Clustering Algorithm For Detection Research Articles

Dynamic clustering of Asian regionalisms: Empirical testing of United Nations multilateral treaty participation by social network analysis

This paper investigates the evolving clustering and historical progression of “Asian regionalisms” concerning their involvement in multilateral treaties deposited in the United Nations system. We employ criteria such as geographic proximity, historical connections, cultural affinities, and economic interdependencies to identify twenty-eight candidate countries from East Asia, Southeast Asia, South Asia, and Central Asia for this empirical testing. Using a social network analysis approach, we model the network of these twenty-eight Asian state actors alongside 600 major treaties from the United Nations system, identifying clusters among Asian states by assessing similarities in their treaty participation behavior. Specifically, we observe dynamic changes in these clusters across three key historical eras: Post-war reconstruction and transformation (1945–1968), Cold War tensions and global transformations (1969–1989), and post-Cold War era and globalization (1990–present). Employing the Louvain cluster detection algorithm, the results reveal the evolution in cluster numbers and changes in membership status throughout the world timeline. The results also identify the current situation of six distinct Asian clusters based on states’ inclinations to engage or abstain from multilateral treaties across six policy domains. These findings provide a foundation for further research on the trajectories of Asian regionalisms amidst evolving global dynamics and offer insights into potential alliances, cooperation, or conflicts within the region.

THE THREE HUNDRED: Contrasting cluster galaxy density in hydrodynamical and dark matter simulations

Cluster number counts at visible and IR wavelengths will be a key cosmological probe in the next decade thanks to the Euclid satellite mission. For this purpose, the performance of cluster detection algorithms, which at these wavelengths are sensitive to the spatial distributions of the cluster galaxy members and their luminosity functions, need to be accurately characterized. Using THE THREE HUNDRED hydrodynamical and dark-matter-only simulations, we studied a complete sample of massive clusters beyond 7 (5) × 1014 M⊙ at redshift 0 (1) on a (1.48 Gpc)3 volume. We find that the mass resolution of the current hydrodynamical simulations (1.5 × 109 M⊙) is not enough to characterize the luminosity function of the sample in the perspective of Euclid data. Nevertheless, these simulations are still useful to characterize the spatial distribution of the cluster substructures assuming a common relative mass threshold for the different flavours and resolutions. By comparing with the dark-matter-only version of these simulations, we demonstrate that baryonic physics preserves significantly low-mass subhalos (galaxies), as has also been observed in previous studies with less statistics. Furthermore, by comparing the hydro simulations with higher resolution dark-matter-only simulations of the same objects and taking the same limit in subhalo mass, we find galaxy density profiles that are significantly more cuspy towards the centre of the clusters, where the low-mass substructures tend to concentrate. We conclude that using a dark-matter-only simulation may lead to some biases on the spatial distribution and density of galaxy cluster members. Based on the preliminary analysis of few high-resolution hydro simulations we conclude that a mass resolution of 1.8 × 108h−1 M⊙ will be needed for THE THREE HUNDRED simulations to approach the expected magnitude limits for the Euclid survey. These simulations are currently under way.

Analysis of professional competency awareness based on visible network graphs

Introduction: With the advancement of society and technology, the importance of professional competency has been increasingly emphasized. This study aims to provide an in-depth analysis of the Baidu search index for “professional competency” from July 3, 2017, to October 6, 2023, across 34 provincial-level administrative regions in China.Methods: The study begins with a literature review to establish a theoretical framework. Subsequently, it delves into the fundamental concepts of complex networks, their parameters, and the principles of visible network graph algorithms. The application of system clustering and community detection algorithms is explored within this framework.Results: The empirical analysis starts with a provincial-level examination, involving the construction of visible network graphs, the exploration of scale-free networks, and discussions on regional system clustering. Following this, a nationwide analysis is conducted, focusing on the construction of visible network graphs to visualize the awareness of professional competency and studying community structures.Discussion: The findings provide insights into the awareness of professional competency across different regions. These insights are valuable for informing policy formulation and corporate decision-making, contributing to a deeper understanding of professional competency awareness and its implications.

