Voter Model Research Articles

Ordering kinetics of the two-dimensional voter model with long-range interactions.

We study analytically the ordering kinetics of the two-dimensional long-range voter model on a two-dimensional lattice, where agents on each vertex take the opinion of others at distance r with probability P(r)∝r^{-α}. The model is characterized by different regimes, as α is varied. For α>4, the behavior is similar to that of the nearest-neighbor model, with the formation of ordered domains of a typical size growing as L(t)∝sqrt[t], until consensus is reached in a time of the order of NlnN, with N being the number of agents. Dynamical scaling is violated due to an excess of interfacial sites whose density decays as slowly as ρ(t)∝1/lnt. Sizable finite-time corrections are also present, which are absent in the case of nearest-neighbor interactions. For 0<α≤4, standard scaling is reinstated and the correlation length increases algebraically as L(t)∝t^{1/z}, with 1/z=2/α for 3<α<4 and 1/z=2/3 for 0<α<3. In addition, for α≤3,L(t) depends on N at any time t>0. Such coarsening, however, only leads the system to a partially ordered metastable state where correlations decay algebraically with distance, and whose lifetime diverges in the N→∞ limit. In finite systems, consensus is reached in a time of the order of N for any α<4.

Voting Model Strategies for Reliable Categorical IoT-DDoS Attack Prediction

This research focuses on developing reliable categorical IoT-DDoS attack prediction models using ensemble voting strategies. The study explores various machine learning algorithms suitable for categorical data analysis, employing feature engineering techniques to preprocess IoT data. Ensemble learning methodologies, including bagging, boosting, and stacking, are then utilized to build robust prediction models. Evaluation metrics such as precision, recall, F1-score, and AUC-ROC are used to assess model performance, demonstrating the effectiveness of ensemble voting models in reliably predicting IoT-DDoS attacks. Comparative analyses with individual classifiers highlight the advantages of ensemble approaches in terms of predictive accuracy and robustness against data imbalances and noise. This work contributes to advancing IoT security by providing a practical framework for deploying predictive models that aid in early detection and mitigation of DDoS attacks, enhancing overall resilience against cyber threats in IoT ecosystems.

Polyadic Opinion Formation: The Adaptive Voter Model on a Hypergraph

AbstractThe adaptive voter model is widely used to model opinion dynamics in social complex networks. However, existing adaptive voter models are limited to only pairwise interactions and fail to capture the intricate social dynamics that arises in groups. This paper extends the adaptive voter model to hypergraphs to explore how forces of peer pressure influence collective decision‐making. The model consists of two processes: individuals can either consult the group and change their opinion or leave the group and join a different one. The interplay between those two processes gives rise to a two‐phase dynamics. In the initial phase, the topology of the hypergraph quickly reaches a new stable state. In the subsequent phase, opinion dynamics plays out on the new topology depending on the mechanism by which opinions spread. If the group always follows the majority, the network rapidly converges to fragmented communities. In contrast, if individuals choose an opinion proportionally to its representation in the group, the system remains in a metastable state for an extended period of time. The results are supported both by stochastic simulations and an analytical mean‐field description in terms of hypergraph moments with a moment closure at the pair level.

An Integrated Approach to Preferential Voting Models with Variable Weights for Rank Positions

An Integrated Approach to Preferential Voting Models with Variable Weights for Rank Positions

An experimental case study of natural ventilation effects on the residential thermal environment and predicted thermal comfort in Kunming

The use of natural ventilation for cooling in summer in mild climate areas can positively improve the indoor thermal environment and thermal comfort. In this paper, Kunming, China was chosen as a case to study experimentally the natural ventilation effects on the residential thermal environment and thermal comfort in mild climate rural areas. The effects of natural ventilation on the indoor thermal environment and thermal comfort under the typical hottest sunny days in summer in Kunming were investigated with seven different combinations of ventilation methods with the opening/closing of doors/windows. The results show that natural ventilation can considerably improve the indoor thermal environment. Furthermore, the uniformity of indoor thermal environments at different heights was assessed. Additionally, the vertical temperature difference between the head and foot areas around the human body remained below 3 °C. Moreover, the adaptive predicted mean vote (APMV) model was used to evaluate the thermal comfort under natural ventilation, and values of APMV ranged from −0.84 to 0.84 under six scenarios, considering an acceptable temperature range based on occupant satisfaction rates of over 80%. This study's outcomes offer valuable insights for the design of self-built housing in the extensive rural areas within mild climate zones in China.

