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Literatura académica sobre el tema "HETEROGENOUS MEAN FIELD"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "HETEROGENOUS MEAN FIELD"

Xu, Can, Yonggang Wu, Zhigang Zheng, and Longkun Tang. "Partial locking in phase-oscillator populations with heterogenous coupling." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (2022): 063106. http://dx.doi.org/10.1063/5.0093318.

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We consider a variant of the mean-field model of coupled phase oscillators with uniform distribution of natural frequencies. By establishing correlations between the quenched disorder of intrinsic frequencies and coupling strength with both in- and out-coupling heterogeneities, we reveal a generic criterion for the onset of partial locking that takes place in a domain with the coexistence of phase-locked oscillators and drifters. The critical points manifesting the instability of the stationary states are obtained analytically. In particular, the bifurcation mechanism of the equilibrium states is uncovered by the use of frequency-dependent version of the Ott–Antonsen reduction consistently with the analysis based on the self-consistent approach. We demonstrate that both the manner of coupling heterogeneity and correlation exponent have influence on the emergent patterns of partial locking. Our research could find applicability in better understanding the phase transitions and related collective phenomena involving synchronization control in networked systems.

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Chicchi, Lorenzo, Gloria Cecchini, Ihusan Adam, et al. "Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging." Journal of Computational Neuroscience 49, no. 2 (2021): 159–74. http://dx.doi.org/10.1007/s10827-020-00774-1.

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AbstractAn inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.

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Forbes, Emily, Matthew Back, Andrew Brooks, et al. "Stability of Patches of Higher Population Density within the Heterogenous Distribution of the Gray Field Slug Deroceras reticulatum in Arable Fields in the UK." Insects 12, no. 1 (2020): 9. http://dx.doi.org/10.3390/insects12010009.

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Exploitation of heterogenous distributions of Deroceras reticulatum, in arable fields by targeting molluscicide applications toward areas with higher slug densities, relies on these patches displaying sufficient spatio-temporal stability. Regular sampling of slug activity/distribution was undertaken using 1 ha rectangular grids of 100 refuge traps established in 22 commercial arable field crops. Activity varied significantly between the three years of the study, and the degree of aggregation (Taylor’s Power Law) was higher in fields with higher mean trap catches. Hot spot analysis detected statistically significant spatial clusters in all fields, and in 162 of the 167 individual assessment visits. The five assessment visits in which no clusters were detected coincided with low slug activity (≤0.07 per trap). Generalized Linear Models showed significant spatial stability of patches in 11 fields, with non-significant fields also characterized by low slug activity (≤1.2 per trap). Mantel’s permutation tests revealed a high degree of correlation between location of individual patches between sampling dates. It was concluded that patches of higher slug density were spatio-temporally stable, but detection using surface refuge traps (which rely on slug activity on the soil surface) was less reliable when adverse environmental conditions resulted in slugs retreating into the upper soil horizons.

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Hamdioui, S., and L. Vaivre-Douret. "Psycho-socio-emotional characteristics in high intellectual potential child regarding IQ profile (Homogenous/Heterogenous)." European Psychiatry 65, S1 (2022): S419. http://dx.doi.org/10.1192/j.eurpsy.2022.1064.

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Introduction Few studies have analyzed the psychometric profile (Homogenous/Heterogenous), established by the Wechsler scale in high intellectual potential children (HIP, IQ>130), regarding the psycho-socio-emotional characteristics. Objectives We aimed to look at the links between the IQ-profile and the psycho-socio-emotional characteristics in HIP. Methods Anamnestic questionnaire and Wechsler-Intelligence-Scale for children (WISC-V) were conducted and analyzed in 58 healthy children with HIP, aged 7-to-13 years-old (mean 10y; SD 1.8). It was possible to distinguished 27 Homogenousvs 30 Heterogenous IQ-profile. Results No significant difference between homogenous/heterogonous groups, FIQ was positively significantly correlated with “Reacting very little emotionally”, “Tendency to isolation”. Visual-Spatial-Index (VSI) with “Ability to adapt to new people” (r=-0.4, p=0.02), “few interests” (r=0.5, p=0.008). Verbal-Comprehension-Index (VCI) with “Reacting strongly to frustration”, “Difficulties to understand limits”, “Separation anxiety”. A significant difference between homogenous/heterogonous groups was shown regarding “few interests” with high rate in the heterogonous group (t= -2.34, p=0.023). Conclusions HIP seems to cover specific psycho-socio-emotional characteristics linked to IQ index distribution. Thus, it appears interesting to assess more the emotional and socio-cognitive field to understand these characteristics in HIP children. Disclosure No significant relationships.

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Allmeier, Sebastian, and Nicolas Gast. "Mean Field and Refined Mean Field Approximations for Heterogeneous Systems." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 103–4. http://dx.doi.org/10.1145/3547353.3522653.

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Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as n interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers. Mean field approximation is asymptotically exact for systems composed of n homogeneous objects under mild conditions. In this paper, we study what happens when objects are heterogeneous. This can represent servers with different speeds or contents with different popularities. We define an interaction model that allows obtaining asymptotic convergence results for stochastic systems with heterogeneous object behavior and show that the error of the mean field approximation is of order O(1/n). More importantly, we show how to adapt the refined mean field approximation, developed by the authors of Gast et al. 2019, and show that the error of this approximation is reduced to O(1/n2). To illustrate the applicability of our result, we present two examples. The first addresses a list-based cache replacement model, RANDOM(m), which is an extension of the RANDOM policy. The second is a heterogeneous supermarket model. These examples show that the proposed approximations are computationally tractable and very accurate. For moderate system sizes (n ≈ 30) the refined mean field approximation tends to be more accurate than simulations for any reasonable simulation time.

