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Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. We demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.

Validating simulations with experimental results is a vital component of modern materials science. Existing workflows require substantial human intervention to interpret their results. We present a statistical approach to identify physically meaningful features, and we construct a visualization system that allows users to interactively and intuitively explore their datasets.

A typical implementation of BB84 protocol for quantum communication uses four laser diodes for transmitting weak coherent pulses, which may not have the same characteristics. We have characterized these lasers for mismatch in various parameters such as spectral width, pulse width, spatial mode, peak wavelength, polarization and their arrival times at the receiver. This information is utilized to calculate possible information leakage through side channel attacks by evaluating mutual information between source and eavesdropper. Based on our experimental observations of cross correlation between parameter values for different laser diodes, we suggest methods to reduce information leakage to Eve.

The study of many extreme weather events requires simulations with high spatiotemporal data that can grow in size quickly. Storing all the raw data from such a large-scale simulation for traditional post hoc analyses is soon going to be prohibitive as the data generation speed is outpacing the data storage capability in supercomputers. In situ analysis has emerged as a solution to this problem; data is analyzed when it is being produced, bypassing the slower disk input/output (I/O). In this work, we develop a new in situ analysis pathway for Energy Exascale Earth System Model (E3SM) and propose an algorithm for analyzing the impacts of sudden stratospheric warmings (SSWs), which can cause extreme cold temperature outbreaks at the surface, resulting in hazardous weather and disrupting many socioeconomic sectors. We detect SSWs and model the surface temperature data distributions in situ and show that post hoc analysis using the distribution models can predict the impact of SSWs in the continental United States.

We present a direct manipulation technique that allows material scientists to interactively highlight relevant parameterized simulation instances located in dimensionally reduced spaces, enabling a user-defined understanding of a continuous parameter space. Our goals are two-fold: first, to build a user-directed intuition of dimensionally reduced data, and second, to provide a mechanism for creatively exploring parameter relationships in parameterized simulation sets, called ensembles. We start by visualizing ensemble data instances in dimensionally reduced scatter plots. To understand these abstract views, we employ user-defined virtual data instances that, through direct manipulation, search an ensemble for similar instances. Users can create multiple of these direct manipulation queries to visually annotate the spaces with sets of highlighted ensemble data instances. User-defined goals are therefore translated into custom illustrations that are projected onto the dimensionally reduced spaces. Combined forward and inverse searches of the parameter space follow naturally allowing for continuous parameter space prediction and visual query comparison in the context of an ensemble. The potential for this visualization technique is confirmed via expert user feedback for a shock physics application and synthetic model analysis.

The ever-expanding demand for ultra-high-speed interconnects has driven the development of wireline TXs operating at >100Gb/s per lane [1]–[4]. This paper presents a PAM-4 TX achieving 200Gb/s with improved output bandwidth and output swing by minimizing the driver capacitance with pull-up current sources, multiplexing with flexible clock timing control, and employing a fully reconfigurable 5-tap FFE architecture.

Due to I/O bandwidth limitations, intelligent in situ data reduction methods are needed to enable post-hoc workflows. Current state-of-the-art sampling methods save data points if they deem them spatially or temporally important. By analyzing the properties of the data values at each time-step, two consecutive steps may be very similar. This research follows the notion that if neighboring time-steps are very similar, samples from both are unnecessary, which leaves storage for adding more useful samples. Here, we present an investigation of the combination of spatial and temporal sampling to drastically reduce data size without the loss of valuable information. We demonstrate that, by reusing samples, our reconstructed data set reduces the overall data size while achieving a higher post-reconstruction quality over other reduction methods.

With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses of single variables. In this work, we propose a multivariate sampling strategy which preserves the original variable relationships and enables different multivariate analyses directly on the sampled data. Our proposed strategy utilizes principal component analysis to capture the variance of multivariate data and can be built on top of any existing state-of-the-art sampling algorithms for single variables. In addition, we also propose variants of different data partitioning schemes (regular and irregular) to efficiently model the local multivariate relationships. Using two real-world multivariate data sets, we demonstrate the efficacy of our proposed multivariate sampling strategy with respect to its data reduction capabilities as well as the ease of performing efficient post-hoc multivariate analyses., To appear as IEEE Vis 2020 Shortpaper

Background & Aims. Chronic hepatitis (CH) has emerged as a distinct outcome of drug-induced liver injury (DILI). Combination therapy of Isoniazid (INH) and Rifampicin (RMP) which is widely used for prolonged periods can cause acute hepatotoxicity and has been also incriminated in chronic DILI. We sought evidence of the production of hepatic fibrosis on long-term INH-RMP treatment through experiments in BALB/c mice exposed to INH-RMP.Methods. A combined dose of INH (50 mg) and RMP (100 mg) per kg body weight per day was administered to mice by oral gavage, 6 days a week, for 4 to 24 weeks for the assessment of liver injury, oxidative stress, and development of hepatic fibrosis, including demonstration of changes in key fibrogenesis linked pathways and mediators.Results. Progressive increase in markers of hepatic stellate cell (HSC) activation associated with changes in matrix turnover was observed between 12 and 24 weeks of INH-RMP treatment along with the elevation of liver collagen content and significant periportal fibrosis. These were associated with concurrent apoptosis of the hepatocytes, increase in hepatic cytochrome P450 2E1 (CYP2E1), NADPH oxidase (NOX) activity, and development of hepatic oxidative stress.Conclusions. INH-RMP can activate HSC through generation of NOX-mediated oxidative stress, leading to the development of liver fibrosis.

