ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Increasing energy efficiency of Massive-MIMO network via base stations switching using reinforcement learning and radio environment maps

Energy Efficiency (EE) is of high importance while considering Massive Multiple-Input Multiple-Output (M-MIMO) networks where base stations (BSs) are equipped with an antenna array composed of up to hundreds of elements. M-MIMO transmission, although highly spectrally efficient, results in high energy consumption growing with the number of antennas. This paper investigates EE improvement through switching on/off underutilized BSs. It is proposed to use the location-aware approach, where data about an optimal active BSs set is stored in a Radio Environment Map (REM). For efficient acquisition, processing and utilization of the REM data, reinforcement learning (RL) algorithms are used. State-of-the-art exploration/exploitation methods including epsilon-greedy, Upper Confidence Bound (UCB), and Gradient Bandit are evaluated. Then analytical action filtering, and an REM-based Exploration Algorithm (REM-EA) are proposed to improve the RL convergence time. Algorithms are evaluated using an advanced, system-level simulator of an M-MIMO Heterogeneous Network (HetNet) utilizing an accurate 3D-ray-tracing radio channel model. The proposed RL-based BSs switching algorithm is proven to provide 70% gains in EE over a state-of-the-art algorithm using an analytical heuristic. Moreover, the proposed action filtering and REM-EA can reduce RL convergence time in relation to the best-performing state-of-the-art exploration method by 60% and 83%, respectively.
Inhibition of NADPH Oxidase-ROS Signal using Hyaluronic Acid Nanoparticles for Overcoming Radioresistance in Cancer Therapy

Upregulation of NADPH oxidases (NOXs) in cancer cells leads to chronic increase in intracellular reactive oxygen species (ROS) and adaptation to a high ROS level for cell survival and, thereby, low sensitivity to radiotherapy. To overcome resistance to radiotherapy, we have developed a bioactive and CD44 targeted hyaluronic acid nanoparticle encapsulated with an NOX inhibitor, GKT831 (HANP/GKT831). We found that HANP/GKT831 had stronger inhibitory effects on ROS generation and cell proliferation than that of GKT831 alone in cancer cells. Systemic delivery of HANP/GKT831 led to the targeted accumulation in breast cancer patient derived xenograft (PDX) tumors in nude mice. Importantly, the combination of systemic delivery of HANP/GKT831 with a low dose of local radiotherapy significantly enhanced tumor growth inhibition in breast cancer PDX models. Our results showed that HANP/GKT831 primed tumor cells to radiation-induced DNA damage and cell death by downregulation of DNA repair function and oncogenic signal pathways.
Fused lasso for feature selection using structural information

Most state-of-the-art feature selection methods tend to overlook the structural relationship between a pair of samples associated with each feature dimension, which may encapsulate useful information for refining the performance of feature selection. Moreover, they usually consider candidate feature relevancy equivalent to selected feature relevancy, and therefore, some less relevant features may be misinterpreted as salient features. To overcome these issues, we propose a new feature selection method based on graph-based feature representations and the Fused Lasso framework in this paper. Unlike stateof-the-art feature selection approaches, our method has two main advantages. First, it can accommodate structural relationship between a pair of samples through a graph-based feature representation. Second, our method can enhance the trade-off between the relevancy of each individual feature on the one hand and its redundancy between pairwise features on the other. This is achieved through the use of a Fused Lasso framework applied to features reordered on the basis of their relevance with respect to the target feature. To effectively solve the optimization problem, an iterative algorithm is developed to identify the most discriminative features. Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets. (c) 2021 Elsevier Ltd. All rights reserved.
A Spatially Separable Attention Mechanism for Massive MIMO CSI Feedback

Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback overhead several compressive sensing (CS) techniques were implemented in recent years but these techniques are often iterative and are computationally complex to realize in power-constrained user equipment (UE). Hence, a data-based deep learning approach took over in these recent years introducing a variety of neural networks for CSI compression. Specifically, transformer-based networks have been shown to achieve state-of-the-art performance. However, the multi-head attention operation, which is at the core of transformers, is computationally complex making transformers difficult to implement on a UE. In this letter, we present a lightweight transformer named STNet which uses a spatially separable attention mechanism that is significantly less complex than the traditional full-attention. Equipped with this, STNet outperformed state-of-the-art models in some scenarios with approximately 1/10(th )of the resources.
A New Necessary Condition for Threshold Function Identification

