Search results for "binary"
showing 10 items of 833 documents
Entropic measure of spatial disorder for systems of finite-sized objects
2000
We consider the relative configurational entropy per cell S_Delta as a measure of the degree of spatial disorder for systems of finite-sized objects. It is highly sensitive to deviations from the most spatially ordered reference configuration of the objects. When applied to a given binary image it provides the quantitatively correct results in comparison to its point object version. On examples of simple cluster configurations, two-dimensional Sierpinski carpets and population of interacting particles, the behaviour of S_Delta is compared with the normalized information entropy H' introduced by Van Siclen [Phys. Rev. E 56, (1997) 5211]. For the latter example, the additional middle-scale fe…
Structure Learning in Nested Effects Models
2007
Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we…
MODERATE DEVIATION PRINCIPLES FOR BIFURCATING MARKOV CHAINS: CASE OF FUNCTIONS DEPENDENT OF ONE VARIABLE
2021
The main purpose of this article is to establish moderate deviation principles for additive functionals of bifurcating Markov chains. Bifurcating Markov chains are a class of processes which are indexed by a regular binary tree. They can be seen as the models which represent the evolution of a trait along a population where each individual has two offsprings. Unlike the previous results of Bitseki, Djellout \& Guillin (2014), we consider here the case of functions which depend only on one variable. So, mainly inspired by the recent works of Bitseki \& Delmas (2020) about the central limit theorem for general additive functionals of bifurcating Markov chains, we give here a moderate deviatio…
Monte Carlo simulation of polymers at interfaces
1993
Abstract Polymers at interfaces pose challenging problems to statistical physics because their configurations often differ greatly from the bulk. Computer simulation of coarse-grained models then gives valuable insight and allows stringent tests of various theoretical predictions. Three examples are briefly treated: chain configurations of B-chains in the surface-enriched B-rich layer of an (AB) binary polymer mixture; “frustrated” lamellar ordering in ultra-thin block-copolymer films; and the collapse of polymer brushes in bad solvents.
Synthesis, structure, and nuclease properties of several binary and ternary complexes of copper(II) with norfloxacin and 1,10 phenantroline
2007
Three new binary Cu(II) complexes of norfloxacin have been synthesized and characterized. We also report the synthesis, characterization and X-ray crystallographic structures of a new binary compound, [Cu(HNor)(2)]Cl(2).2H(2)O (2) and two new ternary complexes norfloxacin-copper(II)-phen, [Cu(Nor)(phen)(H(2)O)](NO(3)).3H(2)O (4), and [Cu(HNor)(phen)(NO(3))](NO(3)).3H(2)O (5). The structure of 2 consists of two crystallographically independent cationic monomeric units of [Cu(HNor)(2)](2+), chloride anions, and uncoordinated water molecules. The Cu(II) ion is placed at a center of symmetry and is coordinated to two norfloxacin ligands which are related through the inversion center. The struct…
Fe-periclase reactivity at Earth's lower mantle conditions: Ab-initio geochemical modelling
2017
Intrinsic and extrinsic stability of the (Mg, Fe) O solid mixture in the Fe-Mg-Si-O system at high P, T conditions relevant to the Earth's mantle is investigated by the combination of quantum mechanical calculations (Hartree-26 Fock/DFT hybrid scheme), cluster expansion techniques and statistical thermodynamics. Iron in the (Mg, Fe) O binary mixture is assumed to be either in the low spin (LS) or in the high spin (HS) state. Un-mixing at solid state is observed only for the LS condition in the 23-42 GPa pressure range, whereas HS does not give rise to un-mixing. LS (Mg, Fe) O un-mixings are shown to be able to incorporate iron by subsolidus reactions with a reservoir of a virtual bridgmanit…
Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (…
2021
Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Plat…
Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.
2016
This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
2020
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…
Morphological Analysis of Binary Scene in APR Integrated Environment
2009
This paper describes principles of binary scene [1] morphological analysis in script based application - APR (Analysis, Processing and Recognition). The aim of the method is to find object on the scene and then to describe theirs basic features like edges, neighbors and surface [2]. The algorithm construction gives benefits in terms speed as well as to computation costs, at the same time being capable of presenting number of attributes values for scene and each of the objects. There are also some practical algorithm applications showed.