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Allmeier, Sebastian, and Nicolas Gast. "Mean Field and Refined Mean Field Approximations for Heterogeneous Systems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 1 (2022): 1–43. http://dx.doi.org/10.1145/3508033.

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Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as n interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers. Mean field approximation is asymptotically exact for systems composed of n homogeneous objects under mild conditions. In this paper, we study what happens when objects are heterogeneous. This can represent servers with different speeds or contents with different popularities. We define an interaction model that allows obtaining asymptotic convergence results for stochastic systems with heterogeneous object behavior, and show that the error of the mean field approximation is of order $O(1/n)$. More importantly, we show how to adapt the refined mean field approximation, developed by Gast et al., and show that the error of this approximation is reduced to O(1/n^2). To illustrate the applicability of our result, we present two examples. The first addresses a list-based cache replacement model, RANDOM(m), which is an extension of the RANDOM policy. The second is a heterogeneous supermarket model. These examples show that the proposed approximations are computationally tractable and very accurate. They also show that for moderate system sizes (30) the refined mean field approximation tends to be more accurate than simulations for any reasonable simulation time.

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Jumaah, Hayder A. "Modified Archie’s parameters for estimating water saturation for carbonate reservoir in north of Iraq." Journal of Petroleum Exploration and Production Technology 11, no. 10 (2021): 3689–97. http://dx.doi.org/10.1007/s13202-021-01258-3.

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AbstractArchie’s parameters, cementation factor (m), saturation exponent (n) and tortuosity factor (a), are general factor that have effects on water saturation magnitude, due to their sensitivity to pores distribution, lithofacies properties and wettability, particularly in carbonate reservoirs. Water saturation magnitude has a direct effect in estimating initial oil-in-place values, and inaccuracy in its values will lead to huge impact errors in initial oil-in-place values, so it would affect the economics of field management and development plans. In this paper, the main objective was to investigate the impact of using conventional and modified Archie’s parameters in the determination of water saturation from well log interpretation for Tertiary reservoir in Khabaz oil field, a heterogenous carbonite reservoir in the north of Iraq which was affected by different digenesis processes that impacted the reservoir quality. Tertiary reservoir of Khabaz field consists of five geological units (A, B, C, D and E), and the selected well penetrated the top of the reservoir at 2200.5 m RTKB and passed through five geological units and reached total depth at 2348 m RTKB. The geothermal gradient of the field was 1.12 ℉ per 100 ft, and formation water resistivity (Rw) was about 0.029 Ω m. Water saturation was at first estimated from resistivity logs by Archie model with conventional known values of parameters (a, m and n) (1, 2 and 2), respectively, and then Archie’s parameters were modified and determined by graphical technique of Pickett plot for each geological unit to estimate water saturation. Finally, the results show the water saturation value was more sensitive for Archie’s parameter in low-porosity and high-clay-volume zone, but less sensitive in clean high-porosity zone, and water saturation values determined by modified Archie model were less about 18.5% at mean than their value by using conventional Archie’s parameters.

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Alabi, Tunrayo R., Julius Adewopo, Ojo Patrick Duke, and P. Lava Kumar. "Banana Mapping in Heterogenous Smallholder Farming Systems Using High-Resolution Remote Sensing Imagery and Machine Learning Models with Implications for Banana Bunchy Top Disease Surveillance." Remote Sensing 14, no. 20 (2022): 5206. http://dx.doi.org/10.3390/rs14205206.

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Banana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA. The BBTV outbreak in West Africa was first recorded in the Benin Republic in 2010 and has spread to the adjoining territories of Nigeria and Togo. Regular surveillance, conducted as part of the containment efforts, requires the identification of banana fields for disease assessment. However, small and fragmented production spread across large areas poses complications for identifying all banana farms using conventional field survey methods, which is also time-consuming and expensive. In this study, we developed a remote sensing approach and machine learning (ML) models that can be used to identify banana fields for targeted BBTV surveillance. We used medium-resolution synthetic aperture radar (SAR), Sentinel 2A satellite imagery, and high-resolution RGB and multispectral aerial imagery from an unmanned aerial vehicle (UAV) to develop an operational banana mapping framework by combining the UAV, SAR, and Sentinel 2A data with the Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. The user accuracy (UA) and producer accuracy (PA) for the banana class were 83% and 78%, respectively. The BBTV surveillance teams used the banana mapping framework to identify banana fields in the BBTV-affected southwest Ogun state of Nigeria, which helped in detecting 17 sites with BBTV infection. These findings suggest that the prediction of banana and other crops in the heterogeneous smallholder farming systems is feasible, with the precision necessary to guide BBTV surveillance in large areas in SSA.

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Dias, C., and M. O. Hase. "Weakening connections in heterogeneous mean-field models." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 1 (2021): 013404. http://dx.doi.org/10.1088/1742-5468/abd4ca.

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Chong, Carsten, and Claudia Klüppelberg. "Partial mean field limits in heterogeneous networks." Stochastic Processes and their Applications 129, no. 12 (2019): 4998–5036. http://dx.doi.org/10.1016/j.spa.2018.12.018.

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