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Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subjective DSM-5 guidelines, and advances in EEG and video monitoring technologies have not been widely adopted due to invasiveness and inconvenience. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. Here, we introduce PRISM—Passive, Real-time Information for Sensing Mental Health. This platform integrates motion, light and heart rate data from a smart watch application with user interactions and text entries from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of 13 subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to develop models that are predictive of user-reported ratings of their emotional state, demonstrating that the data has the potential to be useful for evaluating mental health. This platform could allow patients and clinicians to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.

Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on $k$-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset. $K$-means based on silhouette criterion discovered top three representatives of the optimal numbers of clusters inherent in the flood dataset. Experts’ judgment favored four clusters, while Squared Euclidean distance was the best performing distance measure. SOM provided cluster visuals of the input attributes within the four different groups and transformed the dataset into a labeled one. A 5-layered Adaptive Neuro Fuzzy Inference System (ANFIS) driven by hybrid learning algorithm was applied to classify and predict FSL. ANFIS optimized by Genetic Algorithm (GA) produced root mean squared error (RMSE) of 0.323 and Error Standard Deviation of 0.408 while Particle Swarm Optimized ANFIS model produced 0.288 as the RMSE, depicting 11% improvement when compared with GA optimized model. The result shows significant improvement in the classification and prediction of flood risks using single ML tool.

Associating the regions of a geographic subdivision with the cells of a grid is a basic operation that is used in various types of maps, like spatially ordered treemaps and Origin-Destination maps (OD maps). In these cases the regular shapes of the grid cells allow easy representation of extra information about the regions. The main challenge is to find an association that allows a user to find a region in the grid quickly. We call the representation of a set of regions as a grid a grid map.

We introduce a new approach to solve the association problem for grid maps by formulating it as a point set matching problem: Given two sets $A$ (the centroids of the regions) and $B$ (the grid centres) of $n$ points in the plane, compute an optimal one-to-one matching between $A$ and $B$. We identify three optimisation criteria that are important for grid map layout: maximise the number of adjacencies in the grid that are also adjacencies of the regions, minimise the sum of the distances between matched points, and maximise the number of pairs of points in $A$ for which the matching preserves the directional relation (SW, NW, etc.). We consider matchings that minimise the $L_1$-distance (Manhattan-distance), the ranked $L_1$-distance, and the $L_2^2$-distance, since one can expect that minimising distances implicitly helps to fulfill the other criteria.

We present algorithms to compute such matchings and perform an experimental comparison that also includes a previous method to compute a grid map. The experiments show that our more global, matching-based algorithm outperforms previous, more local approaches with respect to all three optimisation criteria.

Bold color images from telescopes act as extraordinary ambassadors for research astronomers because they pique the public’s curiosity. But are they snapshots documenting physical reality? Or are we looking at artistic spacescapes created by digitally manipulating astronomy images? This paper provides a tour of how original black and white data, from all regimes of the electromagnetic spectrum, are converted into the color images gracing popular magazines, numerous websites, and even clothing. The history and method of the technical construction of these images is outlined. However, the paper focuses on introducing the scientific reader to visual literacy (e.g. human perception) and techniques from art (e.g. composition, color theory) since these techniques can produce not only striking but politically powerful public outreach images. When created by research astronomers, the cultures of science and visual art can be balanced and the image can illuminate scientific results sufficiently strongly that the images are also used in research publications. Included are reflections on how they could feedback into astronomy research endeavors and future forms of visualization as well as on the relevance of outreach images to visual art. (See the color online PDF version at http://dx.doi.org/10.1142/S0218271817300105; the figures can be enlarged in PDF viewers.)

Chua Oscillator exhibits a wide variety of nonlinear behavior and has become a paradigm for theoretical and experimental investigations of chaotic systems. An initial exploration of the parameter space for the circuit shows that the system and its generalizations generates a broad range of very different strange attractors. In the work described in this paper, we constructed "a gallery" of these attractors, including patterns that have never previously been observed. We identified the regions of parameter space occupied by each attractor and the initial conditions leading to production of the attractor. System behavior was characterized using time series, FFT graphs and in some cases Lyapunov exponents. In this way we created a complex picture of chaos, which we divided into six parts. The first, we publish here. The rest of our work will be published in subsequent issues of this journal. In this first paper, we describe how to build Chua Oscillator and some of its generalizations, as proposed in the recent literature. We introduce the main features that characterize the chaotic behavior of each of these systems. Finally, we offer hints on the mechanisms underlying synchronization between pairs of coupled Chua Oscillators. The investigation of chaos allows for the emergence of a complex picture, which could improve our knowledge of these challenging phenomena in contemporary science.