Efficient equilibria in common interest voting games

We develop a unified derivation of asymmetric pure strategy equilibria and their optimality in the canonical common interest voting model of Austen-Smith and Banks (Am Polit Sci Rev 90(1):34–45, 1996). We also study the relationship between the most efficient equilibria, which have a remarkably simple and intuitive structure, and the symmetric mixed strategy equilibrium that has been commonly studied in the literature. In particular, while the efficiency in the symmetric mixed strategy equilibrium under unanimity rule is known to be decreasing in the number of voters, the efficiency does not depend on the number of voters above a threshold in the most efficient equilibria.

DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.

Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degeneration. The aim of this study is to develop a deep learning classifier based on optical coherence tomography (OCT) and OCT angiography (OCTA) to identify nonexudative macular neovascularizations at risk of exudation. Patients with age-related macular degeneration showing OCTA and fluorescein angiography-documented nonexudative macular neovascularization with a 2-year minimum imaging follow-up were retrospectively selected. Patients showing OCT B-scan-documented macular neovascularization exudation within the first 2 years formed the EX GROUP while the others formed the QU GROUP. ResNet-101, Inception-ResNet-v2, and DenseNet-201 were independently trained on OCTA and OCT B-scan images. Combinations of the six models were evaluated with major and soft voting techniques. Eighty-nine eyes of 89 patients with a follow-up of 5.7 ± 1.5 years were recruited (35 EX GROUP and 54 QU GROUP). Inception-ResNet-v2 was the best performing among the three single convolutional neural networks. The major voting model resulting from the association of the three different convolutional neural networks resulted in an improvement of performance both for OCTA and OCT B-scan (both significantly higher than human graders' performance). The soft voting model resulting from the combination of OCTA and OCT B-scan-based major voting models showed a testing accuracy of 94.4%. Peripheral arcades and large vessels on OCTA en face imaging were more prevalent in the QU GROUP. Artificial intelligence shows high performances in identifications of nonexudative macular neovascularizations at risk for exudation within the first 2 years of follow-up, allowing better customization of follow-up timing and avoiding treatment delay. Better results are obtained with the combination of OCTA and OCT B-scan image analysis.

Ordering dynamics of nonlinear voter models.

We study the ordering dynamics of nonlinear voter models with multiple states, also providing a discussion of the two-state model. The rate with which an individual adopts an opinion scales as the qth power of the number of the individual's neighbors in that state. For q>1 the dynamics favor the opinion held by the most agents. The ordering to consensus is driven by deterministic drift, and noise plays only a minor role. For q<1 the dynamics favors minority opinions, and for multistate models the ordering proceeds through a noise-driven succession of metastable states. Unlike linear multistate systems, the nonlinear model cannot be reduced to an effective two-state model. We find that the average density of active interfaces in the model with multiple opinion states does not show a single exponential decay in time for q<1, again at variance with the linear model. This highlights the special character of the conventional (linear) voter model, in which deterministic drift is absent. As part of our analysis, we develop a pair approximation for the multistate model on graphs, valid for any positive real value of q, improving on previous approximations for nonlinear two-state voter models.

The Voter Model with a Slow Membrane

The Voter Model with a Slow Membrane

Condorcet-loser dominance between the plurality rule and other scoring rules

In a voting model with three alternatives, we show that for any non-Borda scoring rule, there exists at least one preference profile under which the scoring rule selects a Condorcet loser, whereas the plurality rule does not.

Identification and interpretation of gait analysis features and foot conditions by explainable AI

Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.

A deep ensemble learning method for cherry classification

AbstractIn many agricultural products, information technologies are utilized in classification processes at the desired quality. It is undesirable to mix different types of cherries, especially in export-type cherries. In this study on cherries, one of the important export products of Turkey, the classification of cherry species was carried out with ensemble learning methods. In this study, a new dataset consisting of 3570 images of seven different cherry species grown in Isparta region was created. The generated new dataset was trained with six different deep learning models with pre-learning on the original and incremental dataset. As a result of the training with incremental data, the best result was obtained from the DenseNet169 model with an accuracy of 99.57%. The two deep learning models with the best results were transferred to ensemble learning and a 100% accuracy rate was obtained with the Maximum Voting model.

