Asymmetric Clusters Research Articles

A study of the coupled dynamics of asymmetric absorbing clusters in a photophoretic trap

Abstract We report a study on the dynamics of absorbing asymmetric carbon clusters trapped by a loosely focused Gaussian beam using photophoretic force. At high laser powers, all the trapped clusters display rotation coupled with oscillation along the axial direction, with a majority spinning about a body fixed axis, while the rest display dual spin as well as orbital motion about a fixed point in space. The spinning and orbiting frequency is inversely proportional to the amplitude of the axial oscillation - with one growing at the expense of the other. Further, the frequencies of these rotations are not proportional to the laser power, but to the trap stiffnesses inferred from the corresponding natural frequencies. The clusters also stop rotating below a certain laser power, and execute random thermal fluctuations. Our work suggests that the dynamics of clusters trapped with photophoretic force are largely dependent on the cluster size and morphology, which could, in principle, be tuned to obtain various motional responses, and help in the design of rotating micromachines in air.

Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information

In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by approximating the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework’s efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.

TDDFT and the x-ray absorption spectrum of liquid water: Finding the "best" functional.

We investigate the performance of time-dependent density functional theory (TDDFT) for reproducing high-level reference x-ray absorption spectra of liquid water and water clusters. For this, we apply the integrated absolute difference (IAD) metric, previously used for x-ray emission spectra of liquid water [T. Fransson and L. G. M. Pettersson, J. Chem. Theory Comput. 19, 7333-7342 (2023)], in order to investigate which exchange-correlation (xc) functionals yield TDDFT spectra in best agreement to reference, as well as to investigate the suitability of IAD for x-ray absorption spectroscopy spectrum calculations. Considering highly asymmetric and symmetric six-molecule clusters, it is seen that long-range corrected xc-functionals are required to yield good agreement with the reference coupled cluster (CC) and algebraic-diagrammatic construction spectra, with 100% asymptotic Hartree-Fock exchange resulting in the lowest IADs. The xc-functionals with best agreement to reference have been adopted for larger water clusters, yielding results in line with recently published CC theory, but which still show some discrepancies in the relative intensity of the features compared to experiment.

New Data on the Reactions of Zirconium and Hafnium Tetrachlorides with Aliphatic Acids

The reaction of ZrCl4 or HfCl4 with excess 2-methylpropanoic acid when boiling under reflux has been studied. The formation of polynuclear Zr and Hf complexes of the composition M2O(i-C3H7CO2)6 during prolonged reflux of the reaction mixtures was found. The complexes are very sensitive to hydrolysis, forming hexanuclear [M6(O)4(OH)4(i-C3H7CO2)12]. The reactions have a general character for aliphatic acids and can be used as an alternative to the known methods for the synthesis of polynuclear carboxylate clusters of Group 4 metals. The crystal and molecular structures of previously undescribed {[Hf6(μ3-O)4(μ3-OH)4(i-C3H7CO2)12(H2O)]·3i-C3H7COOH} have been determined. The molecular structure is a completely asymmetric hexanuclear cluster containing six Hf(IV) atoms united by a 4:4 μ3-O/OH system of bridges, and stabilized by twelve 2-methylpropanoate ligands, eight of which are bidentate bridging, three are chelating, and one is monodentate. The crystal structure of the complex includes three independent solvating 2-methylpropanoic acid molecules. The obtained IR spectroscopy data make it possible to determine the type of complexes in the reaction mixture. The results of the study may be useful for improving the catalytic systems for ethylene oligomerization.

Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

Lithium-ion batteries may suffer an abnormal degradation defined by a significantly accelerated performance drop after a period of linear and low-rate degradation, resulting in severe danger to operational safety and reliability. Existing supervised data-driven prognostics for abnormal degradation rely heavily on adequate high-quality training samples, thus hindering their real-world utilization. Therefore, this paper develops an unsupervised dynamic prognostics framework that is dynamically executed as the cycle number increases to provide accurate and timely warnings for abnormal degradation. In the framework, an extended asymmetric quantum clustering (EAQC) can preliminarily identify the risky battery with the abnormal degradation trend. Its use of multi-dimensional feature degradation rates and potential function for clustering makes it outperform other density-based clustering methods. Then, for the identified battery, a time-varying double-layer autoregression (TVDLAR) can accurately predict its knee point of abnormal degradation. TVDLAR produces much earlier warning than other autoregression methods, which owes to its modeling ability of the time-varying correlation in battery historical degradation data. Through applying to two experimental datasets consisting of 174 lithium-ion batteries of different types under various working conditions, the framework is proven highly effective and shows significant superiority over some alternative approaches; all abnormal degradation can be timely warned before the corresponding knee points, and the average earlier warning cycles are 86 and 116, respectively. Compared with supervised data-driven methods, the proposed unsupervised dynamic prognostics framework has the advantages of low data requirement, low computational consumption, and good interpretability, indicating its substantial potential for application.

