Set Of Points Research Articles

Emotional and psychological safety in the context of digital transformation in healthcare: a mixed-method strategic foresight study

BackgroundPerceived safety has received attention in the digital transformation of healthcare. However, the impact of perceived safety on the future of digital transformation has not been fully elucidated.AimTo investigate perceived...

Lower density soft operators and density soft topologies

The classical density topology is derived from the Lebesgue density points of Lebesgue measurable sets. Wilczynski proposed a more general scheme for defining density points. It turns out that his scheme works in the category sense in addition to the measure-theoretic one. The sets of density points necessitate some properties of the operator of density points. Such an operator is named the lower density operator. In this theme, we introduce lower density soft operators on (abstract) chargeable soft spaces along with their basic properties. Among others, we show that each lower density soft operator defines a system of parametric lower density operators. Then, we study density soft topologies, the soft topologies generated by lower density soft operators. The main attributes and topological properties of density soft topologies are analyzed. We finalize this work by demonstrating several characterizations of (simple) soft density topologies. In particular, we prove that in density soft topologies, the Borel soft σ-algebra coincides with the soft σ-algebra of soft sets with the Baire property.

Weak markov flows and extreme points of convex sets

Weak markov flows and extreme points of convex sets

CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.

Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been proved that exploring and embedding context knowledge in deep network can significantly improve accuracy. To emphasize these tips, we present CDT-CAD, i.e., context-aware deformable transformers for end-to-end chest abnormality detection on X-Ray images. CDT-CAD first constructs an iterative context-aware feature extractor, which not only enlarges receptive fields to encode multi-scale context information via dilated context encoding blocks, but also captures unique and scalable feature variation patterns in wavelet frequency domain via frequency pooling blocks. Afterwards, a deformable transformer detector on the extracted context features is built to accurately classify disease categories and locate regions, where a small set of key points are sampled, thus leading the detector to focus on informative feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det 10 Datasets, CDT-CAD demonstrates its effectiveness in recognizing chest abnormities and outperforms 1.4% and 6.0% than the existing methods in AP50 and AR on VinBig dateset, and 0.9% and 2.1% on Chest Det-10 dataset, respectively.

On a family of billiards-inspired operators over intuitionistic fuzzy sets

In the present work we introduce a family of geometrically inspired operators over intuitionistic fuzzy sets. In essence, if we consider the interpretational triangle as a billiards table with certain properties and each point of an intuitionistic fuzzy set as a ball propelled with a predetermined initial force, then its image after bouncing off from the boundaries of the triangle will, in general, be a new and different intuitionistic fuzzy point. The value of this image depends on the magnitude and direction of the force, which we will describe by using a parameter λ > 0 and another intuitionistic fuzzy set over the same universe.

Unexpected Properties of the Klein Configuration of 60 Points in P3

Felix Klein in course of his study of the regular icosahedron and its symmetries encountered a highly symmetric configuration of $60$ points in ${\mathbb P}^3$. This configuration has appeared in various guises, perhaps post notably as the configuration of points dual to the $60$ reflection planes in the group $G_{31}$ in the Shephard-Todd list. In the present note we show that the $60$ points exhibit interesting properties relevant from the point of view of two paths of research initiated recently. Firstly, they give rise to two completely different unexpected surfaces of degree $6$. Unexpected hypersurfaces have been introduced by Cook II, Harbourne, Migliore, Nagel in 2018. One of unexpected surfaces associated to the configuration of $60$ points is a cone with a single singularity of multiplicity $6$ and the other has three singular points of multiplicities $4,2$ and $2$. Secondly, Chiantini and Migliore observed in 2020 that there are non-trivial sets of points in ${\mathbb P}^3$ with the surprising property that their general projection to ${\mathbb P}^2$ is a complete intersection. They found a family of such sets, which they called grids. An appendix to their paper describes an exotic configuration of $24$ points in ${\mathbb P}^3$ which is not a grid but has the remarkable property that its general projection is a complete intersection. We show that the Klein configuration is also not a grid and it projects to a complete intersections. We identify also its proper subsets, which enjoy the same property. \

LPOT: Locality-Preserving Gromov-Wasserstein Discrepancy for Nonrigid Point Set Registration.

