Set Of Data Points Research Articles

FAKIR: An algorithm for revealing the anatomy and pose of statues from raw point sets

Abstract3D acquisition of archaeological artefacts has become an essential part of cultural heritage research for preservation or restoration purpose. Statues, in particular, have been at the center of many projects. In this paper, we introduce a way to improve the understanding of acquired statues representing real or imaginary creatures by registering a simple and pliable articulated model to the raw point set data. Our approach performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge. We are thus able to detect the pose and elementary anatomy of sculptures, with possibly non realistic body proportions. By adapting our simple skeleton, our method can work on animals and imaginary creatures.

Data clustering via cooperative games: A novel approach and comparative study

Arguably, the main purpose of cluster analysis is to develop algorithms to reveal natural groupings (clusterings) over a set of data points based on their similarity. On the other hand, the focus of cooperative game theory (CGT) is to study the formation of groups (coalitions) of decision makers (players) and ways to split the resulting income among them. Due to the conceptual similitude between these fields, algorithms rooted in CGT have recently emerged for tackling the data clustering problem. In this work, we revisit two such algorithms, one based on cluster prototypes (Biobjective Game Clustering – BiGC) and the other based on dense regions of data points (Density-Restricted Agglomerative Clustering – DRAC). We also present a novel partitional clustering algorithm, referred to as HGC (after Hedonic Game based Clustering), which is grounded on theoretical results stemming from the subclass of hedonic games. Two HGC versions are investigated, which differ in the order of the players in the game, and a detailed factorial simulation study is reported to analyze how sensitive they are to three relevant factors, namely number of clusters, number of features, and noise level. Besides, a heuristic to calibrate the value of HGC’s single control parameter (viz., the number of nearest neighbors of each point) is provided, so as to yield high-quality partitions. To compare the performance of the CGT algorithms, a series of experiments were conducted on UCI and gene-expression data sets, the majority of which being high dimensional. Overall, the results measured by 10 external validation indices evidence that HGC is usually more stable and effective than DRAC and BiGC. They also show that HGC is very competitive (sometimes, considerably better) to well-known clustering algorithms/variants (specifically, k-means, k-means++, affinity propagation, two variants of hierarchical clustering, and the density peak clustering algorithm). Remarkably, HGC could fully recover the true clustering structures for two gene-expression data sets.

D-VPnet: A network for real-time dominant vanishing point detection in natural scenes

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115 fps.

Data-driven fracture mechanics

We present a new data-driven paradigm for variational brittle fracture mechanics. The fracture-related material modeling assumptions are removed and the governing equations stemming from variational principles are combined with a set of discrete data points, leading to a model-free data-driven method of solution. The solution at a given load step is identified as the point within the data set that best satisfies either the Kuhn–Tucker conditions stemming from the variational fracture problem or global minimization of a suitable energy functional, leading to data-driven counterparts of both the local and the global minimization approaches of variational fracture mechanics. Both formulations are tested on different test configurations with and without noise and for Griffith and R-curve type fracture behavior.

Factors influencing the use of modern contraceptive in Nigeria: a multilevel logistic analysis using linked data from performance monitoring and accountability 2020

BackgroundThe population of Nigeria is estimated at over 190 million and it is projected to increase by 44% between 2015 and 2030. However, less than one-quarter of women within reproductive age in Nigeria uses modern contraceptive methods despite its importance. Hence, this study aims at examining the influence of individual and community level factors on the use of modern contraceptive method.MethodsThe study is a secondary analysis of linked household and Service Delivery Point datasets from a 2018 survey conducted by Performance, Monitoring and Accountability in Nigeria. Data was abstracted for a total of 9126 sexually active women within the ages of 15–49 years across 295 enumeration areas in seven States. A 2-level binary logistic regression was used to examine the association between study variables and the use of modern contraceptives while adjusting for the clustering effect.ResultsThere was significant influence of educational level, marital status, parity, socio-economic status, fertility intention, and awareness of family planning methods on the use of modern contraceptives. Also, women who perceived support from someone in the community on family planning were more likely to use modern contraceptive unlike those without such support. Those who believed that contraceptive methods are used by almost all and some of their friends or relatives were more likely to use modern contraceptive compared to those who think otherwise.ConclusionsThe study shows the need to reduce inequalities between FP utilization across women with different socio-economic status as well as increasing the awareness for modern contraceptive methods.

