Probabilistic Interaction Research Articles

PAYOFF-BASED PROBABILISTIC INTERACTION MODEL ON THE EVOLUTION OF COOPERATION IN SPATIAL PUBLIC GOODS GAME

In the spatial public goods game (SPGG), punishment effectively promotes cooperation but often reduces the collective benefits of cooperators and punishers. In order to increase revenue, we propose a probabilistic interaction domain model considering both strategy type and payoff level. In this model, players are divided into two types, successful players with payoffs higher than the payoff threshold and failed players with payoffs lower than the payoff threshold. A successful player is less likely to change the interaction range than a failed player. Through extensive simulations, it is shown to verify that a higher payoff threshold leads to a more pronounced promotion effect on cooperation and corresponds to a higher cooperation return. Moreover, introducing dynamic interaction domain can rapidly remove defectors from the vicinity of cooperative players on regular lattices. Reducing the payoff gap between punishers and cooperators helps mitigate the system’s second-order free-riding problem. Additionally, through analysis of the critical parameters, it is revised that incorporating diversity in interaction structures substantively enhances cooperation level.

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

Temporal interaction and its role in the evolution of cooperation.

This research investigates the impact of dynamic, time-varying interactions on cooperative behavior in social dilemmas. Traditional research has focused on deterministic rules governing pairwise interactions, yet the impact of interaction frequency and synchronization in groups on cooperation remains underexplored. Addressing this gap, our work introduces two temporal interaction mechanisms to model the stochastic or periodic participation of individuals in public goods games, acknowledging real-life variances due to exogenous temporal factors and geographical time differences. We consider that the interaction state significantly influences both game payoff calculations and the strategy updating process, offering new insights into the emergence and sustainability of cooperation. Our results indicate that maximum game participation frequency is suboptimal under a stochastic interaction mechanism. Instead, an intermediate activation probability maximizes cooperation, suggesting a vital balance between interaction frequency and inactivity security. Furthermore, local synchronization of interactions within specific areas is shown to be beneficial, as time differences hinder the spread of cross-structures but promote the formation of dense cooperative clusters with smoother boundaries. We also note that stronger clustering in networks, larger group sizes, and lower noise increase cooperation. This research contributes to understanding the role of node-based temporality and probabilistic interactions in social dilemmas, offering insights into fostering cooperation.

Gaze-Data-Based Probability Inference for Menu Item Position Effect on Information Search

This study examines the effect of menu items placed around a slideshow at the center of a webpage on an information search. Specifically, the study analyzes eye movements of users whose search time is long or short on a mixed-type landing page and considers the cause in relation to “directed search” (which triggers a certain type of mental workload). To this end, a Bayesian network model is developed to elucidate the relation between eye movement measures and search time. This model allows the implementation degree of directed search to be gauged from the levels of the measures that characterize a long or short search time. The model incorporates probabilistic dependencies and interactions among eye movement measures, and hence it enables the association of various combinations of these measure levels with different browsing patterns, helping judge whether directed search is implemented or not. When viewers move their eyes in the direction opposite (identical) to the side on which the target information is located, the search time increases (decreases); this movement is a result of the menu items around the slideshow capturing viewers’ attention. However, viewers’ browsing patterns are not related to the initial eye movement directions, which may be classified into either a series of orderly scans (directed search) to reach the target or long-distance eye movements derived from the desire to promptly reach the target (undirected search). These findings suggest that the menu items of a website should not be basically placed around a slideshow, except in cases where they are intentionally placed in only one direction (e.g., left, right, or below).

Decomposition of interaction indices: alternative interpretations of cardinal–probabilistic interaction indices

Decomposition of interaction indices: alternative interpretations of cardinal–probabilistic interaction indices

Predicting ‘pain genes’: multi-modal data integration using probabilistic classifiers and interaction networks

Abstract Motivation Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans. Here, we use machine learning to identify possible ‘pain genes’. Labelling was based on a gold-standard list with validated involvement across pain conditions, and was trained on a selection of -omics, protein–protein interaction network features, and biological function readouts for each gene. Results The top-performing model was selected to predict a ‘pain score’ per gene. The top-ranked genes were then validated against pain-related human SNPs. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of ‘pain’ genes. As such, a network based on top-ranked genes was constructed to reveal previously uncharacterized pain-related genes. Together, these novel insights into pain pathogenesis can indicate promising directions for future experimental research. Availability and implementation These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines.