The main problems in point registration involve recovering correspondences and estimating transformations, especially in a fully unsupervised way without any feature descriptors. In this work, we propose a robust point matching method using discrete optimal transport (OT), which is a natural and useful approach for assignment tasks, to recover the underlying correspondences and improve the nonrigid registration in the presence of unknown global transformations. Specifically, we cast the registration problem as a joint estimation over local transport couplings and global transformations, observing that the local neighborhood topology structures should be preserved strongly and stably for nonrigid transformations. By solving the Gromov-Wasserstein discrepancy, a smooth assignment matrix from one point set to another can be recovered in a fully unsupervised way. Registration performance can be improved by applying an unsupervised map to guide the transformation estimate under the alternating optimization. Experimental results on several datasets reveal how the presented method is superior to the state-of-the-art methods when facing large data degradations.

Accurate Registration of Cross-Modality Geometry via Consistent Clustering.

The registration of unitary-modality geometric data has been successfully explored over past decades. However, existing approaches typically struggle to handle cross-modality data due to the intrinsic difference between different models. To address this problem, in this article, we formulate the cross-modality registration problem as a consistent clustering process. First, we study the structure similarity between different modalities based on an adaptive fuzzy shape clustering, from which a coarse alignment is successfully operated. Then, we optimize the result using fuzzy clustering consistently, in which the source and target models are formulated as clustering memberships and centroids, respectively. This optimization casts new insight into point set registration, and substantially improves the robustness against outliers. Additionally, we investigate the effect of fuzzier in fuzzy clustering on the cross-modality registration problem, from which we theoretically prove that the classical Iterative Closest Point (ICP) algorithm is a special case of our newly defined objective function. Comprehensive experiments and analysis are conducted on both synthetic and real-world cross-modality datasets. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches with higher accuracy and robustness. Our code is publicly available at https://github.com/zikai1/CrossModReg.

Distance mapping overlap complexity metric for class-imbalance problems

Data complexity for class-imbalance problems is a hot topic in the field of data mining. Classical data complexity measures use techniques such as kNN, which computes the nearest neighbors of each instance. However, the kNN-based method to obtain the nearest neighbors of all instances is an NP-hard problem, which is not conducive to large-scale data complexity calculations. In order to solve this problem, from the global and local perspectives, four distance map complexity metric methods are proposed, and the complexity values is called the distance map overlap index (DMOI). Firstly, we use Mahalanobis distance or standardized Euclidean distance to calculate the distance of the instance to the center point of the data set, and arrange by the distance value of each instance. Then, according to the sequential label vector, iteratively searches the number of map cut-points for each class. Finally, according to the number of map cut-points of different classes, the DMOI of the data set is calculated. Experiments on 50 class-imbalanced datasets show that the DMOI method outperforms state-of-the-art complexity measures for class-imbalanced problems. Although there is still a gap between DMOI and ONB for rule-based or tree-based classifiers, the use of Pearson correlation coefficient shows that DMOI can effectively approximate ONB, and there is a strong positive correlation between them.

Scaling Type-Based Points-to Analysis with Saturation

Designing a whole-program static analysis requires trade-offs between precision and scalability. While a context-insensitive points-to analysis is often considered a good compromise, it still has non-linear complexity that leads to scalability problems when analyzing large applications. On the other hand, rapid type analysis scales well but lacks precision. We use saturation in a context-insensitive type-based points-to analysis to make it as scalable as a rapid type analysis, while preserving most of the precision of the points-to analysis. With saturation, the points-to analysis only propagates small points-to sets for variables. If a variable can have more values than a certain threshold, the variable and all its usages are considered saturated and no longer analyzed. Our implementation in the points-to analysis of GraalVM Native Image, a closed-world approach to build standalone binaries for Java applications, shows that saturation allows GraalVM Native Image to analyze large Java applications with hundreds of thousands of methods in less than two ‍minutes.

