Set Of Data Points Research Articles

Extended atom-based and bond-based group contribution descriptor and its application to melting point prediction of energetic compounds

17817 compounds were collected from the Bradley open melting point data set, including eight elements: C, H, O, N, F, S, Cl, Br, and I. An extended atom-based and bond-based group contribution descriptor was suggested to represent these compounds, which consists of a one-dimensional descriptor based on the molecular formula, a two-dimensional group contribution descriptor based on atoms and bonds, and a structural feature descriptor. Random forest (RF), Partial Least Squares (PLS), and Deep Learning (DL) methods were used to establish models to predict melting points, and the constructed models were evaluated by correlation coefficient (R), mean absolute error (MAE) and root-mean-square error (RMSE). Among them, the best results were obtained using the model constructed by Random forest: the results of out-of-bag (OOB) cross-validation of the training set are R = 0.8977/MAE = 29.57 °C/RMSE = 40.34 °C; the predicted results of the test set are R = 0.8982/MAE = 29.68 °C/RMSE = 40.63 °C. Compared with the results obtained using the subset of this data set in a literature, the results in this study are better than the corresponding results in the literature. The established model was also used to predict an external data set consisting of 74 compounds retrieved from another literature, and the obtained results are R = 0.8946 °C/MAE = 24.51 °C/RMSE = 34.19 °C, which were significantly better than the corresponding results in the literature. If the descriptor suggested in this study is combined with RDKit descriptor that contains charge and electronegativity information and so on, better results were achieved: the results of OOB cross-validation of the training set are R = 0.9013/MAE = 29.25 °C/RMSE = 39.76 °C; the results of the test set are R = 0.9017/MAE = 29.34 °C/RMSE = 40.07 °C.

Improving The Accuracy Of Digital Elevation Model Using Hopfield Neural Network With Additional Elevation Point Dataset

Improving The Accuracy Of Digital Elevation Model Using Hopfield Neural Network With Additional Elevation Point Dataset

Precision of sinewave amplitude estimation in the presence of additive noise and quantization error

Abstract This research paper delves into a comprehensive investigation concerning the impact of additive noise and quantization error on the precision of amplitude, offset, and phase estimates of a sine wave fitted to a set of data points acquired by a waveform digitizer. Simulation results are used to validate the expressions presented.

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data

Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

Pose estimation for swimmers in video surveillance

Traditional models for pose estimation in video surveillance are based on graph structures, in this paper, we propose a method that breaks the limitation of template matching within a range of pose changes to obtain robust results. We implement our swimmer pose estimation method based on deep learning. We take use of High-Resolution Net (HRNet) to extract and fuse visual features of visual object and complete the object detection using the key points of human joint. The proposed model could be applied to all kinds of swimming styles throughout appropriate training. Compared with the methods that require multimodel combinations and training, the proposed method directly achieves the end-to-end prediction, which is easily to be implemented and deployed. In addition, a cross-fusion module is added between parallel networks, which assists the network to make use of the characteristics of multiple resolutions. The proposed network has achieved ideal results in the pose estimation of swimmers by comparing HRNet-W32 and HRNet-W48. In addition, we propose an annotated key point dataset of swimmers which was created from the view of underwater swimmers. Compared with side view, the torso of swimmers collected by the underwater view is much suitable for a broad spectrum of machine vision tasks.

Defining Clusters by Topology Warping Features, an Interpretable Data Clustering Method

Clustering is the task of dividing a data-set into different groups, called clusters, based on similarity. Despite being extensively studied, many state-of-the-art clustering algorithms lack interpretability. While it is useful to partition objects into clusters, it can be equally useful to understand why each cluster has been created. This motivates the desire for a clustering algorithm which can explain its partition. Our proposed method is a clustering process which seeks to explain which variables of a data-set are responsible for each cluster. We analyze the shape of the data, through the mathematical concept of a topological space. A space which is optimal for clustering is one which contains several disconnected islands, quantified as the number of connected components. Subsets of variables can then be selected and the resulting connected components of the topological space calculated. If this space is promising then the resulting disconnected region is the set of data-points which make up a cluster, and the subset of variables are what define it. We chose the variables to consider by grouping them via their correlation or through complex methods, e.g evolutionary algorithms. Our method provides a simple explanation since we can confidently assert that particular variables are why a data point is in a certain cluster. Alongside test data for comparison with existing algorithms, our methodology was applied to a community-based adolescents lipidomics dataset. Results on this dataset revealed 3 distinct clusters which can be explained by the distinct set of lipids that define them. Further optimization showed the existence of a cluster defined by a single lipid.

