ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A Rare Case of Recurrent Hematometra of Unknown Etiology

An abnormal blood collection in the uterus is referred to as hematometra. Obstruction of the genitourinary outflow system caused by earlier surgeries or congenital defects is most frequently related to this rare disorder. The symptoms of hematometra include acute pelvic pain and a history of absent menarche. Here is a case of a 42-year-old female who presented with complaints of severe lower abdominal pain, and pain during urination that was accompanied by vulval itching in June 2021. She had undergone two Caesarean sections and a myomectomy in the past. She was given three monthly injections of gonadotropin-releasing hormone (GnRH) analogue after receiving USG-guided drainage because of a diagnosis of hematometra in January 2021. However, in June 2021, she experienced a recurrence of the same symptoms, necessitating a total abdominal hysterectomy and bilateral salpingo-oophorectomy, which completely resolved the patient's complaints. For a deeper understanding of this issue, further case reporting is necessary.
Moving Object Detection Using Adaptive Blind Update and RGB-D Camera

A novel background subtraction approach using an RCB-D camera and an adaptive blind updating policy is introduced. This method in the initialization creates a model to store background pixels to compare each pixel of the new frame with the model in the same location to identify background pixels. The background-model update presented in this paper uses regular and blind updates which also has different criteria from existing methods. In particular, blind update frequently changes based on the background changes and the speed of moving object. This will allow the scene model to adapt to the changes in the background, detecting the stationary moving object and reducing the ghost phenomenon. In addition, the proposed bootstrapping segmentation and shadow detection are added to the system to improve the accuracy of the algorithm in shadow and depth camouflage scenarios. The proposed method is compared with the original method and the other state of the art algorithms. The experimental results show significant improvement in those videos that stationary object appears. In addition, the benchmark results also indicate strong and stable results compared to the other state of the art algorithms.
Exploring the Role of Deep Learning Technology in the Sustainable Development of the Music Production Industry

This study explores the role of deep learning technology in the sustainable development of the music production industry. This article surveys the opinions of Taiwanese music creation professionals and uses partial least squares (PLS) regression to analyze and elucidate the importance of deep learning technology in the music production industry. We found that deep learning cannot replace human creativity, but greater investment in this technology can improve the quality of music creation. In order to achieve sustainable development in the music production industry, industry participants need to awaken consumers' awareness of music quality, actively enhance the unique value of their art, and strengthen cooperation between industries to provide a friendly environment for listeners.
DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals

In the subsequent decades, the increasing energy will demand renewable resources and intelligent solutions for managing consumption. In this sense, Non-Intrusive Load Monitoring (NILM) techniques detail consumption information for users, allowing better electric power management and avoiding energy losses. In high-frequency NILM methods, state-of-the-art approaches, mainly based on deep learning solutions, do not provide a complete NILM architecture, including all the required steps. To overcome this gap, this work presents an integrated method for detection, feature extraction, and classification of high-frequency NILM signals for the publicly available LIT-Dataset. In terms of detection, the results were above 90% for most cases, whilst the state-of-the-art methods were below 70% for eight loads. For classification, the final accuracies were comparable with other recent works (around 97%). We also include a multi-label procedure to avoid the disaggregation stage, indicating the loads connected at a given time, increasing the recognition of multiple loads. Finally, we present results in an embedded system, a subject also underexplored in the recent literature, demonstrating the proposal's feasibility for real-time signal analysis and practical applications involving NILM.
InP Photonic Integrated Circuits

InP is an ideal integration platform for optical generation, switching, and detection components operating in the range of 1.3-1.6 mu m wavelength, which is preferred for data transmission in the most prevalent silica-based optical fiber. We review the current state of the art in advanced InP photonic ICs.
Evaluation of Small-Molecule HDAC Inhibitors Through In Vitro and In Cellulo Approaches

The aberrant activity of histone deacetylases (HDACs) across a broad range of cancers and other disease indications has led to the development of small-molecule inhibitors that target one or more members of the HDAC protein family. Emerging HDAC inhibitors that show promise in drug discovery programs must be assessed across a range of in vitro assays to establish an inhibitor profile for potency and cellular selectivity towards target HDAC(s) as well as preliminary absorption, distribution, metabolism, and excretion (ADME) features. Here we provide an overview of methods to determine a subset of pivotal in vitro drug-like parameters for HDAC inhibitors (HDACi). We initially describe protocols for parallel artificial membrane permeability assays (PAMPA) to evaluate the passive permeability of small molecules across lipid membranes. Subsequently, we elaborate on cytotoxicity assays using CellTiter-Blue to determine HDACi-induced cell death in healthy/diseased cellular models. We next focus on assessing the target engagement of inhibitors with the appropriate HDAC isoforms in a cellular environment via Western blotting of acetylated HDAC substrates. Finally, we provide detailed guidelines on how to assess the metabolic stability of HDACi through whole blood stability assays. Collectively, these assays provide an overview of the permeability, selectivity, and stability of the HDAC inhibitor under development.
400 GHz HBT Differential Amplifier Using Unbalanced Feed Networks