This article proposes a new necessary condition and the corresponding speedup strategies to the threshold function (TF) identification problem. The state-of-the-art to this identification problem could be very time-consuming when the function-under-identification is a non-TF with the unateness property. The proposed new necessary condition can be seamlessly integrated into this identification algorithm. As compared with the state-of-the-art, the improved identification algorithm with the proposed necessary condition can more effectively and efficiently detect non-TFs. Furthermore, according to the experimental results, the ratio of CPU time overhead in the process of checking the proposed necessary condition for identifying all the 8-input TF is only 0.1%.
Self-Supervised Rigid Registration for Multimodal Retinal Images

The ability to accurately overlay one modality retinal image to another is critical in ophthalmology. Our previous framework achieved the state-of-the-art results for multimodal retinal image registration. However, it requires human-annotated labels due to the supervised approach of the previous work. In this paper, we propose a self-supervised multimodal retina registration method to alleviate the burdens of time and expense to prepare for training data, that is, aiming to automatically register multimodal retinal images without any human annotations. Specially, we focus on registering color fundus images with infrared reflectance and fluorescein angiography images, and compare registration results with several conventional and supervised and unsupervised deep learning methods. From the experimental results, the proposed self-supervised framework achieves a comparable accuracy comparing to the state-of-the-art supervised learning method in terms of registration accuracy and Dice coefficient.
BHIVA position statement on HIV, the law and the work of the clinical team

The purpose of this statement is to outline issues at the interface between HIV transmission and the law and provide guidance to healthcare professionals (HCPs) working in the field of HIV medicine. The guidance is to support work in the UK, and it is important to note that the law in England and Wales differs from that in Scotland and Northern Ireland. Approaches are suggested to deal with these issues consistently, within legal and General Medical Council (GMC) regulatory frameworks and in the context of the public health agenda. The guidance specifically addresses sexual transmission.
Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels

Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep/learning/based cancer detection systems have been developed. Many of the state-of-the- art models are patch/based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. However, such annotations are seldom readily available, in contrast to the clinical reports of pathologists, which contain slide-level labels. As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field. In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet/34) with 21 million parameters end-to-end on 4712 prostate biopsies. Themethod enables the use of entire biopsy images at high-resolution directly by reducing the GPUmemory requirements by 2.4 TB. We show thatmodern CNNs, trained using our streaming approach, can extract meaningful features from high-resolution images without additional heuristics, reaching similar performance as state-of-the-art patch-based and multiple-instance learning methods. By circumventing the need for manual annotations, this approach can function as a blueprint for other tasks in histopathological diagnosis. The source code to reproduce the streaming models is available at https://github.com/DIAGNijmegen/ pathology-streaming-pipeline.
Face age classification based on a deep hybrid model

Face age estimation, a computer vision task facing numerous challenges due to its potential applications in identity authentication, human-computer interface, video retrieval and robot vision, has been attracting increasing attention. In recent years, the deep convolutional neural networks (DCNN) have achieved state-of-the-art performance in age classification of face images. We propose a deep hybrid framework for age classification by exploiting DCNN as the raw feature extractor along with several effective methods, including fine-tuning the DCNN into a fine-tuned deep age feature extraction (FDAFE) model, introducing a new method of feature extracting, applying the maximum joint probability classifier to age classification and a strategy to incorporate information from face images more effectively to improve estimation capabilities further. In addition, we pre-process the original image to represent age information more accurately. Based on the discriminative and compact framework, state-of-the-art performance on several face image data sets has been achieved in terms of classification accuracy.
The Risk of Fatal Arrhythmias in Post-Myocardial Infarction Depression in Association With Venlafaxine