Improving the quality of trauma meetings by implementation of a modern trauma management platform

Trauma meetings constitute the majority of clinical decision-making for new referrals and also act as a crucial tool to coordinate the trauma theatre list. Incorporation of electronic technology has been...

Застосування технології глибинного навчання для побудови інтелектуальних автономних машин

One of the most common tasks that arise in building intelligent machine vision systems for intellectually autonomous machines is the problems of classification and regression. Classification problems are used for the reflexive action of autonomous machines. Prediction tasks can be used to build machine vision systems to provide intelligent autonomous machines with environmental knowledge, which in turn is important for planned predictable movements. Defining a class of task instances is an important procedure for the effective design of deep learning systems. In this context, the possibility of using a multilayered neural network as a regressor to construct elementary functional mappings is explored for further prediction. The study outlines the peculiarities of functioning and configuration of a specialized robotics system, considered in this paper as an intelligent autonomous machine or physical agent, generates a set of data points for elementary functions, analytical modeling and modeling of training systems. Input graph was constructed, neural network architecture was defined, gradient descent algorithm was implemented, and output schedules were finally constructed: learning process, results prediction and comparative graph of predicted results superimposed on the input graph. As a result of the study, an assessment of the machine's intellectual ability to predict was made.

POTENTIAL OF GEOLOCATED CROWDSOURCED IMAGE POSTS IN PREDICTING AN EARLY ESTIMATE OF THE PATTERNS OF STRUCTURAL DAMAGE FOLLOWING A HURRICANE

Abstract. During a disaster, the activity of the crowd represents a very valuable source of the on-the-ground conditions shared by the affected citizens. The approach, presented in the paper, explores the relationship between the spatial distribution of crowdsourced image posts and damaged buildings in order to understand the potential of modelling the spatial distribution of damaged buildings based on geolocated images. The posts related to the hurricane Michael that happened in the United States in October 2018, showing the building damage of Panama City, have been collected by NAPSG Foundation and GISCorps volunteers. The building damage assessment, based on the analysis of high-resolution post-event imagery, has been performed by FEMA. Exploring the two available independent point datasets, the spatial pattern of each individual dataset has been analysed and furthermore the spatial relationship between them has been explored. A set of spatial statistics has been performed with R software. For this purpose, the distance-based methods have been used, that consider the mutual position of points to describe the patterns. The results shown the spatial relationship between the crowdsourced photos and different damage types. Furthermore, potential of crowdsourced images for improving the awareness of the structural damage after the hurricane have been discussed.

Anti-noise FCM image segmentation method based on quadratic polynomial

Image segmentation is an important and challenging task in computer vision and image understanding, in which the segmentation model is the key to improve the quality of image segmentation. In this paper, a new fuzzy c-means clustering(FCM) algorithm based on quadratic polynomials is proposed, which can better distinguish the weak edge region in the image and has certain noise resistance. Firstly, the algorithm proposes to define the segmentation center using a quadratic polynomial surface,and divide the set of data points according to the algebraic distance between data points and the segmentation surfaces. The existing model with constant as the segmentation center is a special case with quadratic polynomial surface as the segmentation center, so the new model has higher segmentation accuracy. Secondly, based on the quadratic polynomial surface as the segmentation center, a new fuzzy factor is designed, and the deviation value is used to represent the difference between the mean algebraic distance of neighborhood points and the algebraic distance of center pixel. By calculating the deviation value, the influence of neighborhood points on the center point can be better measured and the segmentation accuracy can be improved. Experimental results show that the new algorithm has better anti-noise ability as well. Thirdly, select a local window on the edge of the global segmentation result for local window segmentation, which is equivalent to using a clustering center that is more in line with local information to do segmentation in a local small window, optimize misclassified pixels, and obtain the final segmentation result. The experimental results show that in the final segmentation results of medical images with 5% noise, segmentation accuracy can reach above 96%, partition coefficient Vpc has been increased by 0.14. So the algorithm can get the membership matrix with less ambiguity, which means more reliable segmentation results, and can effectively eliminate noise effects, retain the image details.

