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There is a growing trend of applying machine learning methods to medical datasets in order to predict patients' future status. Although some of these methods achieve high performance, challenges still exist in comparing and evaluating different models through their interpretable information. Such analytics can help clinicians improve evidence-based medical decision making. In this work, we develop a visual analytics system that compares multiple models' prediction criteria and evaluates their consistency. With our system, users can generate knowledge on different models' inner criteria and how confidently we can rely on each model's prediction for a certain patient. Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods., This is the author's version of the article that has been accepted to PacificVis 2020 Visualization Meets AI Workshop

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis., Comment: This is the author's version of the article that has been accepted in PacificVis 2020 Visualization Meets AI Workshop

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points. In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications. This paper presents an incremental DR solution. We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data. First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results. Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization. We demonstrate the effectiveness of our design with two case studies using real-world datasets., Comment: This is the author's version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: 10.1109/TVCG.2019.2934433

The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system's effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes., To be included in the IEEE VIS 2021 Short Paper Proceedings

Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine repair and maintenance in an organization often need to be analyzed to diagnose errors and identify abnormal patterns, formalize root-cause analyses, and plan preventive maintenance. Such real-world datasets are also beset by issues such as inconsistent and/or missing entries. To conduct an effective diagnosis, it is important to extract and understand patterns from the data with support from analytic algorithms (e.g., finding that certain kinds of machine complaints occur more in the summer) while involving the human-in-the-loop. To address these challenges, we adopt existing techniques for dimensionality reduction (DR) and clustering of numerical, categorical, and text data dimensions, and introduce a visual analytics approach that uses multiple coordinated views to connect DR + clustering results across each kind of the data dimension stated. To help analysts label the clusters, each clustering view is supplemented with techniques and visualizations that contrast a cluster of interest with the rest of the dataset. Our approach assists analysts to make sense of machine maintenance logs and their errors. Then the gained insights help them carry out preventive maintenance. We illustrate and evaluate our approach through use cases and expert studies respectively, and discuss generalization of the approach to other heterogeneous data.

Finding the similarities and differences between groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used to find the characteristics of each group. However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups. This paper presents an interactive DR framework where we integrate our new DR method, called ULCA (unified linear comparative analysis), with an interactive visual interface. ULCA unifies two DR schemes, discriminant analysis and contrastive learning, to support various comparative analysis tasks. To provide flexibility for comparative analysis, we develop an optimization algorithm that enables analysts to interactively refine ULCA results. Additionally, the interactive visualization interface facilitates interpretation and refinement of the ULCA results. We evaluate ULCA and the optimization algorithm to show their efficiency as well as present multiple case studies using real-world datasets to demonstrate the usefulness of this framework., Comment: This is the author's version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: 10.1109/TVCG.2021.3114807

Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets., Comment: This is the author's version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: 10.1109/TVCG.2020.3028889

A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL., To appear in IEEE Conference on Visual Analytics Science and Technology (VAST) 2020

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers., Comment: This is the author's preprint version that will be published in Proceedings of IEEE Pacific Visualization Symposium, 2020

To optimize the performance and efficiency of HPC applications, programmers and analysts often need to collect various performance metrics for each computer at different time points as well as the communication data between the computers. This results in a complex dataset that consists of multivariate time-series and communication network data, which makes debugging and performance tuning of HPC applications challenging. Automated analytical methods based on statistical analysis and unsupervised learning are often insufficient to support such tasks without the background knowledge from the application programmers. To better explore and analyze a wide spectrum of HPC datasets, effective visual data analytics techniques are needed. In this paper, we present a visual analytics framework for analyzing HPC datasets produced by parallel discrete-event simulations (PDES). Our framework leverages automated time-series analysis methods and effective visualizations to analyze both multivariate time-series and communication network data. Through several case studies for analyzing the performance of PDES, we show that our visual analytics techniques and system can be effective in reasoning multiple performance metrics, temporal behaviors of the simulation, and the communication patterns.

