Merging Algorithm Research Articles

Predicting discharge coefficient of weir–orifice in closed conduit using a neuro-fuzzy model improved by multi-phase PSOGSA

This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.

A sequence-aware merger of genomic structural variations at population scale

Merging structural variations (SVs) at the population level presents a significant challenge, yet it is essential for conducting comprehensive genotypic analyses, especially in the era of pangenomics. Here, we introduce PanPop, a tool that utilizes an advanced sequence-aware SV merging algorithm to efficiently merge SVs of various types. We demonstrate that PanPop can merge and optimize the majority of multiallelic SVs into informative biallelic variants. We show its superior precision and lower rates of missing data compared to alternative software solutions. Our approach not only enables the filtering of SVs by leveraging multiple SV callers for enhanced accuracy but also facilitates the accurate merging of large-scale population SVs. These capabilities of PanPop will help to accelerate future SV-related studies.

A Global Semianalytic Model of the First Stars and Galaxies Including Dark Matter Halo Merger Histories

We present a new self-consistent semianalytic model of the first stars and galaxies to explore the high-redshift (z ≥ 15) Population III (PopIII) and metal-enriched star formation histories. Our model includes the detailed merger history of dark matter halos generated with Monte Carlo merger trees. We calibrate the minimum halo mass for PopIII star formation from recent hydrodynamical cosmological simulations that simultaneously include the baryon–dark matter streaming velocity, Lyman–Werner (LW) feedback, and molecular hydrogen self-shielding. We find an overall increase in the resulting star formation rate density (SFRD) compared to calibrations based on previous simulations (e.g., the PopIII SFRD is over an order of magnitude higher at z = 35−15). We evaluate the effect of the halo-to-halo scatter in this critical mass and find that it increases the PopIII stellar mass density by a factor ∼1.5 at z ≥ 15. Additionally, we assess the impact of various semianalytic/analytic prescriptions for halo assembly and star formation previously adopted in the literature. For example, we find that models assuming smooth halo growth computed via abundance matching predict SFRDs similar to the merger tree model for our fiducial model parameters, but that they may underestimate the PopIII SFRD in cases of strong LW feedback. Finally, we simulate subvolumes of the Universe with our model both to quantify the reduction in total star formation in numerical simulations due to a lack of density fluctuations on spatial scales larger than the simulation box, and to determine spatial fluctuations in SFRD due to the diversity in halo abundances and merger histories.

Diffuse Light in Milky Way–like Haloes

We investigate the diffuse light (DL) content of dark matter haloes in the mass range 11.5≤logMhalo≤13 , a range that also includes the dark matter halo of the Milky Way, taking advantage of a state-of-the-art semianalytic model run on the merger trees extracted from a set of high-resolution cosmological simulations. The fraction of DL in such relatively small haloes is found to progressively decrease from the high- to the low-mass end, in good agreement with analytic and numerical results from simulations, and in good agreement also with the fraction of the DL observed in the Milky Way and M31. Haloes with different masses have different efficiencies in producing DL: logMhalo≃13 is found to be the characteristic halo mass where the production of DL is the most efficient, while the overall efficiency decreases at both larger and smaller scales (this work). The DL content in this range of halo mass is the result of stellar stripping due to tidal interaction between satellites and their hosts (95%) and mergers between satellites and the central galaxy (5%), with preprocessed material, a subchannel of mergers and stripping, and so already included in the 100%, that contributes no more than 8% on average. The halo concentration is the main driver of the DL formation: more concentrated haloes have higher DL fractions that come from the stripping of more massive satellites in the high-halo-mass end, while dwarfs contribute mostly in the low-halo-mass end.

