Search

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent.

Considering urbanization's effects, quantifying individual components of the hydrological cycle, especially at the catchment scale, has been challenging due to the influence of climate change and human activities. Accordingly, the study assesses the impacts of Land Use and Land Cover Changes (LULCC) on the water quantity degradation. It evaluates the effect of urbanization on water balance configuration between the period 2000-2020. The resultant result of LULC reflected a long-term increase in bare land by 11.72% and a marginal increase in built-up areas (0.52%). In contrast, the annual water balance results show an annual negative water deficit ranging from −19.40 mm to −624.955 mm. The result has demonstrated the use of QSWAT and SWMM in a Quick geographic information systems (QGIS) environment to drive the socio-hydrological framework to model spatially distributed and time-varying hydrologic and meteorologic datasets as an effective scientific visualization technique in water balance evaluation. [ABSTRACT FROM AUTHOR]

Tracking time-variant vortex features in unsteady flow fields represents a significant challenge in scientific visualization. In this paper, we propose an innovative new method based on attribute fusion that leverages unsupervised clustering techniques to track time-variant vortex features. This method has the potential for use in large-scale flow field analysis. We apply an attribute matrix integrating the spatial and physical variables of vortex feature points. We then fuse and reduce the dimensionality of this original matrix to create an attribute fusion matrix. Subsequently, we apply coarse clustering to the fusion matrices across time steps. By defining a distribution curve, we derive a similarity measurement matrix among different vortices. Vortex matching is performed based on this similarity measurement to enable the tracking of time-variant vortices. Multiple experimental datasets demonstrate the effectiveness and matching efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of TAY Journal / Türk Akademik Yayınlar Dergisi is the property of Turk Akademik Yayinlar Dergisi (TAY Journal) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volume visualization plays a crucial role in both academia and industry, as volumetric data is extensively utilized in fields such as medicine, geosciences, and engineering. Addressing the complexities of volume rendering, neural rendering has emerged as a potential solution, facilitating the production of high-quality volume rendered images. In this paper, we propose TwineNet, a neural network architecture specifically designed for volume rendering. TwineNet combines features extracted from volume data, transfer functions, and viewpoints by utilizing twining skip connections across multiple feature layers. Building upon the TwineNet architecture, we introduce two neural networks, VolTFNet and PosTFNet, which leverage convolutional encoder–decoders and multilayer perceptrons to synthesize volume rendered images with novel transfer functions and viewpoints. Our experimental findings demonstrate the superiority of our models compared to state-of-the-art approaches in generating high-quality volume rendered images with novel transfer functions and viewpoints. This research contributes to advancing the field of volume rendering and showcases the potential of neural rendering techniques in scientific visualization. [ABSTRACT FROM AUTHOR]

Recently, implicit neural representations (INRs) have demonstrated significant capabilities in handling 3D volume data, especially in the context of data compression. However, the majority of research has predominantly focused on structured grids, which are not commonly found in scientific domains, particularly in physics. To address this limitation, we propose an unstructured grid reduction method via implicit neural representation (UGINR). UGINR employs a divide-and-conquer approach; specifically, we segment the large-scale data into pieces based on values. Subsequently, we employ an INR network for each piece to learn its distinctive features. Finally, we integrate these individual networks to achieve the compression goal. To ensure compatibility with established research methods, we sample only the vertices of each cell in the unstructured grid. Through weight quantization, our model can achieve a high compression ratio. To illustrate the effectiveness of the proposed method, we conduct experiments on various datasets, demonstrating our approach's robustness in scientific visualization and large-scale data compression. [ABSTRACT FROM AUTHOR]