Venlafaxine is a second line anti-depressant and the most commonly used in the treatment of selective serotonin reuptake inhibitor nonresponders in major depression; due to its effects on the noradrenergic and serotonergic systems as a serotonin and norepinephrine reuptake inhibitor, there has been considerable apprehension regarding its use in patients with cardiovascular diseases, particularly post-myocardial infarction depression, some of the feared adverse effects include QT prolongation, arrhythmias including torsades de pointes and sudden cardiac death. We tried to resolve the facts regarding the risks associated with venlafaxine use in cardiac patients. We have reviewed all the relevant information up to May 2022 regarding the risks of venlafaxine use in cardiovascular disease, particularly with a focus on post-myocardial infarction depression, and gathered around 350 articles in our research and narrowed it down to 49 articles. The database used was PubMed and the keywords used were venlafaxine, arrhythmia, major depression, post-myocardial infarction, and ventricular tachycardia. We carefully screened all relevant articles and found articles supporting and refuting the effects of venlafaxine in increasing cardiovascular morbidity and mortality. We have concluded that there is a significant variability due to confounding factors affecting individual cases. Overall there is no increased arrhythmia risk in comparison with other anti-depressants except in high-risk cases such as with pre-existing cardiovascular disease, certain genotypes, and other co-morbidities. Any patient with a high risk of arrhythmias due to any etiology should receive a screening electrocardiogram before venlafaxine prescription for baseline QT interval and periodically while on therapy to check for changes. We encourage further research, including randomized clinical trials and post-marketing surveillance regarding the use of venlafaxine in high-risk cases such as patients with multiple co-morbidities, elderly patients, or patients with certain genotypes.
The Mediating Role of Eco-Friendly Artwork for Urban Hotels to Attract Environmental Educated Consumers

The adoption and implementation of environmental marketing strategies is the hotel industry's new approach to maintaining a competitive advantage and attracting more green consumers. Indeed, hotels with more sustainable practices and eco-friendly artwork generate more trust and make green consumers more loyal and satisfied. However, there is little prior research which has suggested the mediating role of green artwork between customers' levels of green perception and their hotel satisfaction. For this reason, the current authors obtained a total of 659 responses from South Korean consumers and conducted the structural equation analysis (SEM) to identify the indirect effect explaining how green arts boosts green hotel consumers' satisfaction. Our statistical findings offer vital insights regarding the relationship between customers' green perceptions and their hotel satisfaction, with eco-friendly artwork in the hotel interior design as the mediating variable. Finally, the current study provides a detailed understanding of art infusion to urban hotels by highlighting the impact of art and its spillover effects on consumer satisfaction.
Depthwise Spatio-Temporal STFT Convolutiona Neural Networks for Human Action Recognition

Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose spatio-temporal short-term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs. An STFT block consists of non-trainable convolution layers that capture spatially and/or temporally local Fourier information using an STFT kernel at multiple low frequency points, followed by a set of trainable linear weights for learning channel correlations. The STFT blocks significantly reduce the space-time complexity in 3D CNNs. In general, they use 3.5 to 4.5 times less parameters and 1.5 to 1.8 times less computational costs when compared to the state-of-the-art methods. Furthermore, their feature learning capabilities are significantly better than the conventional 3D convolutional layer and its variants. Our extensive evaluation on seven action recognition datasets, including Something(2) v1 and v2, Jester, Diving-48, Kinetics-400, UCF 101, and HMDB 51, demonstrate that STFT blocks based 3D CNNs achieve on par or even better performance compared to the state-of-the-art methods.
Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.
AFSense-ECG: Atrial Fibrillation Condition Sensing From Single Lead Electrocardiogram (ECG) Signals

In this paper, we propose AFSense-ECG, an intelligence-embedded single lead ECG sensor that is enabled with the ability of accurate detection of Atrial Fibrillation (AF) condition, which is the most common sustained cardiac arrhythmia and increased risk of stroke is higher with sub-clinical AF patients. AFSense-ECG acts like an early-warning sensor for AF condition detection. A processing unit (e.g., ESP32WROVERE microcontroller) integrated with off-the-shelf single lead ECG sensor like Alivecor or AD8232 embeds intelligence to the sensing system to augment for inferential sensing for empowering automated decision-making. AFSense-ECG captures the quasi-periodic nature of typical ECG signals with repetitive P-wave, QRS complex and T-wave patterns into its feature extraction and the representation learning process of model construction and learning rate optimization. Our empirical study validates the superiority of proposed ECG signal characteristics-based hyperparameter tuned ECG classification model construction. AFSense-ECG demonstrates F1-measure of 86.13%, where the current state-of-the-art methods report F1-measures of 83.70%, 83.10%, 82.90%, 82.60%, 82.50%, 81.00% over publicly available single lead ECG datasets of Physionet 2017 Challenge. Further, the proposed learning model for the inferential sensing is lean (approximately 25 times simpler in terms of total number of trainable parameters with reduced model size than relevant state-of-the-art model, where the state-of-the-art method with 83.70% F1-measure consists of 10474607 trainable parameters, and our proposed model consists of 433675 trainable parameters) and more effective (better F1-measure than the state-of-the-art methods), which enables us to construct affordable intelligent sensing system.
An Integrated Multi-Channel Biopotential Recording Analog Front-End IC With Area-Efficient Driven-Right-Leg Circuit