Augmented reality (AR) is used in neurosurgery to visualize lesions and plan procedures pre-operatively and intra-operatively, though its use has not been widely adopted in simulation-based neurosurgical training for the same tasks. This work defines metrics to determine performance in drill position and angle identification for neurosurgical training. The metrics were validated intra-operatively and in a simulated training environment, demonstrating that trainees identify drill position and angle faster and more accurately with AR compared with standard techniques. Training using AR and the proposed metrics stands to add value to neurosurgical curricula development.

Decision support for planning and improving software development projects is a crucial success factor. The special characteristics of software development aggregate these tasks in contrast to the planning of many other processes, such as production processes. Process simulation can be used to support decisions on process alternatives on the basis of existing knowledge. Thereby, new development knowledge can be gained faster and more cost effective.

This chapter gives a short introduction to experimental software engineering, describes simulation approaches within that area, and introduces a method for systematically developing discrete-event software process simulation models. Advanced simulation modeling techniques will point out key problems and possible solutions, including the use of visualization techniques for better simulation result interpretation.

By utilizing the molecular dynamics code SPaSM on Livermore's BlueGene/L architecture, consisting of 212 992 IBM PowerPC440 700 MHz processors, a molecular dynamics simulation was run with one trillion atoms. To demonstrate the practicality and future potential of such ultra large-scale simulations, the onset of the mechanical shear instability occurring in a system of Lennard-Jones particles arranged in a simple cubic lattice was simulated. The evolution of the instability was analyzed on-the-fly using the in-house developed massively parallel graphical object-rendering code MD_render.

The visualization of patterns related to chaos is a challenge for those who are part of today's dynamical systems community, especially when we consider the aim of providing users with the ability to visually analyze and explore large, complex datasets related to chaos. Thus visualization could be considered a useful element in the discovery of unexpected relationships and dependencies that may exist inside the domain of chaos, both in the phase and the parameter spaces. In the second part of "A Gallery of Chua attractors", we presented an overview of forms which can only be produced by the physical circuit. In Part III, we illustrate the variety and beauty of the strange attractors produced by the dimensionless version of the system. As in our earlier work, we have used ad hoc methods, such as bifurcation maps and software tools, allowing rapid exploration of parameter space. Applying these techniques, we show how it is possible, starting from attractors described in the literature, to find new families of patterns, with a special focus on the cognitive side of information seeking and on qualitative processes of change in chaos, thus demonstrating that traditional categories of chaos exploration need to be renewed.

After a brief introduction to dimensionless equations for Chua's oscillator, we show 150 attractors, which we represent using three-dimensional images, time series and FFT diagrams. For the most important patterns, we also report Lyapunov exponents. To show the position of dimensionless attractors in parameter space, we use parallel coordinate techniques that facilitate the visualization of high dimensional spaces. We use Principal Components Analysis (PCA) and Mahalanobis Distance to provide additional tools for the exploration and visualization of the structure of the parameter space.

The construction of an artifact to visually represent information is usually required by Information Visualization research projects. The end product of design science research is also an artifact and therefore it can be argued that design science research is an appropriate research paradigm for conducting Information Visualization research. Design science research requires that, during the Rigor Cycle, the design of the artifacts should be based on a scientific knowledge base. This article provides a knowledge base in the form of design guidelines that can guide the design of the view for an Information Visualization solution. The design principles and guidelines presented in this article are identified by means of a literature review.

Motivation: Methods like FBA and kinetic modeling are widely used to calculate fluxes in metabolic networks. For the analysis and understanding of simulation results and experimentally measured fluxes visualization software within the network context is indispensable.