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

Community detection in decentralized social networks with local differential privacy

Community detection under local differential privacy has been a research hotspot recently. Most existing methods primarily rely on the edge local differential privacy model (edge-LDP), compromising privacy strength for high utility. In addition, they adopt a way of agglomerating nodes one by one to construct communities (or clusters) through multiple iterations, suffering from significant noise and low detection efficiency. Moreover, the error accumulation caused by large-scale iterations is not conducive to detection accuracy. To address these issues, we propose a privacy-preserving community detection method, CD-LDP, based on node-LDP, achieving efficient and accurate community construction by aggregating grouped units instead of individual nodes and delimiting the interaction to three rounds. Specifically, we propose a privacy-preserving global social network construction method to gain connectivity information among all nodes through two rounds of interactions. In addition, a novel perturbation mechanism based on the star social network structure is devised, which improves the accuracy of the global social network by reducing the probability of generating fake edges. Secondly, we design a privacy-preserving node grouping approach leveraging clustering coefficients and Haar discrete wavelet transform in the third round, achieving low noise injection and high-quality node grouping through lossless dimensionality reduction. Lastly, we develop a fast unit merging algorithm combining modularity and Huffman trees to realize efficient and accurate community building by reducing the aggregation possibility of nodes with low similarity. Theoretical analysis and experimentation on real-world datasets testify that our solution can efficiently extract high-quality community structures while satisfying node-LDP.

Characterizing the assembly of dark matter haloes with protohalo size histories: I. Redshift evolution, relation to descendant haloes, and halo assembly bias

ABSTRACT We propose a novel method to quantify the assembly histories of dark matter haloes with the redshift evolution of the mass-weighted spatial variance of their progenitor haloes, that is, the protohalo size history. We find that the protohalo size history for each individual halo at z ∼ 0 can be described by a double power-law function. The amplitude of the fitting function strongly correlates to the central-to-total stellar mass ratios of descendant haloes. The variation of the amplitude of the protohalo size history can induce a strong halo assembly bias effect for massive haloes. This effect is detectable in observation using the central-to-total stellar mass ratio as a proxy of the protohalo size. The correlation to the descendant central-to-total stellar mass ratio and the halo assembly bias effect seen in the protohalo size are much stronger than that seen in the commonly adopted half-mass formation time derived from the mass accretion history. This indicates that the information loss caused by the compression of halo merger trees to mass accretion histories can be captured by the protohalo size history. Protohalo size thus provides a useful quantity to connect protoclusters across cosmic time and to link protoclusters with their descendant clusters in observations.

Towards flexibility and robustness of LSM trees

Log-structured merge trees (LSM trees) are increasingly used as part of the storage engine behind several data systems, and are frequently deployed in the cloud. As the number of applications relying on LSM-based storage backends increases, the problem of performance tuning of LSM trees receives increasing attention. We consider both nominal tunings—where workload and execution environment are accurately known a priori—and robust tunings—which consider uncertainty in the workload knowledge. This type of workload uncertainty is common in modern applications, notably in shared infrastructure environments like the public cloud. To address this problem, we introduce Endure, a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policy, size ratio, and memory allocation on the overall performance. Endure considers a robust formulation of the throughput maximization problem and recommends a tuning that offers near-optimal throughput when the executed workload is not the same, instead in a neighborhood of the expected workload. Additionally, we explore the robustness of flexible LSM designs by proposing a new unified design called K-LSM that encompasses existing designs. We deploy our robust tuning system, Endure, on a state-of-the-art key-value store, RocksDB, and demonstrate throughput improvements of up to 5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} in the presence of uncertainty. Our results indicate that the tunings obtained by Endure are more robust than tunings obtained under our expanded LSM design space. This indicates that robustness may not be inherent to a design, instead, it is an outcome of a tuning process that explicitly accounts for uncertainty.

Delineation of endorheic drainage basins in the MERIT-Plus dataset for 5 and 15 minute upscaled river networks

The MERIT-Hydro networks re-gridded by the Iterative Hydrography Upscaling (IHU) algorithm do not retain exo- or endorheic basin attributes from the original data. Here we developed methods to assign such attributes to those and any other digital river networks. The motivation is that endorheic inland drainage basins are essential for hydrologic modelling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. First, we create basin attributes that explicitly label endorheic and exorheic catchments by the criteria of direct or hidden connectivity to the ocean without changing their flow direction grid. In the second step we alter the delineation of endorheic basins by the merging algorithm that eliminates small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion and topology of the inland basins. The data was validated using the Water Balance Model by comparing volume of endorheic inland depressions with modelled water accumulation in their inland lakes.

