Merging Algorithm Research Articles

A link to the past: characterizing wandering black holes in Milky Way-type galaxies

Abstract A population of non-stellar black holes (≳100 M⊙) has been long predicted to wander the Milky Way. We aim to characterize this population by using the L-Galaxies semi-analytical model applied on top of the high resolution Millennium-II merger trees. Our results predict ∼10 wandering black holes with masses ∼2 × 103 M⊙ in a typical z = 0 Milky Way galaxy, accounting for $\sim 2\%$ of the total non-stellar black hole mass budget of the galaxy. We find that the locations of these wanderers correlate with their formation scenario. While the ones concentrated at ≲1 kpc from the galactic nucleus on the disc come from past galactic mergers, the ones formed as a consequence of ejections due to gravitational recoils or the disruption of satellite galaxies are typically located at ≳100 kpc. Such small and large distances might explain the absence of strong observational evidence for wandering black holes in the Milky Way. Our results also indicate that $\sim 67\%$ of the wandering population is conformed by the leftovers of black hole seeds that had little to no growth since their formation. We find that wandering black holes that are leftover seeds become wanderers at an earlier time with respect to grown seeds, and also come from more metal-poor galaxies. Finally, we show that the number of wandering black holes in a Milky Way-type galaxy depends on the seeding efficiency.

Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set

ABSTRACT This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.

A performant energy-conserving particle reweighting method for Particle-in-Cell simulations

A new particle-based reweighting method is developed and demonstrated in the Aleph Particle-in-Cell with Direct Simulation Monte Carlo (PIC-DSMC) program. Novel splitting and merging algorithms ensure that modified particles maintain physically consistent positions and velocities. This method allows a single reweighting simulation to efficiently model plasma evolution over orders of magnitude variation in density, while accurately preserving energy distribution functions (EDFs). Demonstrations on electrostatic sheath and collisional rate dynamics show that reweighting simulations achieve accuracy comparable to fixed weight simulations with substantial computational time savings. This highly performant reweighting method is recommended for modeling plasma applications that require accurate resolution of EDFs or exhibit significant density variations in time or space.

An in-depth study of sorting algorithms

Sorting algorithms are very popular fundamental algorithms in the field of computer science. Its job is sorting statistics so that they are arranged according to certain rules. In this paper, the kinds of sorting algorithms and their research method of them will be discussed. It will consist of discussing bubble sort, selection sorting, insertion sorting, hill sorting, and merge sorting. Those of them are that arrange a group of elements according to a certain number of ones. They are widely used in computer science and data processing in all walks of life. The most common sorting methods are numerical order and lexicographic order. Efficient sorting algorithms are significant in some of the algorithms, such as search and merge algorithms, so that they can arrive at the correct solution. Dealing with textual data and producing readable output are also important applications of sorting. The outcome is a permutation or recombination of the original input. So learning about the principles of those sorting algorithms by recognizing the code or reading pictures is significant. It is also necessary to be familiar with the applications of those algorithms which are beneficial to the daily life.

An Estimate of the Impact of Reionization on Supermassive Black Hole Growth

The supermassive black holes (SMBHs) that power active galactic nuclei found at z ≥ 6 were formed during the Epoch of Reionization. Because reionization is an inhomogeneous process, the physical properties of SMBH host-galaxy environments will vary spatially during reionization. We construct a semi-analytic model to estimate the impact of reionization on SMBH growth. Using a series of merger trees, reionization models, and black hole (BH) growth models, we find that early reionization can reduce a SMBH’s mass by up to [50, 70, 90]% within dark matter halos of mass [1012, 1011, 1010] M ⊙ by z = 6. Our findings also suggest that the redshift range in which BH growth is impacted by reionization strongly depends on whether the Eddington accretion rate can be exceeded. If so, we find that BH masses are significantly suppressed principally during the early phases of reionization (z ≳ 10), while they are more readily suppressed across the full redshift range if super-Eddington growth is not allowed. We find that the global average impact of reionization may be to reduce the masses of BHs residing in ≲1011 M ⊙ halos by a factor of ≳2. The census of SMBHs being uncovered by the JWST may offer a means to test the basic prediction that more massive BHs reside in cosmological volumes that are reionized at later times.

Fast Comparative Analysis of Merge Trees Using Locality Sensitive Hashing.

Scalar field comparison is a fundamental task in scientific visualization. In topological data analysis, we compare topological descriptors of scalar fields-such as persistence diagrams and merge trees-because they provide succinct and robust abstract representations. Several similarity measures for topological descriptors seem to be both asymptotically and practically efficient with polynomial time algorithms, but they do not scale well when handling large-scale, time-varying scientific data and ensembles. In this paper, we propose a new framework to facilitate the comparative analysis of merge trees, inspired by tools from locality sensitive hashing (LSH). LSH hashes similar objects into the same hash buckets with high probability. We propose two new similarity measures for merge trees that can be computed via LSH, using new extensions to Recursive MinHash and subpath signature, respectively. Our similarity measures are extremely efficient to compute and closely resemble the results of existing measures such as merge tree edit distance or geometric interleaving distance. Our experiments demonstrate the utility of our LSH framework in applications such as shape matching, clustering, key event detection, and ensemble summarization.

