다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

https://www.cs.ox.ac.uk/people/bernardo.cuencagrau/publications/PelletDemo.pdf

Pellet: A practical OWL-DL reasoner

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

/pdf/data-integration-a-theoretical-perspective-58aesjvhuk.pdf

Data integration: a theoretical perspective

Knowledge representation techniques for information extraction on the Web

In this paper we present a novel approach to accessing the Web, that enables automatically acquiring data from Web sites and making them accessible to the user through a database query paradigm. The basic idea is to build, once the user has specified a generic domain of interest, the domain conceptual representation, to instantiate it with data extracted from Web sites (so to build a materialized view over the Web), and to query such a conceptual representation through an easy-to-use visual interface. Knowledge representation techniques are used for both the internal modeling of the conceptual representation and for supporting the automatic extraction of data from Web sites to feed the materialized view. We describe a prototype implementation, focusing on the internal representation of information and on the process for analysing Web sites and acquiring data from them. Our preliminary results support our intuition that, at least for certain kinds of queries, the proposed approach can effectively provide the desired information.

Materializing the Web

Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions, and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the design and implementation of architectures for information fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual information into a framework for information fusion.

Context in Robotics and Information Fusion

The problem of assigning tasks to a group of robots acting in a dynamic environment is a fundamental issue for a MRS and is relevant to several real world applications. Several techniques have been studied to address this problem, however when the system needs to scale up with size, communication quickly becomes an important issue to address; moreover, tasks to be executed can be tied by constraints that have to be considered in the task assignment process. In this paper we present a distributed task assignment approach that ensures very low communication overhead and can manage constraints among roles. The basic idea of our approach is to use tokens to represent tasks to be executed, each team member creates, executes and propagates tokens based on a broad knowledge of their team mates. We test and evaluate our approach by means of experiments both on a simulates and on a real robot scenario

Dynamic token generation for constrained tasks in a multi-robot system

Complex service robotics scenarios entail unpredictable task appearance both in space and time This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall Moreover, the proposed solution is robust to limited communication and robot failures

Daniele Nardi

Papers

Knowledge representation techniques for information extraction on the Web

Materializing the Web

Context in Robotics and Information Fusion

Dynamic token generation for constrained tasks in a multi-robot system

Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms