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

In multi-robot reinforcement learning the goal is to enable a group of robots to learn coordinated behaviors from direct interaction with the environment. Here, we provide a comparison of two main approaches designed for tackling this challenge; namely, independent learners (IL) and joint-action learners (JAL). We evaluate these methods in a multi-robot cooperative and adversarial soccer scenario, called 2 versus 2 free-kick task, with simulated NAO humanoid robots as players. Our findings show that both approaches can achieve satisfying solutions, with JAL outperforming IL.

Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains

Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and flight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features are heterogeneously distributed, like weeds appearing in patches over the field. In this case, an adaptive approach that focuses only on relevant areas can perform better, especially when multiple UAVs are employed simultaneously. Leveraging on a swarm-robotics approach, we propose a monitoring and mapping strategy that adaptively chooses the target areas based on the expected information gain, which measures the potential for uncertainty reduction due to further observations. The proposed strategy scales well with group size and leads to smaller mapping errors than optimal pre-planned monitoring approaches. 

/pdf/monitoring-and-mapping-of-crop-fields-with-uav-swarms-based-gofzspec.pdf

Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain

The Petri Net Plans framework (PNPs [2]) allows the representation of high level programs for robotic behavior, providing all the action features needed to describe complex plans in dynamic, partially observable and unpredictable environments. The multi-robot extension of PNPs allows the synchronization of actions among different robots, the performance of deliberate cooperation and the cooperative handling of local failures in a multi-robot system. A multi-robot PNP is automatically divided by each single robot of the system for the individual execution. The robots perform their actions relying on their individual knowledge base, and during the plan execution they are able to communicate through a reliable channel, to attain synchronization and sharing of information. This demonstration shows the use of multi-robot PNPs for the implementation of a robotic-soccer task. Two Sony AIBO quadruped robots are placed on a soccer field with the task of passing a ball (Figure 1). Before the execution of each pass the robots dynamically assign the roles for the execution of the task (Passer, Receiver) according to the information that is exchanged during a synchronization action. The passing robot reaches and grabs the ball, waiting for its partner to be ready for receiving the pass. At the reception of an acknowledgement the ball is kicked. At the end of the pass the robots perform a new synchronization and the proce-

/pdf/a-robotic-soccer-passing-task-using-petri-net-plans-demo-yte2r57o3t.pdf

A Robotic Soccer Passing Task Using Petri Net Plans (Demo Paper)

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g, hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q- CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.

Q-CP: Learning Action Values for Cooperative Planning

The increasing interest towards rescue robotics and the complexity of typical rescue environments make it necessary to use high fidelity 3D simulators during the application development phase. USARSim is an open source high fidelity simulator for rescue environments, based on a commercial game engine. In this paper, we describe the development of an autonomous rescue robot within the USARSim simulation environment. We describe our rescue robotic system and present the extensions we made to USARSim in order to have a satisfying simulation of our robot. Moreover, as a case study, we present an algorithm to avoid obstacles invisible to our laser scanner based mapping process.

/pdf/development-of-an-autonomous-rescue-robot-within-the-usarsim-2cemzqq7ek.pdf

Daniele Nardi

Papers

Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains

Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain

A Robotic Soccer Passing Task Using Petri Net Plans (Demo Paper)

Q-CP: Learning Action Values for Cooperative Planning

Development of an Autonomous Rescue Robot Within the USARSim 3D Virtual Environment