Ensemble learning for multi-channel sleep stage classification

Sleep is a vital process for human well-being. Sleep scoring is performed by experts using polysomnograms, that record several body activities, such as electroencephalograms (EEG), electrooculograms (EOG), and electromyograms (EMG). This task is known to be exhausting, biased, time-consuming, and prone to errors. Current automatic sleep scoring approaches are mostly based on single-channel EEG and do not produce explainable results. Therefore, we propose a heterogeneous ensemble learning-based approach where we combine accuracy-based learning classifier systems with different algorithms to produce a robust, explainable, and enhanced classifier. The efficiency of our approach was evaluated using the Sleep-EDF benchmark dataset. The proposed models have reached an accuracy of 89.2% for the stacking model and 87.9% for the voting model, on a multi-class classification task based on the R&K guidelines.

Study on the indoor thermal comfort of cave dwellings in cold areas of China

Cave dwelling, as a common traditional housing form in rural areas, has long been regarded as a low-cost, green, and environmentally friendly living choice. The purpose of this study was to explore and improve the thermal comfort of cave dwellings, and to systematically evaluate and analyse the thermal comfort of cave dwellings through a combination of visit surveys and indoor environmental parameter monitoring. After statistical analysis and comparison of the collected data, the following conclusions were drawn: (1) The thermal neutrality temperature of 25.15 °C determined by Predicted Mean Vote (PMV) in this study was greater than the thermal neutrality temperature of 19.49 °C determined by Thermal Sensation Vote (TSV) model, indicating that cave dwellers prefer a cooler thermal environments under actual conditions. (2) In cold weather, the maximum value of 80% acceptability is 18.12 °C and the minimum value is 12.00 °C. In hot weather, the maximum value of 80% acceptability is 27.17 °C and the minimum value is 23.66 °C. (3) In winter, the average indoor temperature of 9.71 °C is higher than the average outdoor temperature of 7.85 °C; In summer, the average temperature in the naturally ventilated room of 27.44 °C is much lower than the average outdoor temperature of 35.25 °C. The results showed that the building materials and structure of cave houses can provide a certain degree of thermal insulation, which can provide a basis for the design and improvement of low-cost, energy-saving and environmentally friendly houses in rural areas.

Quasistationary Distribution for The Invasion Model on a Complete Bipartite Graph

The Invasion Model on the complete bipartite graph was introduced and studied by physicists as a rudimentary model for opinion dynamics on complex networks. Our main result provides the limit of the Quasistationary Distribution (QSD) for the model as one partition size tends to infinity. This limit is a highly dispersed measure. The results are in stark contrast to the Voter Model whose QSD is essentially a perturbation to absorbing states. A distinct feature not present in the Voter Model and demanding a new approach developed here is the appearance of two time scales with a non-trivial interaction. Our analysis is based on decoupling of the scales and using a Stein-type operator to identify a limit distribution. It also involves an application of a known duality with coalescing Markov chains through which asymptotic survival rates associated with the QSD are obtained.

First-passage distributions of an asymmetric noisy voter model.

This paper explores the first-passage times in an asymmetric noisy voter model through analytical methods. The noise in the model leads to bistable behavior, and the asymmetry arises from heterogeneous rates for spontaneous switching. We obtain exact analytical expressions for the probability distribution for two different initial conditions, first-passage times for switching transitions and first return times to a stable state for all system sizes, offering a deeper understanding of the model's dynamics. Additionally, we derive exact expressions for the mean switching time, mean return time, and their mean square variants. The findings are verified through numerical simulations. To enhance clarity regarding the model's behavior, we also provide approximate solutions, emphasizing the parameter dependence of first-passage times in the small switching parameter regime. An interesting result in this regime is that while the mean switching time in the leading order is independent of system size, the mean return time depends inversely on system size. This study not only advances our analytical understanding of the asymmetric noisy voter model but also establishes a framework for exploring similar phenomena in social and biological systems where the noisy voter model is applicable.

Approval-based voting with mixed goods

We consider a voting scenario in which the resource to be voted upon may consist of both indivisible and divisible goods. This setting generalizes both the well-studied model of multiwinner voting and the recently introduced model of cake sharing. Under approval votes, we propose two variants of the extended justified representation (EJR) notion from multiwinner voting, a stronger one called EJR for mixed goods (EJR-M) and a weaker one called EJR up to 1 (EJR-1). We extend three multiwinner voting rules to our setting—GreedyEJR, the method of equal shares (MES), and proportional approval voting (PAV)—and show that while all three generalizations satisfy EJR-1, only the first one provides EJR-M. In addition, we derive tight bounds on the proportionality degree implied by EJR-M and EJR-1, and investigate the proportionality degree of our proposed rules.

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles.