Interactive scientific visualization applications have been more and more used to observe, analyze and understand the complex phenomena studied in several scientific disciplines such as (nano-sciences, fluid simulations, etc.). To facilitate the coupling of the different computation, visualization and exploration codes, which are often heterogeneous and difficult to cooperate, scientists must be able to use and reuse the different parts of the applications already built in order to interchange and enrich certain parts without being experts. In this context, a middleware called FlowVR has been developed by the scientific community leading to make applications divided into set of components exchanging messages. Moreover, this middleware enables to encapsulate existing codes in components, interconnect them via data links, and deploy them on distributed computing resources. Nevertheless, FlowVR does not offer any capability to check the functional correctness of new developed component-based applications. This paper presents a framework called Model and Component-based framework for Scientific Applications (MCSA) based on a interplay of model-driven and component-based software engineering. On the one hand, we provide a domain specific language dedicated to physics/chemistry scientists in order to built their MCSA applications. This language also allows to capitalize efforts by proposing existing components that have been already developed. On the other hand, we propose a model transformation to check the correctness of instantiated models, for this, we use a new subclass of Petri net called strict Colored FIFO Nets (sCFN). Our approach has been backed by a case study relies on a molecular dynamics application. [ABSTRACT FROM AUTHOR]

Lagrangian coherent structures (LCS) is an important concept for the visualization of unsteady flows. They describe the boundaries of regions for which material transport stays mostly coherent over time which can help for a better understanding of dynamical systems. One of the most common techniques for their computation is the extraction of ridges from the finite‐time Lyapunov exponent (FTLE) field. FTLE ridges are challenging to extract, both in terms of accuracy and performance, because they expose strong gradients of the underlying field, tend to come close to each other and are dynamic with respect to different time parameters. We present a new method for extracting FTLE ridges for series of integration times which is able to show how coherent regions and their borders evolve over time. Our techniques mainly build on a particle system which is used for sampling the ridges uniformly. This system is highly optimized for the challenges of FTLE ridge extraction. Further, it is able to take advantage of the continuous evolvement of the ridges which makes their sampling for multiple integration times much faster. We test our method on multiple 3D datasets and compare it to the standard Marching Ridges technique. For the extraction examples our method is 13 to over 300 times faster, suggesting a significant advantage. [ABSTRACT FROM AUTHOR]

The VTK-m software library enables scientific visualization on exascale-class supercomputers. Exascale machines are particularly challenging for software development in part because they use GPU accelerators to provide the vast majority of their computational throughput. Algorithmic designs for GPUs and GPU-centric computing often deviate from those that worked well on previous generations of high-performance computers that relied on traditional CPUs. Fortunately, VTK-m provides scientific visualization algorithms for GPUs and other accelerators. VTK-m also provides a framework that simplifies the implementation of new algorithms and adds a porting layer to work across multiple processor types. This paper describes the main challenges encountered when making scientific visualization available at exascale. We document the surprises and obstacles faced when moving from pre-exascale platforms to the final exascale designs and the performance on those systems including scaling studies on Frontier, an exascale machine with over 37,000 AMD GPUs. We also report on the integration of VTK-m with other exascale software technologies. Finally, we show how VTK-m helps scientific discovery for applications such as fusion and particle acceleration that leverage an exascale supercomputer. [ABSTRACT FROM AUTHOR]

We introduce time‐varying extremum graph (tveg), a topological structure to support visualization and analysis of a time‐varying scalar field. The extremum graph is a sub‐structure of the Morse–Smale complex. It captures the adjacency relationship between cells in the Morse decomposition of a scalar field. We define the tveg as a time‐varying extension of the extremum graph and demonstrate how it captures salient feature tracks within a dynamic scalar field. We formulate the construction of the tveg as an optimization problem and describe an algorithm for computing the graph. We also demonstrate the capabilities of tveg towards identification and exploration of topological events such as deletion, generation, split and merge within a dynamic scalar field via comprehensive case studies including a viscous fingers and a 3D von Kármán vortex street dataset. [ABSTRACT FROM AUTHOR]

