ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
U-series and radiocarbon cross dating of speleothems from Nerja Cave (Spain): Evidence of open system behavior. Implication for the Spanish rock art chronology

Two stalagmites from Nerja cave (Andalusia, Spain) were studied. The cave is well known because of its long human occupation from the Upper Palaeolithic to the Chalcolithic and its abundant parietal prehistoric Art. The aims of this study were twofold: i) to compare uranium/thorium (Th-230/U-234) and Carbon-14 (C-14) ages obtained all along the growth axis of the stalagmites in order to understand the consequences of diagenetic processes on the validity of radiometric ages; ii) as one of the stalagmites contains black layers, attributed to combustion soot, to establish when these intense hearths were used and by which culture. Th-230/U-234 and C-14 ages were coupled with mineralogical studies using FTIR (Fourier-transform infrared spectroscopy) and thin section observations. The first stalagmite (GN16-9b) displays Th-230/U-234 ages in stratigraphic order, and compatible with C-14 ages corrected for a few percent of dead carbon. Homogeneous composition of aragonitic crystals characterized by their needle-like texture is observed throughout this speleothem. For the second stalagmite (GN16-7), in contrast,Th- 230/U-234 ages display large significant inversions and discordant results on the upper part and at the base of the stalagmite, suggesting a possible open system behavior for this chronometer. Interestingly, 14C ages are in stratigraphic order all along the stalagmite and are compatible with Th-230/U-234 ages only in its central part. Mineralogical studies display evidence of aragonite to calcite transformation at the top and a complex mineralogical assemblage with interlayered silicates (possibly clays) and calcitic mineralogy for the base of GN16-7. In these parts, discordant Th-230/U-234 ages were measured. In the middle part of the stalagmite, however, where the fibrous aragonite is well preserved, the 14C and Th-230/U-234 ages agree. Our data suggest that in the case of aragonite to calcite transformation as shown here, Th-230/U-234 ages are biased, but C-14 ages seem to remain accurate, as already observed in aragonitic marine bio minerals. C-14 ages obtained are used for the chronology of the soot layer, determined here between 7900 and 5500 years Cal BP, coherent with previous analysis of charcoals in the same sector of the cave. This study highlights the importance of working with at least two chronometers when stratigraphic age verification is not possible, as is the case of some parietal CaCO3 thin layers used for rock art dating. Recent Th-230/U-234 ages published for carbonate deposits on Spanish parietal Art are discussed in light of this demonstration. (C) 2022 Elsevier Ltd. All rights reserved.
Recurrent Poisson Factorization for Temporal Recommendation

Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, they do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a rich family of time-sensitive factorization models. They include Hierarchical RPF that captures the consumption heterogeneity among users and items, Dynamic RPF that handles dynamic user preferences and item specifications, Social RPF that models the social-aspect of product adoption, Item-Item RPF that considers the inter-item correlations, and eXtended Item-Item RPF that utilizes items' metadata to better infer the correlation among engagement patterns of users with items. We also develop an efficient variational algorithm for approximate inference that scales up to massive datasets. We demonstrate RPFs superior performance over many state-of-the-art methods on synthetic dataset, and wide variety of large scale real-world datasets.
Harmonizing Pathological and Normal Pixels for Pseudo-Healthy Synthesis

Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches based on the Generative Adversarial Network (GAN) have achieved promising results in pseudo-healthy synthesis. However, the discriminator (i.e., a classifier) in the GAN cannot accurately identify lesions and further hampers from generating admirable pseudo-healthy images. To address this problem, we present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. Then, we apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem existing in medical image segmentation. Furthermore, a reliable metric is proposed by utilizing two attributes of label noise to measure the health of synthetic images. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods. The method achieves better performance than the existing methods with only 30% of the training data. The effectiveness of the proposed method is also demonstrated on the LiTS and the T1 modality of BraTS. The code and the pre-trained model of this study are publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor.
Fluorescence Molecular Tomography Reconstruction of Small Targets Using Stacked Auto-Encoder Neural Networks