Differential Substrate Sensing in Terpene Synthases from Plants and Microorganisms: Insight from Structural, Bioinformatic, and EnzyDock Analyses.

Terpene synthases (TPSs) catalyze the first step in the formation of terpenoids, which comprise the largest class of natural products in nature. TPSs employ a family of universal natural substrates, composed of isoprenoid units bound to a diphosphate moiety. The intricate structures generated by TPSs are the result of substrate binding and folding in the active site, enzyme-controlled carbocation reaction cascades, and final reaction quenching. A key unaddressed question in class I TPSs is the asymmetric nature of the diphosphate-(Mg2+)3 cluster, which forms a critical part of the active site. In this asymmetric ion cluster, two diphosphate oxygen atoms protrude into the active site pocket. The substrate hydrocarbon tail, which is eventually molded into terpenes, can bind to either of these oxygen atoms, yet to which is unknown. Herein, we employ structural, bioinformatics, and EnzyDock docking tools to address this enigma. We bring initial data suggesting that this difference is rooted in evolutionary differences between TPSs. We hypothesize that this alteration in binding, and subsequent chemistry, is due to TPSs originating from plants or microorganisms. We further suggest that this difference can cast light on the frequent observation that the chiral products or intermediates of plant and bacterial terpene synthases represent opposite enantiomers.

Differential Substrate Sensing in Terpene Synthases from Plants and Microorganisms: Insight from Structural, Bioinformatic, and EnzyDock Analyses

AbstractTerpene synthases (TPSs) catalyze the first step in the formation of terpenoids, which comprise the largest class of natural products in nature. TPSs employ a family of universal natural substrates, composed of isoprenoid units bound to a diphosphate moiety. The intricate structures generated by TPSs are the result of substrate binding and folding in the active site, enzyme‐controlled carbocation reaction cascades, and final reaction quenching. A key unaddressed question in class I TPSs is the asymmetric nature of the diphosphate‐(Mg2+)3 cluster, which forms a critical part of the active site. In this asymmetric ion cluster, two diphosphate oxygen atoms protrude into the active site pocket. The substrate hydrocarbon tail, which is eventually molded into terpenes, can bind to either of these oxygen atoms, yet to which is unknown. Herein, we employ structural, bioinformatics, and EnzyDock docking tools to address this enigma. We bring initial data suggesting that this difference is rooted in evolutionary differences between TPSs. We hypothesize that this alteration in binding, and subsequent chemistry, is due to TPSs originating from plants or microorganisms. We further suggest that this difference can cast light on the frequent observation that the chiral products or intermediates of plant and bacterial terpene synthases represent opposite enantiomers.

Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network

Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network, employing the chaotic and synchronous dynamics of optically interconnected lasers to address the competitive multi-armed bandit (CMAB) problem, is a highly compelling approach due to its scalability and experimental feasibility. We investigated essential network structures for collective decision-making through quantitative stability analysis. Moreover, we demonstrated the asymmetric preferences of players in the CMAB problem, extending its functionality to more practical applications. Our study highlights the capability and significance of machine learning built upon chaotic lasers and photonic devices.

An AstroSat/UVIT study of galaxies in the cluster Abell 2199

We present the newly acquired data for an AstroSat/UVIT field centred on a face-on spiral starburst galaxy UGC 10420, located in the X-ray bright cluster Abell 2199 (z = 0.031). We have analysed the FUV BaF2 data for this field along with the archival FUV and NUV data from the GALEX mission, optical photometric data from the SDSS, spectroscopic data from the literature, and low-frequency radio data from the LoTSS survey, respectively. The stars were separated from the galaxies using the SDSS photometric pipeline classification, while the spectroscopic redshifts available for 35% of the detected UVIT sources were used to identify member galaxies of the cluster Abell 2199. We find that (a) the non-cluster galaxies are on average fainter than the cluster galaxies at fixed magnitude, (b) stars and galaxies are indistinguishable in the r vs. NUV–r plane, and (c) bright stars are ∼ 1.5 mag bluer than the galaxies in the FUV–r vs. NUV–r colour-colour plane. Besides UGC 10420 which is the only known cluster galaxy with an extended-UV disk, we identify five more galaxies with asymmetric FUV morphology and extended radio emission in this field. All the asymmetric member galaxies of Abell 2199, lie within the virial boundaries of the cluster. This observation, together with the fact that these asymmetric cluster galaxies have low-frequency radio tails or FUV emission pointing away from the cluster centre leads us to hypothesise that these galaxies are likely undergoing ram-pressure stripping (RPS) under the influence of cluster-environment related mechanisms. A comparison of optical and FUV star formation rate of UVIT detected galaxies shows enhanced star formation in half of the RPS candidates, suggesting that environment-related mechanisms may lead to a burst of star formation in RPS galaxies.Our analysis indicates the presence of at least two more groups or clusters at z ∼ 0.077 and 0.260, coincident with Abell 2199 along the line of sight of the field of view studied here.

Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.

Porous alumina nanosheet-supported asymmetric platinum clusters for efficient diboration of alkynes.

Precisely designing asymmetrical structures is an effective strategy to optimize the performance of metallic catalysts. Asymmetric Pt clusters were attached to defect-rich porous alumina nanosheets (Pt clu/dp-Al2O3) using a pyrolysis technique coupled with wet impregnation. These Pt-functionalized nanosheets feature a high concentration of active sites, demonstrating remarkable cycling performance and catalytic activity in alkyne diboration. The conversion yield and selectivity can reach up to 97% and 95%, correspondingly.

Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and Neurotypical Controls

Background Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. Objectives We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: (1) identify clusters of walking behaviors in people post-stroke and neurotypical controls and (2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. Methods We gathered data from 81 post-stroke participants across 4 research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. Results We identified 4 stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. Conclusions Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

PixelFrame: A reconfigurable, precast, post-tensioned concrete structural system for a circular building economy

The demand to build for a growing urban population conflicts with the need to reduce the AEC (Architecture, Engineering and Construction) sector’s contributions to global CO2 emissions. Structural systems are a key factor in this challenge, as they contribute substantially to a building’s mass and are typically overdesigned. In this paper, we present PixelFrame, a new precast concrete structural system that is algorithmically designed to use less carbon upfront and be reused across multiple lifecycles. We achieve a high reuse potential for structural elements through building load demand analysis, a segmental externally post-tensioned design, and an integrated optimal assignment strategy. In addition to improving reuse potential, a segmental optimization strategy also yields higher efficiency in comparison to traditional prismatic concrete structural elements. The detailing of concrete mixes is informed by a statistical analysis of anticipated building load demands and a custom clustering asymmetric distance function and clustering algorithm. Compared to previous work in this area, our contribution is original in its bidirectional algorithmic approach; both the target building structure and the circular material inventory are computationally generated to achieve versatility while minimizing emissions.

TgF344-AD Rat Model of Alzheimer's Disease: Spatial Disorientation and Asymmetry in Hemispheric Neurodegeneration.

The TgF344-AD ratline represents a transgenic animal model of Alzheimer's disease. We previously reported spatial memory impairment in TgF344-AD rats, yet the underlying mechanism remained unknown. We, therefore, set out to determine if spatial memory impairment in TgF344-AD rats is attributed to spatial disorientation. Also, we aimed to investigate whether TgF344-AD rats exhibit signs of asymmetry in hemispheric neurodegeneration, similar to what is reported in spatially disoriented AD patients. Finally, we sought to examine how spatial disorientation correlates with working memory performance. TgF344-AD rats were divided into two groups balanced by sex and genotype. The first group underwent the delayed match-to-sample (DMS) task for the assessment of spatial orientation and working memory, while the second group underwent positron emission tomography (PET) for the assessment of glucose metabolism and microglial activity as in-vivo markers of neurodegeneration. Rats were 13 months old during DMS training and 14-16 months old during DMS testing and PET. In the DMS task, TgF344-AD rats were more likely than their wild-type littermates to display strong preference for one of the two levers, preventing working memory testing. Rats without lever-preference showed similar working memory, regardless of their genotype. PET revealed hemispherically asymmetric clusters of increased microglial activity and altered glucose metabolism in TgF344-AD rats. TgF344-AD rats display spatial disorientation and hemispherically asymmetrical neurodegeneration, suggesting a potential causal relationship consistent with past clinical research. In rats with preserved spatial orientation, working memory remains intact.

Star cluster classification using deep transfer learning with PHANGS-HST

ABSTRACT Currently available star cluster catalogues from the Hubble Space Telescope (HST) imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models that accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D ≲ 20 Mpc). Here, we use HST ultraviolet (UV)–optical imaging of over 20 000 sources in 23 galaxies from the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: (i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12, 14–18, and 18–24 Mpc), and (ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (∼60–80 per cent) and shows improvement by a factor of 2 in classifying asymmetric and multipeaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and spectral energy distribution (SED)-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ∼ 200 000) for public release.