With the advent of low-power ultra-fast hardware and GPUs, virtual reality (VR) has gained a lot of prominence in the past few years and is being used in various areas, such as education, entertainment, scientific visualization, and computer-aided design. VR-based applications are highly interactive, and one of the most important performance metrics for these applications is the motion-to-photon-delay (MPD). MPD is the delay from the user's head movement to the time at which the image gets updated on the VR screen. Since the human visual system can even detect an error of a few pixels (very spatially sensitive), the MPD should be as small as possible. Popular VR vendors use the GPU-accelerated Asynchronous Time Warp (ATW) algorithm to reduce the MPD. ATW reduces the MPD if and only if the warping operation finishes just before the display refreshes. However, due to the competition between the different constituent applications for the single, shared GPU, the GPU-accelerated ATW algorithm suffers from an unpredictable ATW latency, making it challenging to find the ideal time instance for starting the time warp and ensuring that it completes with the least amount of lag relative to the screen refresh. Hence, the state-of-the-art is to use a separate hardware unit for the time-warping operation. Our approach, PredATW, uses an ML-based hardware predictor to predict the ATW latency for a VR application, and then schedule it as late as possible while running the time-warping operation on the GPU itself. As far as we know, this is the first work to do so. Our predictor achieves an error of only 0.22 ms across several popular VR applications for predicting the ATW latency. As compared to the baseline architecture, we reduce deadline misses by 80.6%. [ABSTRACT FROM AUTHOR]

Although more and more researchers have paid attention to artificial intelligence research in organizations across different subdivided fields in recent years, there is still a lack of integrative and comprehensive research on AI in organizations. Building upon previous quantitative and qualitative studies in the artificial intelligence literature, this study presents a bibliometric analysis of articles on artificial intelligence in the fields of management, business, and applied psychology up to June 2nd, 2023. The research explores the landscape of artificial intelligence articles, highlighting key intellectual contributions and research constituents such as journals, authors, countries, institutions, and topics. Additionally, the study investigates the intellectual structure and overlay visualization of keywords to identify popular topics and trends in recent artificial intelligence research. The findings offer readers a systematic understanding of artificial intelligence development and provide new insights that expand upon existing knowledge in artificial intelligence within management, business, and applied psychology.

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU‐accelerated AMR rendering has made much progress towards real‐time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high‐quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high‐quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal. [ABSTRACT FROM AUTHOR]

The contour depth methodology enables non‐parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear‐time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear‐time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non‐parametric multimodal analysis. To promote research and adoption, we offer the contour‐depth Python package. [ABSTRACT FROM AUTHOR]

Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response to this, we propose a novel technique, InverseVis, which leverages curved sphere tracing and uses the otherwise unused space to enhance visibility. Our approach combines direct and indirect rendering, allowing camera rays to wrap around the surface and reveal information from the backside. To achieve this, we formulate an energy term that guides the image synthesis in previously unused space, highlighting the most important regions of the backside. By quantifying the amount of visible important features, we optimize the camera position to maximize the visibility of the scalar field on both the front and backsides. InverseVis is benchmarked against state‐of‐the‐art methods and a derived technique, showcasing its effectiveness in revealing essential features and outperforming existing approaches. [ABSTRACT FROM AUTHOR]

In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained. [ABSTRACT FROM AUTHOR]

Russia's "Electronic Structure of Atoms" information and reference system for atomic spectroscopy is described. Its main characteristics and capabilities for supporting scientific research and training of specialists are explained. The data array contains more than 234 000 levels, with great attention given to experimental data and uniform filling across Z, including classified levels and transitions of all rare earth and transuranium elements and their ions. Original means of visualizing scientific data in the form of spectrograms, quantograms, and Grotrian diagrams are proposed. Presenting spectral data in the form of color interactive diagrams promotes understanding and analysis of the properties of an atomic system. In combination with its functionality, the use of spectral data from the "Electronic Structure of Atoms" information system is an effective way of solving different scientific problems and training specialists. [ABSTRACT FROM AUTHOR]

