ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Adaptive multi-task learning using lagrange multiplier for automatic art analysis

Numerous computer vision applications, such as image classification, have benefited from multi-task learning techniques. However, the relative weighting between each task's loss is hard to be tuned by hand, causing multi-task learning prohibitive in real applications. In this paper, we present a novel and principled adaptive multi-task learning method that weights multiple loss functions based on lagrange multiplier strategy. Our method starts from the standard multi-task learning model. Based on Gaussian likelihood and lagrange multiplier, we then design an adaptive multi-task learning model to learn suitable weightings of each task and boost performance. In order to validate the feasibility of proposed method, we conduct automatic art analysis tests, including art classification and cross-modal art retrieval. Experimental results demonstrate that our method outperforms several state-of-the-art techniques, showing that performance is improved by up to 4.2% in art classification and 8.7% in cross-modal art retrieval when compared with the latest automatic loss weights learning method.
Deep Recurrent Network for Fast and Full-Resolution Light Field Deblurring

The popularity of parallax-based image processing is increasing while in contrast early works on recovering sharp light field from its blurry input (deblurring) remain stagnant. State-of-the-art blind light field deblurring methods suffer from several problems such as slow processing, reduced spatial size, and simplified motion blur model. In this paper, we solve these challenging problems by proposing a novel light field recurrent deblurring network that is trained under 6 degree-of-freedom camera motion-blur model. By combining the real light field captured using Lytro Illum and synthetic light field rendering of 3D scenes from UnrealCV, we provide a large-scale blurry light field dataset to train the network. The proposed method outperforms the state-of-the-art methods in terms of deblurring quality, the capability of handling full-resolution, and a fast runtime.
[Oral desensitization with Ketocal® in an infant with ketogenic diet and cow's milk protein allergy]

Introduction: ketogenic diet is a treatment with proven efficacy in drug-refractory childhood epilepsy. Cow's milk protein allergy may be a limitation for treating infants with ketogenic diet, as they need a product that contains cow's milk protein (Ketocal®). Case report: we report the case of an infant with a drug-refractory epileptic encephalopathy and IgE-mediated cow's milk protein allergy, who started a classic ketogenic diet. Oral desensitization was achieved with Ketocal 3:1®, allowing its use in the diet and achieving a clinical improvement with seizure control. Discussion: a patient with epilepsy and cow's milk protein allergy can benefit from the ketogenic diet, since it is possible to perform an oral immunotherapy with Ketocal®, also achieving a probable resolution of his/her allergy.
Photonic nanostructures mimicking floral epidermis for perovskite solar cells

Here, we report photonic nanostructures replicated from the adaxial epidermis of flower petals onto light-polymerized coatings using low-cost nanoimprint lithography at ambient temperature. These multifunctional nanocoatings are applied to confer enhanced light trapping, water repellence, and UV light and environmental moisture protection features in perovskite solar cells. The former feature helps attain a maximum power conversion efficiency of 24.61% (21.01% for the reference cell) without any additional device optimization. Added to these merits, the nanocoatings also enable stable operation under AM 1.5G and UV light continuous illumination or in real-world conditions. Our engineering approach provides a simple way to produce multifunctional nanocoatings optimized by nature's wisdom.
[Surgical treatment of arthrofibrosis of the knee joint]

Arthrofibrosis of the knee joint is a severe complication following trauma and surgical procedures, which often results in long-term impairment of joint function. Early mobilization techniques and anesthesia are still employed without sufficient clarification of the underlying processes. While the early stages of arthrofibrosis can be successfully treated with conservative measures for pain reduction and wound healing, in the late stage tense collagenous scar tissue is frequently present that permanently limits joint mobility. In this stage an improvement of joint mobility has no chance of success without a surgical intervention. In surgical treatment a differentiation should be made between localized (mostly secondary) arthrofibrosis (e.g. cruciate ligament surgery) and generalized arthrofibrosis (in the majority of cases primarily after total knee arthroplasty) and the treatment planned accordingly. Comorbid pathological alterations (transplant position, instability of the total knee endoprosthesis, implant attrition, low-grade infection, patellofemoral instability or maltracking, patella baja) must be taken into consideration in the treatment. A multimodal accompanying treatment including physiotherapy, pain therapy and psychosomatics is necessary to ensure successful treatment.
Network Coding Aided Cooperative Quantum Key Distribution Over Free-Space Optical Channels