The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

Vehicular Localization Based on CSI-Fingerprint and Vector Match

In order to design a fast and accurate short-distance localization system to supplement the gap of Global Position System (GPS) in a toll station, in this paper, an IEEE 802.11p module driver is modified to provide channel state information (CSI), and the signal processing techniques are leveraged in both time-domain and frequency-domain to mitigate multi-path effect. An effective CSI vector provided by a roadside unit (RSU) transceiver (RX) array is treated as the fingerprint of a transmitting spot, the spatial diversities of the multiple antennas are leveraged to highlight its position feature. The position similarity degree of a reference point with regard to the measured vehicle position is proposed and treated as weight to calculate vehicle coordinate, the low variability of CSI-fingerprint is alleviated. A filter is designed to cut off the dataset of remote reference points in localization algorithm, traffic noises are reduced. Finally, the accuracy and performance are verified in enforcement, autopilot and navigation applications. The effects of grid size, speed, traffic and weather are evaluated.

A Gaussian process regression adaptive density guided approach for potential energy surface construction.

We present a new iterative scheme for potential energy surface (PES) construction, which relies on both physical information and information obtained through statistical analysis. The adaptive density guided approach (ADGA) is combined with a machine learning technique, namely, the Gaussian process regression (GPR), in order to obtain the iterative GPR-ADGA for PES construction. The ADGA provides an average density of vibrational states as a physically motivated importance-weighting and an algorithm for choosing points for electronic structure computations employing this information. The GPR provides an approximation to the full PES given a set of data points, while the statistical variance associated with the GPR predictions is used to select the most important among the points suggested by the ADGA. The combination of these two methods, resulting in the GPR-ADGA, can thereby iteratively determine the PES. Our implementation, additionally, allows for incorporating derivative information in the GPR. The iterative process commences from an initial Hessian and does not require any presampling of configurations prior to the PES construction. We assess the performance on the basis of a test set of nine small molecules and fundamental frequencies computed at the full vibrational configuration interaction level. The GPR-ADGA, with appropriate settings, is shown to provide fundamental excitation frequencies of an root mean square deviation (RMSD) below 2 cm-1, when compared to those obtained based on a PES constructed with the standard ADGA. This can be achieved with substantial savings of 65%-90% in the number of single point calculations.

Efficient creation of datasets for data-driven power system applications

Advances in data-driven methods have sparked renewed interest for applications in power systems. Creating datasets for successful application of these methods has proven to be very challenging, especially when considering power system security. This paper proposes a computationally efficient method to create datasets of secure and insecure operating points. We propose an infeasibility certificate based on separating hyperplanes that can a-priori characterize large parts of the input space as insecure, thus significantly reducing both computation time and problem size. Our method can handle an order of magnitude more control variables and creates balanced datasets of secure and insecure operating points, which is essential for data-driven applications. While we focus on N-1 security and uncertainty, our method can extend to dynamic security. For PGLib-OPF networks up to 500 buses and up to 125 control variables, we demonstrate drastic reductions in unclassified input space volumes and computation time, create balanced datasets, and evaluate an illustrative data-driven application.

Digital soil mapping workflow for forest resource applications: a case study in the Hearst Forest, Ontario

Accurate soil information is critically important for forest management planning and operations but is challenging to map. Digital soil mapping (DSM) improves upon the limitations of conventional soil mapping by explicitly linking a variety of environmental data layers to spatial soil point data sets to continuously predict soil variability across a landscape. Thus far, much DSM research has focussed on the development of ultrafine-resolution soil maps within agricultural systems; however, increasing availability of light detection and ranging (LiDAR) data presents new opportunities to apply DSM to support forest resource applications at multiple scales. This project describes a DSM workflow using LiDAR-derived elevation data and machine learning models (MLMs) to predict key forest soil attributes. A case study in the Hearst Forest in northeastern Ontario, Canada, is used to illustrate the workflow. We applied multiple MLMs to the Hearst Forest to predict soil moisture regime and textural class. Both qualitative and quantitative assessment pointed to the random forest MLM producing the best maps (63% accuracy for moisture regime and 66% accuracy for textural class). Where error occurred, soils were typically misclassified to neighbouring classes. This standardized, flexible workflow is a valuable tool for practitioners that want to undertake DSM as part of forest resource management and planning.