Results: We present Flux Viz, an open-source Cytoscape plug-in for the visualization of flux distributions in molecular interaction networks. FluxViz supports (i) import of networks in a variety of formats (SBML, GML, XGMML, SIF, BioPAX, PSI-MI) (ii) import of flux distributions as CSV, Cytoscape attributes or VAL files (iii) limitation of views to flux carrying reactions (flux subnetwork) or network attributes like localization (iv) export of generated views (SVG, EPS, PDF, BMP, PNG). Though FluxViz was primarily developed as tool for the visualization of fluxes in metabolic networks and the analysis of simulation results from FASIMU, a flexible software for batch flux-balance computation in large metabolic networks, it is not limited to biochemical reaction networks and FBA but can be applied to the visualization of arbitrary fluxes in arbitrary graphs.

Availability: The platform-independent program is an open-source project, freely available at http://sourceforge.net/projects/fluxvizplugin/ under GNU public license, including manual, tutorial and examples.

This paper presents methods to visualize bifurcations in flows of nonlinear dynamical systems, using the Lorenz '96 systems as examples. Three techniques are considered; the first two, density and max/min diagrams, are analagous to the bifurcation diagrams used for maps, which indicate how the system's behavior changes with a control parameter. However the diagrams are generally harder to interpret than the corresponding diagrams of maps, due to the continuous nature of the flow. The third technique takes an alternative approach: by calculating the power spectrum at each value of the control parameter, a plot is produced which clearly shows the changes between periodic, quasi-periodic, and chaotic states, and reveals structure not shown by the other methods.

Chua's circuit is a physical system which can be used to investigate chaotic processes. One of its identifying features is the ability to produce a huge variety of strange attractors, each with its own characteristic form, size and model. These characteristics extend to a range of different systems derived from the original circuit.

In the first paper A Gallery of Chua's Attractors. Part I, we presented physical circuits and some generalizations based on Chua's oscillator, together with techniques for building the circuit and a summary description of its chaotic behavior.

In this second part of our work, we present an overview of forms which can only be produced by the physical circuit, using novel techniques of scientific visualization to explore, discover, analyze and validate our large collection of data. Starting with cases already known in the literature, we show that the circuit can produce an infinite set of three-dimensional patterns. A small sample is included in our paper. More specifically, we present 195 strange attractors generated by the circuit. For each attractor we provide three-dimensional images, time series and FFTs. Finally, we provide Lyapunov exponents for a subset of "base attractors".

Big data starts booming in 2013 and has multiple applications in all walks of life. In such an environment, big data for information technology (BDI) and decision making (BDD) have formed some hot topics in common. This paper reviews the body of BDI and BDD research studies from 1994 to 2020, using bibliometrics analysis. The aim of this paper is to explore the current status, the correlation between BDI and BDD, the future trends and challenges. From time and space dimensions, CiteSpace and VOS viewer are used to obtain the annual trends of documents, the distribution of countries and sources, the citations and the h-index of BDI and BDD. The top three productive countries are the USA, China and the UK. From the perspective of h-index, the USA and the UK are at the forefront of the world. The value of big data is realized through information acquisition, storage, analysis, expression transmission and service sharing technologies, and the decision-making techniques exist throughout the process of big data analysis. “Business” and “Information science library science” are the latest hotspots of BDI. The appliances in the organization, supply chain management, education, and the environment are recent themes of BDD. Big data technology processing capabilities and network security issues are the main challenges in the future. This study contributes to the body of knowledge on BDI and BDD, and hops to help in understanding the evolution of them in relevant fields.

To achieve worldwide high productivity and quality assurance of global production, the authors considered the necessity of including the above with strategic application of the TPS, and clarified the Advanced TPS as the global production by manufacturing technology for the strategic administration of production facilities. Nowadays, we have established manufacturing technology by innovative maintenance of Toyota, a most advanced automotive manufacturing enterprise. Therefore, we propose the V-MICS (utilizing Visualization-Maintenance Innovated Computer System) consisting of five steps (AML-1 to AML-5). Concretely for transfer of the maintenance skill, in particular we will accomplish a procedure combining DB (database) and CG (computer graphics). With regard to the former we will construct a DB for easier accumulation of know-how for parts replacement and other jobs. As regards to the latter, we will provide the instructions to enable even a person not familiarized with machine-drawings to carry out dairy inspection. As a result, we will not only attain considerable reduction of the maintenance cost, but also realize a lot of benefits such as more improved availability when we start up the global production in the world.