In recent years, the increased demand for wireless multimedia services has led to huge bandwidth crisis. The concept of Cognitive Radio Networks (CRN) has been proposed as a solution. In CRN, higher Primary User Activity leads to an increased number of Pre-empted Secondary Users (PSU). Since the PSUs are given priority over New Secondary Users (NSU), the NSUs might get starved. Due to this priority of PSUs less number low priority users can be served. The advent of the Internet of Things, the traffic pattern has become unpredictable. All this reasons required a Dynamic Cognitive Channel Allocation (DCCA) algorithm which has been proposed in this work which balances the service among the PSUs and NSUs as per priority of SU as well as avoiding starvation. Analysis of the proposed algorithm using Continuous Time Markov Chain Model shows an improvement over existing literature of 44.44% in NSU dropping probability and 22.8% improvement in NSU blocking probability with respect to arrival of PUs. Varying the arrival of SUs has resulted in an improvement of 58% and 45.75% in the dropping and blocking probabilities of NSUs respectively. The starvation of SUs has lowered by 32.52% with varying arrival rate of PU and 41.38% with varying arrival rate of SUs

Uncertainty quantification in climate ensembles is an important topic for the domain scientists, especially for decision making in the real-world scenarios. With powerful computers, simulations now produce time-varying and multi-resolution ensemble data sets. It is of extreme importance to understand the model sensitivity given the input parameters such that more computation power can be allocated to the parameters with higher influence on the output. Also, when ensemble data is produced at different resolutions, understanding the accuracy of different resolutions helps the total time required to produce a desired quality solution with improved storage and computation cost. In this work, we propose to tackle these non-trivial problems on the Weather Research and Forecasting (WRF) model output. We employ a moment independent sensitivity measure to quantify and analyze parameter sensitivity across spatial regions and time domain. A comparison of clustering structures across three resolutions enables the users to investigate the sensitivity variation over the spatial regions of the five input parameters. The temporal trend in the sensitivity values is explored via an MDS view linked with a line chart for interactive brushing. The spatial and temporal views are connected to provide a full exploration system for complete spatio-temporal sensitivity analysis. To analyze the accuracy across varying resolutions, we formulate a Bayesian approach to identify which regions are better predicted at which resolutions compared to the observed precipitation. This information is aggregated over the time domain and finally encoded in an output image through a custom color map that guides the domain experts towards an adaptive grid implementation given a cost model. Users can select and further analyze the spatial and temporal error patterns for multi-resolution accuracy analysis via brushing and linking on the produced image. In this work, we collaborate with a domain expert whose feedback shows the effectiveness of our proposed exploration work-flow.

A new visualization tool, Cinema:Bandit, and its demonstration with a continuous workflow for analyzing shock physics experiments and visually exploring the data in real time at X-ray light sources is presented. Cinema:Bandit is an open-source, web-based visualization application in which the experimenter may explore an aggregated dataset to inform real-time beamline decisions and enable post hoc data analysis. The tool integrates with experimental workflows that process raw detector data into a simple database format, and it allows visualization of disparate data types, including experimental parameters, line graphs, and images. Use of parallel coordinates accommodates the irregular sampling of experimental parameters and allows for display and filtering of both experimental inputs and measurements. The tool is demonstrated on a dataset of shock-compressed titanium collected at the Matter in Extreme Conditions hutch at the Linac Coherent Light Source.

Visualization of high-fidelity scientific simulations with high-dimensional inputs and outputs is an important task. Existing high-dimensional data visualization approaches generally assume a substantial amount of data are available or can be generated as needed. However, many of these simulations can be very computationally intensive, taking minutes or hours to run. Analysis and visualization of such expensive simulations poses a challenge. Statistical emulators are frequently used to approximate simulations for statistical analyses. In this work, we propose a visualization tool for an emulator of the simulator and describe how emulators can be used to create effective visualization systems. We choose Gaussian process emulators for this purpose as they enable fast and accurate prediction with uncertainty information. Using these predictions, we design a system that enables visualization of high-dimensional input and output spaces of complex physics simulations. Users of our system can get a detailed understanding of the uncertainties associated with the emulator predictions in both input and output space for a high-dimensional simulation.

India is a groundwater civilization. About 90% of India’s agriculture and 85% of India’s domestic water sectors are dependent on groundwater. On an average, about 50% of water supplied to the cities is sourced from groundwater (Suhag 2016). Close to 30 million wells dot the Indian landscape. Indiscriminate pumping from these wells has led to “groundwater anarchy” (Shah 2009). Access to groundwater is almost unrestricted and individual land rights are inextricably linked to groundwater. At the same time, groundwater is mobile and cannot be stored in one location in its pristine state. This makes enforcement of rights difficult. Some of these complexities in resource characteristics are important to understand before we begin to analyze the different dimensions of participation in India’s groundwater management.