Proof of biased behavior of Normalized Mutual Information

The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of communities, and uncovers a significant drawback associated with the metric's behavior as the number of communities increases. Our findings reveal a pronounced bias in the NMI as the number of communities escalates. While previous studies have noted this biased behavior, they have not provided a formal proof and have not addressed the causation of this problem, leaving a gap in the existing literature. In this study, we fill this gap by employing a mathematical approach to formally demonstrate why NMI exhibits biased behavior, thereby establishing its unsuitability as a metric for evaluating clustering and community detection algorithms. Crucially, our study exposes the vulnerability of entropy-based metrics that employ logarithmic functions to similar bias.

A computational topology-based spatiotemporal analysis technique for honeybee aggregation

A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales, at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

Many morphs: Parsing gesture signals from the noise.

Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research depends on identifying and discriminating signals that represent meaningful units of analysis. Early approaches to defining signal repertoires applied top-down approaches, classifying cases into predefined signal types. Recently, more labour-intensive methods have taken a bottom-up approach describing detailed features of each signal and clustering cases based on patterns of similarity in multi-dimensional feature-space that were previously undetectable. Nevertheless, it remains essential to assess whether the resulting repertoires are composed of relevant units from the perspective of the species using them, and redefining repertoires when additional data become available. In this paper we provide a framework that takes data from the largest set of wild chimpanzee (Pan troglodytes) gestures currently available, splitting gesture types at a fine scale based on modifying features of gesture expression using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. Our method allows different features of interest to be incorporated into the splitting process, providing substantial future flexibility across, for example, species, populations, and levels of signal granularity. Doing so, we provide a powerful tool allowing researchers interested in gestural communication to establish repertoires of relevant units for subsequent analyses within and between systems of communication.

In-Depth Immune Profiling in Children with BCP-ALL within the AIEOP-BFM ALL 2017 Study

