Elimination Of Features Research Articles

Sensitivity and Resolution Capacity of Electrode Configurations

This paper reviews the geological conditions, data density, and acquisition geometry that have direct influence on the sensitivity and resolution capacity of several electrode configurations. The parameters appreciate the effectiveness of automated multichannel system which has evolved several electrode arrays that are cost effective, reduction in survey time, high sensitivity, and resolution capacity in 2D and 3D resistivity tomographies. The arrays are pole-pole, pole-dipole, pole-bipole, dipole-dipole, Wenner, Wenner- , , gradient, midpoint-potential-referred, Schlumberger, square, and Lee-partition arrays. The gradient array and midpoint-potential-referred are well suited for multichannel surveying and gradient array images are comparable to dipole-dipole and pole-dipole. 2D electrical resistivity surveys can produce out-of-plane anomaly of the subsurface which could be misleading in the interpretation of subsurface features. Hence, a 3D interpretation model should give more accurate results, because of the increase in the reliability of inversion images and complete elimination of spurious features. Therefore, the reduction of anomaly effects and damping factor due to signal to noise ratio result in better spatial resolution image, thus enhancing its usage in environmental and engineering research.

An Innovative Approach in Tourism Sector: Slow City, an Application in Konya (Turkey)

Derived from the words 'Citta' which is Italian and 'Slow' which is English, the term 'Cittaslow' means 'Slow City'. Since 2012 the Cittaslow network which has 142 members which is in 24 countries; is a town association which is emerged from Slow food movement to prevent the standardization of the texture, residents and the lifestyle of the city and to prevent the elimination of the city's local features because of globalization. It is an association of towns and cities that want to take part in the world stage by protecting their local identities and features and that don't want to be one of those homogeneous venues created by the globalization. It is seen that the number of domestic and foreign tourists in more that 100 cities is increased in the cities which are awarded as 'Slow City' in world. The most prominent feature of 'Slow Cities Movement' is the revitalization of local identity and the introduction and marketing of touristic destinations. In this study, some districts of the province of Konya (Turkey), within the framework of these criteria have been applied and evaluated in analytic hierarchy process. 

A Novel Method of Handling Tolerances for Analog Circuit Fault Diagnosis Based on Normal Quotient Distribution

Abstract While the Slope Fault Model method can solve the soft-fault diagnosis problem in linear analog circuit effectively, the challenging tolerance problem is still unsolved. In this paper, a proposed Normal Quotient Distribution approach was combined with the Slope Fault Model to handle the tolerances problem in soft-fault diagnosis for analog circuit. Firstly, the principle of the Slope Fault Model is presented, and the huge computation of traditional Slope Fault Characteristic set was reduced greatly by the elimination of superfluous features. Several typical tolerance handling methods on the ground of the Slope Fault Model were compared. Then, the approximating distribution function of the Slope Fault Characteristic was deduced and sufficient conditions were given to improve the approximation accuracy. The monotonous and continuous mapping between Normal Quotient Distribution and standard normal distribution was proved. Thus the estimation formulas about the ranges of the Slope Fault Characteristic were deduced. After that, a new test-nodes selection algorithm based on the reduced Slope Fault Characteristic ranges set was designed. Finally, two numerical experiments were done to illustrate the proposed approach and demonstrate its effectiveness.

Reprogramming of H3K27me3 Is Critical for Acquisition of Pluripotency from Cultured Arabidopsis Tissues

