ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A low latency implementation of a non-uniform partitioned convolution algorithm for room acoustic simulation

Finite impulse response convolution is one of the most widely used operations in digital signal processing field for filtering operations. Low computationally demanding techniques are essential for calculating convolutions with low input/output latency in real scenarios, considering that the real-time requirements are strictly related to the impulse response length. In this context, a complete overview of the state of the art relative to the algorithms for fast computation of convolution is described here. Then, a novel perceptual approach employed to reduce the computational cost of fast convolution algorithms is here presented. It is based on the pre-processing of a selected impulse response and it allows to further reduce the number of complex multiplications considering the energy decay relief and the absolute threshold of hearing, as psychoacoustic constraints. Several results are reported in terms of computational cost and perceived audio quality in order to prove the effectiveness of the proposed approach also introducing comparisons with the existing techniques of the state of the art.
Cryo-EM structures of light-harvesting 2 complexes from Rhodopseudomonas palustris reveal the molecular origin of absorption tuning

The genomes of some purple photosynthetic bacteria contain a multigene puc family encoding a series of α- and β-polypeptides that together form a heterogeneous antenna of light-harvesting 2 (LH2) complexes. To unravel this complexity, we generated four sets of puc deletion mutants in Rhodopseudomonas palustris, each encoding a single type of pucBA gene pair and enabling the purification of complexes designated as PucA-LH2, PucB-LH2, PucD-LH2, and PucE-LH2. The structures of all four purified LH2 complexes were determined by cryogenic electron microscopy (cryo-EM) at resolutions ranging from 2.7 to 3.6 Å. Uniquely, each of these complexes contains a hitherto unknown polypeptide, γ, that forms an extended undulating ribbon that lies in the plane of the membrane and that encloses six of the nine LH2 αβ-subunits. The γ-subunit, which is located near to the cytoplasmic side of the complex, breaks the C9 symmetry of the LH2 complex and binds six extra bacteriochlorophylls (BChls) that enhance the 800-nm absorption of each complex. The structures show that all four complexes have two complete rings of BChls, conferring absorption bands centered at 800 and 850 nm on the PucA-LH2, PucB-LH2, and PucE-LH2 complexes, but, unusually, the PucD-LH2 antenna has only a single strong near-infared (NIR) absorption peak at 803 nm. Comparison of the cryo-EM structures of these LH2 complexes reveals altered patterns of hydrogen bonds between LH2 αβ-side chains and the bacteriochlorin rings, further emphasizing the major role that H bonds play in spectral tuning of bacterial antenna complexes.
Tracking Transformer Tap Position in Real-Time Distribution Network Power Flow Applications

The current injection power flow method is commonly used by the power distribution industry in real-time distribution management systems. The inclusion of reactive power control adjustments that simulate local voltage controllers is known to create multiple power flow solutions. The stateof-the-art for simulating local voltage controllers computes the most likely operational solution by considering the different precedences of the controlled devices, based on their speeds of response. This paper presents a novel sensitivity based approach for simulating local controllers, which directly accounts for both controller interactions and the way in which the system state is reached. Numerical results are reported on networks with up to 3146 nodes and voltage dependent load models; the results show that the proposed approach yields probable power How solutions that are virtually identical to those given by an implementation of the state-of-the-art time-coordinated method and with a speed-up factor around three on the large network cases.
Analysis of Cell Cycle and DNA Compaction Dependent Subnuclear Distribution of Histone Marks

In eukaryotes, the organization of DNA wrapped around histones regulates DNA-dependent processes. Changes in epigenetic modifications modulate the compaction of DNA into chromatin and, thus, regulate DNA metabolism in time and space. Hence, to catalog the spatiotemporal epigenetic information and its relation to the dynamic nuclear landscape is of paramount importance. Here, we present a method, based on FiJi and the statistical image analysis tool nucim(R), to classify in 3D the nuclear DNA compaction in single interphase cells. We, furthermore, mapped the distribution of (epi)genetic marks and nuclear proteins/processes to the compaction classes along with their dynamics over the cell cycle. These techniques allow to catalog and quantify the dynamic changes in the epigenome in space and time and in single cells.
Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models