The international multicenter frontline therapy trial AIEOP-BFM ALL 2017 prospectively randomizes CD19-targeting T-cell engager blinatumomab (BLN) against standard of care (SOC) to improve outcomes in pediatric patients (pts) with B-cell precursor acute lymphoblastic leukemia (BCP-ALL) stratified for high (HR) and medium (MR) risks of relapse. In HR arm BLN is given earlier, directly after intensified HR therapy block, while in the MR arm, it is given towards the end of chemotherapy, before maintenance. The immune composition prior to BLN might thus differ between both treatment groups (HR vs MR) which may impact BLN-mediated anti-leukemic activity. Within the Germany-wide immune monitoring program accompanying the AIEOP-BFM ALL 2017 trial we addressed this question and studied a total of 1056 longitudinal peripheral blood samples collected between 11/2018 and 05/2022 from 381 BCP-ALL pts. Samples were prospectively analyzed with multicolor flow cytometry to study immune cell composition (Panel 1) at the time of BLN application (d0) and under ongoing immunotherapy within first 24h (d1), at day 14 (d14), at the end of the cycle I (d28) and end of cycle 2 (day28_II), if applicable (Figure 1A). In-depth T-cell profiling was performed in the BLN arm, utilizing 4 additional 12-color panels concentrated on markers associated with T-cell functionality (measured with standardized cytometer settings). Data were analyzed using classical 2D-manual gating strategies (> 2000 variables) and completed with R-based machine learning algorithms; namely partial Least-Squares Discriminant Analysis (PLS-DA), a multivariate dimensionality-reduction tool for identifying HR- and MR-defining immune signatures and PhenoGraph algorithm for unsupervised T-cell cluster detection. T-cell receptor excision circles (TRECs) and TR-repertoire analysis were also performed among paired samples of 117 BLN-treated pts. Baseline immune composition differed notably between HR and MR pts directly prior to BLN therapy, as shown by PLS-DA analysis on manually gated immune subsets. HR pts showed higher neutrophil counts and lower lymphocyte and monocyte counts compared to MR pts. Interestingly, MR pts had higher levels of B- and T-cells, which are crucial for BLN-mediated T-cell activation. HR pts displayed an elevated CD4+/CD8+ ratio, shifted naïve/memory T-cell phenotypes and increased proportions of immunosuppressive T-regulatory cells. These results suggest that more intensive therapy protocols directly prior to BLN influence the immune composition towards compromised states in the HR arm, potentially affecting BLN-mediated T-cell leukemia control. BLN effectively depleted CD19+ cells in 221 out of 224 pts. We observed significantly stronger T-cell activation/expansion in MR pts compared to HR pts under BLN therapy. This expansion that was primarily attributed to naïve CD8+ T-cell populations, resulting from clonal T-cell proliferation independent of thymic contribution, as supported by TREC and TR-repertoire analysis. The PhenoGraph clustering also identified this expanding naïve CD8+ T-cell populations, surprisingly marked by loss of adhesion molecule DNAM1 (panel 1), normally involved in NK and T-cell mediated cytotoxicity and by gaining the expression of TIM-3 (panel 3), an inhibitory molecule usually co-expressed with PD1 on exhausted T-cells with memory phenotypes. In this setting however, TIM3 was not co-expressed with PD1 (Figure 1B) and its expression was correlated with the B-cell levels at baseline and was strongest in MR pts under BLN therapy. Our data suggest an until now undescribed association between DNAM1 and TIM3 with BLN-mediated T-cell activation, hinting that they may serve as a measure of T-cell activation strength or an early T-cell exhaustion marker. In conclusion our study provides clinically relevant insights in how chemotherapy prior BLN might affect the patients´ immune system, possibly influencing the BLN anti-leukemic activity, and helps to determine the optimal place of immunotherapy with BLN in multimodal treatment schedules. Here, we report until know undescribed effect of BLN on T-cell compartment. Upcoming clinical data, which will be correlated with obtained immune profiles will answer the open question regarding the clinical relevance of these findings, and shed light into the mechanisms determining response to BLN, for its maximum benefit in childhood ALL.

Natural language processing (NLP) aided qualitative method in health research

Qualitative data analysis is produced frequently in healthcare settings, which is a time-consuming and skilled analytic task. The use of qualitative research findings in clinical settings takes years, which is sometimes obsolete knowledge as the health context is dynamic. Artificial Intelligence (AI)-based qualitative data analysis might present with rapid analysis of text-based data in real-time, thereby empowering qualitative researchers to expedite their analysis and facilitate timely use of the research findings. We tested an AI-based method to complement the manual analysis of text-based data from the verbatim transcripts of seven mall managers’ interviews. First, we prepared text data into a machine-calculable format and employed BERT model to extract sentence-level features in our case. Second, we implement TF-IDF-based keywords mining techniques to extract the main candidate themes from the interview transcripts to support text-based analysis, including: 1) primary cluster detection algorithm, and 2) keyword extraction algorithm. The extracted core themes provide qualitative researchers with a more comprehensive overview of the qualitative data. Most of the sentences clustered in meaningful short topics or sentences carrying independent and clear information. The extracted topics and clustered sentences reduced qualitative researchers’ workload by condensing and identifying meaningful concepts and naming them. This method combining contextualized word embeddings, unsupervised clustering, and keyword extraction techniques can significantly reduce the overall workload and time consumed in qualitative research using conventional methods.

