Group-in-a-box (GIB) is a graph-drawing method designed to facilitate the visualization of the group structure of a graph. GIB allows the user to simultaneously view group sizes and inter-and intra-group structures. Several GIB variants have been proposed in the literature; however, their advantages and disadvantages have not been studied from the perspective of human cognition. Therefore, herein, we used eye tracking analysis and user surveys to evaluate the user experience of four GIB variants: Squarified-Treemap GIB(ST-GIB), Croissant-and-Doughnut GIB (CD-GIB), Force-Directed GIB (FD-GIB), and Tree-Reordered GIB (TR-GIB). We found some trade-offs among the methods for each type of user task and that FD-GIB and TR-GIB are superior than the other variants. Although ST-GIB's results were good, links were difficult to read in this graph layout. Eye-tracking data was gathered to determine which elements in each visualization significantly affected user experience. The results of this study will promote the effective use of GIB to analyze networks such as social networks or web graphs.

MOTIVATION: A cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a 'mutation signature.' Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g. non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear. RESULTS: In this study, we present a novel method for estimating the number of mutation signatures-latent Dirichlet allocation with variational Bayes inference (VB-LDA)-where variational lower bounds are utilized for finding a plausible number of mutation patterns. In addition, we performed cluster analyses for estimated mutation signatures to extract novel mutation signatures that appear in multiple primary lesions. In a simulation with artificial data, we confirmed that our method estimated the correct number of mutation signatures. Furthermore, applying our method in combination with clustering procedures for real mutation data revealed many interesting mutation signatures that have not been previously reported. AVAILABILITY AND IMPLEMENTATION: All the predicted mutation signatures with clustering results are freely available at http://www.f.waseda.jp/mhamada/MS/index.html. All the C++ source code and python scripts utilized in this study can be downloaded on the Internet (https://github.com/qkirikigaku/MS_LDA). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

To improve visualization, it is necessary to optimize the design by analyzing the behavior of users as well as improving the evaluation index of the computational experiment and the task performance (e.g., the correct answer rate and completion time) in the user experiment. Although various studies have investigated the influence of user behavior on the evaluation of visualization, majority of these studies focused on simple visualization tasks. A simple task does not indicate a simple visualization comprising a few visualization elements but a task in which the information obtained from visualization is the only clue for completing the task. However, a few studies have targeted complicated tasks in which multiple information obtained from visualization is considered to be a clue for completing the task regardless of the number of elements that are contained in the visualization. Therefore, in this study, we investigated the behavior of the participants who have performed complicated tasks. We selected two types of group-in-a-box (GIB) layouts, which can be considered to be a complicated visualization method , as the target of the user experiment. In the user experiment, participants were asked to perform an exploration task specific to GIB layouts; which group has the maximum number of intra-edges? We also collected the eye-tracking data in addition to task performance. The results showed that the correct answer rate is considerably affected by the visualization factor; whether the correct answer, the box with maximum number of intra-edges, is the box with the largest area. Furthermore, an analysis of the collected eye-tracking data revealed that this visualization factor affected the exploration behavior of the participants; however, it did not affect the location at which the participants were focused on. The obtained results indicated that the visualization elements that were not considered by the visualization designer can influence the task of extracting information from the data. Therefore, designers have to configure the visualization by considering the visual cognitive behavior of the users.

Collision avoidance subject to operator's intention is needed for power-assisted omni-directional mobile robots. In order to realize collision avoidance on operator's initiative, the system which makes the operator notice the existence of an obstacle is necessary. In this paper, existence of the obstacle is notified through a change in operation feeling by adjusting viscosity coefficients of the power-assist system. Two methods of adjusting viscosity coefficients proportionally or exponentially according to the distance between the robot and the obstacle are considered. In an operation experiment at a narrow passage, a subject operated a power-assisted omni-directional mobile robot by wearing a blindfold or a tunnel vision experience goggle. From the results, the subject was able to operate the robot without collision. Additionally, the operator was able to operate the robot smoothly by the exponantial viscosity coefficients adjusting method.

Holonomic omnidirectional mobile robots are useful because of their high level of mobility in narrow or crowded areas, and omnidirectional robots equipped with normal tires are desired for their ability to surmount difference in level as well as their vibration suppression and ride comfort. A caster-drive mechanism using normal tires has been developed to realize a holonomic omnidiredctional robot, but some problems has remain. Here we describe effective systems to control the caster-drive wheels of an omnidirectional mobile robot. We propose a Differential-Drive Steering System (DDSS) using differential gearing to improve the operation ratio of motors. The DDSS generates driving and steering torque effectively from two motors. Simulation and experimental results show that the proposed system is effective for holonomic omnidirectional mobile robots.

This paper presents intelligent control of power assist system considering skill level of operators for wheelchair. In this paper, operability and anti-fluctuation running against the fluctuation of human force added to the handle by operator are progressed by the proposed auto-tuning system of power-assist controller. A method to estimate the operator's skill level and appropriately adjust the parameters of power-assist controller is presented by applying Fuzzy reasoning method. The effectiveness of the proposed method is demonstrated through experiments of OMW wheelchair.