Introduction: Efforts have been made to reduce HIV/AIDS-related mortality by delivering antiretroviral therapy (ART) treatment. However, HIV patients in resource-poor settings are still dying, even if they are on ART treatment. This study aimed to explore the factors associated with HIV/AIDS-related mortality in Southwestern Ethiopia. Method: A non-concurrent retrospective cohort study which collected data from the clinical records of adult HIV/AIDS patients, who initiated ART treatment and were followed between January 2006 and December 2010, was conducted, to explore the factors associated with HIV/AIDS-related mortality at Jimma University Specialized Hospital (JUSH). Survival times (i. e., the time from the onset of ART treatment to the death or censoring) and different characteristics of patients were retrospectively examined. A best-fit model was chosen for the survival data, after the comparison between native semi-parametric Cox regression and parametric survival models (i. e., exponential, Weibull, and log-logistic). Result: A total of 456 HIV patients were included in the study, mostly females (312, 68.4%), with a median age of 30 years (inter-quartile range (IQR): 23-37 years). Estimated follow-up until December 2010 accounted for 1245 person-years at risk (PYAR) and resulted in 66 (14.5%) deaths and 390 censored individuals, representing a median survival time of 34.0 months (IQR: 22.8-42.0 months). The overall mortality rate was 5.3/100 PYAR: 6.5/100 PYAR for males and 4.8/100 PYAR for females. The Weibull survival model was the best model for fitting the data (lowest AIC). The main factors associated with mortality were: baseline age (> 35 years old, AHR = 3.8, 95% CI: 1.6-9.1), baseline weight (AHR = 0.93, 95% CI: 0.90-0.97), baseline WHO stage IV (AHR = 6.2, 95% CI: 2.2-14.2), and low adherence to ART treatment AHR = 4.2, 95% CI: 2.5-7.1). Conclusion: An effective reduction in HIV/AIDS mortality could be achieved through timely ART treatment onset and maintaining high levels of treatment adherence.
LWIR Strained-Layer Superlattice Materials and Devices at Teledyne Imaging Sensors

Strained-layer superlattices (SLS) based on type II InAs/Ga(In)Sb materials are a rapidly maturing technology and are theoretically predicted to exceed the dark-current performance of state-of-the-art HgCdTe. A substantial effort is underway at Teledyne Imaging Sensors in the development of SLS materials for infrared focal-plane arrays. In this paper, we describe state-of-the-art materials, device research and characterization, along with testing results for long-wavelength infrared SLS devices based on double-heterostructure and p(+)-B-n architectures, having n-on-p and p-on-n polarities, respectively. Detector materials exhibited excellent morphological and crystalline characteristics, and electro-optical characterization demonstrated performance comparable to the state of the art.
Benthic studies adjacent to Sakhalin Island, Russia, 2015 II: energy content of the zoobenthos in western gray whale feeding grounds

The waters adjacent to the northeastern coast of Sakhalin Island, Russia, are an important feeding ground for the endangered western gray whale. Data on the energy available to foraging whales from their prey resources is required for researchers interested in modeling the bioenergetics of whale foraging, but little energy content information is available for the benthic prey communities of gray whales in this region. In this study, we describe the energy density (ED), biomass, and total energy availability (ED × biomass) of benthic prey sampled from two gray whale foraging areas adjacent to Sakhalin Island: the nearshore and offshore feeding areas. ED varied almost seven-fold among benthic taxa, ranging from 1.11 to 7.62 kJ/g wet mass. Although there was considerable variation within most prey groups, amphipods had the highest mean ED of all of groups examined (5.58 ± 1.44 kJ/g wet mass). Small sample sizes precluded us from detecting any seasonal or spatial differences in mean ED within or among taxa; however, mean biomass in the offshore feeding area was, in some cases, an order of magnitude higher than mean estimates in the nearshore feeding area, resulting in higher mean total energy available to foraging gray whales offshore (958-3313 kJ/m2) compared to nearshore (223-495 kJ/m2). While the proportion of total energy accounted for by amphipods was variable, this prey group generally made up a higher proportion of the total energy available in the benthos of the offshore feeding area than in the benthos of the nearshore feeding area. Data presented here will be used to inform bioenergetics modeling of the vital rates of mature females in an effort to improve understanding of population growth limits for western gray whales.
A Ring Oscillator-Based PUF With Enhanced Challenge-Response Pairs