UTILIZING DEEP LEARNING AND PERSUASIVE TECHNOLOGY TO INCREASE USER TRUST IN DIGITAL PAYMENTS

The paper examines the foundations of user trust in digital payments, using quantitative analysis of data from 277 participants. The study focuses on the associations between perceived security, convenience, privacy protection, and user trust in digital payments. Our findings suggest that all three attributes, safety, security, and privacy, are powerful positive correlates of trust. Furthermore, past experience with digital payments (PEDP) modifies these relationships, suggesting that the impact of these attributes on trust can have significant implications for engaged user digital payments are greatly enhanced We can use persuasive design principles and advanced deep learning to enhance user trust. The study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for enhanced feature extraction and temporal sequence analysis of user interactions and preferences within digital payment environments. This model is complemented by a hybrid optimization algorithm merging the Zebra Optimization Algorithm (ZOA) and Seagull Optimization Algorithm (SOA), aimed at refining system performance and user engagement through iterative enhancement of persuasive elements. This paper lays the groundwork for further research on other factors affecting the use of trust and cultural diversity in digital payment channels. Combining mind-blowing technologies with sophisticated deep learning techniques, it seeks to develop digital payment systems that not only secure transactions but also provide lasting user trust, satisfaction planning and implementation improvements.

Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams).

This article presents a computational framework for the Wasserstein auto-encoding of merge trees (MT-WAE), a novel extension of the classical auto-encoder neural network architecture to the Wasserstein metric space of merge trees. In contrast to traditional auto-encoders which operate on vectorized data, our formulation explicitly manipulates merge trees on their associated metric space at each layer of the network, resulting in superior accuracy and interpretability. Our novel neural network approach can be interpreted as a non-linear generalization of previous linear attempts (Pont et al. 2023) at merge tree encoding. It also trivially extends to persistence diagrams. Extensive experiments on public ensembles demonstrate the efficiency of our algorithms, with MT-WAE computations in the orders of minutes on average. We show the utility of our contributions in two applications adapted from previous work on merge tree encoding (Pont et al. 2023). First, we apply MT-WAE to merge tree compression, by concisely representing them with their coordinates in the final layer of our auto-encoder. Second, we document an application to dimensionality reduction, by exploiting the latent space of our auto-encoder, for the visual analysis of ensemble data. We illustrate the versatility of our framework by introducing two penalty terms, to help preserve in the latent space both the Wasserstein distances between merge trees, as well as their clusters. In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used for reproducibility.

The study of cooperative merging algorithm based on the assignment model for connected and automated vehicles

The study of cooperative merging algorithm based on the assignment model for connected and automated vehicles

A Kinetic-magnetohydrodynamic Model with Adaptive Mesh Refinement for Modeling Heliosphere Neutral-plasma Interaction

The charge exchange between the interstellar medium and the solar wind plasma is crucial for determining the structures of the heliosphere. Since both the neutral-ion and neutral–neutral collision mean free paths are either comparable to or larger than the size of the heliosphere, the neutral phase space distribution can deviate far away from the Maxwellian distribution. A kinetic description for the neutrals is crucial for accurately modeling the heliosphere. It is computationally challenging to run three-dimensional time-dependent kinetic simulations due to the large number of macroparticles. In this paper, we present the new highly efficient Solar Wind with Hydrogen Ion Exchange and Large-scale Dynamics-2 model with a kinetic model of neutrals and a magnetohydrodynamic model for the ions and electrons. To improve the simulation efficiency, we implement adaptive mesh refinement and particle splitting and merging algorithms for the neutral particles to reduce the particle number that is required for an accurate simulation. We present several tests to verify and demonstrate the capabilities of the model.

Hierarchical Heuristic Species Delimitation under the Multispecies Coalescent Model with Migration.

