRNA and protein expression levels as well as defects in ER structures after HDAC inhibitor-induced ER stress.
Computationally Efficient Pricing and Benefit Distribution Mechanisms for Incentivizing Stable Peer-to-Peer Energy Trading

Peer-to-peer (P2P) energy trading has emerged as a promising market paradigm toward maximizing the value of distributed energy resources (DERs) for electricity prosumers, by enabling direct energy trading among them. However, state-of-the-art P2P mechanisms either fail to adequately incentivize prosumers to participate, prevent prosumers from accessing the highest achievable monetary benefits, or suffer severely from the curse of dimensionality. This article proposes two computationally efficient mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core ttonnement process while the second involves a novel pricing mechanism based on the solution of a single linear program. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.
Integrating electrical impedance tomography and transpulmonary pressure monitoring to personalize PEEP in hypoxemic patients undergoing pressure support ventilation

Monitoring with electrical impedance tomography (EIT) during a decremental PEEP trial has been used to identify the PEEP that yields the optimal balance of pulmonary overdistension and collapse. This method is based on pixel-level changes in respiratory system compliance and depends on fixed or measured airway driving pressure. We developed a novel approach to quantify overdistension and collapse during pressure support ventilation (PSV) by integrating transpulmonary pressure and EIT monitoring and performed pilot tests in three hypoxemic patients. We report that our experimental approach is feasible and capable of identifying a PEEP that balances overdistension and collapse in intubated hypoxemic patients undergoing PSV.
Synthetic image super resolution using FeatureMatch

In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase.
TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network

In this paper, a trust framework is proposed for misbehavior detection in software defined vehicular networks (TFMD-SDVN) to detect the correct events in the network reported by the trusted or untrusted nodes. The trust value of a node is calculated based on rating, recommendation, and similarity. If the trust value is greater than a threshold, then the event reported by the event reporting node (ERN) is assumed to be correct. The performance of the proposed work is evaluated using OMNeT++ network simulator and SUMO traffic simulator in Veins hybrid framework. The performance parameters taken are True Positive Rate (TPR), False Positive Rate (FPR), Detection Time (DT), and Packet Delivery Ratio (PDR). Simulation results show that the proposed approach performs better than ART scheme, RPRep scheme, and BYOR scheme.
Perceived benefits and challenges of repeated exposure to high fidelity simulation experiences of first degree accelerated bachelor nursing students

This study explored perceptions of first-degree entry-level accelerated bachelor nursing students regarding benefits and challenges of exposure to multiple high fidelity simulation (HFS) scenarios, which has not been studied to date. These perceptions conformed to some research findings among Associate Degree, traditional non-accelerated, and second-degree accelerated Bachelor of Science in Nursing (BSN) students faced with one to two simulations. However, first-degree accelerated BSN students faced with multiple complex simulations perceived improvements on all outcomes, including critical thinking, confidence, competence, and theory-practice integration. On the negative side, some reported feeling overwhelmed by the multiple HFS scenarios. Evidence from this study supports HFS as an effective teaching and learning method for nursing students, along with valuable implications for many other fields.
Deep submodular network: An application to multi-document summarization

Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be done in polynomial time and also, by choosing a suitable learning rate and performing enough iterations, a lower empirical error can be ensured. Finally, in order to demonstrate the applicability of DSN for real-world problems, automatic multi-document summarization is considered and a summarizer called DSNSum is introduced. Then, the performance of DSNSum is compared with the state-of-the-art summarizers based on DUC 2004 and CNN/DailyMail corpora. The experimental results show that the performance of the proposed summarizer is comparable with the state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.
Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs

The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently.
Single-Stage Buck-Boost Inverters: A State-of-the-Art Survey

Single-stage buck-boost inverters have attracted the attention of many researchers, due to their ability to increase/decrease the output voltage in one power conversion stage. One of the most important uses of these inverters is in photovoltaic applications, where the voltage of the solar panels varies in a wide range. In recent years, many new inverters have been proposed to improve the performance of existing structures. In this paper, the state of the art of these single-stage buck-boost inverters is discussed. The advantages and disadvantages of each structure are examined from different perspectives, such as the number of components, losses, and performance. Finally, in a general comparison, the properties of all structures are discussed and summarized in a table.
High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment

We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences. Based on a radial golden- angle acquisition trajectory, our method enables a multidimensional self-gating signal to be extracted from the k-space data for more accurate motion representation. The k-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, k-space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2-D and 3-D synthetic and in vivo data sets. On the synthetic data sets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the in vivo data, compared with a state-of-the-art approach based on the center of k-space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy.
Association between semen collection time and semen parameters: an observational study

The process of semen collection plays a key role in the quality of semen specimens. However, the association between semen collection time and semen quality is still unclear. In this study, ejaculates by masturbation from 746 subfertile men or healthy men who underwent semen analysis were examined. The median (interquartile range) semen collection time for all participants was 7.0 (5.0-11.0) min, and the median time taken for semen collection was lower in healthy men than that in subfertile men (6.0 min vs 7.0 min). An increase in the time required to produce semen samples was associated with poorer semen quality. Among those undergoing assisted reproductive technology (ART), the miscarriage rate was positively correlated with the semen collection time. After adjusting for confounders, the highest quartile (Q4) of collection time was negatively associated with semen volume and sperm concentration. A longer time to produce semen samples (Q3 and Q4) was negatively correlated with progressive and total sperm motility. In addition, there was a significant negative linear association between the semen collection time and the sperm morphology. Higher risks of asthenozoospermia (adjusted odds ratio [OR] = 2.06, 95% confidence interval [CI]: 1.31-3.25, P = 0.002) and teratozoospermia (adjusted OR = 1.98, 95% CI: 1.10-3.55, P = 0.02) were observed in Q3 than those in Q1. Our results indicate that a higher risk of abnormal semen parameter values was associated with an increase in time for semen collection, which may be related to male fertility through its association with semen quality.