Insightful visualization of multidimensional scalar fields, in particular parameter spaces, is key to many computational science and engineering disciplines. We propose a principal component-based approach to visualize such fields that accurately reflects their sensitivity to their input parameters. The method performs dimensionality reduction on the space formed by all possible partial functions (i.e., those defined by fixing one or more input parameters to specific values), which are projected to low-dimensional parameterized manifolds such as 3D curves, surfaces, and ensembles thereof. Our mapping provides a direct geometrical and visual interpretation in terms of Sobol's celebrated method for variance-based sensitivity analysis. We furthermore contribute a practical realization of the proposed method by means of tensor decomposition, which enables accurate yet interactive integration and multilinear principal component analysis of high-dimensional models. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive bibliometric review aimed at examining the effectiveness and applicability of the pavement condition index (PCI) method, particularly in assessing airport pavement distress and guiding maintenance and rehabilitation (M&R) strategies. Through bibliometric analysis spanning the last two decades, we investigate the evolution of the PCI method in both road and airport pavement contexts, exploring patterns of keyword co-occurrence, co-authorship, geographical distribution, and publication trends. Our findings reveal significant advancements in the PCI model and associated M&R strategies, largely driven by technological innovations. However, limitations are identified in current bibliometric analysis tools, such as Harzing's Publish or Perish, particularly concerning handling large datasets. Practical implications highlight the importance of integrating bibliometric analysis tools with automated big data handling to enhance research efficiency and reliability. Future research should focus on improving methodological integration to streamline the visualization of scientific network techniques with minimal effort and time investment. [ABSTRACT FROM AUTHOR]

Digital twins, an innovative technology propelled by data and models, play a seminal role in the digital transformation and intelligent upgrade of ships. This study introduces a digital twin methodology for the real-time monitoring of ship structure deformation fields, based on finite discrete strain data, and a visualization tool framework is developed using virtual reality technology. First, the inverse Finite Element Method (iFEM) is employed to derive the deformation field of the ship structure in real time using sensor strain data. Secondly, the deformation field data obtained based on the iFEM algorithm is converted into general visualization data conducive to interpretation within virtual reality (VR) applications. Lastly, a digital twin software tool is built to enable synchronous responses and interactions between the virtual scene and the physical scene, directly superposing particular virtual objects (data acquired by sensors, computer-aided design (CAD) virtual models, and deformation field cloud images) onto the physical scene in real time. The digital twin tool embodies a virtual reality visualization system framework integrating the physical data measurement, reconstruction, analysis, expression, storage, rendering, and interaction of deformation field data. Through practical application, the flexibility, effectiveness, and compatibility of the developed prototype tool are verified. According to the results, the system can enhance the efficiency of scientific communication, model validation, and interdisciplinary sharing during the analysis and evaluation of the mechanical properties of ship structures. [ABSTRACT FROM AUTHOR]

A substantial body of research exists on the use of remote sensing in urban contexts. However, only a limited number of studies have contributed to our understanding of the socioeconomic conditions of different urban areas. This research aims to demonstrate the potential of very high-resolution images and geospatial data by examining the interrelations between socioeconomic data retrieved from household surveys in the city of Kigali and spatial data on urban morphology retrieved by satellite imagery. As the surveys yielded large amounts of data of varying levels of measurement (categorical and numeric), we present different methods of statistical correlation, data mining, and machine learning to highlight socioeconomic patterns within the spatial data. The results demonstrate a significant correlation between the share of different building types, building density, average building heights, and distances to public infrastructure with a range of surveyed data, including building properties, household members, financial resources, and overall lifestyle habits. This highlights the potential of remote sensing and geospatial data to provide valuable insights into the socioeconomic conditions of urban areas. It also underscores the importance of using advanced statistical methods, data mining, and machine learning to enhance our understanding of urban morphology and its socioeconomic implications. However, it is important to acknowledge the limitations of such approaches, including the lack of information on ownership, potential for false inference and the direction of causation, which require further investigation.