Sequential good lattice point sets for computer experiments

Sequential Latin hypercube designs have recently received great attention for computer experiments. Much of the work has been restricted to invariant spaces. The related systematic construction methods are inflexible while algorithmic methods are ineffective for large designs. For such designs in space contraction, systematic construction methods have not been investigated yet. This paper proposes a new method for constructing sequential Latin hypercube designs via good lattice point sets in a variety of experimental spaces. These designs are called sequential good lattice point sets. Moreover, we provide fast and efficient approaches for identifying the (nearly) optimal sequential good lattice point sets under a given criterion. Combining with the linear level permutation technique, we obtain a class of asymptotically optimal sequential Latin hypercube designs in invariant spaces where the $L_1$-distance in each stage is either optimal or asymptotically optimal. Numerical results demonstrate that the sequential good lattice point set has a better space-filling property than the existing sequential Latin hypercube designs in the invariant space. It is also shown that the sequential good lattice point sets have less computational complexity and more adaptability.

Accelerating Globally Optimal Consensus Maximization in Geometric Vision.

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an n-1 dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to four fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, point set registration, and rotation and focal length estimation. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios.

Controlul civil democratic asupra Forțelor Armate ale Republicii Moldova în contextul aspirațiilor de aderare la Uniunea Europeană

Based on the experience of other countries, in the current context of regional security, the enhanced relations with NATO, and the status of candidate country for EU membership, the Republic of Moldova has to pay increased attention to its security and defence. It includes not only increasing budget allocations for the defence sector, professionalizing the armed forces or participating with troops alongside other European countries in various theatres of operations, but also ensuring institutional transparency and rigorous implementation of EU standards of civilian democratic control over the armed forces. The effectiveness of democratic civilian control will contribute to enhancing national security and will bring the Republic of Moldova closer to the European community. This article briefly discusses the evolution of democratic civilian control over the armed forces of the Republic of Moldova from a theoretical and practical point of view and sets up some recommendations.

Democratic Civilian Control over the Armed Forces in the Context of the Aspirations of the Republic of Moldova to Join the European Union

Based on the experience of other countries, in the current context of regional security, the enhanced relations with NATO, and the status of candidate country for EU membership, the Republic of Moldova has to pay increased attention to its security and defence. It includes not only increasing budget allocations for the defence sector, professionalizing the armed forces or participating with troops alongside other European countries in various theatres of operations, but also ensuring institutional transparency and rigorous implementation of EU standards of civilian democratic control over the armed forces. The effectiveness of democratic civilian control will contribute to enhancing national security and will bring the Republic of Moldova closer to the European community. This article briefly discusses the evolution of democratic civilian control over the armed forces of the Republic of Moldova from a theoretical and practical point of view and sets up some recommendations.

On absolutely friendly measures on QSd$\mathbb {Q}_S^d$

AbstractIn this paper, we extend the work of Pollington and Velani [Selecta Math. 11(2005)] to an ‐arithmetic set‐up, where is a finite set of valuations of . In particular, for an absolutely friendly measure supported on a compact set in , we give a summation condition on an approximating function such that almost no point in the compact set is approximable. The crucial ingredient is a version of the simplex lemma that we prove dynamically.

Growth and gut comfort of healthy term infants exclusively fed with a partially hydrolysed protein-based infant formula: a randomized controlled double-blind trial.

This study aimed to investigate growth and gut comfort of healthy infants fed with a partially hydrolysed cow's milk protein-based infant formula (pHF) compared to a standard intact cow's milk protein-based formula (IPF). A double-blind, multi-center, randomized, controlled trial was performed. Healthy full-term, exclusively formula-fed infants (n = 345), aged ≤28 days were allocated to consume either a pHF (n = 173) or an IPF (n = 172) until the age of 17 weeks. The primary outcome was equivalence of weight gain (g/d) until the age of 17 weeks. The secondary outcomes were equivalence of other growth parameters, i.e., infants' weight, length, head circumference, body mass index (BMI) and anthropometric Z-scores, while tertiary outcomes were gut comfort, formula intake, and adverse events (AEs). Overall, 288 infants completed the study (pHF group: 138, IPF group: 150). No differences were observed between the two groups in weight gain (g/d) during the three-months intervention [p = 0.915 for the Per Protocol (PP) population]. The 90% CI was [-1.252 to 1.100] being within the pre-defined equivalence margin of ±3.0 g/d. Similar findings were observed in the Full Analysis Set (FAS) and the sensitivity analysis. Regarding the secondary outcomes, no differences over the intervention period were shown between the two groups in both the PP and FAS analysis sets. Average Z-scores were in the normal range based on World Health Organization (WHO) growth standards for both groups at all time points in both analysis sets. Stool consistency, amount, and colour were different in the two groups. No differences were observed in gut comfort, stool frequency, and formula intake, between the two groups. In total 14 AEs and 22 serious adverse events (SAEs) were reported of which 15 (12%) and 1 (5%) were considered as (possibly) related to the study product, respectively. The study demonstrates that the consumption of pHF results in adequate infant growth, equivalent to that of infants consuming IPF. Furthermore, the overall gut comfort was comparable between the two groups. Therefore, it can be concluded that the pHF is safe for and well tolerated by healthy infants. https://clinicaltrials.gov/study/NCT05757323?id=NCT05757323&rank=1, identifier (NCT05757323).