Enhancement of inverse-distance-weighting 2D interpolation using accelerated decline

Abstract Two-dimensional interpolation – or surface fitting – is an approximation tool with applications in geodetic datum transformations, terrain modelling and geoid determination. It can also be applied to many other forms of geographic point data, including rainfall, chemical concentrations and noise levels. The problem of fitting of a smooth continuous interpolant to a bivariate function is particularly difficult if the dataset of control points is scattered irregularly. A typical approach is a weighted sum of data values where the sum of the weights is always unity. Weighting by inverse distance to a power is one approach, although a power greater than 1 is needed to ensure smooth results. One advantage over other methods is that data values can be incorporated into the interpolated surface. One disadvantage is the influence of distant points. A simple cut-off limit on distance would affect continuity. This study proposes a transition range of accelerated decline by means of an adjoining polynomial. This preserves smoothness and continuity in the interpolating surface. Case studies indicate accuracy advantages over standard versions of inverse-distance weighting.

Λ hypernuclear potentials beyond linear density dependence

In a recent paper [PLB 837 (2023) 137669] we showed that all measured (1sΛ, 1pΛ) pairs of Λ binding energies in Λ-hypernuclei across the periodic table, 12≤A≤208, can be obtained from a Λ-nucleus optical potential with only two adjustable ΛN and ΛNN parameters, associated with leading linear and quadratic terms in the nuclear density, derived by fitting Λ16N binding energies. Here we extend the previous analysis by performing least-squares fits to the full set of data points. Consequences of suppressing ΛNN interactions between ‘core’ nucleons and ‘excess’ neutrons are studied and related predictions are made for (1sΛ, 1pΛ) binding energies in Λ40,48K, obtainable from upcoming 40,48Ca(e,e′K+) JLab experiments. We find Λ-nucleus partial potential depths of DΛ(2)=−38.6±0.8 MeV (ΛN) and DΛ(3)=11.3±1.4 MeV (ΛNN), with a total depth DΛ=−27.3±0.6 MeV at nuclear-matter density ρ0=0.17 fm−3, consistently with our previous results. Extrapolation to higher nuclear densities and possible relevance to the ‘hyperon puzzle’ in neutron-star matter are discussed.

Smoothed separable nonnegative matrix factorization

Given a set of data points belonging to the convex hull of a set of vertices, a key problem in linear algebra, signal processing, data analysis and machine learning is to estimate these vertices in the presence of noise. Many algorithms have been developed under the assumption that there is at least one nearby data point to each vertex; two of the most widely used ones are vertex component analysis (VCA) and the successive projection algorithm (SPA). This assumption is known as the pure-pixel assumption in blind hyperspectral unmixing, and as the separability assumption in nonnegative matrix factorization. More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point to each vertex. In that scenario, ALLS is probabilistically more robust to noise than algorithms based on the separability assumption. In this paper, inspired by ALLS, we propose smoothed VCA (SVCA) and smoothed SPA (SSPA) that generalize VCA and SPA by assuming the presence of several nearby data points to each vertex. We illustrate the effectiveness of SVCA and SSPA over VCA, SPA and ALLS on synthetic data sets, on the unmixing of hyperspectral images, and on feature extraction on facial images data sets. In addition, our study highlights new theoretical results for VCA.

A global time series dataset to facilitate forest greenhouse gas reporting

We have developed a version of the Continuous Change Detection and Classification algorithm within the Google Earth Engine environment. It has been used with 20 years of Landsat data (1999–2019) to produce a new, publicly available global dataset of pre-computed time series break points and harmonic coefficients. We present results from regional use cases demonstrating classification and change detection with this new dataset and compare them to other temporal compositing techniques. Our results demonstrate that gains in overall accuracy using CCDC may be small on a yearly basis, but they are consistent, and improvements in temporal coherence—correctly detecting land use transitions and temporal trends—can be significant. These improvements can translate into better estimates of land use change activity and reduce the uncertainty in the greenhouse gas emissions estimates in REDD+ reporting.