Current treatments for Alzheimer's disease (AD) focus on slowing memory and cognitive decline, but none offer curative outcomes. This study aims to explore and curate the common properties of active, drug-like molecules that modulate glycogen synthase kinase 3β (GSK-3β), a well-documented kinase with increased activity in tau hyperphosphorylation and neurofibrillary tangles-hallmarks of AD pathology. Leveraging quantitative structure-activity relationship (QSAR) data from the PubChem and ChEMBL databases, we employed seven machine learning models: logistic regression (LogR), k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), neural networks (NNs), and ensemble majority voting. Our goal was to correctly predict active and inactive compounds that inhibit GSK-3β activity and identify their key properties. Among the six individual models, the NN demonstrated the highest performance with a 79% AUC-ROC on unbalanced external validation data, while the SVM model was superior in accurately classifying the compounds. The SVM and RF models surpassed NN in terms of Kappa values, and the ensemble majority voting model demonstrated slightly better accuracy to the NN on the external validation data. Feature importance analysis revealed that hydrogen bonds, phenol groups, and specific electronic characteristics are important features of molecular descriptors that positively correlate with active GSK-3β inhibition. Conversely, structural features like imidazole rings, sulfides, and methoxy groups showed a negative correlation. Our study highlights the significance of structural, electronic, and physicochemical descriptors in screening active candidates against GSK-3β. These predictive features could prove useful in therapeutic strategies to understand the important properties of GSK-3β candidate inhibitors that may potentially benefit non-amyloid-based AD treatments targeting neurofibrillary tangles.

Discord in the voter model for complex networks.

Online social networks have become primary means of communication. As they often exhibit undesirable effects such as hostility, polarization, or echo chambers, it is crucial to develop analytical tools that help us better understand them. In this paper we are interested in the evolution of discord in social networks. Formally, we introduce a method to calculate the probability of discord between any two agents in the multistate voter model with and without zealots. Our work applies to any directed, weighted graph with any finite number of possible opinions, allows for various update rates across agents, and does not imply any approximation. Under certain topological conditions, the opinions are independent and the joint distribution can be decoupled. Otherwise, the evolution of discord probabilities is described by a linear system of ordinary differential equations. We prove the existence of a unique equilibrium solution, which can be computed via an iterative algorithm. The classical definition of active links density is generalized to take into account long-range, weighted interactions. We illustrate our findings on real-life and synthetic networks. In particular, we investigate the impact of clustering on discord and uncover a rich landscape of varied behaviors in polarized networks. This sheds lights on the evolution of discord between, and within, antagonistic communities.

Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study.

Attention-deficit/hyperactivity disorder (ADHD) affects a significant proportion of the pediatric population, making early detection crucial for effective intervention. Eye movements are controlled by brain regions associated with neuropsychological functions, such as selective attention, response inhibition, and working memory, and their deficits are related to the core characteristics of ADHD. Herein, we aimed to develop a screening model for ADHD using machine learning (ML) and eye-tracking features from tasks that reflect neuropsychological deficits in ADHD. Fifty-six children (mean age 8.38 ± 1.58, 45 males) diagnosed with ADHD based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition were recruited along with seventy-nine typically developing children (TDC) (mean age 8.80 ± 1.82, 33 males). Eye-tracking data were collected using a digital device during the performance of five behavioral tasks measuring selective attention, working memory, and response inhibition (pro-saccade task, anti-saccade task, memory-guided saccade task, change detection task, and Stroop task). ML was employed to select relevant eye-tracking features for ADHD, and to subsequently construct an optimal model classifying ADHD from TDC. We identified 33 eye-tracking features in the five tasks with the potential to distinguish children with ADHD from TDC. Participants with ADHD showed increased saccade latency and degree, and shorter fixation time in eye-tracking tasks. A soft voting model integrating extra tree and random forest classifiers demonstrated high accuracy (76.3%) at identifying ADHD using eye-tracking features alone. A comparison of the model using only eye-tracking features with models using the Advanced Test of Attention or Stroop test showed no significant difference in the area under the curve (AUC) (p = 0.419 and p=0.235, respectively). Combining demographic, behavioral, and clinical data with eye-tracking features improved accuracy, but did not significantly alter the AUC (p=0.208). Our study suggests that eye-tracking features hold promise as ADHD screening tools, even when obtained using a simple digital device. The current findings emphasize that eye-tracking features could be reliable indicators of impaired neurobiological functioning in individuals with ADHD. To enhance utility as a screening tool, future research should be conducted with a larger sample of participants with a more balanced gender ratio.