Realistic public wireless channels and quantum key distribution (QKD) systems are amalgamated. Explicitly, we conceive network coding aided cooperative QKD over free space optical systems for improving the bit error ratio and either the key rate or the reliable operational distance. Our system has provided a 55% key rate improvement against the state-of-the-art benchmarker.
Highly effective reduction of phosphate and harmful bacterial community in shrimp wastewater using short-term biological treatment with immobilized engineering microalgae

Shrimp farming wastewater includes high amounts of phosphate and microbiological contaminants, necessitating further treatment before release into receiving water bodies. After 24 h of shrimp wastewater treatment, alginate beads containing the blue-green algal Synechocystis strain lacking the phosphate regulator gene (mutant strain ΔSphU) at 150 mg L-1 reduced phosphate content from 17.5 mg L-1 to 5.0 mg L-1, representing 71.5% removal efficiency, with phosphate removal rate reaching 6.9 mg gDW-1 h-1 during photobioreactor operation. For short-term treatment, removal rates of nitrate, ammonium and nitrite were 42.7, 48.5 and 92.9%, respectively. Microalgal encapsulated beads also impacted the bacterial community composition dynamics in shrimp wastewater. Next-generation sequencing targeting the V3-V4 region of the 16S rDNA gene showed significant differences in bacterial community composition after 24 h of treatment. Proteobacteria are the most abundant phylum in shrimp wastewater. After 24 h of bioremediation, reductions of harmful bacteria in the Cellvibrionaceae and Pseudomonadaceae families were recorded at 5.85 and 3.18%, respectively. Engineered microalgal immobilization under optimal conditions can be applied as an alternative short-term bioremediation strategy to remove phosphate and other harmful microbial contamination from shrimp farming wastewater.
Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, in homogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.
Efficient methods for mining weighted clickstream patterns

Pattern mining has been an attractive topic for many researchers since its first introduction. Clickstream mining, a specific version of sequential pattern mining, has been shown to be important in the age of the Internet. However, most previous works have simply exploited and applied existing sequential pattern algorithms to the mining of clickstream patterns, and few have studied clickstreams with weights, which also have a wide range of application. In this paper, we address this problem by proposing an approach based on the average weight measure for clickstream pattern mining and adapting a previous state-of-the-art algorithm to deal with the problem of weighted clickstream pattern mining. Following this, we propose an improved method named Compact-SPADE to enhance both the efficiency and memory consumption. Through various tests on both real-life and synthetic databases, we show that our proposed algorithms outperform state-of-the-art alternatives in terms of efficiency, memory requirements and scalability. (C) 2019 Elsevier Ltd. All rights reserved.
Soft Subspace Based Ensemble Clustering for Multivariate Time Series Data

Recently, multivariate time series (MTS) clustering has gained lots of attention. However, state-of-the-art algorithms suffer from two major issues. First, few existing studies consider correlations and redundancies between variables of MTS data. Second, since different clusters usually exist in different intrinsic variables, how to efficiently enhance the performance by mining the intrinsic variables of a cluster is challenging work. To deal with these issues, we first propose a variable-weighted K-medoids clustering algorithm (VWKM) based on the importance of a variable for a cluster. In VWKM, the proposed variable weighting scheme could identify the important variables for a cluster, which can also provide knowledge and experience to related experts. Then, a Reverse nearest neighborhood-based density Peaks approach (RP) is proposed to handle the problem of initialization sensitivity of VWKM. Next, based on VWKM and the density peaks approach, an ensemble Clustering framework (SSEC) is advanced to further enhance the clustering performance. Experimental results on ten MTS datasets show that our method works well on MTS datasets and outperforms the state-of-the-art clustering ensemble approaches.
The Evaluation of Curative Effect of Acupuncture: A Review of Systematic and Meta-Analysis Studies

