scholarly journals Euler diagrams drawn with ellipses area-proportionally (Edeap)

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Michael Wybrow ◽  
Peter Rodgers ◽  
Fadi K. Dib
Keyword(s):  
State Of The Art ◽  
Data Sets ◽  
Microarray Experiments ◽  
Current State ◽  
Gene Ontology Database ◽  
Euler Diagrams ◽  
Twitter Data ◽  
Extensive Evaluation ◽  
Using Data ◽  
The Web

AbstractBackgroundArea-proportional Euler diagrams are frequently used to visualize data from Microarray experiments, but are also applied to a wide variety of other data from biosciences, social networks and other domains.ResultsThis paper details Edeap, a new simple, scalable method for drawing area-proportional Euler diagrams with ellipses. We use a search-based technique optimizing a multi-criteria objective function that includes measures for both area accuracy and usability, and which can be extended to further user-defined criteria. The Edeap software is available for use on the web, and the code is open source. In addition to describing our system, we present the first extensive evaluation of software for producing area-proportional Euler diagrams, comparing Edeap to the current state-of-the-art; circle-based method, venneuler, and an alternative ellipse-based method, eulerr.ConclusionsOur evaluation—using data from the Gene Ontology database via GoMiner, Twitter data from the SNAP database, and randomly generated data sets—shows an ordering for accuracy (from best to worst) of eulerr, followed by Edeap and then venneuler. In terms of runtime, the results are reversed with venneuler being the fastest, followed by Edeap and finally eulerr. Regarding scalability, eulerr cannot draw non-trivial diagrams beyond 11 sets, whereas no such limitation is present in Edeap or venneuler, both of which draw diagrams up to the tested limit of 20 sets.

scholarly journals LEO: Liquid Exploration Online

2020 ◽  
Vol 2 (1) ◽  
pp. 58-80
Author(s):  
Frank Hoeller
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Simulation Experiments ◽  
Coverage Path Planning ◽  
Current State ◽  
Extensive Evaluation ◽  
Novel Approach ◽  
Local Navigation ◽  
Local Decision ◽  
Real Robot

This article introduces a novel approach to the online complete- coverage path planning (CCPP) problem that is specically tailored to the needs of skid-steer tracked robots. In contrast to most of the current state-of-the-art algorithms for this task, the proposed algorithm reduces the number of turning maneuvers, which are responsible for a large part of the robot's energy consumption. Nevertheless, the approach still keeps the total distance traveled at a competitive level. The algorithm operates on a grid-based environment representation and uses a 3x3 prioritization matrix for local navigation decisions. This matrix prioritizes cardinal di- rections leading to a preference for straight motions. In case no progress can be achieved based on a local decision, global path planning is used to choose a path to the closest known unvisited cell, thereby guaranteeing completeness of the approach. In an extensive evaluation using simulation experiments, we show that the new algorithm indeed generates competi- tively short paths with largely reduced turning costs, compared to other state-of-the-art CCPP algorithms. We also illustrate its performance on a real robot.

scholarly journals Modern Scientific Visualizations on the Web

Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 37
Author(s):  
Loraine Franke ◽  
Daniel Haehn
Keyword(s):  
Scientific Visualization ◽  
State Of The Art ◽  
Three Dimensional ◽  
Emerging Technology ◽  
Research Field ◽  
Future Directions ◽  
Web Based ◽  
Current State ◽  
Visualization Systems ◽  
The Web

Modern scientific visualization is web-based and uses emerging technology such as WebGL (Web Graphics Library) and WebGPU for three-dimensional computer graphics and WebXR for augmented and virtual reality devices. These technologies, paired with the accessibility of websites, potentially offer a user experience beyond traditional standalone visualization systems. We review the state-of-the-art of web-based scientific visualization and present an overview of existing methods categorized by application domain. As part of this analysis, we introduce the Scientific Visualization Future Readiness Score (SciVis FRS) to rank visualizations for a technology-driven disruptive tomorrow. We then summarize challenges, current state of the publication trend, future directions, and opportunities for this exciting research field.

scholarly journals Design Challenges for Entity Linking

2015 ◽  
Vol 3 ◽  
pp. 315-328 ◽  
Author(s):  
Xiao Ling ◽  
Sameer Singh ◽  
Daniel S. Weld
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Data Sets ◽  
Entity Linking ◽  
Joint Inference ◽  
Extensive Evaluation ◽  
Key Aspects ◽  
Design Challenges

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.

scholarly journals Native Language Identification With Classifier Stacking and Ensembles

2018 ◽  
Vol 44 (3) ◽  
pp. 403-446 ◽  
Author(s):  
Shervin Malmasi ◽  
Mark Dras
Keyword(s):  
State Of The Art ◽  
Native Language ◽  
Ensemble Methods ◽  
Large Data ◽  
Language Identification ◽  
Large Data Sets ◽  
Data Sets ◽  
Classification Models ◽  
Multiple Classifiers ◽  
Current State

Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.