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters (such as popula- tion split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (𝑔𝑑𝑖) and implement them in a python pipeline called hhsd. We characterize the behavior of the 𝑔𝑑𝑖 under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as three empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

FLORAH: a generative model for halo assembly histories

ABSTRACT The mass assembly history (MAH) of dark matter haloes plays a crucial role in shaping the formation and evolution of galaxies. MAHs are used extensively in semi-analytic and empirical models of galaxy formation, yet current analytic methods to generate them are inaccurate and unable to capture their relationship with the halo internal structure and large-scale environment. This paper introduces florah (FLOw-based Recurrent model for Assembly Histories), a machine-learning framework for generating assembly histories of ensembles of dark matter haloes. We train florah on the assembly histories from the Gadget at Ultra-high Redshift with Extra Fine Time-steps and vsmdplN-body simulations and demonstrate its ability to recover key properties such as the time evolution of mass and concentration. We obtain similar results for the galaxy stellar mass versus halo mass relation and its residuals when we run the Santa Cruz semi-analytic model on florah-generated assembly histories and halo formation histories extracted from an N-body simulation. We further show that florah also reproduces the dependence of clustering on properties other than mass (assembly bias), which is not captured by other analytic methods. By combining multiple networks trained on a suite of simulations with different redshift ranges and mass resolutions, we are able to construct accurate main progenitor branches with a wide dynamic mass range from $z=0$ up to an ultra-high redshift $z \approx 20$, currently far beyond that of a single N-body simulation. florah is the first step towards a machine learning-based framework for planting full merger trees; this will enable the exploration of different galaxy formation scenarios with great computational efficiency at unprecedented accuracy.

An arbitrarily high order unfitted finite element method for elliptic interface problems with automatic mesh generation, Part II. Piecewise-smooth interfaces

We consider the reliable implementation of an adaptive high-order unfitted finite element method on Cartesian meshes for solving elliptic interface problems with geometrically curved singularities. We extend our previous work on the reliable cell merging algorithm for smooth interfaces to automatically generate the induced mesh for piecewise smooth interfaces. An hp a posteriori error estimate is derived for a new unfitted finite element method whose finite element functions are conforming in each subdomain. Numerical examples illustrate the competitive performance of the method.

Evolution of central galaxy alignments in simulations

Context. Observations suggest that red central galaxies align closely with their group galaxies and the large-scale environment. This finding was also replicated in simulations, which added information about the alignment of the stars that form the galaxies with the dark matter in the halo they inhabit. These results were obtained for the present Universe. Our study aims to build upon previous findings by examining the evolution of central galaxy alignment with the environment, as well as the alignment between the stellar and dark matter components. Aims. Based on previous studies, in this work, we describe the evolution of the alignment of bright central galaxies over time and try to understand the process leading to the current observed alignment. Methods. By employing the merger trees from the simulation, we tracked the alignment evolution of the central galaxy sample at z = 0 used in a previous study, the results of which correspond to observations. In particular, we exploited the anisotropic correlation function to study the alignment of the central galaxies with their environment and the probability distribution of the angle between the axes of the shape tensor calculated for each component to deepen the analysis of the stellar and dark matter components. Results. We provide a description of the evolution of alignment in bright central galaxies with a focus on the distinctions between red and blue galaxies. Furthermore, we find that the alignment of the dark matter halo differs from that of the stellar material within it. According to these findings, the assembly process and mergers influenced the evolution of the alignment.

VISTA: an integrated framework for structural variant discovery.

Structural variation (SV) refers to insertions, deletions, inversions, and duplications in human genomes. SVs are present in approximately 1.5% of the human genome. Still, this small subset of genetic variation has been implicated in the pathogenesis of psoriasis, Crohn's disease and other autoimmune disorders, autism spectrum and other neurodevelopmental disorders, and schizophrenia. Since identifying structural variants is an important problem in genetics, several specialized computational techniques have been developed to detect structural variants directly from sequencing data. With advances in whole-genome sequencing (WGS) technologies, a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge, with the majority of SV detection methods prone to a high false-positive rate, and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here, we report an integrated structural variant calling framework, Variant Identification and Structural Variant Analysis (VISTA), that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensus-based tools which ignore the length and coverage, VISTA overcomes this limitation by executing various combinations of top-performing callers based on variant length and genomic coverage to generate SV events with high accuracy. We evaluated the performance of VISTA on comprehensive gold-standard datasets across varying organisms and coverage. We benchmarked VISTA using the Genome-in-a-Bottle gold standard SV set, haplotype-resolved de novo assemblies from the Human Pangenome Reference Consortium, along with an in-house polymerase chain reaction (PCR)-validated mouse gold standard set. VISTA maintained the highest F1 score among top consensus-based tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode, where the calls can be optimized for precision or recall. VISTA-optimized can attain 100% precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework that outperforms existing consensus-based tools and sets a new standard for SV detection in genomic research.

Evolutionary algorithms simulating molecular evolution: a new field proposal.