As a noninvasive and quantitative method, fluorescence molecular tomography (FMT) has many potential applications in biomedical field. It has the power to resolve in three-dimension (3D), the molecular processes in small animal in-vivo in both theory and practice. This paper proposes to solve the problem of reconstruction error and speed by using stacked auto-encoders (SAE). A finite element method (FEM) solution to the Laplace transformed time-domain coupled diffusion equations is employed as the forward model. The reconstruction model is formulated under the framework of SAE. Numerical simulation experiments were conducted to compare the reconstruction results of SAE and algebraic reconstruction technique (ART). We demonstrated that the proposed reconstruction algorithm can retrieve the positions and shapes of the targets more accurately than ART. This advantage of SAE is especially reflected in the reconstruction for small targets with a radius of 2 mm and 3 mm.
A Single-Blind, Randomized, Placebo Controlled Study to Evaluate the Benefits and Safety of Endourage Targeted Wellness Formula C Sublingual +Drops in People with Post-Acute Coronavirus Disease 2019 Syndrome

Introduction: Coronavirus Disease 2019 (COVID-19) causes a wide range of symptoms, including death. As persons recover, some continue to experience symptoms described as Post-Acute COVID-19 Syndrome (PACS). The objectives of this study were to measure the efficacy of Formula C™, a cannabidiol (CBD)-rich, whole-flower terpene-rich preparation in managing PACS symptoms. Materials and Methods: This randomized, placebo-controlled, single-blind, open-label crossover study was conducted in 2021. Informed consent was obtained from participants, and they were randomized to two treatment groups. Group 1 (n=15) received blinded active product for 28 days, and Group 2 (n=16) received blinded placebo for 28 days (Treatment Period 1). Both groups crossed over to open-label active product for 28 days (Treatment Period 2) with a safety assessment at day 70. Patient-Reported Outcomes Measurement Information System (PROMIS®) scores and the Patient Global Impression of Change (PGIC) score were used to assess primary and secondary objectives. Safety assessments were also done at each visit. Results: Twenty-four participants completed study, with 8 withdrawals, none related to study product. PGIC and PROMIS scores improved across both groups at day 28. This raised questions about the placebo. A reanalysis of the placebo confirmed absence of CBD and unexpected medical concentration of terpenes. The study continued despite no longer having a true placebo. The improved scores on outcome measures were maintained across the open label treatment period. There were no safety events reported throughout the study. Discussion: For persons with PACS who are nonresponsive to conventional therapies, this study demonstrated symptom improvement for participants utilizing Formula C. In addition, the benefits seen in Group 2 suggest the possibility that non-CBD formulations rich in antioxidants, omega-3, and omega-6 fatty acids, gamma-linoleic acid, and terpenes may also have contributed to the overall improvement of the partial active group through the study. Conclusion: Given that both groups demonstrated improvement, both formulations may be contributing to these findings. Limitations include the small number of participants, the lack of a true placebo, and limited time on study products. Additional studies are warranted to explore both CBD-rich hemp products and hempseed oil as treatment options for PACS. Trial Registration ClinicalTrials.gov Identifier: NCT04828668.
HEVC coding-unit decision algorithm using tree-block classification and statistical data analysis

We propose a fast coding unit (CU) depth decision algorithm in the High Efficiency Video Coding (HEVC) procedure based on statistical analysis. First, we derive a set of optimized weights of surrounding CU decisions to predict the current CU decision for 3 different Largest Coding Unit (LCU) classes. Second, for a given predicted current CU decision, we analyze the possible true current CU decisions, aiming to find the correspondence. A corresponding table is found and can be used to achieve target prediction accuracy. Third, for early termination of the encoding processes, the 3 early termination methods in a state-of-the-art work, as well as their different combinations, are evaluated. We show that using one of them is sufficient for saving time while encoding to keep the implementation complexity low. Compared with full CU search in HEVC standards, the proposed method reduces the encoding time by 57 and 49 % on average with Low Delay and Random Access profiles, respectively, with acceptable bitrate and PSNR performances. Compared with two state-of-the-art methods, the encoding time reduction is up to 23 and 13 % with Low Delay profile, 7 and 3 % with Random Access profile, on average, whereas the performances of bitrate and PSNR are similar.
Leveraging event-based semantics for automated text simplification

