Search results for "Mach"

Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Appli…

2022

Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosi…

Biological and Mechanical Characterization of the Random Positioning Machine (RPM) for Microgravity Simulations

2021

The rapid improvement of space technologies is leading to the continuous increase of space missions that will soon bring humans back to the Moon and, in the coming future, toward longer interplanetary missions such as the one to Mars. The idea of living in space is charming and fascinating; however, the space environment is a harsh place to host human life and exposes the crew to many physical challenges. The absence of gravity experienced in space affects many aspects of human biology and can be reproduced in vitro with the help of microgravity simulators. Simulated microgravity (s-μg) is applied in many fields of research, ranging from cell biology to physics, including cancer biology. In…

Improvements and applications of the elements of prototype-based clustering

2018

Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outl…

The manipulation of Euribor: An analysis with machine learning classification techniques

2022

The manipulation of the Euro Interbank Offered Rate (Euribor) was an affair which had a great impact on in ternational financial markets. This study tests whether advanced data processing techniques are capable of classifying Euribor panel banks as either manipulating or non-manipulating on the basis of patterns found in quotes submissions. For this purpose, panel banks’ daily contributions have been studied and monthly variables obtained that denote different contribution patterns for Euribor panel banks. Thus, in accordance with the court verdict, banks are categorized as manipulating and non-manipulating and Machine Learning classification techniques such as Supervised Learning, Anomaly …

Growth and population structure of perch in relation to diet in a small humic lake, Valkea-Kotinen

2011

Lake Valkea-Kotinen is a long-term ecological monitoring site in the Evo region of southern Finland .The aim of this study was to investigate the growth and population structure of perch (Perca fluviatilis L.) in relation to diet in Valkea-Kotinen in order to provide reference data for a study investigating effects of increased loading of dissolved organic matter to nearby Lake Alinen Mustajärvi. Valkea-Kotinen was predominantly inhabited by small benthivorous perch. The growth relationship between opercular bone and total length of perch in the lake is similar to those earlier analysed for nearby lakes. Compared to wider growth rates, the growth of perch was slow and similar to those in si…

De la sauvagerie à la violence créatrice : regards sur les bris de machines dans la France du XIXe siècle

2013

International audience

Evidence for region-specific effects of glucagone-like peptide-2 in mouse stomach

2009

Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

2018

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer. CICYT TIN2015-64210-R In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophy…

Anomaly and Change Detection in Remote Sensing Images

2021

Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexi…

Análisis de técnicas de “aggregation”/“disaggregation” aplicadas a imágenes satélite para la estimación de parámetros térmicos superficiales a difere…

2023

Las aplicaciones que implican la observación de la superficie terrestre desde plataformas satélites a escala inferior a la regional, como por ejemplo, el caso del seguimiento de cultivos, requieren de una mayor disponibilidad de información térmica, en particular de la temperatura de la superficie terrestre (LST), con resoluciones espaciales apropiadas para un alcance local. Por ello, numerosos autores han propuesto y desarrollado métodos para extraer la LST a nivel “subpíxel”, mediante el empleo de productos complementarios de teledetección, con resultados adecuados para su uso en resoluciones superiores. La mayoría de estos métodos se basan en la correlación entre índices de vegetación, c…