Decisions made by domain experts, such as in healthcare and market research, are influenced by the conditional co-occurrence of different events. Learning about conditional co-occurrence is also beneficial for non-experts–the general public. By understanding the co-occurrences of diseases, it is easier to understand which diseases individuals are susceptible to. However, co-occurrence data is often complex. In order for a public understanding of conditional co-occurrence, there needs to be a simpler form to convey such complex information. We introduce an organic visual metaphor, which can provide a summary of the conditional co-occurrences within a large set of items and is accessible to the public with its organic shape. We develop a prototype application offering not only an overview for users to gain insights on how co-occurrence patterns evolve based on user-defined criteria (e.g., how do sex and age affect likelihood), but also functionality to explore the hierarchical data in-depth. We conducted two case studies with this prototype to demonstrate the effectiveness of our design.

SUMMARY In this paper, we propose an agent model based on Nonaka's SECI model for encouraging the propagation of knowledge in organizations. In this model, the complexity of knowledge is expressed as a bit tag, and worker agents and knowledge manager agents are introduced. Parametric studies are performed on the bit tag length and on the internalization rates and communication patterns of the worker agents. We evaluate the acquisition of knowledge in order to verify the present simulation model. Furthermore, a scenario simulation is implemented with respect to employment periods for worker agents and the effectiveness of the multi-agent model is shown by means of a qualitative evaluation of knowledge variations corresponding to joining and leaving an organization.

Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profiled at a is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for Mini AMR application on 4,096 MPI processes.

As virtual reality (VR) hardware technology becomes more mature and affordable, it is timely to develop visualization applications making use of such technology. How to interact with data in an immersive 3D space is both an interesting and challenging problem, demanding more research investigations. In this paper, we present a gesture input system for graph visualization in a stereoscopic 3D space. We compare desktop mouse input with gesture input with bare hands for performing a set of tasks on graphs. Our study results indicate that users are able to effortlessly manipulate and analyze graphs using gesture input. Furthermore, the results also show that using gestures is more efficient when exploring the complicated graph.

Neuroscientists study brain functional connectivity in order to obtain a deeper understanding of how the brain functions. Current studies are mainly based on analyzing the averaged brain connectivity of a group (or groups) due to the high complexity of the collected data in terms of dimensionality, variability, and volume. While it is more desirable for the researchers to explore the potential variability between individual subjects or groups, a data analysis solution meeting this need is absent. In this paper, we present the design and capabilities of such a visual analytics system, which enables neuroscientists to visually compare the differences of brain networks between individual subjects as well as group averages, to explore a large dataset and examine sub-groups of participants that may not have been expected a priori to be of interest, to review detailed information as needed, and to manipulate the data and views to fit their analytical needs with easy interactions. We demonstrate the utility and strengths of this system with case studies using a representative functional connectivity dataset.

Grinding is one of the machining methods for finishing that is performed by using a large number of abrasive grains with irregular shapes and random distribution. While this feature enables accurate and high-quality machining, it complicates analysis of the grinding process. To solve this problem, a great number of computer simulations have been carried out using the Monte Carlo method. Most of them, however, statically calculate geometric interaction between a grain and a workpiece, and have not provided enough achievements for practical applications. In this study, taking this background into account, a simulation program has been developed based on the elastic support model of a grain in which the elasticity of grain support changes according to its location. The program focuses on the generation process of a workpiece surface, and simulates the interaction between grains and a workpiece, which includes the elastic and plastic deformation as well as the removal of workpiece material. Examples of the simulation shown in this study verify the fact that the simulation program facilitates analysis of the microscopic grinding phenomena, and can be used as a practical tool for predicting the grinding results and for optimizing grinding parameters.

Twin axes synchronous controlled type CNC crankshaft pin grinder has been widely used, but grinding mechanism with this type of pin grinder is not cleared well. In this study, the grinding mechanism is analyzed, and it is found that the cylindrical plunge grinding process carries on. From the investigation of substantial surface speed of both the wheel and the workpiece, grinding speed ratio is formulated. Furthermore, a simulation method, which predicts the grinding results, is developed. In order to realize high precision pin grinding, two methods, which control workpiece rotation speed adequately, are proposed, such as the controlled grinding speed ratio method and the controlled residual stock method. From the analysis, it is made clear that stable grinding force and surface roughness are generated by the controlled grinding speed ratio method, and also high dimensional accracy will be realized by the controlled residual stock method.

