Structure Of Space Research Articles

Utilizing unsupervised machine learning to uncover novel phenogroups within the broad QRS complex

Abstract Background Conduction disease can manifest as a broad QRS complex on the ECG. Broad QRS is conventionally categorised as left bundle branch block (LBBB) and right bundle branch block (RBBB) or non-specific intraventricular conduction delay (IVCD), based on QRS morphology. These subgroups were coined over a century ago and have been relevant for heart failure with reduced ejection fraction and cardiac resynchronization therapy (CRT). However, there may be more precise phenogroups underlying broad QRS complexes. Purpose Using unsupervised machine learning to define novel broad QRS phenogroups and characterise them using clinical variables and disease outcomes. Methods We extracted 51 relevant features using a Variational Autoencoder from 47,456 median-beat ECGs with broad QRS intervals (QRS ≥ 130ms) from a secondary care cohort. These were input into Discriminative Dimensionality Reduction via Learning a Tree (DDRTree), a clustering method that models the continuum of heterogeneity by projecting the underlying distribution as a tree structure in a low-dimensional space, while assigning distinct branch/cluster labels. Results Six distinct phenogroups were identified within broad QRS morphologies (Figure-1). Two branches were RBBB dominant (Branch 1: more males, high-comorbidity; Branch 6: low-comorbidity/healthy-RBBB). Two branches were LBBB dominant (Branch 4: mixed IVCD with CHB burden; Branch 5: more females, CRT responders). Two branches were IVCD/mixed-phenotype (Branch 2: IVCD-dominant, high risk of future cardiovascular events; Branch 3: average/core branch). Within LBBB, Branch 4 and 5 showed similar trends with improving echocardiography profiles and CRT response when adjusted for covariates, each compared against the core branch. Branch 5 was distinct due its high risk of future HF (HR 1.19 [1.07-1.33] p < 0.01), cardiovascular death (HR 1.35 [1.20-1.50] p < 0.0001), all-cause mortality (HR 1.08 [1.01-1.16] p < 0.05) and CHB (HR 1.35 [1.16-1.56] p < 0.0001) (Figure-2). Conversely within RBBB, Branch 1 and 6 showed stark differences across various traits. Branch 1 captured a high-risk profile associated to higher risk of cardiovascular death (HR 1.37 [1.24-1.52] p < 0.0001), all-cause mortality (HR 1.20 [1.12-1.29] p < 0.0001) and CHB (HR 1.15 [1.01-1.32] p < 0.05) (Figure-2), while Branch 6 retained a low-risk phenotype. The tree dimensions also revealed relevant trends in cardiac anatomy and valvular dysfunction; the top-left region in Figure-1 associated with higher left ventricular ejection fraction and lower left ventricular end-diastolic dimension, lower left ventricular end-systolic dimension and a greater CRT response. Conclusion Through unsupervised methods we have identified conduction disease phenogroups with additive value for disease prognosis and CRT response prediction beyond traditional LBBB and RBBB labels. These novel phenogroups may help guide precision care for patients with a broad QRS complex.Figure-1Figure-2

Synthesis and observation of optical skyrmionic structure in free space

Synthesis and observation of optical skyrmionic structure in free space

Research on the Green Construction Technology of Stilt Houses Based on the Climate Adaptation of Transitional Seasons

Stilt houses are extremely adaptable to terrain and climate. However, current indoor thermal environment research in transitional seasons is not prominent. Therefore, in this research, a typical stilt house in southwest China was chosen as the research object and analyzed by combining Climate Consultant climate analysis software and field measurement data. The results showed that the indoor thermal stability of a stilt house was excellent during the transition season, and the attic had the most obvious climate regulation, with the maximum temperature difference between indoors and outdoors being 9 °C when the outdoor temperature was the highest. The difference between the mean radiant temperature and the average air temperature was only 0.04 °C, and the radiant effect of the enclosure on the interior was small. The indoor relative humidity ranged from 63.2% to 85.1%, showing high relative humidity, but the fluctuation was relatively stable. Stilt floors did not play a significant role in climate regulation during the transition season, and the semi-open space structure was more prone to moisture accumulation when the outdoor humidity was high. Regarding practical application, the climate adaptation strategies of shading, cooling, and dehumidification were applied in the transition season, but dehumidification was ineffective.

