Search results for "Programming Language"
showing 10 items of 624 documents
Geomeasure: GIS and Scripting for Measuring Morphometric Variability
2019
This paper presents Geomeasure, a methodological tool developed to recover typometric information with a twofold objective. First, to speed up the process of gathering data by automatizing the way in which it is recovered. Second, it adds higher accuracy and the possibility of re-measuring archeological items without further directly interacting with the piece. Based on a combination of R scripting with GIS features, Geomeasure is at the time able to automatically gather 125–130 typometric variables per archaeological item, with the only input of vectorized photographs. It can be used as a reliable methodological aid to extract detailed information on patterns and trends of shape variabilit…
Influence of Patagonian Lactiplantibacillus plantarum and Oenococcus oeni strains on sensory perception of Pinot Noir wine after malolactic fermentat…
2020
[Background and Aims]: The aim of this work was to study the effect of two Patagonian malolactic starters, Oenococcus oeni UNQOe 73.2 and Lactiplantibacillus plantarum UNQLp 11, on the wine composition and sensory perception after MLF of Pinot Noir wine.
Images are not and should not ever be type specimens: a rebuttal to GarraffoniFreitas.
2017
Note. This original form of this rebuttal was submitted to Science on 3 March 2017 (limited to 300 words as per Science editorial policy) but rejected on 13 March 2017. Herein, we elaborate on our original Science submission in order to more fully address the issue without the length limitations. This rebuttal is followed by the list of the signatories who supported our original submission.
Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing
2018
International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…
George-Veeramani Fuzzy Metrics Revised
2018
In this note, we present an alternative approach to the concept of a fuzzy metric, calling it a revised fuzzy metric. In contrast to the traditional approach to the theory of fuzzy metric spaces which is based on the use of a t-norm, we proceed from a t-conorm in the definition of a revised fuzzy metric. Here, we restrict our study to the case of fuzzy metrics as they are defined by George-Veeramani, however, similar revision can be done also for some other approaches to the concept of a fuzzy metric.
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
2021
Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive …
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition
2019
Abstract Background Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneo…
The Hidden Charm of Life
2019
Synthetic biology is an engineering view on biotechnology, which has revolutionized genetic engineering. The field has seen a constant development of metaphors that tend to highlight the similarities of cells with machines. I argue here that living organisms, particularly bacterial cells, are not machine-like, engineerable entities, but, instead, factory-like complex systems shaped by evolution. A change of the comparative paradigm in synthetic biology from machines to factories, from hardware to software, and from informatics to economy is discussed.
SpCLUST: Towards a fast and reliable clustering for potentially divergent biological sequences
2019
International audience; This paper presents SpCLUST, a new C++ package that takes a list of sequences as input, aligns them with MUSCLE, computes their similarity matrix in parallel and then performs the clustering. SpCLUST extends a previously released software by integrating additional scoring matrices which enables it to cover the clustering of amino-acid sequences. The similarity matrix is now computed in parallel according to the master/slave distributed architecture, using MPI. Performance analysis, realized on two real datasets of 100 nucleotide sequences and 1049 amino-acids ones, show that the resulting library substantially outperforms the original Python package. The proposed pac…