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manipulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Aim: The purpose of this article is to give an analysis of developing trends and research hotspots within the KLE Academy of Higher Education and Research (KAHER) Group using bibliometric methodologies. Materials and Methods: Using co-authorship, co-citation, and co-occurrence analysis, publications from the Scopus database for the years 1973–2022 are examined and analyzed on a variety of factors. R-Programming (Biblioshiny) and VOSViewer, an application for scientific visualization, are used to do the analysis. Results: There were 1721 publications in Scopus throughout the study period, with 83.9% of those being journal articles and the remaining 6% being letters and case studies. Research generally shows a constant rise with growing multi-authorship. The most sought-after magazine for publishing by the writers at KAHER is "Reproductive Health," and they prefer to collaborate on research pieces. In addition, health-care science services and medical informatics have steadily developed into new areas of scientific interest. Originality: This study offers a thorough and comprehensive analysis of the KAHERs' formative environment. This study can serve as a solid starting point for future research and can be used to analyze how health science research has changed overtime. Conclusion: The study reveals KAHER's robust publishing productivity and impact, with a diverse output among authors and a consistent growth in the number of publications. The dominance of collaborative research, high citation rates, and the prevalence of medical research suggest the importance of international collaborations and the implementation of an institutional online repository system for enhanced research efficacy and accessibility. [ABSTRACT FROM AUTHOR]

In this research, all the scientific publications of the Iran University of Medical Sciences (IUMS) from 1980 to 2020 are studied using bibliometric analysis and scientific network visualization. This research applied quantitative research using bibliometrics and visualization of scientific publications. The research population included all the scientific publications of IUSM on the Web of Science Core Collection (WOSCC) from 1980 to 2020. Data from the WOSCC were extracted via the advanced search by searching the Iran University of Medical Sciences in the affiliation field. Microsoft Excel and VOSviewer were used for data analysis. First, the frequency distribution of the scientific publications was identified. Then, the level of international collaborations was analyzed. Finally, the citation clusters of researchers' scientific publications and keyword co-occurrence were examined. IUMS had 9950 documents indexed in the WOSCC. Malekzadeh jointly ranked first as the most prolific author. The Iranian Red Crescent Medical Journal, with 207 articles, has the highest number of articles. All highly-cited papers were published in high-level Q1 journals. The highest collaboration rate at a national level was with the Tehran University of Medical Sciences. Internationally, IUMS's researchers had the highest collaboration with authors from the United States, the United Kingdom, Canada, and Australia, respectively. Term clustering demonstrated five main clusters: pharmacological studies, epidemiological studies, general & and internal medicine, meta-analysis and systematic review, and Immunological studies. The methods and techniques of bibliometrics and visualization are optimal for depicting and analyzing the scientific status of researchers, publications, journals, universities, countries, and even the world. The current study can be a model for analyzing bibliometric indices of other universities and research institutes in Iran and elsewhere. [ABSTRACT FROM AUTHOR]

This essay reads Anna Atkins's Photographs of British Algae: Cyanotype Impressions (1843–1853) as an example of Victorian imitative art by reading it through the lens of Victorian domestic handicraft. It does so in order to resituate Atkins's work within the history of scientific visualization and to contribute to the increasing complexity scholars of visual culture and of the scientific image have added to prevailing accounts of the rise of the "objective" scientific image in the nineteenth‐century. Building on the work of historians of photography, art, science, and literature, it argues that her cyanotypes point toward an alternative history of scientific image as a form of craft and collection that resonates with recent calls in the study of the scientific image and scientific practices to move "beyond representation." [ABSTRACT FROM AUTHOR]

Taking the identification of glucose as an example, this article outlines the instructional design and video production process for integrating scientific visualization into the pharmaceutical analysis experiment. The principle, procedure, and phenomenon of the experiment are vividly demonstrated through the pictures and videos, enhancing the interest and attraction of the experiment course. A series of related videos have been applied to classroom teaching and uploaded to the video website, achieving favorable educational outcomes and facilitating broader sharing and communication. [ABSTRACT FROM AUTHOR]

The problem of determining the wave function of a physical system based on the graphical representation of its zeros is considered. It can be dealt with by invoking the Bargmann representation in which the wave functions are represented by analytic functions with an appropriate definition of the scalar product. The Weierstrass factorization theorem can then be applied. Examples of states that can be guessed from the pictorial representation of zeros by both the human eye and, possibly, by machine learning systems are given. The quality of recognition by the latter has been tested using Convolutional Neural Networks. [ABSTRACT FROM AUTHOR]