Vortices on cylinders and warped exponential networks

We study 3d N=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {N}=2$$\\end{document}U(1) Chern–Simons-Matter QFT on a cylinder C×R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C\ imes \\mathbb {R}$$\\end{document}. The topology of C gives rise to BPS sectors of low-energy solitons known as kinky vortices, which interpolate between (possibly) different vacua at the ends of the cylinder and at the same time carry magnetic flux. We compute the spectrum of BPS vortices on the cylinder in an isolated Higgs vacuum, through the framework of warped exponential networks, which we introduce. We then conjecture a relation between these and standard vortices on R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {R}^2$$\\end{document}, which are related to genus-zero open Gromov–Witten invariants of toric branes. More specifically, we show that in the limit of large Fayet–Iliopoulos coupling, the spectrum of kinky vortices on C undergoes an infinite sequence of wall-crossing transitions and eventually stabilizes. We then propose an exact relation between a generating series of stabilized CFIV indices and the Gromov–Witten disk potential and discuss its consequences for the structure of moduli spaces of vortices.

Aboveground and belowground sizes are aligned in the unified spectrum of plant form and function

Understanding the global variation of plant strategies is essential for unravelling eco-evolutionary processes and ecosystem functions. Variation in ten fundamental aboveground and fine-root traits is summarised in four dimensions, the first of which relates to aboveground plant size. However, there is no consensus about how root size fits within this scheme. Here, we add rooting depth and lateral spread, compiling a set of twelve key traits that define the fundamental investments of plants in growth, reproduction, and survival. We examine whether the inclusion of root size alters the dimensionality and structure of trait correlations defining plant functional strategies. Our results show that including root size traits does not alter the fundamental structure and dimensionality of the plant functional space, regardless of trait completeness and phylogenetic relatedness. Plant size defines a single continuum of allometric investments at the global scale, independent from leaf and root economic strategies.

Fractional Einstein–Gauss–Bonnet Scalar Field Cosmology

Our paper introduces a new theoretical framework called the Fractional Einstein–Gauss–Bonnet scalar field cosmology, which has important physical implications. Using fractional calculus to modify the gravitational action integral, we derived a modified Friedmann equation and a modified Klein–Gordon equation. Our research reveals non-trivial solutions associated with exponential potential, exponential couplings to the Gauss–Bonnet term, and a logarithmic scalar field, which are dependent on two cosmological parameters, m and α0=t0H0 and the fractional derivative order μ. By employing linear stability theory, we reveal the phase space structure and analyze the dynamic effects of the Gauss–Bonnet couplings. The scaling behavior at some equilibrium points reveals that the geometric corrections in the coupling to the Gauss–Bonnet scalar can mimic the behavior of the dark sector in modified gravity. Using data from cosmic chronometers, type Ia supernovae, supermassive Black Hole Shadows, and strong gravitational lensing, we estimated the values of m and α0, indicating that the solution is consistent with an accelerated expansion at late times with the values α0=1.38±0.05, m=1.44±0.05, and μ=1.48±0.17 (consistent with Ωm,0=0.311±0.016 and h=0.712±0.007), resulting in an age of the Universe t0=19.0±0.7 [Gyr] at 1σ CL. Ultimately, we obtained late-time accelerating power-law solutions supported by the most recent cosmological data, and we proposed an alternative explanation for the origin of cosmic acceleration other than ΛCDM. Our results generalize and significantly improve previous achievements in the literature, highlighting the practical implications of fractional calculus in cosmology.

Graph Regularized Sparse L2,1 Semi‐Nonnegative Matrix Factorization for Data Reduction

ABSTRACTNon‐negative Matrix Factorization (NMF) is an effective algorithm for multivariate data analysis, including applications to feature selection, pattern recognition, and computer vision. Its variant, Semi‐Nonnegative Matrix Factorization (SNF), extends the ability of NMF to render parts‐based data representations to include mixed‐sign data. Graph Regularized SNF builds upon this paradigm by adding a graph regularization term to preserve the local geometrical structure of the data space. Despite their successes, SNF‐related algorithms to date still suffer from instability caused by the Frobenius norm due to the effects of outliers and noise. In this paper, we present a new SNF algorithm that utilizes the noise‐insensitive norm. We provide monotonic convergence analysis of the SNF algorithm. In addition, we conduct numerical experiments on three benchmark mixed‐sign datasets as well as several randomized mixed‐sign matrices to demonstrate the performance superiority of SNF over conventional SNF algorithms under the influence of Gaussian noise at different levels.