In plants, multiple detached tissues are capable of forming a pluripotent cell mass, termed callus, when cultured on media containing appropriate plant hormones. Recent studies demonstrated that callus resembles the root-tip meristem, even if it is derived from aerial organs. This finding improves our understanding of the regeneration process of plant cells; however, the molecular mechanism that guides cells of different tissue types to form a callus still remains elusive. Here, we show that genome-wide reprogramming of histone H3 lysine 27 trimethylation (H3K27me3) is a critical step in the leaf-to-callus transition. The Polycomb Repressive Complex 2 (PRC2) is known to function in establishing H3K27me3. By analyzing callus formation of mutants corresponding to different histone modification pathways, we found that leaf blades and/or cotyledons of the PRC2 mutants curly leaf swinger (clf swn) and embryonic flower2 (emf2) were defective in callus formation. We identified the H3K27me3-covered loci in leaves and calli by a ChIP–chip assay, and we found that in the callus H3K27me3 levels decreased first at certain auxin-pathway genes. The levels were then increased at specific leaf genes but decreased at a number of root-regulatory genes. Changes in H3K27me3 levels were negatively correlated with expression levels of the corresponding genes. One possible role of PRC2-mediated H3K27me3 in the leaf-to-callus transition might relate to elimination of leaf features by silencing leaf-regulatory genes, as most leaf-preferentially expressed regulatory genes could not be silenced in the leaf explants of clf swn. In contrast to the leaf explants, the root explants of both clf swn and emf2 formed calli normally, possibly because the root-to-callus transition bypasses the leaf gene silencing process. Furthermore, our data show that PRC2-mediated H3K27me3 and H3K27 demethylation act in parallel in the reprogramming of H3K27me3 during the leaf-to-callus transition, suggesting a general mechanism for cell fate transition in plants.

Role of mature sphingolipids in yeast: new tools

Sphingolipids of yeast have been described as being important for numerous cell biological phenomena such as heat resistance, endocytosis, stress resistance and many others. The genetic or pharmacological elimination of specific features or entire classes of sphingolipids has pinpointed specific sphingolipids as pivotal regulators in many processes. The report by Epstein et al. adds two new tools for such studies: a strain being completely resistant to aureobasidin A, a specific inhibitor of inositol phosphorylceramide synthase and a second strain where this synthase is deleted. The resulting phenotypes advocate new roles of complex sphingolipids in cytokinesis, lipid droplet biogenesis and cell survival.

Rapid detection of hairline cracks on the surface of piezoelectric ceramics

Lead zirconate titanate, also known as PZT, is a type of piezoelectric ceramics commonly used for actuators in modern hard disk drives (HDDs). These PZT actuators are prone to hairline surface cracks, prompting detection and removal during the HDD production. Machine vision is then utilized for automatic detection of these cracks. The developed image processing approach comprises three steps: extraction of the region of interest, enhancement of crack regions, and elimination of irrelevant features. The key step, crack region enhancement, employs image filtering with a specifically designed filter kernel, capable of extracting thin crack regions from the rough surface of PZT actuators. The experiments show that the algorithm reveals cracks with high accuracy and high sensitivity, whereas the overall processing time satisfies the industrial environment.

A new brain-inspired robust face recognition through elimination of variation features

Independent perceptual feature extraction and modeling the interactions among them are important issues in brain-inspired pattern recognition models. In the face recognition task, person code and different variation codes can be considered as these features. Here, besides extracting the elementary features, perceptual feature modules and their relatedness are modeled. This feature extraction method is a very powerful preprocessing tool in dealing with variation and noise. It would be shown that recognition accuracy for noisy and varied data is highly improved if this classification is implemented in the perceptual feature space instead of the elementary feature space.

Elimination of Negative Character Features as an Element of Building a Positive Image of Physical Education Teacher

Elimination of Negative Character Features as an Element of Building a Positive Image of Physical Education Teacher Introduction. In the era of the development of a number of ways and forms of communication, positive image has become an important medium of information, including teacher-student relationships. One can now recognize it as one of the elements of effective implementation of the mandates of teaching and upbringing. The aim of this study was to determine what physical education teacher's features are most undesirable for senior high school students, and thus negatively affect his image. Material and methods. The study used the method of diagnostic survey, in which the questionnaire technique was used. The data obtained from 763 students of two senior high schools (484 females and 279 males) was analyzed. For statistical evaluation of the data the log-linear analysis method was used. Results. Quick temper and severity were found to be the features that the students found to be most undesirable in the physical education teacher. The choice of such features as: compliant, indulgent, moody and quick tempered was significantly dependent on gender (p<0.05), and such as strict, hesitant and distracted, significantly depended on both gender and grade (p<0.05). The school turned out to be a variable that did not differentiate significantly choices made by students. None of the independent variables conditioned the selection of such features as passive and unreliable. Conclusions. Knowing what features should not characterize a physical education teacher is one of the important elements that should be taken into account in the process of building a positive image of teachers. It can be assumed that the elimination or restriction of the expression of these features contributes significantly to the positive perception of teachers, and it is followed by improved relations with students and increased effectiveness of teaching and educational interactions.