Utilizing data visualization facilitates the analysis and comprehension of common data provided by the media, individuals, governments, and other sectors. Python is a well-known programming language that excels at scientific data visualization. This thesis utilizes a variety of Python modules, including Pandas, NumPy, Matplotlib, Seaborn, Plotly, and Bokeh, to illustrate female genital mutilation. The purpose of this thesis is to illustrate female genital mutilation and explain its performance pattern using a complex, interactive diagram that integrates multiple types of Python libraries. In comparison to other libraries, Plotly is the simplest, yet it performs at the highest level. NumPy and Matplotlib are combined to produce Hexbins charts. NumPy provides an N-dimensional plot, and Matplotlib allows for the plot's colours to be customized. Despite its limited customization options, the Seaborn library is suitable for both data visualization and statistical modelling. Due to this deficiency, the Seaborn library is frequently combined with Matplotlib to generate superior visualizations. As a result, this thesis will be recommended to both specialists and novices as worthwhile reading. In addition, it will assist the government in drafting legislation to end female genital mutilation. They will comprehend the significance of combining multiple Python modules to generate intricate interactive diagrams for data visualization in the field of data science. This information will be posted online to contribute to the corpus of knowledge. [ABSTRACT FROM AUTHOR]

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parameterized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output. [Display omitted] • Implementing an image-wise implicit neural representation for compressive representation of visualization images. • Proposing SIREN-based residual blocks for performance improvement. • Presenting mask loss for addressing the foreground–background issue well and capturing small objects well. • Achieving high-quality visualization images compression with a high compression rate (705 to 1,263). [ABSTRACT FROM AUTHOR]

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization datawithout first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, wherefine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Thirdis the ability to use all available resources, CPU’s and accelerators, in the computation of analysis products. This STAR paperbrings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to presentexisting methods, infrastructures, and a range of computational science and engineering applications using in situ analysis andvisualization.

We describe a Python library project for generating animations of simple 3D motion capture data points. The motion capture method is agnostic with respect to this visualizaiton tool, expecting a time series of time stamped coordinates captured in a 3-dimensional space. One of the advantages of this tool is the simple data format design. This tool was developed originally for a Kinect system implementing skeleton tracking of 15 joint positions, where each data point consists of an accurate time of capture (time stamp), and 3 accurate coordinate (x, y, z) positions of each of the 15 joints at each time step. In addition to the motion capture time series, all that is needed is a joint graph file describing the relationship of the joint edges to one another. This library can be extended to visualize and create animations of motion capture data that can be reformulated using this basic structure of a time series of time stamped 3D coordinates and a joint graph description file. This library should be useful as is, or with easy modification, for many such visualization requirements of similar motion capture data.

We introduce HydroLang Markup Language (HL-ML), a programming interface that uses a markup language to perform environmental analyses using the hydrological and environmental framework HydroLang. The software acts as a self-contained interface that uses HTML tags powered by the web component specification to generate simple hydrological computations that enable data analysis, visualization, and manipulation via semantically driven instructions. It enables hydrological researchers and professionals to use markup language to retrieve, analyze, visualize, and map data with basic programming skills. The components' adaptability enables users to run analytical routines that perform simple and complex analyses on the client side. We present the implementation details of the approach, the use of custom elements in web technologies and academia, and share sample usages to demonstrate the simplicity of use of the human-readable and computer-executable framework. HIGHLIGHTS An HTML-based computational framework has been developed to facilitate hydrology research and education.; The software is based on an effortless interface designed for environmental and hydrological analyses.; By employing architecture-agnostic web components, the library ensures reusability throughout web platforms.; The software has a modular design using a powerful core engine with third-party geospatial tools.;

Visualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head‐mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes' rule. This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost. [ABSTRACT FROM AUTHOR]