Gentrius: generating trees compatible with a set of unrooted subtrees and its application to phylogenetic terraces.

For a set of binary unrooted subtrees generating all binary unrooted trees compatible with them, i.e. generating their stand, is one of the classical problems in phylogenetics. Here, we introduce Gentrius - an efficient algorithm to tackle this task. The algorithm has a direct application in practice. Namely, Gentrius generates phylogenetic terraces - topologically distinct, equally scoring trees due to missing data. Despite stand generation being computationally intractable, we showed on simulated and biological datasets that Gentrius generates stands with millions of trees in feasible time. We exemplify that depending on the distribution of missing data across species and loci and the inferred phylogeny, the number of equally optimal terrace trees varies tremendously. The strict consensus tree computed from them displays all the branches unaffected by the pattern of missing data. Thus, by solving the problem of stand generation, in practice Gentrius provides an important systematic assessment of phylogenetic trees inferred from incomplete data. Furthermore, Gentrius can aid theoretical research by fostering understanding of tree space structure imposed by missing data.

AdS/CFT correspondence for the O(N) invariant critical φ4 model in 3-dimensions by the conformal smearing

We investigate a structure of a 4-dimensional bulk space constructed from the O(N) invariant critical φ4 model in 3-dimension using the conformal smearing. We calculate a bulk metric corresponding to the information metric and the bulk-to-boundary propagator for a composite scalar field φ2 in the large N expansion. We show that the bulk metric describes an asymptotic AdS space at both UV (near boundary) and IR (deep in the bulk) limits, which correspond to the asymptotic free UV fixed point and the Wilson-Fisher IR fixed point of the 3-dimensional φ4 model, respectively. The bulk-to-boundary scalar propagator, on the other hand, encodes ∆φ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\Delta }_{\\varphi^2} $$\\end{document} (the conformal dimension of φ2) into its z (a coordinate in the extra direction of the AdS space) dependence. Namely it correctly reproduces not only ∆φ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\Delta }_{\\varphi^2} $$\\end{document} = 1 at UV fixed point but also ∆φ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\\Delta }_{\\varphi^2} $$\\end{document} = 2 at the IR fixed point for the boundary theory. Moreover, we confirm consistency with the GKP-Witten relation in the interacting theory that the coefficient of the z∆φ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {z}^{\\Delta _{\\varphi^2}} $$\\end{document} term in z → 0 limit agrees exactly with the two-point function of φ2 including an effect of the φ4 interaction.

Effective action and black hole solutions in asymptotically safe quantum gravity

We derive the quantum effective action and the respective quantum equations of motion from multi-graviton correlation functions in asymptotically safe quantum gravity. The fully momentum-dependent couplings of three- and four-graviton scatterings are computed within the functional renormalization group approach and the effective action is reconstructed from these vertices. The resulting quantum equations of motion are solved numerically for quantum black hole geometries. Importantly, the black hole solutions show signatures of quantum gravity outside the classical horizon, which manifest in the behavior of the temporal and radial components of the metric. Three different types of solutions with distinct causal structures are identified and the phase structure of the solution space is investigated. Published by the American Physical Society 2024

The Study of Fixed Point Theorem over Modular F-metric Spaces

One of the most significant discoveries in the annals of mathematical history is Schauder's fixed-point theorem, which is generally recognized to be among the most important discoveries. The fact that the Brüwer fixed point hypothesis cannot be used in dimensions of space that are infinitely large is one of the most important discoveries in the history of mathematics. The facts that have been supplied make it feasible to claim that the great majority of them are of a topological nature. In 2019, N. Manav and D. Turkoglu introduced a new class of generalized metric space called modular F metric space as a generalization of metric space. It is generally agreed upon that this is one of the most significant discoveries that has been made in the subject of mathematics that has ever been produced. In this article, we study the basic structure of modular F-metric spaces and the concept of an equivalent relationship between modular F metric space and modular F metric bounded space. Moreover, we study the fixed point theorem (New version of Banach Contraction Principle) over modular F metric spaces. This is something that one would be able to predict occurring given the circumstances that are present. These results extend, broaden, and integrate many previously published results.

