Search results for " Mach"
showing 10 items of 1388 documents
Two-way automata with multiplicity
2005
We introduce the notion of two-way automata with multiplicity in a semiring. Our main result is the extension of Rabin, Scott and Shepherdson's Theorem to this more general case. We in fact show that it holds in the case of automata with multiplicity in a commutative semiring, provided that an additional condition is satisfied. We prove that this condition is also necessary in a particular case. An application is given to zig-zag codes using special two-way automata.
Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces
2019
AbstractRecognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of su…
Automated quality control protocol for MR spectra of brain tumors.
2008
Item does not contain fulltext eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis …
Application of machine-vision techniques to fish-quality assessment
2012
Abstract Machine vision is a non-destructive, rapid, economic, consistent and objective inspection tool and is also an evaluation technique based on image analysis and processing with a variety of applications. We review the use of machine vision and imaging technologies for fish-quality assessment. This review updates and condenses a representative selection of recent research and industrial solutions proposed in order to evaluate the general trends of machine vision and image processing in the visible range applied for inspection of fish and fish products. In order to determine freshness and composition, it is necessary to measure and to evaluate size and volume, to estimate weight, to me…
An Application of Spike-Timing-Dependent Plasticity to Readout Circuit for Liquid State Machine
2007
Liquid state machine (LSM) is a neural system based on spiking neurons that implements a mapping between functions of time. A typical application of LSM is classification of time functions obtained observing the state of the liquid by using a memoryless readout circuit, usually implemented by a linear perceptron. Due to the high number of neurons in the liquid the training of the readout is difficult. In this paper we show that using the Spike-Timing-Dependent Plasticity (STDP) a single neuron with short training session can be used to recognize the state of the liquid due to an input signal. Using STDP it is possible to identify the spikes timing of the neurons in the liquid and this allow…
Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.
2017
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector…
<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>
2015
The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .
Predictive modeling of aryl hydrocarbon receptor (AhR) agonism
2020
Abstract The aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolic machinery and detoxification systems. In the recent years, this receptor has attracted interest as a therapeutic target for immunological, oncogenic and inflammatory conditions. In the present report, in silico and in vitro approaches were combined to study the activation of the AhR. To this end, a large database of chemical compounds with known AhR agonistic activity was employed to build 5 classifiers based on the Adaboost (AdB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms, respectively. The built classifier…
Potential and limitations of quantum extreme learning machines
2023
Quantum reservoir computers (QRC) and quantum extreme learning machines (QELM) aim to efficiently post-process the outcome of fixed -- generally uncalibrated -- quantum devices to solve tasks such as the estimation of the properties of quantum states. The characterisation of their potential and limitations, which is currently lacking, will enable the full deployment of such approaches to problems of system identification, device performance optimization, and state or process reconstruction. We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements, and provide an explicit characterisation of the information exactly retriev…
Looking for magnetic monopoles at LHC with diphoton events
2012
Magnetic monopoles have been a subject of interest since Dirac established the relation between the existence of monopoles and charge quantization. The intense experimental search carried thus far has not met with success. The Large Hadron Collider is reaching energies never achieved before allowing the search for exotic particles in the TeV mass range. In a continuing effort to discover these rare particles we propose here other ways to detect them. We study the observability of monopoles and monopolium, a monopole-antimonopole bound state, at the Large Hadron Collider in the $\gamma \gamma$ channel for monopole masses in the range 500-1000 GeV. We conclude that LHC is an ideal machine to …