A Novel Method of Curve Fitting Based on Optimized Extreme Learning Machine

ABSTRACT In this article, we present a new method based on extreme learning machine (ELM) algorithm for solving nonlinear curve fitting problems. Curve fitting is a computational problem in which we seek an underlying target function with a set of data points given. We proposed that the unknown target function is realized by an ELM with introducing an additional linear neuron to correct the localized behavior caused by Gaussian type neurons. The number of hidden layer neurons of ELM is a crucial factor to achieve a good performance. An evolutionary computation algorithm–particle swarm optimization (PSO) technique is applied to determine the optimal number of hidden nodes. Several numerical experiments with benchmark datasets, simulated spectral data and measured data from high energy physics experiments have been conducted to test the proposed method. Accurate fitting has been accomplished for various tough curve fitting tasks. Comparing with the results of other methods, the proposed method outperforms the traditional numerical-based technique. This work clearly demonstrates that the classical numerical analysis problem-curve fitting can be satisfactorily resolved via the approach of artificial intelligence.

Sparse Graph Connectivity for Image Segmentation

It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point ( e.g., a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of data points for achieving graph connectivity. However, previous methods only focused on either subspace preservation or graph connectivity. In this article, we propose a Sparse Graph Connectivity (SGC) method for image segmentation to automatically learn the affinity matrix from the low-dimensional space of original data, which aims at simultaneously achieving subspace preservation and graph connectivity. To do this, the proposed SGC simultaneously learns a self-representation affinity matrix for subspace preservation and a sparse affinity matrix for graph connectivity, from the intrinsic low-dimensional feature space of high-dimensional original data. Meanwhile, the self-representation affinity matrix is pushed to be similar to the sparse affinity as well as be the final segmentation results. Experimental result on synthetic and real-image datasets showed that our SGC method achieved the best segmentation performance, compared to state-of-the-art segmentation methods.

Mapeamento da COVID-19 por meio da densidade de Kernel

A densidade de Kernel consiste em quantificar as relações dos pontos dentro de um raio (R) de influência, com base em determinada função estatística, analisando os padrões traçados por determinado conjunto de dados pontuais, estimando a sua densidade na área de estudo (BERGAMASCHI, 2010). De acordo com Kawamoto (2012, p. 16-17), a técnica de Kernel “[...] consiste num estimador probabilístico de intensidade do processo pontual não-paramétrico através de função Kernel". Dentro dessa perspectiva metodológica, o objetivo deste trabalho é apresentar um mapa de densidade de Kernel da incidência de COVID-19 para área urbana de Santa Maria, RS, utilizando os dados do dia 05 de junho de 2020 (n = 282). Os procedimentos metodológicos se dividiram em cinco etapas: (1) definição do raio (R); (2) função k escolhida; (3) procedimentos realizados no QGIS para a geração da densidade de Kernel; (4) determinação do número de classes e intervalo; e (5) finalização do mapa. Os dados levaram a observar uma maior densidade de casos na área central da cidade, especificamente nas proximidades dos bairros Centro, Bonfim, Nossa Senhora de Fátima e Nonoai, com uma maior circulação de pessoas, serviços e residentes. Contudo, é possível identificar a dispersão da COVID-19 para áreas periféricas da cidade, como já ressaltado em Rizzatti et al (2020). O bairro Camobi, na região leste da cidade, demonstra comportamento semelhante ao bairro Centro, porém em menor densidade, formando o que poderíamos chamar de cluster secundário, além da zona Oeste da cidade, que demonstra a formação de um novo cluster.