The genetic blueprint for the essential functions of life is encoded in DNA, which is translated into proteins-the engines driving most of our metabolic processes. Recent advancements in genome sequencing have unveiled a vast diversity of protein families, but compared with the massive search space of all possible amino acid sequences, the set of known functional families is minimal. One could say nature has a limited protein "vocabulary." A major question for computational biologists, therefore, is whether this vocabulary can be expanded to include useful proteins that went extinct long ago or have never evolved (yet). By merging evolutionary algorithms, machine learning, and bioinformatics, we can develop highly customized "designer proteins." We dub the new subfield of computational evolution, which employs evolutionary algorithms with DNA string representations, biologically accurate molecular evolution, and bioinformatics-informed fitness functions, Evolutionary Algorithms Simulating Molecular Evolution.

Metallicity distributions of halo stars: do they trace the Galactic accretion history?

The standard cosmological scenario predicts a hierarchical formation for galaxies. Many substructures have been found in the Galactic halo, usually identified as clumps in kinematic spaces, like the energy-angular momentum space ($E-L_z$), under the hypothesis that these quantities should be conserved during the interaction. If these clumps also feature different chemical abundances, such as the metallicity distribution function (MDF), these two arguments together (different kinematic and chemical properties) are often used to motivate their association with distinct and independent merger debris. The aim of this study is to explore to what extent we can couple kinematic characteristics and metallicities of stars in the Galactic halo to reconstruct the accretion history of the Milky Way (MW). In particular, we want to understand whether different clumps in the $E-L_z$ space with different MDFs should be associated with distinct merger debris. We analysed dissipationless, self-consistent, high-resolution N-body simulations of a MW-type galaxy accreting a satellite with a mass ratio of 1:10, with different orbital parameters and different metallicity gradients, which were assigned a posteriori. We confirm that accreted stars from a sim 1:10 mass ratio merger event redistribute in a wide range of $E$ and $L_z$, due to the dynamical friction process, and are thus not associated with a single region. Because satellite stars with different metallicities can be deposited in different regions of the $E-L_z$ space (on average the more metal-rich ones end up more gravitationally bound to the MW), this implies that a single accretion of sim 1:10 can manifest with different MDFs, in different regions of the $E-L_z$ space. Groups of stars with different $E$, $L_z$, and metallicities may be interpreted as originating from different satellite galaxies, but our analysis shows that these interpretations are not physically motivated. In fact, as we show, the coupling of kinematic information with MDFs to reconstruct the accretion history of the MW can bias the reconstructed merger tree towards increasing the number of past accretions and decreasing the masses of the progenitor galaxies.

Municipal street‐sweeping area generation with route optimization

AbstractStreet sweeping is used in urban municipalities worldwide but requires a great deal of planning for large‐scale implementation. Municipalities typically make use of the macro‐ and microapproach by using operational areas in which routes are assigned, but creating operational areas without an understanding of the expected workload, number of routes, and the travel distance to and from the depot may lead to increased statistics. In this paper, a combination of heuristic approaches is used to assign street‐sweeping areas on the macroscale and generate street‐sweeping routes on the microscale. For the area assignment, a two‐stage cluster approach is proposed, making use of the weighted k‐means algorithm and differential evolution. For the route optimization, a three‐phase augment merge algorithm is used to create initial solutions, the u‐turns are minimized with a modified version of Hierholzer's algorithm and tabu search, and the remaining u‐turns are removed using a forward‐searching ant colony optimization. A case study in The City of Oshawa, Canada, was used to verify the proposed methodology, and all metrics were theoretically improved.

ProcaryaSV: structural variation detection pipeline for bacterial genomes using short-read sequencing

BackgroundStructural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results.ResultsWe developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool.ConclusionsThe ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV.

Formation Channels of Diffuse Lights in Groups and Clusters over Time

We explore the formation of the intragroup light and intracluster light, representing diffuse lights within groups and clusters, from the point that z = 1.5. For this, we perform multiresolution cosmological N-body simulations using the “galaxy replacement technique” and identify the progenitors in which the diffuse light stars existed when they fell into the groups or clusters. Our findings reveal that typical progenitors contributing to diffuse lights enter the host halo with massive galaxies containing a stellar mass of 10<logMgal[M⊙]<11 , regardless of the mass or dynamical state of the host halos at z = 0. In cases where the host halos are dynamically unrelaxed or more massive, diffuse lights from massive progenitors with logMgal[M⊙]>11 are more prominent, with over half of them already preprocessed before entering the host halo. Additionally, we find that the main formation mechanism of diffuse lights is the stripping process of satellites, and a substantial fraction (40%–45%) of diffuse light stars are linked to the merger tree of the brightest cluster galaxy. Remarkably, all trends persist for groups and clusters at higher redshifts. The fraction of diffuse lights in the host halos with a similar mass decreases as the redshift increases, but they are already substantial at z = 1.5 (∼10%). However, it is crucial to acknowledge that detection limits related to the observable radius and faint-end surface brightness may obscure numerous diffuse light stars and even alter the main formation channel of diffuse lights.