Background:Hemangiomas are unique endothelial cell tumors that involute spontaneously, which makes interpreting their response to therapies difficult. The objective of this work was to identify a potential biomarker in the urine of children with infantile hemangiomas that would facilitate testing ne

Information theory provides a theoretical framework for measuring information content for an observed variable, and has attracted much attention from visualization researchers for its ability to quantify saliency and similarity among variables. In this paper, we present a new approach towards building an exploration framework based on information theory to guide the users through the multivariate data exploration process. In our framework, we compute the total entropy of the multivariate data set and identify the contribution of individual variables to the total entropy. The variables are classified into groups based on a novel graph model where a node represents a variable and the links encode the mutual information shared between the variables. The variables inside the groups are analyzed for their representativeness and an information based importance is assigned. We exploit specific information metrics to analyze the relationship between the variables and use the metrics to choose isocontours of selected variables. For a chosen group of points, parallel coordinates plots (PCP) are used to show the states of the variables and provide an interface for the user to select values of interest. Experiments with different data sets reveal the effectiveness of our proposed framework in depicting the interesting regions of the data sets taking into account the interaction among the variables.

Although vortex analysis and detection have been extensively investigated in the past, none of the existing techniques are able to provide fully robust and reliable identification results. Local vortex detection methods are popular as they are efficient and easy to implement, and produce binary outputs based on a user-specified, hard threshold. However, vortices are global features, which present challenges for local detectors. On the other hand, global detectors are computationally intensive and require considerable user input. In this work, we propose a consensus-based uncertainty model and introduce spatial proximity to enhance vortex detection results obtained using point-based methods. We use four existing local vortex detectors and convert their outputs into fuzzy possibility values using a sigmoid-based soft-thresholding approach. We apply a majority voting scheme that enables us to identify candidate vortex regions with a higher degree of confidence. Then, we introduce spatial proximity- based analysis to discern the final vortical regions. Thus, by using spatial proximity coupled with fuzzy inputs, we propose a novel uncertainty analysis approach for vortex detection. We use expert's input to better estimate the system parameters and results from two real-world data sets demonstrate the efficacy of our method.

With growing computational capabilities of parallel machines, scientific simulations are being performed at finer spatial and temporal scales, leading to a data explosion. Careful analysis of this data holds much promise for future scientific discoveries. Particularly, correlation analysis, which focuses on studying the potential relationships among multiple variables, is becoming a useful method for scientific analysis. This paper focuses on the problem of correlation analysis across large-scale simulation datasets, including 1) accelerating this analysis with the use of bitmap indexing as a representative summary of the data, 2) developing efficient algorithms for parallel execution, 3) performing analysis in distributed environments, i.e., for cases where different attributes are stored in geographically distributed repositories, and 4) combining sampling with correlation analysis. These algorithms have been implemented in a system that provides a high-level API for specification of the analyses, including allowing correlation analysis on specified value-based and dimension-based subsets of the data, and supports interactive and incremental analysis. We have extensively evaluated our framework for efficiency, and have also carried out case studies with domain scientists to establish how it can aid data-driven discovery process.

Background and Aims: Chronic hepatitis has emerged as a distinct clinical outcome in large DILI registries. AT drugs being the commonest DILI agents in India, we investigated the pathways that mediate the development of hepatic fibrosis in long-term treatment of AT drugs in mice. Methods: In a combined in vivo and in vitro experiment-based study, the extent of liver injury and development of hepatic fibrosis were evaluated in wild type BALB/c mice at different time points of AT drugs (cotreatment of INH 50 mg/kg and RMP 100 mg/kg body weight) treatment for 24 weeks. Markers of oxidative stress, hepatic inflammation, apoptosis and fibrosis were evaluated. In vitro studies with mediators released from INH treated E47 cells on LX2 cells were carried out to establish the pathways in greater detail. Results: Development of hepatic oxidative stress associated with increased expression of NADPH oxidase was observed in mice during long-term treatment of AT drugs in mice. Immunohistochemistry and quantitative polymerase chain reaction demonstrated a gradual increase of HSC activation during AT drugs treatment. Histological evidence of liver fibrosis was first observed at 3 months of these drugs treatment. In addition, increasing apoptosis of hepatocytes associated with duration of AT drugs treatment accelerated hepatic fibrosis, suggesting a causative contribution of apoptosis in this process. In vitro study on stable human cell lines also confirmed the involvement of mediators (products of oxidative stress) released from INH-treated hepatocytes in the activation of hepatic stellate cell and increased collagen synthesis. Conclusions: In mice, long-term treatment of AT drugs contributes HSC activation leading to development of hepatic fibrosis. These findings will help in understanding of the pathobiology of AT druginduced liver fibrosis.