Mathematical model of patella T-reflex and clinical evaluation with Ashworth scales.

Spasticity is one of the major problems that arise in different neurological diseases and seriously affect the quality of human life. Research on the understanding of mechanism of spasticity remains as important as the studies on the spasticity therapy and rehabilitation. In this study, the spasticity mechanism which develops concerning the upper motor neuron lesions is investigated by modelling "Patella tendon reflex triggered patella pendulum". The mathematical model based on the pendulum phenomenon is developed by solving the curve-fitting problem as finding the curve that best fits a set of data points. Electrophysiological and dynamic measurement data were taken from 76 spastic subjects and 20 healthy participants. The mathematical model is determined by the morphological properties of the goniometric variations. The results denote that the mathematical model containing two clinically relevant parameters -frequency component of the damped oscillatory motion defined as "f 0 " with the maximum angle of the reflex defined as "a 0 " ensures to distinguish spasticity from healthy subjects.

Sparse probabilistic K-means

Abstract The goal of clustering is to partition a set of data points into groups of similar data points, called clusters. Clustering algorithms can be classified into two categories: hard and soft clustering. Hard clustering assigns each data point to one cluster exclusively. On the other hand, soft clustering allows probabilistic assignments to clusters. In this paper, we propose a new model which combines the benefits of these two models: clarity of hard clustering and probabilistic assignments of soft clustering. Since the majority of data usually have a clear association, only a few points may require a probabilistic interpretation. Thus, we apply the l1 norm constraint to impose sparsity on probabilistic assignments. Moreover, we also incorporate outlier detection in our clustering model to simultaneously detect outliers which can cause serious problems in statistical analyses. To optimize the model, we introduce an alternating minimization method and prove its convergence. Numerical experiments and comparisons with existing models show the soundness and effectiveness of the proposed model.

Test\u2013retest variability of microperimetry in geographic atrophy

PurposeMicroperimetry (MP) allows for measurement of retinal sensitivity at precise locations and is now commonly employed as a clinical trial endpoint. Test–retest reliability is important when evaluating treatment effects in patients with geographic atrophy (GA). This study aimed to determine the test–retest variability of MP in patients with moderate to severe GA using the MAIA MP device.MethodsIn this prospective study, patients with a confirmed diagnosis of foveal-involving GA were enrolled. Participants performed three MP assessments of a selected eye over two visits with the Macular Integrity Assessment (MAIA) 2 instrument (Centervue, Padova, Italy) utilizing a wide 30° grid, consisting of 93 stimuli (Goldmann III) using a 4-2 representation strategy, encompassing the entire area of GA and beyond. Mean retinal sensitivity (MS) was expressed as an average threshold value (dB) for the entire field tested. Coefficients of Repeatability at a 95% level (CoR95) were calculated for Point Wise Sensitivity (PWS). Fixation stability (FS) was assessed by evaluating the area of an elliptical representation encompassing 95% of the cloud of fixation points (CFP) dataset generated by the MAIA MP, known as the bivariate contour ellipse area (BCEA).ResultsA total of 8 subjects were enrolled (21 tests), with six subjects completing 3 MP assessments. BCVA in these patients ranged from 20/100 to 20/800. The mean area of GA was 18.7 ± 12.3 mm2. The average time to complete one MP assessment was 13 min 9 s and mean BCEA@95% was 38.5 ± 19.3°2. The MS was 14.3 ± 4.5 dB. No significant increase in MS was noted between testing pairs 1&2 and 2&3. The preferred retinal locus was maintained in the same quadrant on successive tests. The mean CoR95 for PWS were similar for testing pairs 1&2 (± 3.50 dB) and 2&3 (± 3.40).ConclusionMicroperimetry using a wide grid can be reliably performed in a reasonable amount of time in patients with moderate and severe vision loss secondary to GA. There was no learning effect seen between sequential assessments when analyzing MS or PWS. A change of approximately 4 dB in PWS provides a threshold for considering a true change in this patient cohort.