Constructing an Innovative Interactive Experience of Stage Visual Design for Augmented Reality in Costume Performance

Abstract Augmented reality (AR) technology has rapidly advanced across various domains, propelled by its robust interactive immersion and the seamless integration of real and virtual environments. However, its exploration and deployment in theatrical contexts remain limited. This study leverages the Kinect system to capture images during costume performances, employing algorithms for dynamic frame difference merging and human-computer interaction to detect performers’ body movements. Building on this, the study constructs a visually innovative stage for costume performances that enhances the interactive experience for the audience. Additionally, a multimodal emotion analysis model is utilized to assess audience emotions, demonstrating significantly higher accuracy and F1 scores compared to other emotion analysis models. This model effectively integrates speech, expression, and action, surpassing the performance of unimodal analyses in emotion recognition. Furthermore, the audience's experiential perception of stage lighting effects notably exceeds expectations (P=0.013 < 0.05), underscoring an enhanced interaction experience. This research substantiates the transformative potential of AR technology in stage design, offering audiences a more innovative visual and interactive experience, and serves as a valuable reference for future applications in this field.

Dark Sage: Next-generation semi-analytic galaxy evolution with multidimensional structure and minimal free parameters

Abstract After more than five years of development, we present a new version of Dark Sage, a semi-analytic model (SAM) of galaxy formation that breaks the mould for models of its kind. Included among the major changes is an overhauled treatment of stellar feedback that is derived from energy conservation, operates on local scales, affects gas gradually over time rather than instantaneously, and predicts a mass loading factor for every galaxy. Building on the model’s resolved angular momentum structure of galaxies, we now consider the heating of stellar discs, delivering predictions for disc structure both radially and vertically. We add a further dimension to stellar discs by tracking the distribution of stellar ages in each annulus. Each annulus–age bin has its own velocity dispersion and metallicity evolved in the model. This allows Dark Sage to make structural predictions for galaxies that previously only hydrodynamic simulations could. We present the model as run on the merger trees of the highest-resolution gravity-only simulation of the MillenniumTNG suite. Despite its additional complexity relative to other SAMs, Dark Sage only has three free parameters, the least of any SAM, which we calibrate exclusively against the cosmic star formation history and the $z = 0$ stellar and H i mass functions using a particle-swarm optimisation method. The Dark Sage codebase, written in C and python, is publicly available at https://github.com/arhstevens/DarkSage.

Rapid and Precise Topological Comparison with Merge Tree Neural Networks

Rapid and Precise Topological Comparison with Merge Tree Neural Networks

AI-assisted super-resolution cosmological simulations III: time evolution

ABSTRACT In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time-consistent evolving representations of the particle phase-space distribution. We employ a style-based constrained generative adversarial network (StyleGAN), where the changing cosmic time is an input style parameter to the network. The matter power spectrum and halo mass function agree well with results from high-resolution N-body simulations over the full trained redshift range (10 ≤ z ≤ 0). Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees. We examine progenitors, descendants, and mass growth along the tree branches. All statistical indicators demonstrate the ability of our SR model to generate satisfactory high-resolution simulations based on low-resolution inputs.

Deep learning-based multifrequency ground penetrating radar data merging

Ground-penetrating radar systems with a single central frequency suffer limitations due to the unavoidable trade-off between resolution and penetration depth that multifrequency equipments can overcome. A new semisupervised multifrequency merging algorithm was developed based on deep learning and specifically on bi-directional long-short term memory to automatically merge varying numbers of data sets collected at different frequencies. A new training strategy, based only on the data set of interest, without synthetic or real training data sets was implemented. The proposed methodology is tested on synthetic and field data, to evaluate its performance and robustness. The merging algorithm can manage the complementarity of information at different central frequencies, properly merging different types of data. Results indicate not only a smooth transition in time, but, even more important, a remarkable broadening of the bandwidth thus increasing the overall resolution. Our approach is not limited to specific frequency components or geologic settings but can be potentially exploited to merge any type of data set with different spectral components.

