Hidden Structure Research Articles

CoolBox: a flexible toolkit for visual analysis of genomics data

BackgroundData visualization, especially the genome track plots, is crucial for genomics researchers to discover patterns in large-scale sequencing dataset. Although existing tools works well for producing a normal view of the input data, they are not convenient when users want to create customized data representations. Such gap between the visualization and data processing, prevents the users to uncover more hidden structure of the dataset.ResultsWe developed CoolBox—an open-source toolkit for visual analysis of genomics data. This user-friendly toolkit is highly compatible with the Python ecosystem and customizable with a well-designed user interface. It can be used in various visualization situations like a Swiss army knife. For example, to produce high-quality genome track plots or fetch commonly used genomic data files with a Python script or command line, to explore genomic data interactively within Jupyter environment or web browser. Moreover, owing to the highly extensible Application Programming Interface design, users can customize their own tracks without difficulty, which greatly facilitate analytical, comparative genomic data visualization tasks.ConclusionsCoolBox allows users to produce high-quality visualization plots and explore their data in a flexible, programmable and user-friendly way.

FEA and Machine Learning Techniques for Hidden Structure Analysis.

This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root structure and depth of small plants and to detect the diameter and depth of bubbles in plexiglass. A finite element analysis (FEA) model was built to simulate the infrared imaging process to realize the detection and prediction given the amount of heat flux required to obtain thermal images and data. For the root system, based on a tree structure thermal profile over time derived from the FEA model, a line scan method was developed to predict root structure. The main root branches can be viewed from the detection results. Polynomial regression, support vector machine (SVM), and artificial neural network (ANN) models were designed to predict root depth. For bubble defects, three ANN models were developed to predict bubble size using temperature data generated by the FEA model. Results indicated that these models provide valid predictions. Statistical tests were applied to evaluate and compare the above predictive models. Results suggest that infrared imaging and machine learning models can be used to provide information on both hidden structures.

Fluctuation spectra of large random dynamical systems reveal hidden structure in ecological networks

Understanding the relationship between complexity and stability in large dynamical systems—such as ecosystems—remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years. The vast majority of this theory addresses asymptotic linear stability around equilibrium points, but the idea of ‘stability’ in fact has other uses in the empirical ecological literature. The important notion of ‘temporal stability’ describes the character of fluctuations in population dynamics, driven by intrinsic or extrinsic noise. Here we apply tools from random matrix theory to the problem of temporal stability, deriving analytical predictions for the fluctuation spectra of complex ecological networks. We show that different network structures leave distinct signatures in the spectrum of fluctuations, and demonstrate the application of our theory to the analysis of ecological time-series data of plankton abundances.

Development of a Tool for evaluating the Social Capital of the academic system in Mashhad University of Medical Sciences in 2020

Background Universities are the first line of social capital production centers in every society, among which the medical universities are the most important because of their great duties to promote common health services and educate professional students. Despite this, there is no standard social capital assessment tool in medical science university in Persian. Therefore, the present study was designed to develop a tool for measuring social capital in Mashhad University of Medical Sciences, in order to improve the level of social capital of faculty members, staff and managers to have a significant impact on the development of this index in the field of health education and student performance. Methods Integrating quantitative and qualitative method has been applied in the present study in Mashhad University of medical sciences during 2019-2020. In the quantitative part, different variables affecting on social capital, obtained using related literature reviews and then they were finalized using Delphi method and expert panels' opinions. In the qualitative part, the proper questionnaire prepared and data analysis performed using structural equation method. Finally, descriptive statistics methods were used to characterize the sociological features of the sample and confirmatory factor analysis was used to evaluate the social capital model, using SPSS software version 20 and LISREL version 8.8. Results We found 7 dimensions for social capital questionnaire and 27 components affecting on social capital dimensions. We also found that there are more different factors influencing on social capital such as structural, communicational, cognitive, management, organizational, group and individual. Also, the overall reliability of the Social Capital Questionnaire was calculated to be 0.893 in Cronbach's alpha, which indicates the high reliability of the research instrument, moreover the calculated values of t for each factor load of each indicator with its hidden structure or variable is above 1.96. Therefore, the Validity of the questionnaire questions to measure the concepts can be validated. Conclusion Based on the findings of this study; we observed that effective components to develop the social capital model in Mashhad university of medical sciences include the component of social accountability system in the structural dimension of social capital, trust in the communication dimension of social capital, perception of justice and job satisfaction in the individual dimension of social capital and knowledge sharing in the organizational dimension of social capital. These components should be socially noticed by university faculty members.