Discovery of XEN907, a spirooxindole blocker of NaV1.7 for the treatment of pain

Starting from the oxindole 2a identified through a high-throughput screening campaign, a series of Na(V)1.7 blockers were developed. Following the elimination of undesirable structural features, preliminary optimization of the oxindole C-3 and N-1 substituents afforded the simplified analogue 9b, which demonstrated a 10-fold increase in target potency versus the original HTS hit. A scaffold rigidification strategy then led to the discovery of XEN907, a novel spirooxindole Na(V)1.7 blocker. This lead compound, which in turn showed a further 10-fold increase in potency, represents a promising structure for further optimization efforts.

Psychophysical and physiological evidence contradicts a model of dynamic image stabilization

Humans can see fine structures with excellent precision even if these are moving (1). To achieve this, the visual system may also compensate for the fixational image drift projected on the retina (2). In a recent issue of PNAS, Burak et al. (2) proposed that the effects of such eye movements may be compensated for by two parallel mechanisms possibly implemented in V1. One mechanism, called where, is tracking the image; the other retrieves the image as such and is called what. The authors write about their model: “A salient prediction is that in high acuity tasks, fixed features within the visual scene are beneficial because they provide information about the drifting position of the image. Therefore, complete elimination of peripheral features in the visual scene should degrade performance on high acuity tasks involving very small stimuli.”

Evaluating the importance of different temporal features of calling songs in cricket phonotaxis

Event Abstract Back to Event Evaluating the importance of different temporal features of calling songs in cricket phonotaxis Gundula Meckenhäuser1, 2*, Matthias R. Hennig3 and Martin P. Nawrot1, 2 1 Freie Universität Berlin, Institute of Biology, Germany 2 Bernstein Center for Computational Neuroscience Berlin, Germany 3 Humboldt-Universität zu Berlin, Department of Biology, Germany Acoustic signals are used by Gryllus bimaculatus to communicate and they play a key role in mate choice. Males produce calling songs to attract females, females in turn use these songs to discriminate conspecific songs from the signals of other species. If they are willing to pair females approach the singing male - a behavior referred to as phonotaxis. So far, extensive behavioral experiments in which phonotactic responses of female crickets were tested by varying song features, revealed that song patterns are processed in the time rather than in the spectral domain [1]. Here, we explored the relevant cues for song pattern discrimination on several time scales. We present feed-forward artificial neural networks (ANNs) that quantitatively predict the phonotactic value for untested calling songs that are described by temporal features such as duration, pause, period and duty cycle for both pulses and chirps. We employ ANNs with the following architecture: Input neurons representing temporal song features project to nonlinear neurons in the hidden layer which project to the output neuron that relates the phonotactic value. For training and testing the networks we used 218 artificial songs for which the phonotactic values had already been determined in experiments. Our complete ANNs that use eight temporal song features show a mean squared error (MSE) of MSE = 0.034. By a greedy backward elimination of features, we came up with minimal ANNs using just pulse period, chirp duration and chirp duty cycle as features. The dimensionality reduction of the feature space also improved the performance: The mean squared error for minimal networks is MSE = 0.019. Thus, our findings suggest that among all tested temporal features, pulse period on the short time scale and chirp duration and chirp duty cycle on the long time scale are the most relevant ones for the evaluation of conspecific signals. Further, the minimal ANNs show high predictive power and can be used to complement experimental testing of female phonotaxis in the laboratory. Acknowledgements This work is funded by the German Research Council (DFG) within the Collaborative Research Center "Theoretical Biology" (SFB 618).

Bayesian model of dynamic image stabilization in the visual system

Humans can resolve the fine details of visual stimuli although the image projected on the retina is constantly drifting relative to the photoreceptor array. Here we demonstrate that the brain must take this drift into account when performing high acuity visual tasks. Further, we propose a decoding strategy for interpreting the spikes emitted by the retina, which takes into account the ambiguity caused by retinal noise and the unknown trajectory of the projected image on the retina. A main difficulty, addressed in our proposal, is the exponentially large number of possible stimuli, which renders the ideal Bayesian solution to the problem computationally intractable. In contrast, the strategy that we propose suggests a realistic implementation in the visual cortex. The implementation involves two populations of cells, one that tracks the position of the image and another that represents a stabilized estimate of the image itself. Spikes from the retina are dynamically routed to the two populations and are interpreted in a probabilistic manner. We consider the architecture of neural circuitry that could implement this strategy and its performance under measured statistics of human fixational eye motion. A salient prediction is that in high acuity tasks, fixed features within the visual scene are beneficial because they provide information about the drifting position of the image. Therefore, complete elimination of peripheral features in the visual scene should degrade performance on high acuity tasks involving very small stimuli.