Linear Fuzzy Information-Granule-Based Fuzzy C-Means Algorithm for Clustering Time Series.

This article aims to design a trend-oriented-granulation-based fuzzy C -means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-oriented granulation of a time series, l1 trend filtering is firstly carried out to result in segments which are then optimized by the proposed segment merging algorithm. By constructing a linear fuzzy information granule (LFIG) on each segment, a granular time series which well reflects the linear trend characteristic of the original time series is produced. With the novel designed distance that can well measure the trend similarity of two LFIGs, the distance between two granular time series is calculated by the modified dynamic time warping (DTW) algorithm. Based on this distance, the LFIG-based FCM algorithm is developed for clustering time series. In this algorithm, cluster prototypes are iteratively updated by the specifically designed granule splitting and merging algorithm, which allows the lengths of prototypes to change in the process of iteration. This overcomes the serious drawback of the existing approaches, where the lengths of prototypes cannot be changed. Experimental studies demonstrate the superior performance of the proposed algorithm in clustering time series with different shapes or trends.

Probing the Mass Relation between Supermassive Black Holes and Dark Matter Halos at High Redshifts by Gravitational Wave Experiments

Numerous observations have shown that almost all galaxies in our Universe host supermassive black holes (SMBHs), but there is still much debate about their formation and evolutionary processes. Recently, gravitational waves (GWs) have been expected to be a new and important informative observation, in particular, in the low-frequency region by making use of the Laser Interferometer Space Antenna (LISA) and Pulsar Timing Arrays (PTAs). As an evolutionary process of the SMBHs, we revisit a dark matter (DM) halo–SMBH coevolution model based on the halo merger tree employing an ansatz for the mass relation between the DM halos and the SMBHs at z = 6. In this model, the mass of SMBHs grows through their mergers associated with the halo mergers, and hence, the evolutionary information must be stored in the GWs emitted at the mergers. We investigate the stochastic gravitational background from the coalescing SMBH binaries, which the PTAs can detect, and also the GW bursts emitted at the mergers, which can be detected by the mHz band observations such as LISA. We also discuss the possibility of probing the mass relation between the DM halos and the SMBHs at high redshift by future GW observations.

Identify Alzheimer’s disease subtypes and markers from multi‐omic data of human brain and blood with a subspace merging algorithm

AbstractBackgroundAlzheimer’s disease (AD) is a highly heterogeneous disease with different disease trajectory, brain pathological changes, and risk factors. Identifying subtypes of AD can have greatly impact AD diagnosis, treatment, and disease management. Our goal is to identify subtypes of AD by integrating multiple types of omics data from AD patients’ brain samples and blood samples.MethodWe applied a subspace merging algorithm on AD patient brain samples with matched transcriptomic, DNA methylation, and proteomics data collected from the ROSMAP study. This algorithm merges patient graphs generated from these three datasets on a Grassmann manifold followed by spectral clustering. Differential gene expression and pathway analysis were applied to establish the gene signatures between the subtypes (patient clusters) and to the transcriptomic data of the peripheral blood monocyte (PBMC) from the matching patients to identify possible blood markers.ResultOur analysis identified two patient clusters between which the cognitive test scores or the degree of AD pathology are significantly different. Differentially expressed genes between the two groups were identified from both brain and blood samples. Gene set enrichment analysis identified a group of genes significantly enriched with synaptic functions in the brain samples. The Ingenuity Pathway Analysis was used to examine any connections between the significantly differentially expressed genes in the brain samples with the ones identified from the PBMC samples. The low density lipoprotein receptor (LDLR) gene, which was an APOE receptor observed from the blood sample is connected with 12 genes from the brain sample. The results suggested that LDLR could be a novel blood biomarker for AD subtyping.ConclusionThe subspace merging algorithm integrated multimodal omics data from the patients and successfully identified two clusters with distinct clinical progression. Differential expression analysis identified 388 significant genes from the brain samples and 18 from the PBMC samples. The pathway analysis indicated that LDLR could be a novel blood biomarker for AD subtyping. Future work will focus on validating LDLR as a candidate blood marker for AD subtyping using other public datasets with similar study design. The clinical relevance and molecular mechanism basis for the findings will also be investigated.