Multivariate phase combination improves automated crystallographic model building

2010 ◽  
Vol 66 (7) ◽  
pp. 783-788 ◽  
Author(s):  
Pavol Skubák ◽  
Willem-Jan Waterreus ◽  
Navraj S. Pannu
Keyword(s):  
Model Building ◽  
State Of The Art ◽  
Standard Technique ◽  
Real Data ◽  
Data Sets ◽  
Macromolecular Crystallography ◽  
Current State ◽  
Phase Combination ◽  
Density Map ◽  
Electron Density Map

Density modification is a standard technique in macromolecular crystallography that can significantly improve an initial electron-density map. To obtain optimal results, the initial and density-modified map are combined. Current methods assume that these two maps are independent and propagate the initial map information and its accuracy indirectly through previously determined coefficients. A multivariate equation has been derived that no longer assumes independence between the initial and density-modified map, considers the observed diffraction data directly and refines the errors that can occur in a single-wavelength anomalous diffraction experiment. The equation has been implemented and tested on over 100 real data sets. The results are dramatic: the method provides significantly improved maps over the current state of the art and leads to many more structures being built automatically.

scholarly journals Supervised and Unsupervised Neural Approaches to Text Readability

2021 ◽  
Vol 47 (1) ◽  
pp. 141-179
Author(s):  
Matej Martinc ◽  
Senja Pollak ◽  
Marko Robnik-Šikonja
Keyword(s):  
State Of The Art ◽  
Comprehensive Analysis ◽  
Language Models ◽  
Feature Engineering ◽  
Data Sets ◽  
Data Set ◽  
Systematic Comparison ◽  
Current State ◽  
Text Readability ◽  
Unsupervised Approach

Abstract We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.

GA2RM: A GA-Based Action Rule Mining Method

2021 ◽  
pp. 2150012
Author(s):  
Shervin Hashemi ◽  
Pirooz Shamsinejad
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Search Space ◽  
Data Sets ◽  
Mining Method ◽  
Classification Rules ◽  
Classification Methods ◽  
Rule Mining ◽  
Current State ◽  
Traditional Classification

Action Mining is a subfield of Data Mining that tries to extract actions from traditional data sets. Action Rule is a type of rule that suggests some changes in its consequent part. Extracting action rules from data has been one of the research interests in recent years. Current state-of-the-art action rule mining methods like DEAR typically take classification rules as their input; Since traditional classification methods have been designed for prediction and not for manipulation, therefore extracting action rules directly from data can result in more valuable action rules. Here, we have proposed a method to generate action rules directly from data. To tackle the problem of huge search space of action rules, a Genetic Algorithm has been devised. Different metrics have been defined for investigating the effectiveness of our proposed method and a large number of experiments have been done on real and synthetic data sets. The results show that our method can find from 20% to 10 times more interesting (in case of support and confidence) action rules in comparison with its competitors.

Learning on the Move in the Web 2.0

2013 ◽  
pp. 1693-1714
Author(s):  
Carlos Baladrón ◽  
Javier M. Aguiar ◽  
Lorena Calavia ◽  
Belén Carro ◽  
Antonio Sánchez-Esguevillas
Keyword(s):  
Web 2.0 ◽  
Mobile Devices ◽  
State Of The Art ◽  
New Approach ◽  
Collaborative Tools ◽  
Research And Innovation ◽  
Current State ◽  
Pros And Cons ◽  
Approach To Teaching ◽  
The Web

This work aims at presenting the current state of the art of the m-learning trend, an innovative new approach to teaching focused on taking advantage of mobile devices for learning anytime, anywhere and anyhow, usually employing collaborative tools. However, this new trend is still young, and research and innovation results are still fragmented. This work aims at providing an overview of the state of the art through the analysis of the most interesting initiatives published and reported, studying the different approaches followed, their pros and cons, and their results. And after that, this chapter provides a discussion of where we stand nowadays regarding m-learning, what has been achieved so far, which are the open challenges and where we are heading.

scholarly journals Natural product biosynthesis: What's next? An introduction to the JBC Reviews Thematic Series

2019 ◽  
Vol 295 (2) ◽  
pp. 335-336
Author(s):  
Satish K. Nair ◽  
Joseph M. Jez
Keyword(s):  
Natural Product ◽  
State Of The Art ◽  
Data Sets ◽  
Specialized Metabolites ◽  
Current State ◽  
Modified Peptides ◽  
Thematic Review ◽  
Global Threat ◽  
Derivatives Of ◽  
Academic Sector

The diversity of natural products not only fascinates us intellectually, but also provides an armamentarium against the microbes that threaten our health. The increased prevalence of pathogens that are resistant to one or more classes of available medicines continues to be a growing global threat. As drug-resistant pathogens erode the effectiveness of the current reserve of antibiotics and antifungals, methodological advances open additional avenues for discovery of new classes of drugs, as well as novel derivatives of existing (and proven) classes of compounds. In this Thematic Review Series, we aim to provide a snapshot of the current state of the art in natural product discovery. The reviews in this series encapsulate convergent approaches toward the identification of different classes of primary and specialized metabolites, including nonribosomal peptides, polyketides, and ribosomally synthesized and post-translationally modified peptides, from all kingdoms of life. Traction in unraveling new and diverse classes of molecules has come largely from the academic sector, which ensures availability of methods and data sets. Such knowledge is needed to thwart serious threats to human health and calls to mind the proverb praemonitus praemunitus (forewarned is forearmed).

scholarly journals WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web

2016 ◽  
Vol 22 (3) ◽  
pp. 364-407 ◽  
Author(s):  
Tim Taylor ◽  
Joshua E. Auerbach ◽  
Josh Bongard ◽  
Jeff Clune ◽  
Simon Hickinbotham ◽  
...  
Keyword(s):  
Recent Work ◽  
Artificial Life ◽  
State Of The Art ◽  
Web Based ◽  
Web Technologies ◽  
Current State ◽  
The Many ◽  
Exciting Area ◽  
Future Work ◽  
The Web

We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994—when the first WebAL work appeared—up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010–2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area.

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