Receptiveness angle: A new surrogate safety measure for monitoring traffic safety

This paper presents a framework for monitoring highway traffic-stream measures using quality trajectory data of mixed (heterogeneous) traffic. The framework includes a new measure that reflects the attentiveness of the follower driver, called receptiveness angle, in the vehicle-following process. This measure is integrated with the traditional measures (distance gap between the leader and follower vehicles and their speeds) to model the probabilistic rear-end collision interactions between the two vehicles. To verify the proposed framework, two road sections in India with mixed traffic conditions, located along the same road, were used. One section has no construction activity (base section) and the other has construction activity. The verification consisted of two tasks. First, to trace the movements of the vehicles, trajectory data over the study sections were developed for three traffic-flow levels, where two flow levels between the two sections were comparable. Second, the trajectory data were used to verify the proposed framework which was evaluated for the traffic streams of the two sections at the three traffic-flow levels. The results showed that smaller vehicles in the traffic stream exhibited a higher receptiveness angle (paid less attention) compared to other vehicle classes. Interestingly, the study revealed variations in safety among the three traffic-flow levels. It was observed that the traffic stream was safer at stop-and-go conditions than at other flow conditions. Furthermore, due to the pre-cautioning measures for the construction section, vehicles in this section were more attentive than those in the base section.

Risk aggregation considering probabilistic and consequential interactions: A general formulation with computational cost handling.

Complicated interaction between risk events is the critical obstacle preventing accurate risk aggregation, which is an important issue in risk management. Recent research integrates interaction into risk aggregation with different perspectives and lacks a comprehensive discussion of this issue, making the risk aggregation process not universal for diverse cases, especially in subjective risk assessment contexts. Therefore, this article proposes a theoretically convincing risk aggregation method embedding different types of interaction to support decision analysis more effectively. The main contributions of this article are as follows: (a) more in-depth and stricter definitions, measures, and graphical descriptions of different types of interaction are developed to ensure the accuracy of risk aggregation; (b) a formal risk aggregation approach that could apply in both objective and subjective risk assessment contexts while elegantly embedding risk interactions is proposed; (c) the additivity of risks and risk sets in the risk aggregation process is discussed in detail and the conditions for additivity are clarified; (d) the quasi-two/three-additive measures, which approximately obtain the aggregate risk value within sufficient reliability, are proposed to greatly reduce the computational cost. To examine the applicability of the proposed general risk aggregation method, a case study is finally presented to show the complete risk aggregation process and its application in the decision-making stage.

A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Harnessing the Hubble Space Telescope Archives: A Catalog of 21,926 Interacting Galaxies

Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems requires ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalog of interacting galaxies from the Hubble Space Telescope science archives; this catalog is larger than previously published catalogs by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalog. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty-five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and “backlit” overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalog publicly available for use by the community. In addition to providing a new catalog of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user.

Probabilistic establishment of speckle-associated inter-chromosomal interactions.

Inter-chromosomal interactions play a crucial role in genome organization, yet the organizational principles remain elusive. Here, we introduce a novel computational method to systematically characterize inter-chromosomal interactions using in situ Hi-C results from various cell types. Our method successfully identifies two apparently hub-like inter-chromosomal contacts associated with nuclear speckles and nucleoli, respectively. Interestingly, we discover that nuclear speckle-associated inter-chromosomal interactions are highly cell-type invariant with a marked enrichment of cell-type common super-enhancers (CSEs). Validation using DNA Oligopaint fluorescence in situ hybridization (FISH) shows a strong but probabilistic interaction behavior between nuclear speckles and CSE-harboring genomic regions. Strikingly, we find that the likelihood of speckle-CSE associations can accurately predict two experimentally measured inter-chromosomal contacts from Hi-C and Oligopaint DNA FISH. Our probabilistic establishment model well describes the hub-like structure observed at the population level as a cumulative effect of summing individual stochastic chromatin-speckle interactions. Lastly, we observe that CSEs are highly co-occupied by MAZ binding and MAZ depletion leads to significant disorganization of speckle-associated inter-chromosomal contacts. Taken together, our results propose a simple organizational principle of inter-chromosomal interactions mediated by MAZ-occupied CSEs.