YOLO–CL: Galaxy cluster detection in the SDSS with deep machine learning

Galaxy clusters are powerful probes for cosmological models. Next-generation, large-scale optical and infrared surveys are poised to reach unprecedented depths and, thus, they require highly complete and pure cluster catalogs, with a well-defined selection function. We have developed a new cluster detection algorithm named YOLO for CLuster detection (YOLO–CL), which is a modified version of the state-of-the-art object detection deep convolutional network named You only look once (YOLO) that has been optimized for the detection of galaxy clusters. We trained YOLO–CL on the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) cluster catalog, based on Sloan Digital Sky Survey (SDSS) color images. We find that YOLO–CL detects 95–98% of the redMaPPer clusters, with a purity of 95–98%, that is calculated by applying the network to SDSS blank fields. When compared to the Meta-Catalog of X-Ray Detected Clusters of Galaxies 2021 (MCXC2021) X-ray catalog in the SDSS footprint, YOLO–CL recovers all clusters at LX ≳ 2–3 × 1044 erg s−1, M500 ≳ 2–3 × 1014M⊙, R500≳0.75–0.8 Mpc and 0.4 ≲ z ≲ 0.6. When compared to the redMaPPer detection of the same MCXC2021 clusters, we find that YOLO–CL is more complete than redMaPPer, which means that the neural network has indeed improved the cluster detection efficiency of its training sample. In fact, YOLO–CL detects ~98% of the MCXC2021 clusters with an X-ray surface brightness of IX,500 ≳ 20 × 10−15 erg s−1 cm−2 arcmin−2 at 0.2 ≲ z ≲ 0.6 and ~100% of the MCXC2021 clusters with IX,500 ≳ 30 × 10−15 erg s−1 cm−2 arcmin−2 at 0.3 ≲ z ≲ 0.6; while redMaPPer detects ~98% of the MCXC2021 clusters with IX,500 ≳ 55 × 10−15 erg s−1 cm−2 arcmin−2 at 0.2 ≲ z ≲ 0.6 and ~100% of the MCXC2021 clusters with IX,500 ≳ 20 × 10−15 erg s−1 cm−2 arcmin−2 at 0.5 ≲ z ≲ 0.6. The YOLO–CL selection function is approximately constant with redshift, with respect to the MCXC2021 cluster X-ray surface brightness. YOLO–CL exhibits a high level of performance when compared to traditional detection algorithms applied to SDSS. Deep learning networks display a strong advantage over traditional galaxy cluster detection techniques because they do not require the galaxy’s photometric and photometric redshift catalogs. This eliminates systematic uncertainties that may be introduced during source detections and photometry, as well as photometric redshift measurements. Our results show that YOLO–CL is an efficient alternative to traditional cluster detection methods. In general, this work shows that it is worth exploring the performance of deep convolution networks for future cosmological cluster surveys, such as the Rubin/Legacy Survey of Space and Time (Rubin/LSST), Euclid, and Roman Space Telescope surveys.

UAV cluster detection algorithm based on weighted tri-layer window local contrast

Infrared images of unmanned aerial vehicle (UAV) cluster targets are characterized by the low signal-to-noise ratio (SCR), closely spaced, and limited detail characteristics, which exacerbate the detection complexity of contemporary infrared (IR) small target detection algorithms. This paper proposes a weighted tri-layer sliding window local contrast (NTLLCM) algorithm for UAV cluster targets detection. Firstly, the IR imaging characteristics of UAV cluster targets are modeled and evaluated. Secondly, after evaluating the limitations of LCM, the local contrast of the filtered images is calculated by designing a tri-layer sliding window structure with core, middle, and outermost layers. The local contrast is then weighted utilizing a new regional intensity level (NRIL) strategy to suppress the background further, and the target is detected by adaptive threshold segmentation. Finally, a considerable number of experimental results illustrate the superiority of this algorithm in detecting UAV cluster targets under sophisticated backgrounds, especially the proposed algorithm detects multiscale targets utilizing single-scale windows instead of using multiscale windows, which significantly decreases the computationally costs. Furthermore, the algorithm can reduce the overlapping effect of UAV cluster target imaging and correctly detect closely spaced targets.

Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19.