Assessing the impact of the unfinished part of the Cascade architectural and monumental complex as a communicative core between Kentron and Kanaker-Zeytun districts in Yerevan and a proposal for solving the urban planning problem

The paper considers the problem of communication between Kentron and Kanaker-Zeytun districts in Yerevan. Particular attention is paid to the Cascade architectural and monumental complex. It obtains an important urban planning significance in the structure of public spaces in Yerevan and is considered by citizens as one of main symbols of the capital of Armenia. The study is aimed at identifying the grounds for poor pedestrian communication between the districts of the city and determining the prospects of development, transformation, and modernization of the territory. The methodology involved a comparative analysis of public spaces in the city center, publicly available GPS tracks and the results of a sociological survey. The results of the urban planning analysis outline the problem of pedestrian accessibility in the city, and demonstrate the relationship between public spaces. The sociological survey depicts the demands of the citizens and guests of Yerevan for the further development of the Cascade architectural and monumental complex. Studying the qualitative and quantitative characteristics of public spaces revealed the problems of communication between the districts of the city on the northsouth compositional axis, which are associated with the incomplete construction of the fifth terrace of the complex. Sociological research necessitates the construction to be completed. The survey shows the opinions of citizens and guests of the city about the potential development of the Cascade environment.

Composite dyadic models for spatio-temporal data.

Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

On fuzzy inner products constructed by fuzzy numbers in linear spaces

In this paper a new definition for fuzzy inner product spaces is presented. Just as the set of real numbers can be embedded in a set of fuzzy numbers, a crisp inner product space can be considered as a special case of fuzzy inner product spaces. An example is given to demonstrate that, the new definition is a nontrivial generalization for the crisp inner product spaces. Under certain restrictions, a fuzzy inner product space can become a fuzzy normed space. Based on some elementary properties for the families of semi-inner products of endpoints, the linearly topological structure of new spaces is discussed. Moreover, the orthogonality between two vectors is considered and a fuzzy version of Pythagorean theorem is given.

Quasi-One-Dimensional Spin Transport in Altermagnetic Z^{3} Nodal Net Metals.

In three dimensions, quasi-one-dimensional (Q1D) transport has traditionally been associated with systems featuring a Q1D chain structure. Here, based on first-principle calculations, we go beyond this understanding to show that the Q1D transport can also be realized in certain three-dimensional (3D) altermagnetic (AM) metals with a topological nodal net in momentum space but lacking Q1D chain structure in real space, including the existing compounds β-Fe_{2}(PO_{4})O, Co_{2}(PO_{4})O, and LiTi_{2}O_{4}. These materials exhibit an AM ground state and feature an ideal crossed Z^{3} Weyl nodal line in each spin channel around Fermi level, formed by three straight and flat nodal lines traversing the entire Brillouin zone. These nodal lines eventually lead to an AM Z^{3} nodal net. Surprisingly, the electronic conductivity σ_{xx} in these topological nodal net metals is dozens of times larger than σ_{yy} and σ_{zz} in the up-spin channel, while σ_{yy} dominates transport in the down-spin channel. This suggests a distinctive Q1D transport signature in each spin channel, and the principal moving directions for the two spin channels are orthogonal, resulting in Q1D direction-dependent spin transport. This novel phenomenon cannot be found in both conventional 3D bulk materials and Q1D chain materials. In particular, the Q1D spin transport gradually disappears as the Fermi energy moves away from the nodal net, further confirming its topological origin. Our Letter not only enhances the comprehension of topological physics in altermagnets but also opens a new direction for the exploration of topological spintronics.

The cosmological constant and energy quantization in hydrogen atom in light of a doubly special relativity with a minimum speed

We show the existence of an invariant minimum speed in space–time as a new fundamental constant of nature by forming the basis of a doubly special relativity so-called Symmetrical Special Relativity (SSR). Such an observer-independent minimum speed emerges from the Dirac’s Large Number Hypothesis (LNH), which leads us to build SSR-theory. This allows us to understand that the hydrogen atom represents the most stable bound state in the universe as being a fundamental structure in space–time with two speed limits, i.e., the speed of light c and a minimum speed V. So the symmetry in space–time given by an invariant minimum speed, which has origin in the electrical and gravitational bound states in hydrogen atom is able to provide a deeper understanding of the proton-electron mass ratio. We investigate how the minimum speed plays a fundamental role in the quantization of energy in hydrogen atom. The minimum speed V is related to a preferred background frame (ultra-referential SV) represented by the surface of a dark cone forbidden to the world line of any particle. It is related to the vacuum energy that leads to the cosmological constant. An investigation of SSR is done and a hypothetical experiment of Bose–Einstein condensation is proposed to detect the minimum speed as a possible Lorentz violation at very low energies.