A multi-channel biopotential recording analog front-end (AFE) with a fully integrated area-efficient driven-right-leg (DRL) circuit is presented in this paper. The proposed AFE includes 10 channels of low-noise capacitive coupled instrumentation amplifier (CCIA), one shared 10-bit SAR ADC and a fully integrated DRL to enhance the system-level common-mode rejection ratio (CMRR). The proposed DRL circuit senses the common-mode at the CCIA output so that the AFE gain is reused as the DRL loop gain. Therefore, area efficient unit-gain buffer with small averaging capacitors can be used in DRL circuit to reduce the circuit area significantly. The proposed AFE has been implemented in a standard 0.18-mu m CMOS process. The DRL circuit achieved more than 85% chip area reduction compared to the state-of-art on-chip DRL circuits and maximum 60 dB enhancement to system-level CMRR. Measurement results show high/low AFE gain of 60 dB/54 dB respectively with 1 mu A/channel current consumption under 1.0 V power supply. The measured AFE input-referred noise in 1 Hz - 10k Hz range is 4.2 mu V-rms and the maximum system-level CMRR is 110 dB.
Detecting Covert Channels in Computer Networks Based on Chaos Theory

Covert channels via the widely used TCP/IP protocols have become a new challenging issue for network security. In this paper, we analyze the information hiding in TCP/IP protocols and propose a new effective method to detect the existence of hidden information in TCP initial sequence numbers (ISNs), which is known as one of the most difficult covert channels to be detected. Our method uses phase space reconstruction to create a processing space called reconstructed phase space, where a statistical model is proposed for detecting covert channels in TCP ISNs. Based on the model, a classification algorithm is developed to identify the existence of information hidden in ISNs. Simulation results have demonstrated that our proposed detection method outperforms the state-of-the-art technique in terms of high detection accuracy and greatly reduced computational complexity. Instead of offline processing as the state-of-the-art does, our new scheme can be used for online detection.
Effects of dietary calcium and phosphorus restrictions on growth performance, intestinal morphology, nutrient retention, and tibia characteristics in broiler chickens

1. This study evaluated the effects of dietary calcium (Ca)‎ and available phosphorus (aP) restrictions on growth performance, intestinal morphology, nutrient apparent total tract retention (ATTR), and tibia characteristics.2. A total of 1296, one-day-old male Ross-308 broilers were reared for 42 d. During the ‎starter phase (1-10 d), all birds were fed a nutrient-adequate diet (C). Diets fed during the grower phase (11-24 d) included: 1. C; 2. 15% of the Ca and aP in C; 3. 30% of the Ca and aP in C. At the beginning of the finisher phase (25 d), chickens fed the C diet were divided into two subgroups including C, and C+ phytase (500 FTU/kg). Restricted treatments were divided into eight subgroups as 1. C; 2. 10% of the Ca and aP in C; 3. 20% of the Ca and aP in C; 4. 30% of the Ca and aP in C; 5. C+ phytase; 6. 10% of the Ca and aP in C+ phytase; 7. 20% of the Ca and aP in C+ phytase and 8. 30% of the Ca and aP in C+ phytase. 3. On d 24 and 42, ATTR of Ca and phytate phosphorus (pP) were linearly increased by decreasing Ca and aP levels (P < 0.05). Birds receiving phytase showed higher nutrient ATTR ‎compared to those fed non-phytase supplemented diets (P < 0.05). Tibia Ca and P were linearly decreased at 24 d (P < 0.05) and tibial ash was linearly decreased (P < 0.05) at 42 d by decreasing levels of Ca and aP in finisher diets (without phytase)‎. By decreasing the levels of Ca and aP in the finisher diets (with phytase) with a 30% reduction of Ca and aP in the grower phase, tibia ash linearly decreased (P < 0.05). Using 500 FTU/kg phytase improved tibia traits compared to non-phytase supplemented treatments (P < 0.05).4. In general, decreasing dietary Ca and aP (up to 30%) during grower and finisher phases increased ATTR of minerals and decreased Ca, P and breaking strength (BS) of tibia without any negative effect on growth performance or intestinal morphology. Reduced dietary Ca and aP decreased tibial ash content, although 500 FTU/kg phytase improved ATTR of minerals and tibia attributes.