Spatial-temporal analysis of the COVID-19 cases is critical to find its transmitting behaviour and to detect the possible emerging clusters. Poisson's prospective space-time analysis has been successfully implemented for cluster detection of geospatial time series data. However, its accuracy, number of clusters, and processing time are still a major problem for detecting small-sized clusters. The aim of this research is to improve the accuracy of cluster detection of COVID-19 at the county level in the U.S.A. by detecting small-sized clusters and reducing the noisy data. The proposed system consists of the Poisson prospective space-time analysis along with Enhanced cluster detection and noise reduction algorithm (ECDeNR) to improve the number of clusters and decrease the processing time. The results of accuracy, processing time, number of clusters, and relative risk are obtained by using different COVID-19 datasets in SaTScan. The proposed system increases the average number of clusters by 7 and the average relative risk by 9.19. Also, it provides a cluster detection accuracy of 91.35% against the current accuracy of 83.32%. It also gives a processing time of 5.69 minutes against the current processing time of 7.36 minutes on average. The proposed system focuses on improving the accuracy, number of clusters, and relative risk and reducing the processing time of the cluster detection by using ECDeNR algorithm. This study solves the issues of detecting the small-sized clusters at the early stage and enhances the overall cluster detection accuracy while decreasing the processing time.

On Clustering of Floating Tracers in Random Velocity Fields

AbstractIn this paper, we investigate the aggregation of a floating tracer into clusters. Motivated by observations of dense patches of buoyant material in the real ocean (e.g., microplastic pollutants, plankton, and sargassum), we develop an idealized model that can reproduce the clustering process. A stochastic, kinematic 2D velocity field is chosen to represent turbulent oceanic surface currents, with a weakly divergent component. Lagrangian particles are introduced and we track their concentrations. We differ from delta‐correlated fields used in previous studies by including finite time correlations. Clustering in these fields can be compared to the traditional setting, through global measures and cluster detection algorithms. The enhanced velocity fields can be deformed using various interpolation methods. We can then investigate the sensitivity of clustering to the representation of temporal/spatial velocity structure to inform future studies of this phenomenon. We find coherency of time‐correlated velocities leads to significantly faster rates of clustering, causing a larger number of longer lived/more populated clusters to form. Clustering is likely relevant to a host of biogeochemical processes of urgent interest, such as phytoplankton blooms and the ecological risk of microplastic pollutants. This work aims to establish an accurate basis for clustering simulations, to enable further exploration.

The XMM cluster survey: exploring scaling relations and completeness of the dark energy survey year 3 redMaPPer cluster catalogue

ABSTRACT We cross-match and compare characteristics of galaxy clusters identified in observations from two sky surveys using two completely different techniques. One sample is optically selected from the analysis of 3 years of Dark Energy Survey observations using the redMaPPer cluster detection algorithm. The second is X-ray selected from XMM observations analysed by the XMM Cluster Survey. The samples comprise a total area of 57.4 deg2, bounded by the area of four contiguous XMM survey regions that overlap the DES footprint. We find that the X-ray-selected sample is fully matched with entries in the redMaPPer catalogue, above λ > 20 and within 0.1 <$z$ <0.9. Conversely, only 38 per cent of the redMaPPer catalogue is matched to an X-ray extended source. Next, using 120 optically clusters and 184 X-ray-selected clusters, we investigate the form of the X-ray luminosity–temperature (LX –TX ), luminosity–richness (LX –λ), and temperature–richness (TX –λ) scaling relations. We find that the fitted forms of the LX –TX relations are consistent between the two selection methods and also with other studies in the literature. However, we find tentative evidence for a steepening of the slope of the relation for low richness systems in the X-ray-selected sample. When considering the scaling of richness with X-ray properties, we again find consistency in the relations (i.e. LX –λ and TX –λ) between the optical and X-ray-selected samples. This is contrary to previous similar works that find a significant increase in the scatter of the luminosity scaling relation for X-ray-selected samples compared to optically selected samples.