A terahertz differential eight-stage amplifier fabricated using state-of-the-art 125 nm double-heterojunction bipolar transistors (DHBT) is presented. The four-port unit-cell chain is designed for optimum forward differential gain with no even and odd-mode reverse gains. Unbalanced single-ended feed networks are added to preserve the amplifier gain without inducing oscillations. The proposed feed scheme is validated by a stable amplifier operation in 325-to-450 GHz range with the peak gain of 22 dB at 375 GHz.
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain. Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning. We have extensively evaluated our method with cardiac substructure segmentation and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images, and outperforms the state-of-the-art domain adaptation approaches by a large margin.
Enhancing Differential Evolution With Novel Parameter Control

In this paper, we proposed a novel DE variant named DE-NPC for real parameter single objective optimization. In DE-NPC algorithm, a novel adaptation scheme for the scale factor Sis first proposed, which is based on the location information of the population rather than the fitness difference. The adaptation scheme of crossover rate in our DE-NPC is based on its success probability. Furthermore, a novel population size reduction scheme is also employed in DE-NPC, which can get a better perception of the landscape of objectives and consequently obtain an overall better performance. The algorithm validation is conducted under our test suite containing 88 benchmarks from CEC2013, CEC2014 and CEC2017 in comparison with several state-of-the-art DE variants. The experiment results show that our novel DE-NPC algorithm is competitive with these state-of-the-art DE variants.
Multi-atlas segmentation of optic disc in retinal images via convolutional neural network

Multi-atlas segmentation is widely accepted as an essential image segmentation approach. Through leveraging on the information from the atlases instead of utilizing the model-based segmentation techniques, the multi-atlas segmentation could significantly enhance the accuracy of segmentation. However, label fusion, which plays an important role for multi-atlas segmentation still remains the primary challenge. Bearing this in mind, a deep learning-based approach is presented through integrating feature extraction and label fusion. The proposed deep learning architecture consists of two independent channels composing of continuous convolutional layers. To evaluate the performance our approach, we conducted comparison experiments between state-of-the-art techniques and the proposed approach on publicly available datasets. Experimental results demonstrate that the accuracy of the proposed approach outperforms state-of-the-art techniques both in efficiency and effectiveness.
A Survey on Observability of Distributed Edge & Container-Based Microservices

Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network's edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.
State of the Art Review of Reinforcement Strategies and Technologies for 3D Printing of Concrete

This state of the art review paper aims to discuss the results of a literature survey on possible ways to reinforce printed concrete based on existing reinforcement strategies. Just as conventional concrete, for 3D printed concrete to be suitable for large-scale construction, reinforcement is needed to increase the tensile capacity of concrete members and reduce temperature and shrinkage cracking. Despite efforts that are currently underway, the development of proper reinforcement suitable for printed concrete is still very active on the research agenda. As an initial step for designing suitable reinforcement for printed concrete, the existing reinforcement methods for printed concrete as well as conventional cast concrete from the literature are reviewed and summarized. Through the preliminary evaluation of the suitability and effectiveness of various reinforcement methods, guidelines are proposed to better understand possible solutions to reinforce printed concrete and inspire new practical ideas to fill the current technology void. The conclusions also include the possible improvements of the existing reinforcement methods to be considered in future applications.
Interplay between hydrogen and chalcogen bonds in cysteine

Protein structures are stabilized by several types of chemical interactions between amino acids, which can compete with each other. This is the case of chalcogen and hydrogen bonds formed by the thiol group of cysteine, which can form three hydrogen bonds with one hydrogen acceptor and two hydrogen donors and a chalcogen bond with a nucleophile along the extension of the CS bond. A survey of the Protein Data Bank shows that hydrogen bonds are about 40-50 more common than chalcogen bonds, suggesting that they are stronger and, consequently, prevail, though not always. It is also observed that frequently a thiol group that forms a chalcogen bond is also involved, as a hydrogen donor, in a hydrogen bond.
Tricuspid Valve-in-Valve Procedure with An Edwards S3 Valve in a 15-kg Child in Latin America

A 5-year-old child, weighing 15 kg, with three previous sternotomies, presented with right heart failure due to severe stenosis and regurgitation of the bioprosthetic tricuspid valve. A percutaneous tricuspid valve-in-valve procedure with an Edwards S3 valve was ofered for compassionate use, performed with no complications and with a significant clinical condition improvement.
Two decades of stem cell transplantation in patients with Fanconi anemia: Analysis of factors affecting transplant outcomes