Research on Key Point Detection Method of Mechanical Arm

Accurate keypoint localization is an important research direction in the field of robotic arms. Deep learning is being used more and more for motion capture and pose estimation. This is due to their ability to detect user-defined key points without the use of labels. We present an approach for detecting robot 3D key points coordinate based on two perspectives. Two images of different perspectives are processed by the same deep neural network to detect 2D projections of key points (such as joints) associated with the robot. Triangularization is then applied to recover 3D coordinates. In addition, the existing dataset of robot critical points is obtained by simulation. It seriously impedes the advancement of this field. We establish an actual robot key points dataset and corresponding evaluation criterion. The experimental results demonstrate that our framework achieves cutting-edge detection performance. It is reusable for other robot models.

Betti Number for Point Sets

Topology is the foundation for many industrial applications ranging from CAD to simulation analysis. Computational topology mostly focuses on structured data such as mesh. However, unstructured datasets such as point sets remain a virgin land for topology scientists. The significance of point-based topology can never be overemphasized, especially in the area of reverse engineering, geometric modeling, and algorithmic analysis. In this paper, we propose a novel approach to compute the Betti number for point set data and illustrate its usefulness in real-world examples. To the best of our knowledge, our work is pioneering and the first of its kind in the field of computational topology.

Uncertain characterization of reservoir fluids due to brittleness of equation of state regression

Equations of state (EoS) play a central role in modeling the phase equilibrium of fluid mixtures. Their parameterization involves fitting a model to experimental data, i.e., solving a nonlinear, non-convex, multivariate optimization problem. The latter requires one to select design variables, domains of definition for each variable, and weights assigned to individual measurements. We demonstrate that subjective choices of an optimization algorithm and an initial guess also impact the regression process. Consequently, EoS predictions are fundamentally uncertain even after the EoS tuning to a limited set of experimental data points. We demonstrate this observation for two hydrocarbon reservoir fluids, in which five properties of the heaviest carbon fraction are treated as design variables. While all the optimization algorithms and initial guesses match experimental data for the gas and liquid properties, the resulting EoS parameterizations lead to dramatically different predictions of the fluid’s thermophysical behavior in the unsampled pressure and temperature regions. We propose the probabilistic treatment of design variables to quantify the predictive uncertainty of the resulting fluid models.

IDENTIFIABLE BOUNDED COMPONENT ANALYSIS VIA MINIMUM VOLUME ENCLOSING PARALLELOTOPE.

In this paper, we revisit bounded component analysis (BCA) and formulate it as a geometric problem of finding the minimum volume enclosing parallelotope (MVEP) of a set of data points in the Euclidean space. A parallelotope is an affine transformation of the standard box, also known as the -norm ball. An immediate benefit of the novel formulation is that the bounds on the supports of the latent components can be arbitrary, unlike most existing BCA works that assume the bounds are symmetric around zero. The main contribution is that the MVEP solution exactly recovers the latent components, up to the inherent (and inconsequential) permutation, shift, and scaling ambiguities, if the groundtruth components satisfy a so-called "sufficiently scattered" condition in the standard box. This is a great improvement to the existing result that requires all vertices of the box are contained in the data set, which requires exponentially many data points, or that of ICA, which essentially requires infinite amount of data points to guarantee exact recovery. We also present a new learning algorithm to solve the (NP-hard) MVEP problem based on Frank-Wolfe, and show numerically that the performance is surprisingly effective.

Sustainability Reporting: A Financial Reporting Perspective

ABSTRACT This paper examines incentive effects of sustainability reporting, based on proposals for mandatory sustainability reporting standards in the EU, the US, and the IFRS Foundation, and highlights conceptual differences between sustainability and financial reporting. Sustainability reporting is an instrument of transparency regulation intended to influence management decisions. It requires disclosure of a large set of data points but does not provide aggregate measures. It is production-oriented and does not include accruals. It expands reporting to include disclosure of long-term policies and targets, and of information of firms in the value chain. Consequently, sustainability reporting is not very useful for tracking sustainability performance and for comparisons across firms. Overall, it would benefit from applying more generally accepted accounting concepts.