Grape-Bunch Identification and Location of Picking Points on Occluded Fruit Axis Based on YOLOv5-GAP

Due to the short fruit axis, many leaves, and complex background of grapes, most grape cluster axes are blocked from view, which increases robot positioning difficulty in harvesting. This study discussed the location method for picking points in the case of partial occlusion and proposed a grape cluster-detection algorithm “You Only Look Once v5-GAP” based on “You Only Look Once v5”. First, the Conv layer of the first layer of the YOLOv5 algorithm Backbone was changed to the Focus layer, then a convolution attention operation was performed on the first three C3 structures, the C3 structure layer was changed, and the Transformer in the Bottleneck module of the last layer of the C3 structure was used to reduce the computational amount and execute a better extraction of global feature information. Second, on the basis of bidirectional feature fusion, jump links were added and variable weights were used to strengthen the fusion of feature information for different resolutions. Then, the adaptive activation function was used to learn and decide whether neurons needed to be activated, such that the dynamic control of the network nonlinear degree was realized. Finally, the combination of a digital image processing algorithm and mathematical geometry was used to segment grape bunches identified by YOLOv5-GAP, and picking points were determined after finding centroid coordinates. Experimental results showed that the average precision of YOLOv5-GAP was 95.13%, which was 16.13%, 4.34%, and 2.35% higher than YOLOv4, YOLOv5, and YOLOv7 algorithms, respectively. The average positioning pixel error of the point was 6.3 pixels, which verified that the algorithm effectively detected grapes quickly and accurately.

VST ATLAS galaxy cluster catalogue I: cluster detection and mass calibration

ABSTRACT Taking advantage of ∼4700 deg2 optical coverage of the Southern sky offered by the VST ATLAS survey, we construct a new catalogue of photometrically selected galaxy groups and clusters using the orca cluster detection algorithm. The catalogue contains ∼22 000 detections with N200 > 10 and ∼9000 with N200 > 20. We estimate the photometric redshifts of the clusters using machine learning and find the redshift distribution of the sample to extend to z ∼ 0.7, peaking at z ∼ 0.25. We calibrate the ATLAS cluster mass-richness scaling relation using masses from the MCXC, Planck, ACT DR5, and SDSS redMaPPer cluster samples. We estimate the ATLAS sample to be $\gt 95~{{\ \rm per\ cent}}$ complete and $\gt 85~{{\ \rm per\ cent}}$ pure at z < 0.35 and in the M200m>$1\times 10^{14}h^{-1}\, \mbox{M}_\odot$ mass range. At z < 0.35, we also find the ATLAS sample to be more complete than redMaPPer, recovering a $\sim 40~{{\ \rm per\ cent}}$ higher fraction of Abell clusters. This higher sample completeness places the amplitude of the z < 0.35 ATLAS cluster mass function closer to the predictions of a ΛCDM model with parameters based on the Planck CMB analyses, compared to the mass functions of the other cluster samples. However, strong tensions between the observed ATLAS mass functions and models remain. We shall present a detailed cosmological analysis of the ATLAS cluster mass functions in paper II. In the future, optical counterparts to X-ray-detected eROSITA clusters can be identified using the ATLAS sample. The catalogue is also well suited for auxiliary spectroscopic target selection in 4MOST. The ATLAS cluster catalogue is publicly available at http://astro.dur.ac.uk/cosmology/vstatlas/cluster_catalogue/.

Research Overview on Edge Detection Algorithms Based on Deep Learning and Image Fusion

Edge detection is a boundary-based segmentation method to extract important information from an image, and it is a research hotspot in the fields of computer vision and image analysis. Especially feature extraction is also the basis of image segmentation, target detection, and recognition. In recent years, in order to solve the problems of edge detection refinement and low detection accuracy, the industry has proposed multiscale fusion wavelet edge, spectral clustering, network reconstruction, and other edge detection algorithms based on deep learning. In order to enable researchers to understand the current research status of edge detection, this paper first introduces the classic algorithm of traditional edge detection, compare with advantages and disadvantages of different edge detection algorithms. Then, it summarizes the main edge detection methods based on deep learning in recent years and classifies and compares them according to the implementation technology. Finally, it shows the development direction of edge detection algorithm research.