Ghostly Galaxies: Accretion-dominated Stellar Systems in Low-mass Dark Matter Halos

Wide-area deep imaging surveys have discovered large numbers of extremely low surface brightness (LSB) dwarf galaxies, which challenge galaxy formation theory and, potentially, offer new constraints on the nature of dark matter. Here we discuss one as-yet-unexplored formation mechanism that may account for a fraction of LSB dwarfs. We call this the “ghost galaxy” scenario. In this scenario, inefficient radiative cooling prevents star formation in the “main branch” of the merger tree of a low-mass dark matter halo, such that almost all its stellar mass is acquired through mergers with less massive (but nevertheless star-forming) progenitors. Present-day systems formed in this way would be “ghostly” isolated stellar halos with no central galaxy. We use merger trees based on the extended Press–Schechter formalism and the Copernicus Complexio cosmological N-body simulation to demonstrate that mass assembly histories of this kind can occur for low-mass halos in ΛCDM, but they are rare. They are most probable in isolated halos of present-day mass ∼4 × 109 M ⊙, occurring for ∼5% of all halos of that mass under standard assumptions about the timing and effect of cosmic reionization. The stellar masses of star-forming progenitors in these systems are highly uncertain; abundance-matching arguments imply a bimodal present-day mass function having a brighter population (median M ⋆ ∼ 3 × 106 M ⊙) consistent with the tail of the observed luminosity function of ultradiffuse galaxies. This suggests that observable analogs of these systems may await discovery. We find that a stronger ionizing background (globally or locally) produces brighter and more extended ghost galaxies.

Graphically Contracted Function Construction with the Recursive Pairwise Merge Algorithm.

A new implementation of a recursive pairwise merge algorithm to construct a GCF from a list of CSF expansion coefficients is presented. The essential new feature is the preallocation of some work arrays used within the intermediate steps of the merge procedure. This results in roughly an order of magnitude improvement in overall efficiency and also approximately eliminates a factor of n, the molecular orbital dimension, from the original implementation. Initial application of this merge procedure to a series of Hm molecules shows that the GCF wave functions can be represented well both with delocalized canonical Hartree-Fock orbitals and with localized molecular orbitals. For a given wave function complexity, as measured by the average facet count, f̅, the delocalized Hartree-Fock orbitals show smaller errors for small f̅ values, while the localized orbitals show smaller errors for larger f̅ values.

Are the ultra-high-redshift galaxies at z > 10 surprising in the context of standard galaxy formation models?

ABSTRACT A substantial number of ultra-high redshift (8 ≲ z ≲ 17) galaxy candidates have been detected with JWST, posing the question: Are these observational results surprising in the context of current galaxy formation models? We address this question using the well-established Santa Cruz semi-analytic models, implemented within merger trees from the new suite of cosmological N-body simulations gureft, which were carefully designed for ultra-high redshift studies. Using our fiducial models calibrated at z = 0, we present predictions for stellar mass functions, rest-frame UV luminosity functions, and various scaling relations. We find that our (dust-free) models predict galaxy number densities at z ∼ 11 (z ∼ 13) that are an order of magnitude (a factor of ∼30) lower than the observational estimates. We estimate the uncertainty in the observed number densities due to cosmic variance, and find that it leads to a fractional error of ∼20–30 per cent at z = 11 (∼30–80 per cent at z = 14) for a 100 arcmin2 field. We explore which processes in our models are most likely to be rate-limiting for the formation of luminous galaxies at these early epochs, considering the halo formation rate, gas cooling, star formation, and stellar feedback, and conclude that it is mainly efficient stellar-driven winds. We find that a modest boost of a factor of ∼4 to the UV luminosities, which could arise from a top-heavy stellar initial mass function, would bring our current models into agreement with the observations. Adding a stochastic component to the UV luminosity can also reconcile our results with the observations.