Minimizing profile error when estimating the sieve-size distribution of iron ore pellets using ordinal logistic regression

Size measurement of pellets in industry is usually performed by manual sampling and sieving techniques. Automatic on-line analysis of pellet size based on image analysis techniques would allow non-invasive, frequent and consistent measurement. We evaluate the statistical significance of the ability of commonly used size and shape measurement methods to discriminate among different sieve-size classes using multivariate techniques. Literature review indicates that earlier works did not perform this analysis and selected a sizing method without evaluating its statistical significance. Backward elimination and forward selection of features are used to select two feature sets that are statistically significant for discriminating among different sieve-size classes of pellets. The diameter of a circle of equivalent area is shown to be the most effective feature based on the forward selection strategy, but an unexpected five-feature classifier is the result using the backward elimination strategy. The discrepancy between the two selected feature sets can be explained by how the selection procedures calculate a feature's significance and that the property of the 3D data provides an orientational bias that favours combination of Feret-box measurements. Size estimates of the surface of a pellet pile using the two feature sets show that the estimated sieve-size distribution follows the known sieve-size distribution.

Simultaneous feature selection and classification using kernel-penalized support vector machines

We introduce an embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature’s use in the dual formulation of support vector machines (SVM). This approach called kernel-penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel eliminating features that have low relevance for the classifier. Additionally, KP-SVM employs an explicit stopping condition, avoiding the elimination of features that would negatively affect the classifier’s performance. We performed experiments on four real-world benchmark problems comparing our approach with well-known feature selection techniques. KP-SVM outperformed the alternative approaches and determined consistently fewer relevant features.

Intelligent and nature inspired optimization methods in medicine: the Pap smear cell classification problem

Abstract: The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Feature selection is widely used as the first stage of the classification task to reduce the dimension of the problem, decrease noise and improve speed by the elimination of irrelevant or redundant features. The present paper deals with the optimization of nearest neighbour classifiers via intelligent and nature inspired algorithms for a very significant medical problem, the Pap smear cell classification problem. The algorithms used include tabu search, genetic algorithms, particle swarm optimization and ant colony optimization. The proposed complete algorithmic scheme is tested on two sets of data. The first consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully by expert cyto‐technicians and doctors. Each cell is described by 20 numerical features, and the cells fall into seven classes representing a variety of normal and abnormal cases. Nevertheless, from the medical diagnosis viewpoint, a minimum requirement corresponds to the general two‐class problem of correct separation between normal and abnormal cells.

Durability of Endovascular Infrarenal Aneurysm Repair: When Does Late Failure Occur and Why?

The first commercially available stent grafts were unable to withstand the hemodynamic forces of the vascular environment. The past 15 years have seen a gradual improvement in long-term stent graft performance as designs evolved through the elimination of features associated with late failure and the replication of features associated with durable success. Clinical experience provides the following principles on which to base device design and implantation techniques. Few patients have an adequate length of non-dilated aorta distal to the aneurysm to allow implantation of an aorto-aortic stent graft; bifurcated stent grafts are usually required for AAA repair. Friction, column strength and tissue ingrowth do not prevent migration of the stent graft from its attachment within the neck into the aneurysm; some form of active fixation is required, usually in the form of barbs. Any movement between the apex of a stent and the overlying graft material will erode the fabric; stents and grafts need to move as a single unit. Nitinol is versatile, but fragile; Nitinol components must be polished to eliminate all surface irregularities and they cannot be subjected to compression loading, or excessive pulsatile movement. The neck of an aneurysm is unstable; it will dilate unless protected by a securely fixed, non-compliant stent graft. The aneurysm does not heal; freedom from risk of rupture depends on durable depressurization of the sac. The sole objective of image-based follow-up is the early detection, and catheter-based correction, of device failure. Once any given design has been in use long enough to identify its failure modes, the frequency of follow-up studies can be adjusted accordingly. However, it takes a long time to identify all the potential forms of late failure, and pre-clinical testing remains an imprecise science. New, or recently modified, devices cannot necessarily be assumed to be as durable as their predecessors.