How Do the Factors Associated With Frailty Change With Sex? An Exploratory Network Analysis.

It is important to study multiple social, physical, and psychosocial factors associated with frailty in populations characterized by social and health disparities, such as men and women. This was a cross-sectional population-based study with older adults 65 years or older from the FIBRA (Frailty in Brazilian Older Adults) 2008-2009 study. We carried out a comparative analysis of the factors associated with the frailty phenotype in older men (N = 706) and women (N = 1 251) using networks based on mixed graphical models according to sex. In the male network, frailty was most strongly associated with years of schooling, overall satisfaction with life, and falls; in the female network, the syndrome was associated with satisfaction with problem-solving, depression, and diabetes in addition to years of schooling. Furthermore, permutation tests showed that the networks for males and females were statistically different in terms of their structure, the global strength of the relationships, and the strength of the relationships between frailty and diabetes; frailty and falls; frailty and depression; frailty and overall satisfaction with life; and frailty and satisfaction with problem-solving (p < .05). The walktrap network cluster detection algorithm revealed that in men, frailty was in a physical and social dimension while in women the syndrome was in a cardiometabolic and psychosocial dimension. Network analysis showed that different factors are associated with frailty for each sex. The findings suggest that different strategies for dealing with frailty should be adopted for men and women so that care and prevention efforts can be directed appropriately.

832Detecting spatial clusters of human salmonellosis in Victoria

Abstract Focus of Presentation ‘Cluster Tracker’ is an automated tool for spatial cluster detection of notifiable disease data collected by the Department of Health (DH), Victoria. The tool combines R statistical software and a SaTScan cluster detection algorithm (prospective space-time permutation scan statistic) to detect notifiable disease case clusters in Victoria and is presently implemented for salmonellosis (categorised by type and/or MLVA). The objective of the tool is to conduct an initial screening of case data to improve the prioritisation of salmonellosis cases for epidemiological investigation. Findings The Cluster Tracker tool parameters have been validated using historical data from 2017-2018, comparing DH outbreak and cluster investigations identified by usual surveillance activities with clusters detected by the Cluster Tracker tool. Parameter selection considered cluster detection agreement and disagreement, disease-specific epidemiological characteristics, and operational requirements. The Cluster Tracker tool was able to provide closely-aligned agreement with existing DH outbreak and cluster investigations using the validated parameters. Implications This automated spatial cluster detection tool complements existing desktop surveillance of salmonellosis notifications to enhance public health decision making, and serves as an example of how spatial methods can improve real-time surveillance. Key messages Advanced spatial statistical tools have a role alongside traditional methods to make better use of limited epidemiological capacity and improve the timeliness and prioritisation of surveillance activities for notifiable diseases.

Evolutionary clustering and community detection algorithms for social media health surveillance

The prominent rise of social networks within the past decade have become a gold mine for data mining operations seeking to model the real world through these virtual worlds. One of the most important applications that has been proposed is utilizing information generated from social networks as a supplemental health surveillance system to monitor disease epidemics. At the time this research was conducted in 2020, the COVID-19 virus had evolved into a global pandemic, forcing many countries to implement preventative measures to halt its expanse. Health surveillance has been a powerful tool in placing further preventative measures, however it is not a perfect system, and slowly collected, misidentified information can prove detrimental to these efforts. This research proposes a new potential surveillance avenue through unsupervised machine learning using dynamic, evolutionary variants of clustering algorithms DBSCAN and the Louvain method to allow for community detection in temporal networks. This technique is paired with geographical data collected directly from the social media Twitter, to create an effective and accurate health surveillance system that grows as time passes. The experimental results show that the proposed system is promising and has the potential to be an advancement on current machine learning health surveillance techniques.