Despite the increasing concern on workplace diversity, no research has been conducted to providing an objective visualization of the development of this fast-developing area and to complementing prior qualitative and meta-analytic reviews in the Asia-Pacific region. This research uses co-citation and co-occurrence analyses to trace how the landscape of diversity evolved from January 1993 to August 2021, using the scientific visualization tool CiteSpace. Based on a dataset of 636 diversity-related publications in the Asia-Pacific region collected from the Web of Science database, we conduct a bibliometric review to map the landscape, with the focus on identifying landmark researchers, articles, and journals, and the intellectual structure of, and changes in, diversity-related research themes. Further, a narrative review was adopted to summarize the frequently studied diversity themes, to capture the developing trends of diversity research, and to build an integrated model of workplace diversity in the Asia-Pacific context. Based on these findings, this review displays the evolution of diversity literature and uncovers future research directions to spur follow-up diversity studies. [ABSTRACT FROM AUTHOR]

With the increasing demand for high precision in numerical simulations using computational fluid dynamics (CFD), the use of large-scale grids for discretized solutions has become a trend, resulting in an explosive growth of flow-field data size. To address the challenges posed by large-scale flow-field data for real-time interactive visualization, this paper proposes novel strategies for data partitioning and communication management. Firstly, we propose a data-partitioning strategy based on grid segmentation. This approach constructs metadata to create file viewports for each process and performs grid partitioning. Subsequently, it reconstructs sub-grids within each process and utilizes a coordinate-mapping algorithm to map global coordinates to local process coordinates, facilitating access to attribute variables through a lookup table. Secondly, we introduce a real-time interactive method for large-scale flow fields. This method leverages the system architecture of high-speed interconnection among compute nodes in a cluster environment and low-speed interconnection between service nodes and rendering nodes. It enables coordinated management of parallel rendering and synchronized rendering methods. The experimental results on typical flow-field data demonstrate that the proposed data-partitioning strategy improves the loading speed of millions of grid-level data by a factor of 7, surpassing ParaView's performance by 1.5 times. Furthermore, it achieves system load balancing. Real-time interaction experiments with datasets containing 500 million and 800 million grid cells exhibit millisecond-level latencies, demonstrating the effectiveness of the proposed communication management method in meeting the real-time interactive visualization demands of large-scale flow-field data. [ABSTRACT FROM AUTHOR]

The interest in real-time volume graphics has grown rapidly in the last few years, driven by the increasing demands from both academia and industry. GPU-based volume rendering has been used in a wide variety of fields, including scientific visualization, visual effects, and video games. Similarly, real-time volume editing has been used to build terrain and create visual effects during game development; it has even become an integral part of gameplay in various video games (e.g., Minecraft). Nowadays, as the size of volume data increases, processing large volume data in real time is inevitable in many modern application scenarios. However, manipulation and editing of large volume data are associated with various challenges, such as dramatically increasing memory usage and computational burden. In this paper, we present a framework for interactive manipulation and editing of large volume data to address these challenges. A robust and efficient method for large signed distance function (SDF) volume generation is presented and incorporated into the framework. Also, a complete implementation with specialized GPU optimization is introduced to demonstrate its usefulness and effectiveness—it is included in the framework as well. The framework can be an easy-to-use middleware or a plugin that is able to integrate into game engines for the development of various types of applications (e.g., video games). It can also contribute to the research looking at large volume data from a user-centered perspective (e.g., for human–computer interaction researchers). [ABSTRACT FROM AUTHOR]

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.

Natural disasters and epidemics are unfortunate recurring events that lead to huge societal and economic loss. Recent advances in supercomputing can facilitate simulations of such scenarios in (or even ahead of) real-time, therefore supporting the design of adequate responses by public authorities. By incorporating high-velocity data from sensors and modern high-performance computing systems, ensembles of simulations and advanced analysis enable urgent decision-makers to better monitor the disaster and to employ necessary actions (e.g., to evacuate populated areas) for mitigating these events. Unfortunately, frameworks to support such versatile and complex workflows for urgent decision-making are only rarely available and often lack in functionalities. This paper gives an overview of the VESTEC project and framework, which unifies orchestration, simulation, in-situ data analysis, and visualization of natural disasters that can be driven by external sensor data or interactive intervention by the user. We show how different components interact and work together in VESTEC and describe implementation details. To disseminate our experience three different types of disasters are evaluated: a Wildfire in La Jonquera (Spain), a Mosquito-Borne disease in two regions of Italy, and the magnetic reconnection in the Earth magnetosphere.