Search results for "Binary"
showing 10 items of 833 documents
Automatic mass spectra recognition for Ultra High Vacuum systems using multilabel classification
2021
Abstract In Ultra High-Vacuum (UHV) systems it is common to find a mixture of many gases originating from surface outgassing, leaks and permeation that contaminate vacuum chambers and cause issues to reach ultimate pressures. The identification of these contaminants is, in general, done manually by trained technicians from the analysis of mass spectra. This task is time consuming and can lead to misinterpretation or partial understanding of issues. The challenge resides in the rapid identification of these contaminants by using some automatic gas identification technique. This paper explores the automatic and simultaneous identification of 80 molecules, including some of the most commonly p…
A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem
2012
Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_78 This paper deals with the Stochastic-Point Location (SPL) problem. It presents a solution which is novel in both philosophy and strategy to all the reported related learning algorithms. The SPL problem concerns the task of a Learning Mechanism attempting to locate a point on a line. The mechanism interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point which also is the current guess. The first pioneering work […
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
2019
Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…
EvalMSA: A Program to Evaluate Multiple Sequence Alignments and Detect Outliers
2016
8 páginas, 3 figuras, 2 tablas.
On the Use of Binary Trees for DNA Hydroxymethylation Analysis
2017
DNA methylation (mC) and hydroxymethylation (hmC) can have a significant effect on normal human development, health and disease status. Hydroxymethylation studies require specific treatment of DNA, as well as software tools for their analysis. In this paper, we propose a parallel software tool for analyzing the DNA hydroxymethylation data obtained by TAB-seq. The software is based on the use of binary trees for searching the different occurrences of methylation and hydroxymethylation in DNA samples. The binary trees allow to efficiently store and access the information about the methylation of each methylated/hydroxymethylated cytosines in the samples. Evaluation results shows that the perf…
Measuring the clustering effect of BWT via RLE
2017
Abstract The Burrows–Wheeler Transform (BWT) is a reversible transformation on which are based several text compressors and many other tools used in Bioinformatics and Computational Biology. The BWT is not actually a compressor, but a transformation that performs a context-dependent permutation of the letters of the input text that often create runs of equal letters (clusters) longer than the ones in the original text, usually referred to as the “clustering effect” of BWT. In particular, from a combinatorial point of view, great attention has been given to the case in which the BWT produces the fewest number of clusters (cf. [5] , [16] , [21] , [23] ). In this paper we are concerned about t…
Machine learning–XGBoost analysis of language networks to classify patients with epilepsy
2017
Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one sho…
Deep neural attention-based model for the evaluation of italian sentences complexity
2020
In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Eventual Consistency Formalized
2019
Distribution of computation is well-known, and there are several frameworks, including some formal frameworks, that capture distributed computation. As yet, however, models of distributed computation are based on the idea that data is conceptually centralized. That is, they assume that data, even if it is distributed, is consistent. This assumption is not valid for many of the database systems in use today, where consistency is compromised to ensure availability and partition tolerance. Starting with an informal definition of eventual consistency, this paper explores several measures of inconsistency that quantify how far from consistency a system is. These measures capture key aspects of e…
Densities, Viscosities, and Refractive Indices of the Binary Systems Methyl tert-Butyl Ether + 2-Methylpentane, + 3-Methylpentane, + 2,3-Dimethylpent…
2000
This paper reports experimental densities, viscosities, and refractive indices of the binary systems methyl tert-butyl ether (MTBE) + 2-methylpentane, + 3-methylpentane, + 2,3-dimethylpentane, and + 2,2,4-trimethylpentane over the entire range of composition, at 298.15 K and atmospheric pressure. Excess molar volumes and viscosity deviations were evaluated from the experimental data. These excess or derived properties were fitted to the Redlich−Kister equation to estimate the binary interaction parameters. The experimental values of viscosity have been compared to values predicted by means of the GC−UNIMOD model.