Physical unclonable functions (PUFs) are powerful security primitives that provide cheap and secure solutions for security-related applications. Strong PUFs provide a large set of challenge-response pairs (CRPs) and are suitable for device authentication. Weak PUFs produce a small number of CRPs and can be used for key extraction. In this paper, we propose a novel method to enhance the CRP set of traditional ring oscillatorbased PUFs (RO-PUFs). RO-PUFs are one of the most reliable types of PUFs and the best fit to implement on the field-programmable gate arrays. To the best of our knowledge, our method provides the maximum number of CRPs compared with the state of the art. In addition, the number of response bits that can be extracted by our method for each challenge is n - 1 times more than the state of the art, where n is the number of ROs. The large number of response bits results in the authentication of more devices and generation of more keys. Evaluation of the PUF responses produced by applying our method shows a significant improvement in unpredictability and randomness compared with the related works. Moreover, we show that the responses are unique and reliable.
Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.
An Oil Well Dataset Derived from Satellite-Based Remote Sensing

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University-Oil Well Object Detection Version 1.0 (NEPU-OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.
Reliability Issues in State-of-the-Art Microfluidic Biochips: A Survey

Microfluidic biochips are a prominent class of lab-on-a-chip, i.e. LoC system, guided with some activation sequences devised for a bioprotocol to manipulate fluids used in biomedical and biochemical laboratory syntheses. This paper is a survey of reliability issues for state-of-the-art microfluidic biochips. First, this paper presents the basic structure and working principles for different types of available microfluidic biochips. After that, it elaborates on a systematic analysis of security and reliability issues in performing different fluidic operations using microfluidic biochips. This paper also presents the current attack models and types with their scenarios in different remedy actions, both proactive and reactive. Finally, this paper concludes with future research directions so that the effects due to attacks on biochips will be minimized within the stipulated time for a reliable and sustainable microfluidic biochip world.
Extraembryonic tissue in chelicerates: a review and outlook

The formation of extraembryonic membranes (EEMs) contributes to the proper development of many animals. In arthropods, the formation and function of EEMs have been studied best in insects. Regarding the development of extraembryonic tissue in chelicerates (spiders and relatives), most information is available for spiders (Araneae). Especially two populations of cells have been considered to represent EEMs in spiders. The first of these potential EEMs develops shortly after egg deposition, opposite to a radially symmetrical germ disc that forms in one hemisphere of the egg and encloses the yolk. The second tissue, which has been described as being extraembryonic is the so-called dorsal field, which is required to cover the dorsal part of the developing spider germ rudiment before proper dorsal closure. In this review, we summarize the current knowledge regarding the formation of potential extraembryonic structures in the Chelicerata. We describe the early embryogenesis of spiders and other chelicerates, with a special focus on the formation of the potential extraembryonic tissues. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
Model for Detection of Masquerade Attacks Based on Variable-Length Sequences

A masquerader is an attacker who gains illegitimate access to a user's account. Masquerade detection is one of the key problems of intrusion detection systems. Deep learning models that obtained state-of-the-art results in masquerade detection have failed to exhibit very high detection performance when data samples contain limited information. Alternatively, computationally cheaper and more memory-efficient traditional machine learning models suffer from less robust features, which hinders them in achieving high detection performance. The contributions of this article are as follows: we introduce new features of variable-length UNIX command sequences (i.e., weighted occurrence frequencies of different orders) and integrate these features into an extended Markov-chain-based variable-length model. The detection performance of our model is evaluated on three publicly available and free datasets: Schonlau (SEA), Purdue (PU), and Greenberg. The results demonstrate that our model significantly improves the true positive rate (TPR), false positive rate, receiver operator characteristic, and threshold variance compared to the baselines (other Markov-chain-based variable-length models). Furthermore, in terms of the TPR, the proposed method is superior to a state-of-the-art deep learning model that uses a convolutional neural network on the PU and Greenberg datasets and a state-of-the-art sequence-alignment-hidden Markov model on the SEA dataset. Moreover, the proposed method is much more lightweight than the state-of-the-art models in terms of computational and memory complexity, and thus more suitable for real-time masquerade detection.
PoolNet plus : Exploring the Potential of Pooling for Salient Object Detection