A good part of grinding operations is still dependent on skilled operators. Against such a background, this study takes up cylindrical plunge grinding, and presents a skill-formation model based on the learning process similar to that in which a grinding operator acquires skills through trial and error. The model repeats learning by giving only the total grinding stock and initial infeed rate, changing infeed rate, and evaluating the size and the removal rate in the process end. The simulation result has proved that the model shows a skill-formation process similar to that of an operator and generates desirable infeed processes.

本論文で得られた結論は以下のとおりである, (1) 研削抵抗による砥石軸の弾性変位量をうず電流式非接触変位計で検出する新しいインプロセス研削抵抗測定システムを開発し, 加工現場での研削抵抗監視に十分な性能を持つことが確認された.(2) 圧電式動力計は, 工作物の固定方法の影響で, 工作物と動力計流入する加工熱に起因する見掛けの力がノイズとして測定信号に重畳する.しかし, 砥石軸動力計の場合このような加工熱に起因する測定ノイズは本質的に混入しない.(3) 従来の研削理論では, 研削点における工作物の曲率半径すなわち半径が等しい区間を加工する際の研削抵抗は一定と考えられるが, 輪郭加工中は研削抵抗の大きさが異なる.このことは, 実質の砥石切込み量に対する工作物および/あるいは砥石の熱変形の影響を示唆していると考えられる.

喉頭気管食道裂type 3の根治術の麻酔を経験した。この疾患の頻度は,まれではあるが,放置すれば呼吸器合併症を併発し,致死的転帰をたどる。症例は,3,000gの女児で,生後8日に腹部食道結紮術および空腸瘻を造設し,生後30日に一期的根治手術を行なった。麻酔導入後,気管内挿管を行ない,内頸動静脈より膜型人工肺(ECMO)を導入した。ECMO使用により,非換気下に食道気管切開後,術野より特注のY型の気管支用チューブを左右両気管支に挿入し,逆行性に経鼻気管内挿管とした。術後は筋弛緩剤投与下に人工呼吸により呼吸管理を行ない,術中,術後とも経過良好であった。

Visualization of high-dimensional data is difficult to realize and manipulate with 2D display. For example, visualizing time-varying volume data (4D) with volume rendering and animation has spatial and temporal shielding, and data of 5 or more dimensions cannot be visualized on 2D display with existing methods. In this paper, we propose a method that expands high-dimensional data onto a 2D image plane. The proposed method uses the self-similarity of the fractal shape and achieves multi-scale high-dimensional data visualization on 2D display. With this method, we can visualize the entire domain of high-dimensional data without occlusions. Also, one-to-one correspondence in the elements of high-dimensional data and its 2D expansion enables us to manipulate high-dimensional data with 2D expanded result as an interface.

In cam grinding under constant rotation speed of a cam, grinding force and surface roughness become larger at position with higher speed ratio, because curvature radius of a cam changes continuously in one revolution of workpiece. In order to establish precision CNC cam grinding method, therefore it is indispensable to develop optimum rotation speed control of a workpiece. Then, cam grinding process with CNC camshaft grinder is analyzed theoretically, and horizontal travel speed setting of wheel head correspond to the rotation speed of a workpiece is made clear in this study. Furthermore, simulation analysis method is developed, and proposed unique 3 grinding techniques to control the rotation speed of a workpiece are investigated. In the methods, it is certified that fluctuations of the grinding results are reduced, and the precision cam grinding method is realized.

SUMMARY The effects of propranolol on arterial blood pressure, heart rate, and cardiac rhythm during ventricular arrhythmias initiated by hypercarbia were investigated in thirteen subjects during 1 per cent halothane anaesthesia. Propranolol was consistently effective in the treatment of ventricular arrhythmia induced by hypercarbia in all subjects. The successful use of propranolol suggests that the mechanism for ventricular arrhythmia produced by respiratory acidosis during halothane anaesthesia was related to beta adrenergic receptors. Blockade of these receptors by beta adrenergic agents decreased markedly the sensitization of myocardium to sympathetic stimulation.