Atomic configurations for materials research: A case study of some simple binary compounds

Data obtained from computational studies are crucial in building the necessary infrastructure for materials informatics. This computational foundation supplemented with experimental observations can then be employed in the extraction of possible hidden structure–property relationships through machine learning. There are limited attempts to sample the materials configuration space, even for the simplest chemical formulas. Advances in computational methods have now made it possible to accomplish this task. In this study, we analyze four chemical formulas, i.e., BSb, AlSb, MgSi2, and Sn3S, using first-principles computations. We show that numerous thermodynamically more stable crystal structures can be predicted computationally for these relatively simple chemical formulas, while the configuration space can be significantly and effectively mapped out. This approach allows for the prediction of new ground state structures, thereby expanding the available data on these materials. It also provides an understanding of the underlying potential energy topography and adds quality data for materials informatics.

Three-dimensional structural characteristics of the Zhaxikang ore-concentration area based on multiple attribute constraints

The Zhaxikang ore-concentration area (ZOCA) is in the Gangdese metallogenic belt in the southern Tibet Plateau. It has huge prospecting potential. Most of the mineral resources on the Gangdese metallogenic belt are controlled by the dome and fault. The deep structure and the coupling relationship between the Cuonadong dome and the deep structure are of great significance for understanding ore-controlling structures and hydrothermal channels. Existing studies have been controversial in the study of whether there are hidden structures in the deep part of the Zhaxikang deposit, and whether the Cuonadong dome extends to the bottom of the Zhaxikang deposit in the Gangdese metallogenic belt. Therefore, we comprehensively analyzed a large number of gravities, magnetic, and magnetotelluric data in the Zhaxikang area and carried out a fusion display of multiple information in the Zhaxikang ore-concentration area. Understand the relationship between the Cuonadong dome, fault system and deep hydrothermal channel through fusion display to analyze the background of Zhaxikang metallogenesis. The deep structure of the Cuonadong dome was identified, and it was found that there was a hidden structure at the bottom of Zhaxikang that was not connected to the Cuonadong dome. These findings are a great significance for further theoretical research and breakthroughs in ore prospecting.

Hidden structure in the spectra of strongly correlated electrons in 1D

We identify a structure in the spectra of 1D lattice models of interacting electrons, characterised by an anomalous gapped branch of elementary excitations. Focussing on a family of Bethe ansatz solvable models, where all excitations are stable against decay, we make a four-way classification of the energy spectrum along with a model belonging to each class. We find in particular that the anomalous excitation branch may switch between the spin and charge symmetry sectors without a change in the ground state. Instead it changes its nature by becoming inaccessible to the ground state.

An approach to exploring associations between hospital structural measures and patient satisfaction by distance\u2010based analysis

BackgroundPatient satisfaction studies have explored domains of patient satisfaction, the determinants of domains, and score differences of domains by patient/hospital structural measures but reports on the structure of patient satisfaction with respect to similarities among domains are scarce. This study is to explore by distance-based analysis whether similarities among patient-satisfaction domains are influenced by hospital structural measures, and to design a model evaluating relationships between the structure of patient satisfaction and hospital structural measures.MethodsThe Hospital Consumer Assessment of Healthcare Providers and Systems 2012 survey scores and their structural measures from the Hospital Compare website reported adjusted percentages of scale for each hospital. Contingency tables of nine measures and their ratings were designed based on hospital structural measures, followed by three different distance-based analyses - clustering, correspondence analysis, and ordinal multidimensional scaling – for robustness to identify homogenous groups with respect to similarities.ResultsOf 4,677 hospitals, 3,711 (79.3%) met the inclusion criteria and were analyzed. The measures were divided into three groups plus cleanliness. Certain combinations of these groups were shown to be dependent on hospital structural measures. High value ratings for communication and low value ratings for medication explanation, quietness and staff responsiveness were not influenced by hospital structural measures, but the varied-ratings domain group similarities, including items such as global evaluation and pain management, were affected by hospital structural measures.ConclusionsDistance-based analysis can reveal the hidden structure of patient satisfaction. This study suggests that hospital structural measures including hospital size, the ability to provide acute surgical treatment, and hospital interest in improving medical care quality are factors which may influence the structure of patient satisfaction.