The present study attempts to critically evaluate previously published research articles on the efficiency of acupuncture in the treatment of diseases. First, 35 systematic reviews or meta-analysis were found in the Cochrane database. Second, 54 related articles were selected by searching important scientific databases. Based on the results obtained regarding the efficacy of acupuncture for the treatment of various diseases, the articles were divided into 3 groups. The first group of articles confirmed the efficacy of treatment by acupuncture. In the second group of articles, the therapeutic effect of acupuncture was shown; however, further research is required to verify the results. In the third group of articles there is no evidence regarding the therapeutic effect of acupuncture till now. There is an urgent need to design and conduct double-blinded randomized clinical trials with high-quality methodologies. This provides a more careful evaluation of acupuncture efficiency in relation to the treatment of a vast array of diseases, based on scientific evidence.
Optimizing CDK4/6 inhibitors in advanced HR+/HER2- breast cancer: A personalized approach

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) combined with endocrine therapy (ET) are now a backbone of treatment for hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer. CDK4/6i plus ET is more effective than ET alone in this setting; however, the risk of grade 3-4 adverse events also increases. Approved agents in this class have similar efficacies, but important differences due to their structural and pharmacological properties. We review biomarkers and discuss determinants to inform a rational approach to therapy choice when selecting the most appropriate ET and CDK4/6i partners. We also identify subgroups that may benefit from specific ET-CDK4/6i combinations and discuss strategies to overcome resistance. This personalized approach aims to minimize treatment-related toxicities that may affect patient QoL and compliance, and ultimately therapy efficacy.
Sampling Optimization for Printer Characterization by Direct Search

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the alpha = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.
Generic Proposal Evaluator: A Lazy Learning Strategy Toward Blind Proposal Quality Assessment

Existing detection or recognition systems typically select one state-of-the-art proposal algorithm to produce massive object-covered candidate windows, and a quality metric specifically designed for this algorithm is utilized to single out small amounts of proposals. However, in practice, the accuracies of different proposal algorithms significantly change from one image content to another one. To obtain more robust proposal results, a generic proposal evaluator (GPE) is highly desired, which could choose optimal candidate windows across multiple proposal algorithms. In this paper, we propose a lazy learning strategy to train the GPE, which aims to blindly estimate the quality of each proposal without accessing to its manual annotation. Unlike the traditional end-to-end framework that learns a universal model from all training samples, we try to build query-specific training subset for each given proposal, where only its k-nearest-neighborhoods are collected from all labeled candidate windows. Benefits from the capability of updating the regression parameters for different visual contents, the proposed method delivers a higher quality prediction accuracy even with respect to the deep neural network learned by end-to-end method. Experimental results confirm that the proposed algorithm significantly outperforms many state-of-the-art proposal quality metrics.
Cell cultures in assessing radioresistance of glioblastomas

To date, no modern methods of treatment allow overcoming malignant potential of glial neoplasms and significant increase of survival. Analysis of glioblastoma radioresistance using cancer cell cultures is one of the perspective directions, as radiotherapy is standard and available treatment method for these neoplasms. This review summarizes current studies identifying many factors of radioresistance of glial tumors, such as hypoxia, microenvironment and metabolic features of tumor, stem cells, internal heterogeneity of tumor, microRNA, features of cell cycle, DNA damage and reparation. We obtained data on involvement of various molecular pathways in development of radioresistance such as MEK/ERK, c-MYC, PI3K/Akt, PTEN, Wnt, JAK/STAT, Notch, etc. Changes in activity of RAD51 APC, FZD1, LEF1, TCF4, WISP1, p53 and many others are determined in radioresistant cells. Further study of radioresistance pathways will allow development of specific target aptamers and inhibitors.
Missing Value Imputation for Multi-View Urban Statistical Data via Spatial Correlation Learning