Concepts of Picture Fuzzy Line Graphs and Their Applications in Data Analysis

The process of bundling and clustering hasno clear boundaries; hence, their analysis contains uncertainities. Thus, it is more suitable to deal withbundling and clusteringby usingfuzzy graphs. Since picture fuzzy sets (PFSs) are more accurate, compatible, and flexible compared to the other generalizations of fuzzy sets (FSs),hence, it would be more effective to present edge bundling and clustering usingpicture fuzzy line graphs (PFLGs). The aim of our study is to introduce the notions of picture fuzzy intersection graphs (PFIGs) and picture fuzzy line graphs (PFLGs). These concepts are the generalizations of fuzzy intersection graphs (FIGs) and fuzzy line graphs (FLGs), respectively. We begin our discussion by introducing some fresh and useful terminologies in the theory of fuzzy graphs such as fuzzy intersection number, picture fuzzy intersection number, etc., and we explore few interesting results related to them. Based on these concepts, first we introduce the notion of picture fuzzy intersection graphs (PFIGs) and discuss manyimportant characteristics of these graphs. Afterwards, we introduce the notion of picture fuzzy line graphs (PFLGs) and discuss their various properties. We also investigate some structural properties of our newly established fuzzy graphs usingweak isomorphism and isomorphism. Finally, we provide an outline of the applications of picture fuzzy line graphs (PFLGs) in terms of cluster-based picture fuzzy edge bundling (CBPFEB) and the picture fuzzy c-mean algorithm. Since asymmetrical clusters ensure that the databases remain identical across the clusters, our study is deeply related to symmety.

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.

Topological and quantum stability of low-dimensional crystalline lattices with multiple nonequivalent sublattices* *Dedicated to the memory of our close friend, fellow colleague, former student (PA) and teacher (AK) Prof. A A Kuzubov (1974–2016).

The terms of topological and quantum stabilities of low-dimensional crystalline carbon lattices with multiple non-equivalent sublattices are coined using theoretical analysis, multilevel simulations, and available experimental structural data. It is demonstrated that complex low-dimensional lattices are prone to periodicity breakdown caused by structural deformations generated by linear periodic boundary conditions (PBC). To impose PBC mandatory limitations for complex low-dimensional lattices, the topology conservation theorem (TCT) is introduced, formulated and proved. It is shown that the lack of perfect filling of planar 2D crystalline space by structural units may cause the formation of (i) structure waves of either variable or constant wavelength; (ii) nanotubes or rolls; (iii) saddle structures; (iv) aperiodic ensembles of irregular asymmetric atomic clusters. In some cases the lattice can be stabilized by aromatic resonance, correlation effects, or van-der-Waals interactions. The effect of quantum instability and periodicity breakdown of infinite structural waves is studied using quasiparticle approach. It is found that both perfect finite-sized, or stabilized structural waves can exist and can be synthesized. It is shown that for low-dimensional lattices prone to breakdown of translation invariance (TI), complete active space of normal coordinates cannot be reduced to a subspace of TI normal coordinates. As a result, constrained TI subspace structural minimization may artificially return a regular point at the potential energy surface as either a global/local minimum/maximum. It is proved that for such lattices, phonon dispersion cannot be used as solid and final proof of either stability or metastability. It is shown that ab initio molecular dynamics (MD) PBC Nosé–Hoover thermostat algorithm constrains the linear dimensions of the periodic slabs in MD box preventing their thermostated equilibration. Based on rigorous TCT analysis, a flowchart algorithm for structural analysis of low-dimensional crystals is proposed and proved to be a powerful tool for theoretical design of advanced complex nanomaterials.

Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image

Due to the complex properties of hyperspectral images (HSI), such as spatial-spectral structure, high dimension, and great spectral variability, HSI clustering is a challenging operation. In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net) which employs a convolutional autoencoder (CAE) and an asymmetric autoencoder to investigate spatial-spectral information. First, we use a CAE to extract spatial-spectral features. Then, we introduce an asymmetric autoencoder between the encoder and decoder of CAE to suppress some non-material-related spatial information in latten feature like shading and texture. By using a collaborative strategy to train the proposed networks, we obtain the representation features in a low dimension. Furthermore, we improve the k -means algorithm by using the concept of over-clustering to handle fuzzy representation which is difficult to distinguish the cluster, and utilize it to obtain the final HSI clustering result. The results of the experiments demonstrated that the proposed methodology outperforms other methods on the frequently used hyperspectral image dataset.

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada.

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran's I spatial autocorrelation, and Local Moran's I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.