We explore the potential of pooling techniques on the task of salient object detection by expanding its role in convolutional neural networks. In general, two pooling-based modules are proposed. A global guidance module (GGM) is first built based on the bottom-up pathway of the U-shape architecture, which aims to guide the location information of the potential salient objects into layers at different feature levels. A feature aggregation module (FAM) is further designed to seamlessly fuse the coarse-level semantic information with the fine-level features in the top-down pathway. We can progressively refine the high-level semantic features with these two modules and obtain detail enriched saliency maps. Experimental results show that our proposed approach can locate the salient objects more accurately with sharpened details and substantially improve the performance compared with the existing state-of-the-art methods. Besides, our approach is fast and can run at a speed of 53 FPS when processing a $300 \times 400$300x400 image. To make our approach better applied to mobile applications, we take MobileNetV2 as our backbone and re-tailor the structure of our pooling-based modules. Our mobile version model achieves a running speed of 66 FPS yet still performs better than most existing state-of-the-art methods. To verify the generalization ability of the proposed method, we apply it to the edge detection, RGB-D salient object detection, and camouflaged object detection tasks, and our method achieves better results than the corresponding state-of-the-art methods of these three tasks. Code can be found at http://mmcheng.net/poolnet/.
Accelerated reweighted nuclear norm minimization algorithm for low rank matrix recovery

In this paper we propose an accelerated reweighted nuclear norm minimization algorithm to recover a low rank matrix. Our approach differs from other iterative reweighted algorithms, as we design an accelerated procedure which makes the objective function descend further at every iteration. The proposed algorithm is the accelerated version of a state-of-the-art algorithm. We provide a new analysis of the original algorithm to derive our own accelerated version, and prove that our algorithm is guaranteed to converge to a stationary point of the reweighted nuclear norm minimization problem. Numerical results show that our algorithm requires distinctly fewer iterations and less computational time than the original one to achieve the same (or very close) accuracy, in some problem instances even require only about 50% computational time of the original one, and is also notably faster than several other state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved.
Hybrid Feature Model for Emotion Recognition in Arabic Text

In recent years, research into developing state-of-the-art models for Arabic natural language processing tasks has gained momentum. These models must address the added difficulties related to the nature and structure of the Arabic language. In this paper, we propose three models, a human-engineered feature-based (HEF) model, a deep feature-based (DF) model, and a hybrid of both models (HEF& x002B;DF) for emotion recognition in Arabic text. We evaluated the performance of the proposed models on the SemEval-2018, IAEDS, and AETD datasets by comparing the performances of those models on each emotion label. We also compared the model performances with those of other state-of-the-art models. The results show that the HEF& x002B;DF model outperformed the DF and HEF models on all datasets. The DF model performed better than the HEF model on the SemEval-2018 and AETD datasets, while the HEF model performed better than the DF model on the IAEDS dataset. The HEF& x002B;DF model outperformed the state-of-the-art models in terms of accuracy, weighted-average precision, weighted-average recall, and weighted-average F-score on the AETD dataset and in terms of accuracy, macro-averaged precision, macro-averaged recall, and macro-averaged F-score on the IAEDS dataset. It also achieved the best macro-averaged F-score and the second-best Jaccard accuracy and micro-averaged F-score on the SemEval-2018 dataset.
Gestational malnutrition, hyperemesis gravidarum, and Wernicke's encephalopathy: What is missing?

Hyperemesis gravidarum (HG), or the severe nausea and vomiting of pregnancy, is one of the most dreaded complications of gestation, affecting between 1.5% and 3.0% of pregnant women. From the late 1800s to the mid-1980s, the etiology was frequently cited to have psychological and/or-later-perhaps hormonal origins, which have numbered at least 10. Current research has unearthed a genetic basis for HG that implicates growth differentiation factor 15, insulin-like growth factor binding protein 7, and hormone receptors (namely, glial cell line-derived neurogenic factor family receptor alpha-like and the progesterone receptor). Whatever the origins of this disease, it has caused immeasurable physiological and psychological damage to women, their fetuses, and their families. The psychological trauma includes a high rate of suicidal ideation as well as posttraumatic stress disorder. Whereas the healthcare costs are substantial for the mother with HG, the lifetime costs to the neonate include that which accompanies reduced employment earnings related to cognitive compromise. Another devastating outcome of severe HG can be Wernicke's encephalopathy (WE), which has a high fetal and maternal mortality rate. Our study explored 18 current reports of HG and WE. We highlighted additional presenting features we believe also accompany, and sometimes replace, the classically taught triad components of WE: ataxia, confabulation, and nystagmus. We agree with the conclusion made by Sheehan and Ironside in 1939 that thiamin alone may not reverse WE, and we offer possible explanations. Lastly, we offer suggestions for remediation.