A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification

AbstractMotivationDifferential network (D-Net) analysis has attracted great attention in systems biology for its ability to identify genetic variations in response to different conditions. Current approaches either estimate the condition-specific networks separately followed by post-procedures to determine the differential edges or estimate the D-Net directly. Both types of analysis overlook the probabilistic inference and can only provide deterministic inference of the edges.ResultsHere, we propose a Bayesian solution and translate the probabilistic estimation in the regression model to an inferential D-Net analysis for genetic association and classification studies. The proposed PRobabilistic Interaction for Differential Edges (PRIDE) focuses on inferring the D-Net with uncertainty so that the existence of the differential edges can be evaluated with probability and even prioritized if comparison among these edges is of interest. The performance of the proposed model is compared with state-of-the-art methods in simulations and is demonstrated in glioblastoma and breast cancer studies. The proposed PRIDE performs comparably to or outperforms most existing tools under deterministic evaluation criteria. Additionally, it offers the unique advantages, including prioritizing the differential edges with probabilities, highlighting the relative importance of hub nodes, and identifying potential sub-networks in a D-Net.Availability and implementationAll the data analyzed in this research can be downloaded at https://xenabrowser.net/datapages/. The R code for implementing PRIDE is available at https://github.com/YJGene0806/PRIDE_Code.

RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles.

Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.

A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures

High-reliability life design process not only can ensure system safety in service, but also can provide scientific life management during the maintenance period. The objective of the present work is to develop a roadmap for creep-fatigue reliability assessment. Material-level data accumulations and theoretical foundations of creep-fatigue including creep-fatigue constitutive and multi-axial damage models are introduced. Afterwards, a low-pressure turbine disk under a typical creep-fatigue load waveform is applied as a case study to demonstrate how to perform creep-fatigue reliability assessment by using this roadmap in practice. Precise weakness hotspots are identified at the mortise joint of turbine disk. Based on hotspot-based strategy, it is found that the surrogate model assisted by an optimized machine learning method enhances significantly the computational efficiency. Accordingly, the probabilistic creep-fatigue life with considering multi-sources uncertainty obeys lognormal distributions. In the aspect of failure probability analysis, the current probabilistic damage interaction diagram method with creep-fatigue interaction gives conservative reliability assessments and excellent universality as compared to traditional ones mainly used in the low cycle fatigue field. Last but not least, joint failure evaluation of the turbine disk is discussed to comprehensively consider potential failure occurrence in an averaged hot region instead of a single hotspot.

Use and perception of weather forecast information across Europe

AbstractAlthough European studies have become more common in recent years, published research on perception and use of weather information has been dominated by studies from the United States with some scattered contributions across Europe. The present study gives a broad European context, by providing perspectives from 18 countries and several user professions as well as from 14 National Meteorological and Hydrological Services (NMHSs), and by combining new insights from probabilistic forecasting, warning and interaction between NMHSs and their users. These insights are based on two surveys undertaken in the framework of the EUMETNET Nowcasting (E‐NWC) Programme, where EUMETNET represents the European Meteorological Services' Network: one survey for the participating NMHSs in the E‐NWC Programme, and the other one for their respective users. Both surveys were distributed in autumn 2019, and open for responses until spring 2020. Several findings from the surveys support conclusions of previous research, for example, concerning the perception of probabilities or taking measures in case of severe weather (many users would start their preliminary measures at a probability level of 60%). Although most of the NMHSs and their users are in regular contact, there is room for increasing the frequency of face‐to‐face meetings between them. Nearly one‐third of NMHSs never meet face‐to‐face with users from the public. The two surveys indicate that there might be benefits of increased collaboration and sharing of data between European NMHSs to be able to offer their users more training, and to learn from each other in areas where insight already exists.