Analysis of numerical modeling of steady-state modes of methane–hydrogen mixture transportation through a compressor station to reduce CO2 emissions

This study presents a mathematical model to evaluate the performance of gas pipelines during hydrogen injection in a gas pipeline-compressor station. The developed model presents the calculation of methane–hydrogen mixture (CH4/H2) transportation through the compressor station, where the compensation of pressure drops in the mass and energy balance takes place. Simultaneously, in the operation of the centrifugal blower system of gas compressor stations, the emissions of CO2 are considered, considering the mixing of gas media and the compression of CH4/H2. This mathematical model is realized for the pipeline transportation of hydrogen, at which the principle of mixture expansion occurs. The aim is to solve the problem of CO2 emissions at compressor stations. The optimization procedure has been formulated using a system of nonlinear algebraic equalities. The research focuses on the adaptation of existing gas transportation systems to CH4/H2 transportation and the impact of environmental risks on the operation of compressor station equipment. In this case, it is possible to determine the quantitative amount of hydrogen that can be added to natural gas. By solving the problem of finding the inner point of sets using the system of nonlinear algebraic equalities, it is possible to obtain the control parameters for safety control of technological modes of CH4/H2 mixture transportation. The study findings reveal that the consumption of gas charger and hydrogen was 50.67 and 0.184 kg/s, respectively, and the estimated efficiency resulting from the modified turbine design was 75.1 percent. These results indicate that the equipment operates more efficiently when hydrogen is being transported. The numerical analytical results indicated in this study hold practical significance for design applications. It will assist in identifying and evaluating the restrictions that may develop during the technological, operational, and design stages of decision-making.

High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.

Populist Storytelling and Negative Affective Polarization: Social Media Evidence from Mexico

ABSTRACT The ideational definition of populism proposes that a narrative is populist if it is characterized by a Manichean cosmology that divides the political community between a “people,” conceived as a homogeneously virtuous entity, and an “elite,” conceived as a homogeneously corrupt entity. Departing from that conceptualization, this work first investigates the specific stories that Andrés Manuel López Obrador uses to spread his populist worldview, which we call “storytelling.” We define the idea of storytelling as the art of telling a story where emotions, characters and other details are applied in order to promote a particular point of view or set of values. Second, we explore whether some of those stories produce greater negative affective polarization, here defined as the extent to which rival sociopolitical camps view each other as a disliked out-group. Findings suggest that some specific stories—in particular, what we call “stories of conspiracy” and “stories of ostracism”—indeed tend to induce more polarized attitudes among citizens.

Real-time Welding Machine Status Recognition using Embedded Perceptron Color Classification Model

This paper aims to utilize the perceptron model in machine learning to achieve precise classification of the working status of welding machines by analyzing image data. We collected static images of welding and non-welding operations, extracted the R, G, B color values of the images, and labeled them accordingly (+1 for welding status, -1 for idle status). By employing the stochastic gradient descent method for training the perceptron model, we obtained the weights w and bias b of the model under the condition that all data points in the training set are correctly classified. The model's recognition accuracy was validated on the test set. Ultimately, we applied this model to the STM32f103zet6 microcontroller chip and successfully achieved real-time recognition of the welding machine's operational status during the welding process via a camera. Through testing, the classification accuracy reached 83.6%. Further optimization of classification criteria can be combined with relevant expert suggestions, providing a reference solution for monitoring and identifying the working status of welding machines in steel pipe production.