Allogeneic hematopoietic stem cell transplantation (HSCT) is currently the only curative treatment for the hematological complications of patients with Fanconi anemia (FA). Over the last two decades, HSCT outcomes have improved dramatically following the development of regimens tailored for FA patients. In this study, we analyzed genetic, clinical, and transplant data of 41 patients with FA who underwent HSCT at Hadassah Medical Center between November 1996 and September 2020. Overall survival (OS) was 82.9% with a median follow-up time of 2.11-years (95% CI, .48-16.56). Thirteen patients (31.7%) developed acute graft-versus-host disease (GVHD), three of them with grades 3-4. Nine patients developed chronic GVHD, five had extensive disease. Twelve patients (29.3%) developed stable mixed-chimerism with complete resolution of bone marrow failure (BMF); none of them had acute nor chronic GVHD. Significantly higher GVHD rates were observed in transplants from peripheral blood stem cell grafts as compared to other stem cell sources (p = .002 for acute and p = .004 for chronic GVHD). Outcome parameters were comparable between HSCT from matched-sibling (n = 20) to other donors (n = 21), including survival rates (p = .1), time to engraftment (p = .69 and p = .14 for neutrophil and platelet engraftment time, respectively), chimerism status (p = .36 and p = .83 for full-donor and mixed chimerism, respectively), and GVHD prevalence (p = 1). Our results demonstrate the vast improvements in HSCT outcomes of patients with FA, narrowing the gap between matched-sibling versus alternative donor transplantations. Our data identifies factors that may significantly affect transplant outcomes such as graft source and chimerism status.
Deep softmax collaborative representation for robust degraded face recognition

Deep convolutional neural networks (DCNN) have attracted much attention in the field of face recognition because they have achieved high performance than other approaches in the so-called in-the-wild datasets. However, in many real-world applications of face recognition, the performance of CNN-based algorithms is significantly decreased when images contain various kinds of degradations caused by random noise, motion blur, compression artifacts, uncontrolled illumination, and occlusion. Moreover, this is because the main weakness of existing DCNN models is the overfitting problem. To boost the recognition performance of stateof-the-art deep learning networks, we propose a deep softmax collaborative representation-based network, which can be used as a divide-and-conquer algorithm to help multiple DCCNs work together more effectively to solve multiple sub-problems of face reconstruction and classification. We demonstrated several experiments with challenging face recognition datasets. Our extensive experiments demonstrate that our proposed method is more robust and efficient in dealing with the challenging real-world problems in face recognition compared to related state-of-the-art methods.
Application Areas of Information-Centric Networking: State-of-the-Art and Challenges

The Information-Centric Network (ICN) paradigm has gained popularity since its inception. The host-based IP networks were not primarily designed to handle scenarios that it is exposed to on the current Internet. In that direction lot of research has been happening to develop applications such as web applications, multimedia streaming, the Internet of Things, Wireless Sensor Networks and Vehicular networks. In addition, new ICN application areas, such as social networks, Industrial IoTs, etc., are emerging. This review investigates the possible application areas and their deficiencies evenly, broadly and at a certain level of depth with focus on security, scalability, IP interoperability, modularity and other application specific aspects. We discuss the current state-of-the-art in these ICN-based applications and the existing limitations. A comparative analysis of the literary works available is performed to understand the research gaps available, and a detailed discussion of the challenges in each area is provided. We conclude the review with future challenges in the application development with the ICN paradigm to reap its architectural benefits.
Pattern-Based Dynamic Compilation System for CGRAs With Online Configuration Transformation

Prevailing data-intensive applications, such as artificial intelligence and internet of things, demand considerable compute capability. Coarse-grained reconfigurable architectures (CGRAs) can meet this demand via providing abundant compute resources. However, compilation has become an essential problem because the increasing resources need to be orchestrated efficiently. Static compilation is insufficient due to conservative resource allocation and exponentially increasing time cost while state-of-the-art dynamic compilation still performs poorly in both generality and efficiency. This article proposes a dynamic compilation system for CGRAs through online pattern-based configuration transformation, which enables virtualization to improve resource utilization and flexibility. It utilizes statically-generated patterns to straightforwardly determine dynamic placement of registers and operations so that the transformation algorithm has a low complexity. Domain-specific features are extracted by a k-means clustering algorithm to help improve the quality of patterns. The experimental results show that statically compiled applications can be transformed onto arbitrary resources at runtime, reserving 73.5 (22.8-163.3 percent) of the original performance/resource on average, 9.1 (0-52.9 percent) better than the state-of-the-art non-general methods.
Multiresolution Reservoir Graph Neural Network

Graph neural networks are receiving increasing attention as state-of-the-art methods to process graph-structured data. However, similar to other neural networks, they tend to suffer from a high computational cost to perform training. Reservoir computing (RC) is an effective way to define neural networks that are very efficient to train, often obtaining comparable predictive performance with respect to the fully trained counterparts. Different proposals of reservoir graph neural networks have been proposed in the literature. However, their predictive performances are still slightly below the ones of fully trained graph neural networks on many benchmark datasets, arguably because of the oversmoothing problem that arises when iterating over the graph structure in the reservoir computation. In this work, we aim to reduce this gap defining a multiresolution reservoir graph neural network (MRGNN) inspired by graph spectral filtering. Instead of iterating on the nonlinearity in the reservoir and using a shallow readout function, we aim to generate an explicit k-hop unsupervised graph representation amenable for further, possibly nonlinear, processing. Experiments on several datasets from various application areas show that our approach is extremely fast and it achieves in most of the cases comparable or even higher results with respect to state-of-the-art approaches.
IDRLP: Image Dehazing Using Region Line Prior

In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into $n$ regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this "joint optimization" strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.