Automated Text Simplification (ATS) aims to transform complex texts into their simpler variants which are easier to understand to wider audiences and easier to process with natural language processing (NLP) tools. While simplification can be applied on lexical, syntactic, and discourse level, all previously proposed ATS systems only operated on the first two levels, thus failing at simplifying texts on the discourse level. We present a semantically-motivated ATS system which is the first system that is applied on the discourse level. By exploiting the state-of-the-art event extraction system, it is the first ATS system able to eliminate large portions of irrelevant information from texts, by maintaining only those parts of the original text that belong to factual event mentions. A few handcrafted rules ensure that the output of the system is syntactically simple, by placing each factual event mention in a separate short sentence, while the state-of-the-art unsupervised lexical simplification module, based on using word embeddings, replaces complex and infrequent words with their simpler variants. We perform a thorough evaluation, both automatic and manual, showing that our system produces more readable and simpler texts than the state-of-the-art ATS systems. Our newly proposed post-editing evaluation further reveals that our system requires less human effort for correcting grammaticality and meaning preservation on news articles than the state-of-the-art ATS system. (C) 2017 Elsevier Ltd. All rights reserved.
A Simple Model of Speech Communication and its Application to Intelligibility Enhancement

We introduce a model of communication that includes noise inherent in the message production process as well as noise inherent in the message interpretation process. The production and interpretation noise processes have a fixed signal-to-noise ratio. The resulting system is a simple but effective model of human communication. The model naturally leads to a method to enhance the intelligibility of speech rendered in a noisy environment. State-of-the-art experimental results confirm the practical value of the model.
Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem

The multiobjective permutation flowshop scheduling problem (MOPFSSP) is one of the most popular machine scheduling problems with extensive engineering relevance of manufacturing systems. There have been many attempts at solving MOPFSSP using heuristic and meta-heuristic methods, such as evolutionary algorithm. In this paper, a novel multiobjective memetic search algorithm (MMSA), is proposed to solve the MOPFSSP with makespan and total flowtime. First, a problem-specific Nawaz-Enscore-Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a global search embedded with a perturbation operation is used to improve the solution of the entire population. Then, a single insert-based local search is used to improve each individual and then a further local search strategy is used to find the better solution for the non-improved individual in the single insert-based local search. The performance of our proposed algorithm is validated and compared with the four state-of-the-art algorithms on a number of benchmark problem. The experimental results show that the proposed MMSA provides better solutions than several state-of-the-art algorithms.
Fine-tuning Convolutional Neural Networks for fine art classification

The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections. (C) 2018 Elsevier Ltd. All rights reserved.
Remote dielectric sensing and lung ultrasound to assess pulmonary congestion

We investigated the agreement between remote dielectric sensing (ReDS) system, which is a recently introduced non-invasive technology to quantify the degree of pulmonary congestion, and lung ultrasound (LUS), which is a gold standard to assess the existence of severe pulmonary congestion. Consecutive patients who were hospitalized to examine the cause of heart failure and treat their heart failure in our institute were prospectively included. They received LUS and simultaneous ReDS measurements. Three or more B-lines at each LUS zone was assigned to B-profile positive, indicating the existence of significant pulmonary congestion. ReDS values ≥ 35% were defined as significant pulmonary congestion. A total of 19 heart failure patients were included (77 years, 13 men). Plasma B-type natriuretic peptide level was 131 (36, 416) pg/ml. Three patients had B-profile, indicating significant pulmonary congestion, and two of them had ≥ 35% of ReDS (sensitivity 66.7%, specificity 87.5%, and negative predictive value 93.3%). Most of the patients (79%) had lower B-lines below 3 and did not satisfy the criteria of B-profile, irrespective of wide ranges of ReDS values. ReDS system had as acceptable predictability as LUS in assessing the existence of significant pulmonary congestion. ReDS would be recommended to rule out significant pulmonary congestion or quantify the degree of less significant pulmonary congestion.
Meta-Parameter Free Unsupervised Sparse Feature Learning

