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 the last years, robots are moving out of research laboratories to enter everyday life. Competitions aiming at benchmarking the capabilities of a robot in everyday scenarios are useful to make a step forward in this path. In fact, they foster the development of robust architectures capable of solving issues that might occur during human-robot coexistence in human-shaped scenarios. One of those competitions is SciRoc that, in its second edition, proposed new benchmarking environments. In particular, Episode 1 of SciRoc 2 proposed three different modalities of participation while preserving the Human-Robot Interaction (HRI), being a fundamental benchmarking functionality. The Coffee Shop environment, used to challenge the participating teams, represented an excellent testbed enabling for the benchmarking of different robotics functionalities, but also an exceptional opportunity for proposing novel solutions to guarantee real human-robot interaction procedures despite the Covid-19 pandemic restrictions. The developed software is publicly released.

Preserving HRI Capabilities: Physical, Remote and Simulated Modalities in the SciRoc 2021 Competition

Oral tumors are responsible for about 170,000 deaths every year in the World. In this paper, we focus on oral squamous cell carcinoma (OSCC), which represents up to 80–90 % of all malignant neoplasms of the oral cavity. We present a novel deep learning-based method for segmenting whole slide image (WSI) samples at the pixel level. The proposed method is a modification of the well-known U-Net architecture through a multi-encoder structure. In particular, our network, called Multi-encoder U-Net, is a multi-encoder single decoder network that takes as input an image and splits it in tiles. For each tile, there is an encoder responsible for encoding it in the latent space, then a convolutional layer is responsible for merging the tiles into a single layer. Each layer of the decoder takes as input the previous up-sampled layer and concatenate it with the layer made by merging the corresponding layers of the multiple encoders. Experiments have been carried out on the publicly available ORal Cancer Annotated (ORCA) dataset, which contains annotated data from the TCGA repository. Quantitative experimental results, obtained using three different quality metrics, demonstrate the effectiveness of the proposed approach, which achieves 82% Pixel-wise Accuracy, 0.82 Dice similarity score, and 0.72 Mean Intersection Over Union.

Multi-encoder U-Net for Oral Squamous Cell Carcinoma Image Segmentation

In agriculture, the detection of parasites on the crops is required to protect the growth of the plants, increase the yield, and reduce the farming costs. A suitable solution includes the use of mobile robotic platforms to inspect the fields and collect information about the status of the crop. Then, by using machine learning techniques the classification of infected and healthy samples can be performed. Such approach requires a large amount of data to train the classifiers, which in most of the cases is not available given constraints such as weather conditions in the inspection area and the hardware limitations of robotic platforms. In this work, we propose a solution to detect the downy mildew parasite in sunflowers fields. A classification pipeline detects infected sunflowers by using a UAV that overflies the field and captures crop images. Our method uses visual information and morphological features to perform infected crop classification. Additionally, we design a simulation environment for precision agriculture able to generate synthetic data to face the lack of training samples due to the limitations to perform the collection of real crop information. Such simulator allows to test and tune the data acquisition procedures thus making the field operations more effective and less failure prone.

Mapping Infected Crops Through UAV Inspection: The Sunflower Downy Mildew Parasite Case

Task Assignment for Multi-Robot Systems is a main issue to attain good performance in complex real world environments. In several application domains tasks to be executed are not inserted into the system by an external entity but are perceived by robots during mission execution. In this paper we explicitly focus on detecting and solving conflicts that may arise during the task assignment process. We propose a conflict resolution method based only on point to point messages. The approach is able to guarantee a conflict free allocation using a very limited communication bandwidth. We also present an extension to make the system robust to possible network failures.

Conflict Resolution with Minimal Communication Bandwidth

In order for robots to become integrated into society, we need to be able to prove that robots do their jobs reliably. Robot benchmarking competitions in smart cities offer a glimpse into our future.

Daniele Nardi

Papers

Preserving HRI Capabilities: Physical, Remote and Simulated Modalities in the SciRoc 2021 Competition

Multi-encoder U-Net for Oral Squamous Cell Carcinoma Image Segmentation

Mapping Infected Crops Through UAV Inspection: The Sunflower Downy Mildew Parasite Case

Conflict Resolution with Minimal Communication Bandwidth

Benchmarking robots in smart cities