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This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition., Comment: For team website, see https://costar.jpl.nasa.gov/. Accepted for publication in the Journal of Field Robotics, 2021

Objective The purpose of this study is to investigate recent clinical studies on the effect of herbal medicine in flat warts in China, and to seek better methods to treat and study for flat warts in Korea. Methods The clinical studies from the CAJ (China Academic Journal) of CNKI (China National Kno...

The behavioral finance literature attributes failed M&As to CEO overconfidence. We investigate the source of CEO overconfidence that leads to failed M&As. Among various determinants of CEO overconfidence, we propose that power-led CEO overconfidence delivers undesirable consequences in corporate investments. Using CEO-level data, we find that CEO power increases the probability of a CEO being overconfident. We also show that power-driven overconfident CEOs tend to complete more deals regardless of circumstances, do stock acquisitions, and make value-destroying acquisitions, relative to non-overconfident CEOs. The results suggest that previous studies on M&As by overconfident CEOs are mainly led by power-driven overconfident CEOs.

Link prediction is a key technique in various applications such as prediction of existence of relationship in biological network. Most existing works focus the link prediction on homogeneous information networks. However, most applications in the real world require heterogeneous information networks that are multiple types of nodes and links. The heterogeneous information network has complex correlation between a type of link and a type of path, which is an important clue for link prediction. In this paper, we propose a method of link prediction in the heterogeneous information network that takes a type correlation into account. We introduce the Local Relatedness Measure (LRM) that indicates possibility of existence of a link between different types of nodes. The correlation between a link type and path type, called TypeCorr is formulated to quantitatively capture the correlation between them. We perform the link prediction based on a supervised learning method, by using features obtained by combining TypeCorr together with other relevant properties. Our experiments show that the proposed method improves accuracy of the link prediction on a real world network.