Class-modelling of overlapping classes. A two-step authentication approach

2021 ◽  
pp. 339284
Author(s):  
Zuzanna Małyjurek ◽  
Dalene de Beer ◽  
Hèlené van Schoor ◽  
Janine Colling ◽  
Elizabeth Joubert ◽  
...  
Keyword(s):  
Overlapping Classes

scholarly journals Two Pleiotropic Classes of daf-2 Mutation Affect Larval Arrest, Adult Behavior, Reproduction and Longevity in Caenorhabditis elegans

Genetics ◽  
1998 ◽  
Vol 150 (1) ◽  
pp. 129-155 ◽  
Author(s):  
David Gems ◽  
Amy J Sutton ◽  
Mark L Sundermeyer ◽  
Patrice S Albert ◽  
Kevin V King ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Adult Longevity ◽  
Temperature Sensitive ◽  
Adult Behavior ◽  
Dauer Larva ◽  
Class 1 ◽  
Low Levels ◽  
Dauer Larvae ◽  
Overlapping Classes ◽  
Receptor Family

Abstract The nematode Caenorhabditis elegans responds to overcrowding and scarcity of food by arresting development as a dauer larva, a nonfeeding, long-lived, stress-resistant, alternative third-larval stage. Previous work has shown that mutations in the genes daf-2 (encoding a member of the insulin receptor family) and age-1 (encoding a PI 3-kinase) result in constitutive formation of dauer larvae (Daf-c), increased adult longevity (Age), and increased intrinsic thermotolerance (Itt). Some daf-2 mutants have additional developmental, behavioral, and reproductive defects. We have characterized in detail 15 temperature-sensitive and 1 nonconditional daf-2 allele to investigate the extent of daf-2 mutant defects and to examine whether specific mutant traits correlate with each other. The greatest longevity seen in daf-2 mutant adults was approximately three times that of wild type. The temperature-sensitive daf-2 mutants fell into two overlapping classes, including eight class 1 mutants, which are Daf-c, Age, and Itt, and exhibit low levels of L1 arrest at 25.5°. Seven class 2 mutants also exhibit the class 1 defects as well as some or all of the following: reduced adult motility, abnormal adult body and gonad morphology, high levels of embryonic and L1 arrest, production of progeny late in life, and reduced brood size. The strengths of the Daf-c, Age, and Itt phenotypes largely correlated with each other but not with the strength of class 2-specific defects. This suggests that the DAF-2 receptor is bifunctional. Examination of the null phenotype revealed a maternally rescued egg, L1 lethal component, and a nonconditional Daf-c component. With respect to the Daf-c phenotype, the dauer-defective (Daf-d) mutation daf-12(m20) was epistatic to daf-2 class 1 alleles but not the severe class 2 alleles tested. All daf-2 mutant defects were suppressed by the daf-d mutation daf-16(m26). Our findings suggest a new model for daf-2, age-1, daf-12, and daf-16 interactions.

scholarly journals Matrix Neuro-Fuzzy Self-Organizing Clustering Network

2011 ◽  
Vol 45 (1) ◽  
pp. 54-58 ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Valentyna Volkova ◽  
Mark Skuratov
Keyword(s):  
Fuzzy Inference ◽  
Data Sets ◽  
Fuzzy Membership Functions ◽  
Neuro Fuzzy ◽  
The Matrix ◽  
On Line ◽  
Winner Take All ◽  
Overlapping Classes ◽  
Self Learning ◽  
Self Organizing

Matrix Neuro-Fuzzy Self-Organizing Clustering NetworkIn this article the problem of clustering massive data sets, which are represented in the matrix form, is considered. The article represents the 2-D self-organizing Kohonen map and its self-learning algorithms based on the winner-take-all (WTA) and winner-take-more (WTM) rules with Gaussian and Epanechnikov functions as the fuzzy membership functions, and without the winner. The fuzzy inference for processing data with overlapping classes in a neural network is introduced. It allows one to estimate membership levels for every sample to every class. This network is the generalization of a vector neuro- and neuro-fuzzy Kohonen network and allows for data processing as they are fed in the on-line mode.