Exploration and application of directional long boreholes in coal measure strata

two roadways in adjacent working face of Baode Mine may have the risk of water inrush at the same time, so it is necessary to construct long borehole to cover the roadway excavation. On the basis of the hydrogeological conditions of the mine, the safe water insulation thickness and water inrush coefficient of coal seam No .8 are calculated. The results show that the water inrush coefficient is 0.035-0.037 MPa/m, which is less than the critical value 0.06 MPa/m and the bottom plate has no sudden water hazard. In the construction of No .10 coal seam, the directional long borehole is used to detect whether there is a hidden structure communicating with the floor limestone and to drain water. The test shows that there is no effluent phenomenon in the borehole, which proves that there is no hidden structure in No .10 coal seam.

In plain sight: implicit priming of patterns and faces using change symmetry

Aksentijevic–Gibson complexity is an original complexity measure based on the amount of change in a string or 2D array that has been successfully implemented on data from psychology to physics. The key ingredient to computing the measure is a change symmetry (CS)—a novel form of structure (also known as generalised palindrome) which represents a central or mirror symmetry based on the redundant arrangement not of symbols but of changes. This results in patterns that although globally symmetrical do not appear as such when inspected locally. We used this property to (a) affect the registration of a target, (b) prime the symmetry judgment of 2D arrays and (c) faces using 1D patterns possessing change symmetry. In Experiment 2, we applied the lock and key principle to complete the prime without showing its structure at once. In Experiments 3 and 4, we presented subjects with fast sequences of CSs such that the configuration of an individual pattern was masked by the subsequent pattern leaving only the structural “essence” of the prime symmetry. The results strongly support the contention that higher-level hidden structure of change symmetry successfully primes the symmetry perception of 2D arrays as well as facial attractiveness.

Segmentation of High Dimensional Time-Series Data Using Mixture of Sparse Principal Component Regression Model with Information Complexity.

This paper presents a new and novel hybrid modeling method for the segmentation of high dimensional time-series data using the mixture of the sparse principal components regression (MIX-SPCR) model with information complexity () criterion as the fitness function. Our approach encompasses dimension reduction in high dimensional time-series data and, at the same time, determines the number of component clusters (i.e., number of segments across time-series data) and selects the best subset of predictors. A large-scale Monte Carlo simulation is performed to show the capability of the MIX-SPCR model to identify the correct structure of the time-series data successfully. MIX-SPCR model is also applied to a high dimensional Standard & Poor’s 500 (S&P 500) index data to uncover the time-series’s hidden structure and identify the structure change points. The approach presented in this paper determines both the relationships among the predictor variables and how various predictor variables contribute to the explanatory power of the response variable through the sparsity settings cluster wise.

Wall Mosaics: A Review of On-Site Non-Invasive Methods, Application Challenges and New Frontiers for Their Study and Preservation.

This review concerns the challenges and perspectives of on-site non-invasive measurements applied to wall mosaics. Wall mosaics, during the centuries, decorated numerous buildings, nowadays being part of world cultural heritage. The preservation and maintenance of these valuable decorations are undoubtedly directly dependent on identifying possible problems that could affect their hidden structure. On-site non-invasive methods, using different contact or no-contact technologies, can offer support in this specific field of application. The choice of the appropriate technique or combination of different techniques depends, in general, on the depth of investigation, the resolution, the possibility to have direct contact with the surfaces or, on the contrary, limited accessibility of the wall mosaics due to their location (e.g., vaults), as well as deterioration problems, (e.g., voids, detachments, or humidity effects). This review paper provides a brief overview of selected recent studies regarding non-invasive methods applied to the analysis of wall mosaics. This review, discussing the assessment of advantages and limitations for each method here considered, also considers possible future developments of imaging techniques in this specific context for cultural heritage applications.