Speedup of the k-Means Algorithm for Partitioning Large Datasets of Flat Points by a Preliminary Partition and Selecting Initial Centroids

Abstract A problem of partitioning large datasets of flat points is considered. Known as the centroid-based clustering problem, it is mainly addressed by the k-means algorithm and its modifications. As the k-means performance becomes poorer on large datasets, including the dataset shape stretching, the goal is to study a possibility of improving the centroid-based clustering for such cases. It is quite noticeable on non-sparse datasets that the resulting clusters produced by k-means resemble beehive honeycomb. It is natural for rectangular-shaped datasets because the hexagonal cells make efficient use of space owing to which the sum of the within-cluster squared Euclidean distances to the centroids is approximated to its minimum. Therefore, the lattices of rectangular and hexagonal clusters, consisting of stretched rectangles and regular hexagons, are suggested to be successively applied. Then the initial centroids are calculated by averaging within respective hexagons. These centroids are used as initial seeds to start the k-means algorithm. This ensures faster and more accurate convergence, where at least the expected speedup is 1.7 to 2.1 times by a 0.7 to 0.9 % accuracy gain. The lattice of rectangular clusters applied first makes rather rough but effective partition allowing to optionally run further clustering on parallel processor cores. The lattice of hexagonal clusters applied to every rectangle allows obtaining initial centroids very quickly. Such centroids are far closer to the solution than the initial centroids in the k-means++ algorithm. Another approach to the k-means update, where initial centroids are selected separately within every rectangle hexagons, can be used as well. It is faster than selecting initial centroids across all hexagons but is less accurate. The speedup is 9 to 11 times by a possible accuracy loss of 0.3 %. However, this approach may outperform the k-means algorithm. The speedup increases as both the lattices become denser and the dataset becomes larger reaching 30 to 50 times.

Automatic Schelling Point Detection From Meshes.

Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh Schelling points is time-consuming and expensive since the existing techniques are mostly based on participant observation studies. To overcome these limitations, we propose to employ powerful deep learning techniques to detect mesh Schelling points in an automatic manner, free from participant observation studies. Specifically, we utilize the mesh convolution and pooling operations to extract informative features from mesh objects, and then predict the 3D heat map of Schelling points in an end-to-end manner. In addition, we propose a Deep Schelling Network (DS-Net) to automatically detect the Schelling points, including a multi-scale fusion component and a novel region-specific loss function to improve our network for a better regression of heat maps. To the best of our knowledge, DS-Net is the first deep neural network for detecting Schelling points from 3D meshes. We evaluate DS-Net on a mesh Schelling point dataset obtained from participant observation studies. The experimental results demonstrate that DS-Net is capable of detecting mesh Schelling points effectively and outperforms various state-of-the-art mesh saliency methods and deep learning models, both qualitatively and quantitatively.

Qualitative inverse problems: mapping data to the features of trajectories and parameter values of an ODE model

Our recent work on linear and affine dynamical systems has laid out a general framework for inferring the parameters of a differential equation model from a discrete set of data points collected from a system being modeled. It introduced a new class of inverse problems where qualitative information about the parameters and the associated dynamics of the system is determined for regions of the data space, rather than just for isolated experiments. Rigorous mathematical results have justified this approach and have identified common features that arise for certain classes of integrable models. In this work we present a thorough numerical investigation that shows that several of these core features extend to a paradigmatic linear-in-parameters model, the Lotka–Volterra (LV) system, which we consider in the conservative case as well as under the addition of terms that perturb the system away from this regime. A central construct for this analysis is a concise representation of parameter and dynamical features in the data space that we call the P n -diagram, which is particularly useful for visualization of the qualitative dependence of the system dynamics on data for low-dimensional (small n) systems. Our work also exposes some new properties related to non-uniqueness that arise for these LV systems, with non-uniqueness manifesting as a multi-layered structure in the associated P 2-diagrams.

A [formula omitted] nearest neighbour ensemble via extended neighbourhood rule and feature subsets

kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting. The ordinary ensembles based on kNN find out the k nearest observations in a region (bounded by a sphere) based on a predefined value of k. This scenario, however, might not work in situations where the test observation follows the pattern of the closest data points with the same class that lie on a certain path not contained in the given sphere. This paper proposes a k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. This new ensemble method is applied on 20 benchmark datasets and compared with other classical methods, including kNN based models, in terms of classification accuracy, kappa and Brier score as performance metrics. Boxplots are also utilised to illustrate the difference in the results given by the proposed and other state-of-the-art methods. The proposed method outperformed the considered classical methods in the majority of cases. The proposed method is further assessed through a detailed simulation study.

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.