Most of the current computational models for splice junction prediction are based on the identification of canonical splice junctions. However, it is observed that the junctions lacking the consensus dimers GT and AG also undergo splicing. Identification of such splice junctions, called the non-canonical splice junctions, is also essential for a comprehensive understanding of the splicing phenomenon. This work focuses on the identification of non-canonical splice junctions through the application of a bidirectional long short-term memory (BLSTM) network. Furthermore, we apply a back-propagation-based (integrated gradient) and a perturbation-based (occlusion) visualization techniques to extract the non-canonical splicing features learned by the model. The features obtained are validated with the existing knowledge from the literature. Integrated gradient extracts features that comprise contiguous nucleotides, whereas occlusion extracts features that are individual nucleotides distributed across the sequence.

Modeling and visualization of user attention in Virtual Reality (VR) is important for many applications, such as gaze prediction, robotics, retargeting, video compression, and rendering. Several methods have been proposed to model eye tracking data as saliency maps. We benchmark the performance of four such methods for 360^∘ images. We provide a comprehensive analysis and implementations of these methods to assist researchers and practitioners. Finally, we make recommendations based on our benchmark analyses and the ease of implementation.

Electrochromism synchronous to the charge/discharge of a novel Li ion battery having Li₃Fe₂(PO₄)₃ and Li₄Ti₅O₁₂ thin-film electrodes fabricated by a chemical process, the molecular precursor method, was discovered. A cathode of transparent Li₃Fe₂(PO₄)₃ thin film with a thickness of 80 nm was fabricated by heat treating a precursor ethanol solution including a Li(I) complex of nitrilotriacetic acid, an Fe(III) complex of ethylenediaminetetraacetic acid, and (dibutylammonium)₂H₂P₂O₇ ⋅ 0.5H₂O at 550°C for 10 min in air. An anode of transparent Li₄Ti₅O₁₂ thin film with a thickness of 90 nm was fabricated by heat treating a precursor ethanol solution including a Li(I) complex of nitrilotriacetic acid, a Ti(IV) complex of the identical organic ligand, and hydrogen peroxide at 550°C for 30 min in air. The precursor films for both electrodes were fabricated with a spin-coating method. The thermal reactions of the novel precursors were examined in detail by means of thermogravimetry and differential thermal analysis in order to examine the components and heat-treatment temperature. The crystal structure and surface morphology of the thin-film electrodes fabricated on glass substrates pre-coated with a fluorine-doped tin oxide film were examined with X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM). The rechargeable function of the assembled sandwich-type battery using an electrolytic solution containing LiPF₆ was measured by the repeated charge and discharge test at a constant current of 10 μA; a maximum voltage of 3.6 V was recorded. The color changes of the transparent thin-film battery between colorless before charging and a blue-gray color after charging occurred synchronously and repeatedly with the charge/discharge cycles. The intercalation of Li⁺ ions into the Li₄Ti₅O₁₂ thin-film anode may be related to the drastic color change and the unprecedented visualization of the electrochemical reaction of a novel Li ion battery.

This paper summarizes content of the workshop focused on data quality. The first speaker (VH) described data quality infrastructure and data quality evaluation methods currently in place within the Observational Data Science and Informatics (OHDSI) consortium. The speaker described in detail a data quality tool called Achilles Heel and latest development for extending this tool. Interim results of an ongoing Data Quality study within the OHDSI consortium were also presented. The second speaker (MK) described lessons learned and new data quality checks developed by the PEDsNet pediatric research network. The last two speakers (JB, RG) described tools developed by the Sentinel Initiative and University of Utah’s service oriented framework. The workshop discussed at the end and throughout how data quality assessment can be advanced by combining best features of each network.

Visualization of the large-scale collections of information became one of the essential purpose in data analysis. The new methods of visualization are increasingly applied as a significant component in scientific research. Particularly qualitative nature of Infoviz studies (Information visualization) can be combined with quantitative character of digital libraries volumes. This paper describes and demonstrates the case of hierarchical structure visualization i.e. visual representation of both classification adopted by ACM (Association for Computing Machinery) digital library and classification universe. Given maps were processed by nonlinear graphical filters. Finally fractal dimension (FD) and derived techniques have used to analyze the patterns of clusters on the visualization maps. Quantification of output graphical representation by means of fractals makes possible to adjust visualization parameters as well as evaluate initial classification scheme and its dynamical characteristics.