Efficient Semi-Supervised Learning and Sparse Structural Learning for Feature Selection of Leukemia Dataset

The amount of data produced in health informatics growing large and as a result analysis of this huge amount of data requires a great knowledge which is to be gained. The basic aim of health informatics is to take in real world medical data from all levels of human existence to help improve our understanding of medicine and medical practices. Huge amount of unlabeled data are obtainable in lots of real-life data-mining tasks, e.g., uncategorized messages in an automatic email categorization system, unknown genes functions for doing gene function calculation, and so on. Labelled data is frequently restricted and expensive to produce, while labelling classically needs human proficiency. Consequently, semi-supervised learning has become a topic of significant recent interest. This research work proposed a new semi-supervised grouping, where the performance of unsupervised clustering algorithms is enhanced with restricted numbers of supervision in labels form on constraints or data. The previous system designed a Clustering Guided Hybrid support vector machine based Sparse Structural Learning (CGHSSL) for feature selection. However, it does not produce a satisfactory accuracy results. In this research, proposed clustering-guided with Convolution Neural Network (CNN) based sparse structural learning clustering algorithm. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is progressed for learning cluster labels of input samples having more accuracy guiding features election at same time. Concurrently, prediction of cluster labels is as well performed by CNN by means of using hidden structure which is shared by various characteristics. The parameters of CNN are then optimized maximizing Multi-objective Bee Colony (MBO) algorithm that can unravel feature correlations to render outcomes with additional consistency. Row-wise sparse designs are then balanced to yield design depicted to suit for feature selection. This semi supervised algorithm is utilized to choose important characteristics from Leukemia1 dataset additional resourcefully. Therefore dataset size is decreased significantly utilizing semi supervised algorithm prominently. As well proposed Semi Supervised Clustering-Guided Sparse Structural Learning (SSCGSSL) technique is utilized to increase the clustering performance in higher. The experimental results show that the proposed system achieves better performance compared with the existing system in terms of Accuracy, Entropy, Purity, Normalized Mutual Information (NMI) and F-measure.

Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments

We simultaneously revisited the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) with a comprehensive data-analytics strategy. Here, the combination of pattern-analysis algorithms and extensive data resources (n = 266 patients aged 7–49 years) allowed identifying coherent clinical constellations in and across ADI-R and ADOS assessments widespread in clinical practice. Our clustering approach revealed low- and high-severity patient groups, as well as a group scoring high only in the ADI-R domains, providing quantitative contours for the widely assumed autism subtypes. Sparse regression approaches uncovered the most clinically predictive questionnaire domains. The social and communication domains of the ADI-R showed convincing performance to predict the patients’ symptom severity. Finally, we explored the relative importance of each of the ADI-R and ADOS domains conditioning on age, sex, and fluid IQ in our sample. The collective results suggest that (i) identifying autism subtypes and severity for a given individual may be most manifested in the ADI-R social and communication domains, (ii) the ADI-R might be a more appropriate tool to accurately capture symptom severity, and (iii) the ADOS domains were more relevant than the ADI-R domains to capture sex differences.

SaaS software performance issue diagnosis using independent component analysis and restricted Boltzmann machine

SummarySaaS software performance issue diagnosis aims to classify the type of the performance records. Deep classification method has gained much attention as a way to construct hierarchical representations from a small amount of labeled data. However, there are few researches on how to solve the classification problem of performance issues by using the deep classification method. In addition, shallow classification methods exist some problems, such as the training sample is large and the ability to fit complex functions is weak. In this article, we proposed a deep performance issue classification method based on Independent Component Analysis (ICA) and Restricted Boltzmann Machine (RBM). ICA is used to extract the features, after this process, the classification feature is obtained as RBM input, and the extracted information about performance issue is transformed into identifiable information for the classifier via visible structure of input; Hidden layer for RBM is built to realize the data transmission between hidden structure, keeping the key information; And the classification algorithm is implemented to solve our performance issue diagnosis problem of SaaS software. Experiments show that the performance of our approach is superior to the classical shallow classification algorithm, and it also meet the efficiency requirement.

A CHARACTERIZATION OF CAVAFY’S 18: CASCADING ENJAMBMENT AND THE SYLLABIC FORM

The influence of Constantine Cavafy’s (1863–1933) poetry outside of Greece has mainly proceeded through translations. Only in the last two decades has some attention been given to formal structures, such as meter and rhyme, which play a significant role in at least a third of his poems. Cavafy shows particular ingenuity creating one form, which is found in eighteen examples. It consists of visible hemistiches and a fixed syllabics structure. Cavafy built on the traditional structure of the Dekapentasyllavo or Greek folk song but allowed a flexible number of syllables, six or seven for either hemistich, together with a clear caesura. In later years, Cavafy would compromise the integrity of the meaningful phrase in each hemistich but never violated his own rules of syllabics. Syllabics look, feel, and sound different when they are applied in translation. They are a hidden structure with historical antecedents. Cavafy’s 18 are not limited to a theme or subject, yet the lack of attention to the form of these poems obfuscates Cavafy’s engagement with Modern Greek folk songs and his attempts to modernize their form.