Sound Categories Are Represented as Distributed Patterns in the Human Auditory Cortex

The ability to recognize sounds allows humans and animals to efficiently detect behaviorally relevant events, even in the absence of visual information. Sound recognition in the human brain has been assumed to proceed through several functionally specialized areas, culminating in cortical modules where category-specific processing is carried out. In the present high-resolution fMRI experiment, we challenged this model by using well-controlled natural auditory stimuli and by employing an advanced analysis strategy based on an iterative machine-learning algorithm that allows modeling of spatially distributed, as well as localized, response patterns. Sounds of cats, female singers, acoustic guitars, and tones were controlled for their time-varying spectral characteristics and presented to subjects at three different pitch levels. Sound category information--not detectable with conventional contrast-based methods analysis--could be detected with multivoxel pattern analyses and attributed to spatially distributed areas over the supratemporal cortices. A more localized pattern was observed for processing of pitch laterally to primary auditory areas. Our findings indicate that distributed neuronal populations within the human auditory cortices, including areas conventionally associated with lower-level auditory processing, entail categorical representations of sounds beyond their physical properties.

Combination of Ant Colony Optimization and Bayesian Classification for Feature Selection in a Bioinformatics Dataset

Feature selection is widely used as the first stage of classification task to reduce the dimension of problem, decrease noise, improve speed and relieve memory constraints by the elimination of irrelevant or redundant features. One approach in the feature selection area is employing population-based optimization algorithms such as particle swarm optimization (PSO)-based method and ant colony optimization (ACO)-based method. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. This paper empowers the ant colony optimization algorithm by enabling the ACO to select features for a Bayesian classification method. The naive Bayesian classifier is a straightforward and frequently used method for supervised learning. It provides a flexible way for dealing with any number of features or classes, and is based on probability theory. This paper then compares the performance of the proposed ACO algorithm against the performance of a standard binary particle swarm optimization algorithm on the task of selecting features on Postsynaptic dataset. The criteria used for this comparison are maximizing predictive accuracy and finding the smallest subset of features. Simulation results on Postsynaptic dataset show that proposed method simplifies features effectively and obtains a higher classification accuracy compared to other feature selection methods.

Negative Borrowing in an Indigenouslanguage Shift to the Dominant National Language

Receding languages in contact with an expanding language are susceptible to various forms of transfer, including covert transfer or negative borrowing, the elimination of features not shared by the expanding language. Retention of two Scottish Gaelic grammatical features with English parallels and of two grammatical features without English parallel is compared in the Gaelic of bilingual speakers across a 55-year age-and-proficiency continuum. Both kinds of features show change in progress, but only very modest support for negative borrowing emerges, chiefly among speakers who were less than fully fluent in Gaelic by reason of incomplete acquisition or long disuse. The roles of simplification and gender-related speech style in producing these results are considered. The former appears to play a very limited role, while the latter may play a larger role in the unexpectedly strong retention of one unmatched structure among young and imperfect female speakers. Insofar as structural congruity increases the likelihood of negative borrowing, the quite different structures of Gaelic and English may contribute to its weak presence in this case. Long-term language contact before the period of obsolescence may be more productive of negative-borrowing effects than late-stage obsolescence itself.

Novel Unsupervised Feature Filtering of Biological Data

Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR); forward selection by accumulating features according to which set produces highest entropy (FS1); forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2); backward elimination (BE) of features with the lowest CE. We apply our methods to different benchmarks. In each case we evaluate the success of clustering the data in the selected feature spaces, by measuring Jaccard scores with respect to known classifications. We demonstrate that feature filtering according to CE outperforms the variance method and gene-shaving. There are cases where the analysis, based on a small set of selected features, outperforms the best score reported when all information was used. Our method calls for an optimal size of the relevant feature set. This turns out to be just a few percents of the number of genes in the two Leukemia datasets that we have analyzed. Moreover, the most favored selected genes turn out to have significant GO enrichment in relevant cellular processes.