BCS: THE SCIENTIFIC "LOVE OF MY LIFE"

2010 ◽  
Vol 24 (20n21) ◽  
pp. 3983-3998 ◽  
Author(s):  
Philip W. Anderson
Keyword(s):  
Magnetic Transition ◽  
Spinor Representation ◽  
S Wave ◽  
Metal Compounds ◽  
Dynamic Screening ◽  
Magnetic Insulators ◽  
Heavy Electron ◽  
Relative Wave Function ◽  
Electron Superconductors ◽  
Overlapping Classes

After short comments on my early addenda to BCS — gauge invariance and the Anderson–Higgs mechanism, the dirty superconductor "theorem," and the spinor representation — I focus on the interaction mechanisms which cause electron–electron pairing. These bifurcate into two almost non-overlapping classes. In order to cause electrons to pair in spite of the strong, repulsive, instantaneous Coulomb vertex, the electrons can evade each others' propinquity on the same site at the same time either dynamically, by retaining Γ0 (s-wave) relative symmetry, but avoiding each other in time — called "dynamic screening" — or by assuming a non-symmetric relative wave function, avoiding each other in space. All simple metals and alloys, including all the (so far) technically useful superconductors, follow the former scheme. But starting with the first discovery of "heavy-electron" superconductors in 1979, and continuing with the "organics" and the magnetic transition metal compounds such as the cuprates and the iron pnictides, it appears that the second class may turn out to be numerically superior and theoretically more fascinating. The basic interaction in many of these cases appears to be the "kinetic exchange" or superexchange characteristic of magnetic insulators.

scholarly journals Retrosplenial cortex and its role in spatial cognition

2017 ◽  
Author(s):  
Anna Mitchell ◽  
Rafal Czajkowksi ◽  
Ningyu Zhang ◽  
Kate Jeffery ◽  
Andrew Nelson
Keyword(s):  
Spatial Cognition ◽  
Animal Studies ◽  
Reference Frames ◽  
Retrosplenial Cortex ◽  
Spatial Knowledge ◽  
Exact Nature ◽  
General Function ◽  
Spatial Reference Frames ◽  
Landmark Location ◽  
Overlapping Classes

AbstractRetrosplenial cortex (RSC) is a region within the posterior neocortical system, heavily interconnected with an array of brain networks, both cortical and subcortical, that is engaged by a myriad of cognitive tasks. Although there is no consensus as to its precise function, evidence from both human and animal studies clearly points to a role in spatial cognition. However, the spatial processing impairments that follow RSC damage are not straightforward to characterise, leading to difficulties in defining the exact nature of its role. In the present article we review this literature and classify the types of ideas that have been put forward into three broad, somewhat overlapping classes: (i) Learning of landmark location, stability and permanence; (ii) Integration between spatial reference frames, and (iii) Consolidation and retrieval of spatial knowledge (“schemas”). We evaluate these models and suggest ways to test them, before briefly discussing whether the spatial function may be a subset of a more general function in episodic memory.

scholarly journals An Investigation on Land Cover Mapping Capability of Classical and Fuzzy based Maximum Likelihood Classifiers

2018 ◽  
Vol 7 (2) ◽  
pp. 939 ◽  
Author(s):  
Shivakumar B R ◽  
Rajashekararadhya S V
Keyword(s):  
Maximum Likelihood ◽  
Land Cover ◽  
Classification Accuracy ◽  
Distance Measure ◽  
Accuracy Assessment ◽  
Sensor Data ◽  
Remote Sensor ◽  
Mixed Pixels ◽  
Overlapping Classes

In the past two decades, a significant amount of research has been conducted in the area of information extraction from heterogeneous remotely sensed (RS) datasets. However, it is arduous to exactly predict the behaviour of the classification technique employed due to issues such as the type of the dataset, resolution of the imagery, the presence of mixed pixels, and spectrally overlapping of classes. In this paper, land cover classification of the heterogeneous dataset using classical and Fuzzy based Maximum Likelihood Classifiers (MLC) is presented and compared. Three decision parameters and their significance in pixel assignment is illustrated. The presented Fuzzy based MLC uses a weighted inverse distance measure for defuzzification process. 10 pixels were randomly selected from the study area to illustrate pixel assignment for both the classifiers. The study aims at enhancing the classification accuracy of heterogeneous multispectral remote sensor data characterized by spectrally overlapping classes and mixed pixels. The study additionally aims at obtaining classification results with a confidence level of 95% with ±4% error margin. Classification success rate was analysed using accuracy assessment. Fuzzy based MLC produced significantly higher classification accuracy as compared to classical MLC. The conducted research achieves the expected classification accuracy and proves to be a valuable technique for classification of heterogeneous RS multispectral imagery. 

scholarly journals Frequent monoallelic or skewed expression for developmental genes in CNS-derived cells and evidence for balancing selection

2018 ◽  
Vol 115 (44) ◽  
pp. E10379-E10386 ◽  
Author(s):  
Sergio Branciamore ◽  
Zuzana Valo ◽  
Min Li ◽  
Jinhui Wang ◽  
Arthur D. Riggs ◽  
...  
Keyword(s):  
Balancing Selection ◽  
Developmental State ◽  
Mouse Strains ◽  
Cpg Dinucleotides ◽  
Clonal Cell Lines ◽  
Cell Type Specific ◽  
Selection For ◽  
Overlapping Classes ◽  
Two Populations ◽  
Development And Evolution

