scholarly journals SoS TextVis: An Extended Survey of Surveys on Text Visualization

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Mohammad Alharbi ◽  
Robert Laramee
Keyword(s):  
Visual Analytics ◽  
State Of The Art ◽  
Leading Edge ◽  
Research Literature ◽  
Web Pages ◽  
Text Visualization ◽  
Current State ◽  
Starting Point ◽  
Wide Range ◽  
Visualization Techniques

Text visualization is a rapidly growing sub-field of information visualization and visual analytics. There are many approaches and techniques introduced every year to address a wide range of challenges and analysis tasks, enabling researchers from different disciplines to obtain leading-edge knowledge from digitized collections of text. This can be challenging particularly when the data is massive. Additionally, the sources of digital text have spread substantially in the last decades in various forms, such as web pages, blogs, twitter, email, electronic publications, and digitized books. In response to the explosion of text visualization research literature, the first text visualization survey article was published in 2010. Furthermore, there are a growing number of surveys that review existing techniques and classify them based on text research methodology. In this work, we aim to present the first Survey of Surveys (SoS) that review all of the surveys and state-of-the-art papers on text visualization techniques and provide an SoS classification. We study and compare the 14 surveys, and categorize them into five groups: (1) Document-centered, (2) user task analysis, (3) cross-disciplinary, (4) multi-faceted, and (5) satellite-themed. We provide survey recommendations for researchers in the field of text visualization. The result is a very unique, valuable starting point and overview of the current state-of-the-art in text visualization research literature.

scholarly journals Introduction to redundancy rules: the continuous wavelet transform comes of age

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170258 ◽  
Author(s):  
Paul S. Addison
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Continuous Wavelet ◽  
Theme Issue ◽  
Current State ◽  
Analysis Methods ◽  
Wide Range ◽  
New Form

Redundancy: it is a word heavy with connotations of lacking usefulness. I often hear that the rationale for not using the continuous wavelet transform (CWT)—even when it appears most appropriate for the problem at hand—is that it is ‘redundant’. Sometimes the conversation ends there, as if self-explanatory. However, in the context of the CWT, ‘redundant’ is not a pejorative term, it simply refers to a less compact form used to represent the information within the signal. The benefit of this new form—the CWT—is that it allows for intricate structural characteristics of the signal information to be made manifest within the transform space, where it can be more amenable to study: resolution over redundancy. Once the signal information is in CWT form, a range of powerful analysis methods can then be employed for its extraction, interpretation and/or manipulation. This theme issue is intended to provide the reader with an overview of the current state of the art of CWT analysis methods from across a wide range of numerate disciplines, including fluid dynamics, structural mechanics, geophysics, medicine, astronomy and finance. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

scholarly journals AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

2021 ◽  
Vol 7 ◽  
pp. e495
Author(s):  
Saleh Albahli ◽  
Hafiz Tayyab Rauf ◽  
Abdulelah Algosaibi ◽  
Valentina Emilia Balas
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Pathology Classification ◽  
Wide Range ◽  
Multi Class Classification

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

scholarly journals Biomarkers of adverse drug reactions

2017 ◽  
Vol 243 (3) ◽  
pp. 291-299 ◽  
Author(s):  
Daniel F Carr ◽  
Munir Pirmohamed
Keyword(s):  
Pharmaceutical Industry ◽  
Adverse Drug Reactions ◽  
State Of The Art ◽  
Tissue Injury ◽  
Healthcare Providers ◽  
Drug Reactions ◽  
Circulating Biomarkers ◽  
Current State ◽  
Organ Systems ◽  
Wide Range

Adverse drug reactions can be caused by a wide range of therapeutics. Adverse drug reactions affect many bodily organ systems and vary widely in severity. Milder adverse drug reactions often resolve quickly following withdrawal of the casual drug or sometimes after dose reduction. Some adverse drug reactions are severe and lead to significant organ/tissue injury which can be fatal. Adverse drug reactions also represent a financial burden to both healthcare providers and the pharmaceutical industry. Thus, a number of stakeholders would benefit from development of new, robust biomarkers for the prediction, diagnosis, and prognostication of adverse drug reactions. There has been significant recent progress in identifying predictive genomic biomarkers with the potential to be used in clinical settings to reduce the burden of adverse drug reactions. These have included biomarkers that can be used to alter drug dose (for example, Thiopurine methyltransferase (TPMT) and azathioprine dose) and drug choice. The latter have in particular included human leukocyte antigen (HLA) biomarkers which identify susceptibility to immune-mediated injuries to major organs such as skin, liver, and bone marrow from a variety of drugs. This review covers both the current state of the art with regard to genomic adverse drug reaction biomarkers. We also review circulating biomarkers that have the potential to be used for both diagnosis and prognosis, and have the added advantage of providing mechanistic information. In the future, we will not be relying on single biomarkers (genomic/non-genomic), but on multiple biomarker panels, integrated through the application of different omics technologies, which will provide information on predisposition, early diagnosis, prognosis, and mechanisms. Impact statement • Genetic and circulating biomarkers present significant opportunities to personalize patient therapy to minimize the risk of adverse drug reactions. ADRs are a significant heath issue and represent a significant burden to patients, healthcare providers, and the pharmaceutical industry. • This review details the current state of the art in biomarkers of ADRs (both genetic and circulating). There is still significant variability in patient response which cannot be explained by current knowledge of genetic risk factors for ADRs; however, we discussed how specific advances in genomics have the potential to yield better and more predictive models. • Many current clinically utilized circulating biomarkers of tissue injury are valid biomarkers for a number of ADRs. However, they often give little insight into the specific cell or tissue subtype which may be affected. Emerging circulating biomarkers with potential to provide greater information on the etiology/pathophysiology of ADRs are described.

