Search results for "Data type"
showing 10 items of 1183 documents
Querying and reasoning over large scale building data sets
2016
International audience; The architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is avai…
ViziQuer: A Visual Notation for RDF Data Analysis Queries
2019
Visual SPARQL query notations aim at easing the RDF data querying task. At the current state of the art there is still no generally accepted visual graph-based notation suitable to describe RDF data analysis queries that involve aggregation and subqueries. In this paper we present a visual diagram-centered notation for SPARQL select query formulation, capable to handle aggregate/statistics queries and hierarchic queries with subquery structure. The notation is supported by a web-based prototype tool. We present the notation examples, describe its syntax and semantics and describe studies with possible end users, involving both IT and medicine students.
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data
2021
Constrained joint analysis of data from multiple sources has received widespread attention for that it allows us to explore potential connections and extract meaningful hidden components. In this paper, we formulate a flexible joint source separation model termed as group nonnegative matrix factorization with sparse regularization (GNMF-SR), which aims to jointly analyze the partially coupled multi-set data. In the GNMF-SR model, common and individual patterns of particular underlying factors can be extracted simultaneously with imposing nonnegative constraint and sparse penalty. Alternating optimization and alternating direction method of multipliers (ADMM) are combined to solve the GNMF-S…
Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe
2012
One of the major drawbacks of land cover products is the lack of interoperability among them. Since their development was driven by different national or international initiatives, they were developed for different purposes and hold diverse technical characteristics. Thus, comparison among products and quality monitoring is necessary in assessing their usefulness. This paper provides a methodology to compare global land cover maps that allows for differences in legend definitions among products. Two different approaches were considered for map comparison, a Boolean approach and a new methodology based on fuzzy set theory in which the Land Cover Classification System (LCCS) acted as a genera…
Advanced C++11 Multithreading
2018
Abstract The previous chapter introduced the basic concepts of multithreading using the C++11 threading API starting with basic spawn and join approaches, while finishing with non-trivial synchronization based on mutexes and condition variables. However, the major bottleneck of application performance is usually caused by contention for a shared resource. In case of mutex-based programming all participating threads usually try to acquire the same lock in parallel which effectively serializes the program for lightweight operations such as increment/decrement or updates of a single scalar value. Fortunately, modern CPUs provide dedicated commands that allow for the efficient execution of unin…
Distributed Particle Metropolis-Hastings Schemes
2018
We introduce a Particle Metropolis-Hastings algorithm driven by several parallel particle filters. The communication with the central node requires the transmission of only a set of weighted samples, one per filter. Furthermore, the marginal version of the previous scheme, called Distributed Particle Marginal Metropolis-Hastings (DPMMH) method, is also presented. DPMMH can be used for making inference on both a dynamical and static variable of interest. The ergodicity is guaranteed, and numerical simulations show the advantages of the novel schemes.
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…
Decentralized Subspace Projection for Asymmetric Sensor Networks
2020
A large number of applications in Wireless Sensor Networks include projecting a vector of noisy observations onto a subspace dictated by prior information about the field being monitored. In general, accomplishing such a task in a centralized fashion, entails a large power consumption, congestion at certain nodes and suffers from robustness issues against possible node failures. Computing such projections in a decentralized fashion is an alternative solution that solves these issues. Recent works have shown that this task can be done via the so-called graph filters where only local inter-node communication is performed in a distributed manner using a graph shift operator. Most of the existi…
Open Set Audio Classification Using Autoencoders Trained on Few Data.
2020
Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solution…