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  1. Ishigami G., Yoshida K. (Ed.)
    This paper develops an autonomous tethered aerial visual assistant for robot operations in unstructured or confined environments. Robotic tele-operation in remote environments is difficult due to the lack of sufficient situational awareness, mostly caused by stationary and limited field-of-view and lack of depth perception from the robot’s onboard camera. The emerging state of the practice is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the onboard sensors by providing an external viewpoint. However, problems exist when using a tele-operated visual assistant: extra manpower, manually chosen suboptimal viewpoint,more »and extra teamwork demand between primary and secondary operators. In this work, we use an autonomous tethered aerial visual assistant to replace the secondary robot and operator, reducing the human-robot ratio from 2:2 to 1:2. This visual assistant is able to autonomously navigate through unstructured or confined spaces in a risk-aware manner, while continuously maintaining good viewpoint quality to increase the primary operator’s situational awareness. With the proposed co-robots team, tele-operation missions in nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments could benefit from reduced manpower and teamwork demand, along with improved visual assistance quality based on trustworthy risk-aware motion in cluttered environments.« less
  2. Motif mining is a classical data mining problem which aims to extract relevant information and discover knowledge from voluminous datasets in a variety of domains. Specifically, for the temporal data containing real numbers, it is formulated as time series motif mining (TSMM) problem. If the input is alphabetical and edit-distance is considered, this is called Edit-distance Motif Search (EMS). In EMS, the problem of interest is to find a pattern of length l which occurs with an edit-distance of at most d in each of the input sequences. There exist some algorithms proposed in the literature to solve EMS problem.more »However, in terms of challenging instances and large datasets, they are still not efficient. In this paper, EMS3, a motif mining algorithm, that advances the state-of-the-art EMS solvers by exploiting the idea of projection is proposed. Solid theoretical analyses and extensive experiments on commonly used benchmark datasets show that EMS3 is efficient and outperforms the existing state-of-the-art algorithm (EMS2).« less
  3. Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hardmore »scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.« less
    Free, publicly-accessible full text available December 1, 2023