scholarly journals Tensile Testing of Soils: History, Equipment and Methodologies

2020 ◽  
Vol 6 (3) ◽  
pp. 591-601
Author(s):  
Ausamah Al Houri ◽  
Ahed Habib ◽  
Ahmed Elzokra ◽  
Maan Habib
Keyword(s):  
Tensile Strength ◽  
State Of The Art ◽  
Retaining Walls ◽  
Tensile Tests ◽  
Testing Methods ◽  
Embankment Dams ◽  
Current State ◽  
Wide Range ◽  
Indirect Tensile ◽  
And Behavior

Tensile strength of soil is indeed one of the important parameters to many civil engineering applications. It is related to wide range of cracks specially in places such as slops, embankment dams, retaining walls or landfills. Despite of the fact that tensile strength is usually presumed to be zero or negligible, its effect on the erosion and cracks development in soil is significant. Thus, to study the tensile strength and behavior of soil several techniques and devices were introduced. These testing methods are classified into direct and indirect ways depending on the loading conditions. The direct techniques including c-shaped mold and 8-shaped mold are in general complicated tests and require high accuracy as they are based on applying a uniaxial tension load directly to the specimen. On the other hand, the indirect tensile tests such as the Brazilian, flexure beam, double punch and hollow cylinder tests provide easy ways to assess the tensile strength of soil under controlled conditions. Although there are many studies in this topic the current state of the art lack of a detailed article that reviews these methodologies. Therefore, this paper is intended to summarize and compare available tests for investigating the tensile behavior of soils.

scholarly journals Rapid discovery of novel prophages using biological feature engineering and machine learning

2020 ◽  
Author(s):  
Kimmo Sirén ◽  
Andrew Millard ◽  
Bent Petersen ◽  
M Thomas P Gilbert ◽  
Martha RJ Clokie ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Feature Space ◽  
Machine Learning Method ◽  
Biological Feature ◽  
Bacterial Genomes ◽  
Current State ◽  
Starting Point ◽  
Feature Based

ABSTRACTProphages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

Veterinary vaccines against toxoplasmosis

Parasitology ◽  
2014 ◽  
Vol 141 (11) ◽  
pp. 1365-1378 ◽  
Author(s):  
ELŻBIETA HISZCZYŃSKA-SAWICKA ◽  
JUSTYNA M. GATKOWSKA ◽  
MARCIN M. GRZYBOWSKI ◽  
HENRYKA DŁUGOŃSKA
Keyword(s):  
Bird Species ◽  
Congenital Toxoplasmosis ◽  
Protozoan Parasite ◽  
Farm Animals ◽  
Laboratory Animals ◽  
Effective Vaccine ◽  
Current State ◽  
Starting Point ◽  
Wide Range ◽  
Good Starting Point

SUMMARYToxoplasma gondii is a cosmopolitan protozoan parasite that infects a wide range of mammal and bird species. Common infection leads to high economic (e.g. abortions in sheep) and human (e.g. congenital toxoplasmosis or neurotoxoplasmosis in humans) losses. With one exception (Toxovax™ for sheep), there are no vaccines to prevent human or animal toxoplasmosis. The paper presents the current state and challenges in the development of a vaccine against toxoplasmosis, designed for farm animals either bred for consumption or commonly kept on farms and involved in parasite transmission. So far, the trials have mostly revolved around conventional vaccines and, compared with the research using laboratory animals (mainly mice), they have not been very numerous. However, the results obtained are promising and could be a good starting point for developing an effective vaccine to prevent toxoplasmosis.

scholarly journals Fungi-Based Microbial Fuel Cells

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2827 ◽  
Author(s):  
Anna Sekrecka-Belniak ◽  
Renata Toczyłowska-Mamińska
Keyword(s):  
Electron Transfer ◽  
Fuel Cells ◽  
Microbial Fuel Cells ◽  
State Of The Art ◽  
Metabolic Processes ◽  
Cathode Catalysts ◽  
Current State ◽  
Starting Point ◽  
Potential Applicability ◽  
Current Production

Fungi are among the microorganisms able to generate electricity as a result of their metabolic processes. Throughout the last several years, a large number of papers on various microorganisms for current production in microbial fuel cells (MFCs) have been published; however, fungi still lack sufficient evaluation in this regard. In this review, we focus on fungi, paying special attention to their potential applicability to MFCs. Fungi used as anodic or cathodic catalysts, in different reactor configurations, with or without the addition of an exogenous mediator, are described. Contrary to bacteria, in which the mechanism of electron transfer is pretty well known, the mechanism of electron transfer in fungi-based MFCs has not been studied intensively. Thus, here we describe the main findings, which can be used as the starting point for future investigations. We show that fungi have the potential to act as electrogens or cathode catalysts, but MFCs based on bacteria–fungus interactions are especially interesting. The review presents the current state-of-the-art in the field of MFC systems exploiting fungi.

scholarly journals The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 18
Author(s):  
Deepani B. Guruge ◽  
Rajan Kadel ◽  
Sharly J. Halder
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Current Knowledge ◽  
Research Literature ◽  
The State ◽  
Knowledge Based ◽  
Emerging Trends ◽  
Wide Range ◽  
Research Findings ◽  
Research Outcome

In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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