We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL-10 and UCMerced show that the method achieves the state-of-the-art performance, providing discriminative features that generalize well.
Regression-Based Cardiac Motion Prediction From Single-Phase CTA

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3 +/- 0.5 mm, compared to values of 2.7 +/- 0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.
Providing Re-Essure-ance to the Nickel-Allergic Patient Considering Hysteroscopic Sterilization

Essure is a popular method of permanent sterilization that offers a minimally invasive approach that avoids the risks of traditional sterilization procedures in the operating room. Despite the rarity of complications, there has been concern in the popular media over the safety of Essure. We describe the third reported case of systemic contact dermatitis due to the nickel component of the device, with a resolution of symptoms following surgical removal of the inserts. Despite these cases, we believe that extremely rare complications such as this should not dissuade patients from choosing this safe, effective method of sterilization.
State of the Art Lower Limb Robotic Exoskeletons for Elderly Assistance

The number of elderly populations is rapidly increasing. Majority of elderly people face difficulties while walking because the muscular activity or other gait-related parameters start to deteriorate with aging. Therefore, the quality of life among them can be suffered. To make their life more comfortable, service providing robotic solutions in terms of wearable powered exoskeletons should be realized. Assistive powered exoskeletons are capable of providing additional torque to support various activities, such as walking, sit to stand, and stand to sit motions to subjects with mobility impairments. Specifically, the powered exoskeletons try to maintain and keep subjects' limbs on the specified motion trajectory. The state of the art of currently available lower limb assistive exoskeletons for weak and elderly people is presented in this paper. The technology employed in the assistive devices, such as actuation and power supply types, control strategies, their functional abilities, and the mechanism design, is thoroughly described. The outcome of studied literature reveals that there is still much work to be done in the improvement of assistive exoskeletons in terms of their technological aspects, such as choosing proper and effective control methods, developing user friendly interfaces, and decreasing the costs of device to make it more affordable, meanwhile ensuring safe interaction for the end-users.
Coverage path planning with targetted viewpoint sampling for robotic free-form surface inspection

Surface metrology systems are increasingly used for inspecting dimensional quality in manufacturing. The gauge of these measurement systems is often mounted as an end-effector on robotic systems to exploit the robots' high degrees of freedom to reposition the gauge to different viewpoints. With this repositioning flexibility, a planning methodology becomes necessary in order to carefully plan the viewpoints, as well as the optimal sequence and quickest path to move the gauge to each viewpoint. This paper investigates coverage path planning for robotic single-sided dimensional inspection of free-form surfaces. Reviewing existing feasible state-of-the-art methodologies to solve this problem led to identifying an unexplored opportunity to improve the coverage path planning, specifically by replacing random viewpoint sampling strategy. This study reveals that a non-random targetted viewpoint sampling strategy significantly contributes to solution quality of the resulting planned coverage path. By deploying optimisation during the viewpoint sampling, an optimal set of admissible viewpoints can be obtained, which consequently significantly shortens the cycle-time for the inspection task. Results that evaluate the proposed viewpoint sampling strategy for two industrial sheet metal parts, as well as a comparison with the state-of-the-art are presented. The results show up to 23.8% reduction in cycle-time for the inspection task when using targetted viewpoints sampling.