Bootstrap bandwidth selection in time-varying coefficient models with jumps

The time-varying coefficient models (TVCMs) are very important tools to explore the hidden structure between the time series response variable and its predictors. The assumption that the coefficient functions are smooth can be restrictive in practice. The TVCMs with jumps are needed to be considered. Selection of smoothing parameters plays a critical role in assessing the performance of estimations of coefficient functions. In this article, we first develop a nonparametric two-step estimation procedure for estimating a finite number of jumps of the coefficient functions. Then, based on the estimated jumps, we propose a bootstrapping bandwidth selection procedure in the TVCMs with jumps. Monte Carlo simulations are conducted to evaluate the finite sample performance of the proposed data-driven estimation and bootstrap bandwidth selection procedures. As an illustration, the proposed methodologies are further applied to a real data example.

Hidden structure and value network: shedding light on position assessment

This study focuses on hierarchical structures of value networks of service systems. But how can the central and influential network positions be found in the value network? The position of actors is extracted using the hidden structure method (HSM) and centrality metrics of social network analysis (SNA). Both analyses apply adjacency matrix data that reveal direct dependencies of actors in a value network. We measured indegree, outdegree, betweenness, and closeness centralities to each actor to compare SNA and HSM results. HSM can reveal the actors who conduct the most significant activities, as well as more peripheral actors who have a strong influence on core actors. Thus, HSM does not neglect the hierarchy between actors. Our methodological contribution suggests that in parallel with widely used SNA centrality measures, researchers should leverage HSM to define influential actors. Two pharmacy concepts in Finland provide the empirical research context.

Map Reduce based REmoving Dependency on K and Initial Centroid Selection MR-REDIC Algorithm for clustering of Mixed Data

In machine learning, clustering is recognized as widely used task to find hidden structure of data. While handling the massive amount of data, the traditional clustering algorithm degrades in performance due to size and mixed type of at-tributes. The Removal Dependency on K and Initial Centroid Selection (REDIC) algorithm is designed to handle mixed data with frequency based dissimilarity measurement for categorical attributes. The selection of initial centroids and prior decision for number of cluster improves the efficiency of REDIC algorithm. To deal with the large scale data, the REDIC algorithm is migrated to Map Reduce paradigm,and Map Reduce based REDIC( MR-REDIC) algorithm is proposed. The large amount of data is divided into small chunks and parallel approach is used to reduce the execution time of algorithm.The proposed algorithm inherits the feature of REDIC algorithm to cluster the data.The algorithm is implemented in Hadoop environment with three different configuration and evaluated using five bench mark data sets. Experimental results show that the Speed up value of data is gradually shifting towards linear by increasing number of data nodes from one to four. The algorithm also achieves the near to closer value for Scale up parameter, while maintaining the accuracy of algorithm.

Locality-aware group sparse coding on Grassmann manifolds for image set classification

Riemannian sparse coding methods are attracting increasing interest in many computer vision applications, relying on its non-Euclidean structure. One such recently successful task is image set classification by the aid of Grassmann Manifolds, where an image set can be seen as a point. However, due to irrelevant information and outliers, the probe set may be represented by misleading sets with large sparse coefficients. Meanwhile, it is difficult for a single subspace to cover changes within an image set and the hidden structure among samples is relaxed. In this paper, we propose a novel Grassmann Locality-Aware Group Sparse Coding model (GLGSC) that attempts to preserve locality information and take advantage of the relationship among image sets to capture the inter and intra-set variations simultaneously. Since the contributions of different gallery subspaces to the probe subspace should vary in importance, we then introduce a novel representation adaption term. In addition, a kernelised version of GLGSC is proposed to handle non-linearity in data. To reveal the effectiveness of our algorithm over state-of-the-art, several classification tasks are conducted, including face recognition, object recognition and gesture recognition.