Multiple Heterogeneous Networks Representation With Latent Space for Synthetic Lethality Prediction.

Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.

RNA infrastructure profiling illuminates transcriptome structure in crowded spaces.

RNAs fold into compact structures and undergo protein interactions in cells. These occluded environments can block reagents that probe the underlying RNAs. Probes that can analyze structure in crowded settings can shed light on RNA biology. Here, we employ 2'-OH-reactive probes that are small enough to access folded RNA structure underlying close molecular contacts within cells, providing considerably broader coverage for intracellular RNA structural analysis. The data are analyzed first with well-characterized human ribosomal RNAs and then applied transcriptome-wide to polyadenylated transcripts. The smallest probe acetylimidazole (AcIm) yields 80% greater structural coverage than larger conventional reagent NAIN3, providing enhanced structural information in hundreds of transcripts. The acetyl probe also provides superior signals for identifying m6A modification sites in transcripts, particularly in sites that are inaccessible to a standard probe. Our strategy enables profiling RNA infrastructure, enhancing analysis of transcriptome structure, modification, and intracellular interactions, especially in spatially crowded settings.

Preserving text space integrity for robust compositional zero-shot learning via mixture of pretrained experts

In the current landscape of Compositional Zero-Shot Learning (CZSL) methods that leverage CLIP, the predominant approach is based on prompt learning paradigms. These methods encounter significant computational complexity when dealing with a large number of categories. Additionally, when confronted with new classification tasks, there is a necessity to learn the prompts again, which can be both time-consuming and resource-intensive. To address these challenges, We present a new methodology, named the Mixture of Pretrained Expert (MoPE), for enhancing Compositional Zero-shot Learning through Logit-Level Fusion with Mutile Expert Fusion Module. The MoPE skillfully blends the benefits of extensive pre-trained models like CLIP, Bert, GPT-3 and Word2Vec for effectively tackling Compositional Zero-shot Learning. Firstly, we extract the text label space for each language model individually, then map the visual feature vectors to their respective text spaces. This maintains the integrity and structure of the original text space. During this process, the pre-trained expert parameters are kept frozen. The mapping of visual features to the corresponding text spaces is subject to learning and could be considered as multiple learnable visual experts. In the model fusion phase, we propose a new fusion strategy that features a gating mechanism that adjusts the contributions of various models dynamically. This enables our approach to adapt more effectively to a range of tasks and data sets. The method’s robustness is demonstrated by the fact that the language model is not tailored to specific downstream task datasets or losses. This preserves the larger model’s topology and expands the potential for application. Preliminary experiments conducted on the UT-Zappos, AO-Clever, and C-GQA datasets indicate that MoPE performs competitively when compared to existing techniques.

Filling in the blanks

Context. In the solar neighbourhood, only ∼2% of stars in the Gaia survey have a line-of-sight velocity (vlos) contained within the RVS catalogue. These limitations restrict conventional dynamical analysis, such as finding and studying substructures in the stellar halo. Aims. We aim to present and test a method to infer a probability density function (PDF) for the missing vlos of a star with 5D information within 2.5 kpc. This technique also allows us to infer the probability that a 5D star is associated with the Milky Way’s stellar Disc or the stellar Halo, which can be further decomposed into known stellar substructures. Methods. We use stars from the Gaia DR3 RVS catalogue to describe the local orbital structure in action space. The method is tested on a 6D Gaia DR3 RVS sample and a 6D Gaia sample crossmatched to ground-based spectroscopic surveys, stripped of their true vlos. The stars predicted vlos, membership probabilities, and inferred structure properties are then compared to the true 6D equivalents, allowing the method’s accuracy and limitations to be studied in detail. Results. Our predicted vlos PDFs are statistically consistent with the true vlos, with accurate uncertainties. We find that the vlos of Disc stars can be well-constrained, with a median uncertainty of 26 km s−1. Halo stars are typically less well-constrained with a median uncertainty of 72 km s−1, but those found likely to belong to Halo substructures can be better constrained. The dynamical properties of the total sample and subgroups, such as distributions of integrals of motion and velocities, are also accurately recovered. The group membership probabilities are statistically consistent with our initial labelling, allowing high-quality sets to be selected from 5D samples by choosing a trade-off between higher expected purity and decreasing expected completeness. Conclusions. We have developed a method to estimate 5D stars’ vlos and substructure membership. We have demonstrated that it is possible to find likely substructure members and statistically infer the group’s dynamical properties.