As a developing trend of urbanization, massive amounts of urban statistical data with multiple views (e.g., views of Population and Economy) are increasingly collected and benefited to diverse domains, including transportation service, regional analysis, etc. Unfortunately, these statistical data that are divided into fine-grained regions usually suffer from missing value problem during the acquisition and storage processes. It is mianly caused by some inevitable circumstances, e.g., the document defacement, statistical difficulty in remote districts, and inaccurate information cleaning, etc. Those missing entries which make valuable information invisible may distort the further urban analysis. To improve the quality of missing data imputation, we propose an improved spatial multi-kernel learning method to guide the imputation process incorporating with the adaptive-weight non-negative matrix factorization strategy. Our model takes into account the regional latent similarities and the real geographical positions as well as the correlations among various views that are able to complete missing values precisely. We conduct intensive experiments to evaluate our method and compare with other state-of-the-art approaches on real-world datasets. All the empirical results show that the proposed model outperforms all the other state-of-the-art methods. Additionally, our model represents a strong generalization ability across multiple cities.
Evolutionary and functional analysis reveals the crucial roles of receptor-like proteins in resistance to Valsa canker in Rosaceae

Rosaceae is an economically important plant family that can be affected by a multitude of pathogenic microbes, some of which can cause dramatic losses in production. As a type of pattern-recognition receptor, receptor-like proteins (RLPs) are considered vital regulators of plant immunity. Based on genome-wide identification, bioinformatic analysis, and functional determination, we investigated the evolutionary characteristics of RLPs, and specifically those that regulate Valsa canker, a devastating fungal disease affecting apple and pear production. A total of 3028 RLPs from the genomes of 19 species, including nine Rosaceae, were divided into 24 subfamilies. Five subfamilies and seven co-expression modules were found to be involved in the responses to Valsa canker signals of the resistant pear rootstock Pyrus betulifolia 'Duli-G03'. Fourteen RLPs were subsequently screened as candidate genes for regulation of resistance. Among these, PbeRP23 (Chr13.g24394) and PbeRP27 (Chr16.g31400) were identified as key resistance genes that rapidly enhance the resistance of 'Duli-G03' and strongly initiate immune responses, and hence they have potential for further functional exploration and breeding applications for resistance to Valsa canker. In addition, as a consequence of this work we have established optimal methods for the classification and screening of disease-resistant RLPs.
Orchid classification using homogeneous ensemble of small deep convolutional neural network

Orchids are flowering plants in the large and diverse family Orchidaceae. Orchid flowers may share similar visual characteristics even they are from different species. Thus, classifying orchid species from images is a hugely challenging task. Motivated by the inadequacy of the current state-of-the-art general-purpose image classification methods in differentiating subtle differences between orchid flower images, we propose a hybrid model architecture to better classify the orchid species from images. The model architecture is composed of three parts: the global prediction network (GPN), the local prediction network (LPN), and the ensemble neural network (ENN). The GPN predicts the orchid species by global features of orchid flowers. The LPN looks into local features such as the organs of orchid plant via a spatial transformer network. Finally, the ENN fuses the intermediate predictions from the GPN and the LPN modules and produces the final prediction. All modules are implemented based on a robust convolutional neural network with transfer learning methodology from notable existing models. Due to the interplay between the modules, we also guidelined the training steps necessary for achieving higher predictive performance. The classification results based on an extensive in-house Orchids-52 dataset demonstrated the superiority of the proposed method compared to the state of the art.
Hidden images in Atxurra Cave (Northern Spain): A new proposal for visibility analyses of Palaeolithic rock art in subterranean environments

Visibility has been the subject of study in Palaeolithic rock art research ever since the discovery of Altamira Cave in 1879. Nevertheless, until now, the different approaches have been based on subjective assessments, due to computational limitations for a more objective methodology. Nowadays, cutting-edge technologies such as GIS allow us to address spatial studies in caves and overcome their geomorphologically complex and closed characteristics. Here we describe an innovative methodology that uses computing tools available to any researcher to study the viewsheds of the graphic units in decorated caves. We have tested its validity on the recently discovered rock art ensemble of Atxurra Cave, in Northern Spain. We demonstrate that this technology (GIS), widely used in other fields of archaeology, especially in outdoor studies, is also useable in caverns, taking into account the complex morphologies -ceilings and diverse floor-levels, for example. These programmes have also allowed us to consider the lighting systems used by the prehistoric groups inside the cave, as well as various data previously estimated by other authors, such as the height of individuals during the European LUP. The dynamism of these tools -2.5D-, as well as the advancement of new 3D GIS technologies, will allow in the future remarkable progress in these types of structural studies for a better understanding of Palaeolithic cave art phenomena.