Cellular mosaicism due to monoallelic autosomal expression (MAE), with cell selection during development, is becoming increasingly recognized as prevalent in mammals, leading to interest in understanding its extent and mechanism(s). We report here use of clonal cell lines derived from the CNS of adult female F1 hybrid (C57BL/6 X JF1) mice to characterize MAE as neural stem cells (nscs) differentiate to astrocyte-like cells (asls). We found that different subsets of genes show MAE in the two populations of cells; in each case, there is strong enrichment for genes specific to the respective developmental state. Genes that exhibit MAE are 22% of nsc-specific genes and 26% of asl-specific genes. Moreover, the promoters of genes with MAE have reduced CpG dinucleotides but increased CpG differences between the two parental mouse strains. Extending the study of variability to wild populations of mice, we found evidence for balancing selection as a contributing force in evolution of those genes showing developmental specificity (i.e., expressed in either nsc or asl), not just for genes showing MAE. Furthermore, we found that genes showing skewed allelic expression (SKE) were similarly enriched among cell type-specific genes and also showed a heightened probability of balancing selection. Thus, developmental stage-specific genes and genes with MAE or SKE seem to make up overlapping classes subject to selection for increased diversity. The implications of these results for development and evolution are discussed in the context of a model with stochastic epigenetic modifications taking place only during a relatively brief developmental window.

scholarly journals The ‘nouniness’ of attributive adjectives and ‘verbiness’ of predicative adjectives: evidence from phonology

2020 ◽  
pp. 1-23
Author(s):  
WILLEM B. HOLLMANN
Keyword(s):  
American English ◽  
Psycholinguistic Research ◽  
Substantial Body ◽  
Phonological Structure ◽  
Word Classes ◽  
Entire Word ◽  
Radical Construction ◽  
Overlapping Classes ◽  
Theoretical Issues ◽  
Attributive Adjectives

This article investigates prototypically attributive versus predicative adjectives in English in terms of the phonological properties that have been associated especially with nouns versus verbs in a substantial body of psycholinguistic research (e.g. Kelly 1992) – often ignored in theoretical linguistic work on word classes. Inspired by Berg's (2000, 2009) ‘cross-level harmony constraint’, the hypothesis I test is that prototypically attributive adjectives not only align more with nouns than with verbs syntactically, semantically and pragmatically, but also phonologically – and likewise for prototypically predicative adjectives and verbs. I analyse the phonological structure of frequent adjectives from the Corpus of Contemporary American English (COCA), and show that the data do indeed support the hypothesis. Berg's ‘cross-level harmony constraint’ may thus apply not only to the entire word classes noun, verb and adjective, but also to these two adjectival subclasses. I discuss several theoretical issues that emerge. The facts are most readily accommodated in a usage-based model, such as Radical Construction Grammar (Croft 2001), where these adjectives are seen as forming two distinct but overlapping classes. Drawing also on recent research by Boyd & Goldberg (2011) and Hao (2015), I explore the possible nature and emergence of these classes in some detail.

scholarly journals Building the microbiome in health and disease: niche construction and social conflict in bacteria

2015 ◽  
Vol 370 (1675) ◽  
pp. 20140298 ◽  
Author(s):  
Luke McNally ◽  
Sam P. Brown
Keyword(s):  
Human Health ◽  
Social Evolution ◽  
Niche Construction ◽  
Environmental Changes ◽  
Microbiome Composition ◽  
Biofilm Production ◽  
Host Health ◽  
Health And Disease ◽  
Overlapping Classes ◽  
By Products

Microbes collectively shape their environment in remarkable ways via the products of their metabolism. The diverse environmental impacts of macro-organisms have been collated and reviewed under the banner of ‘niche construction’. Here, we identify and review a series of broad and overlapping classes of bacterial niche construction, ranging from biofilm production to detoxification or release of toxins, enzymes, metabolites and viruses, and review their role in shaping microbiome composition, human health and disease. Some bacterial niche-constructing traits can be seen as extended phenotypes, where individuals actively tailor their environment to their benefit (and potentially to the benefit of others, generating social dilemmas). Other modifications can be viewed as non-adaptive by-products from a producer perspective, yet they may lead to remarkable within-host environmental changes. We illustrate how social evolution and niche construction perspectives offer complementary insights into the dynamics and consequences of these traits across distinct timescales. This review highlights that by understanding the coupled bacterial and biochemical dynamics in human health and disease we can better manage host health.

scholarly journals Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

2021 ◽  
Vol 14 (1) ◽  
pp. 123-129
Author(s):  
Yevgeniy Bodyanskiy ◽  
Anastasiia Deineko ◽  
Iryna Pliss ◽  
Olha Chala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Hybrid Learning ◽  
Medical Diagnostics ◽  
Speed Of Information Processing ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Overlapping Classes

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

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