Environment-adaptive learning from demonstration for proactive assistance in human–robot collaborative tasks

Proactive assistance in human–robot collaboration remains a challenging objective, as the spatial–temporal coordination of the human–robot motion must be considered in conjunction with the object and environmental context. In this paper, we propose an environment-adaptive probabilistic interaction primitive method using learning-from-demonstration. In particular, we propose a novel phase estimation algorithm called Single-axis Uniform Interval Interpolation, which alleviates the restriction of Gaussian or uniform distribution of phase variables. In addition, the environmental constraints in human–robot interactive skills are learned via the regression between environmental parameters and the weight vectors. The proposed method is implemented in a proactive robotic system for typical industrial-motivated human–robot collaborative scenarios, such as assistive push-button assembly and human–robot collaborative object covering. The experimental result validates the effectiveness of the proposed approach.

IPS/Zeta correspondence

Our previous works presented zeta functions by the Konno-Sato theorem or the Fourier analysis for one-particle models including random walks, correlated random walks, quantum walks, and open quantum random walks. This paper introduces a new zeta function for multi-particle models with probabilistic or quantum interactions, called the interacting particle system (IPS). We compute the zeta function for some tensor-type IPSs.

A taxonomy of weight learning methods for statistical relational learning

Statistical relational learning (SRL) frameworks are effective at defining probabilistic models over complex relational data. They often use weighted first-order logical rules where the weights of the rules govern probabilistic interactions and are usually learned from data. Existing weight learning approaches typically attempt to learn a set of weights that maximizes some function of data likelihood; however, this does not always translate to optimal performance on a desired domain metric, such as accuracy or F1 score. In this paper, we introduce a taxonomy of search-based weight learning approaches for SRL frameworks that directly optimize weights on a chosen domain performance metric. To effectively apply these search-based approaches, we introduce a novel projection, referred to as scaled space (SS), that is an accurate representation of the true weight space. We show that SS removes redundancies in the weight space and captures the semantic distance between the possible weight configurations. In order to improve the efficiency of search, we also introduce an approximation of SS which simplifies the process of sampling weight configurations. We demonstrate these approaches on two state-of-the-art SRL frameworks: Markov logic networks and probabilistic soft logic. We perform empirical evaluation on five real-world datasets and evaluate them each on two different metrics. We also compare them against four other weight learning approaches. Our experimental results show that our proposed search-based approaches outperform likelihood-based approaches and yield up to a 10% improvement across a variety of performance metrics. Further, we perform an extensive evaluation to measure the robustness of our approach to different initializations and hyperparameters. The results indicate that our approach is both accurate and robust.

Potential use of Bayesian Networks for estimating relationship among rotational dynamics of floating offshore wind turbine tower in extreme environmental conditions

The rotational stability of the floating offshore wind turbine tower plays a vital role in the structural stability of the whole system. So, the protection of the system from probabilistic structural damages and failures due to instability needs planning for robust operation and maintenance methods with different application principles such as structural health monitoring, early failure detection methods, and other novel structural control systems. Besides all the other operation and maintenance methods, early detection and diagnosis for probabilistic failures are effective approaches to save costs and maintain operational safety. This study outlines the potential use of Bayesian Networks (BNs) for the estimation of technical relationships among structural dynamic responses of the tower of a floating spar-type offshore wind turbine. In the method, rotational dynamic responses (rolling and pitching) of a floating turbine are recorded in six different loading scenarios considering two stages of normal operation and parked conditions due to extreme environmental excitations. Bayesian network models are created from these data to observe the probabilistic interactions with the effects of all involving parameters in the rotational vibration of the tower. Thus, obtained final BN models for both normal operational status and extreme environmental conditions can be introduced as feasible decision-support tools to be potentially used in the prediction of relationships and interactions among the dynamic responses of floating turbine's tower.

Application of Cellular Automata in Bi-Objective Operation of Multi Reservoir Systems

Optimal operation of multi-reservoir systems is one the most challenging problems in water resource management due to their multi-objective nature and time-consuming solving process. In this paper, Multi-Reservoir Parallel Cellular Automata-Simulated Annealing (MPCA-SA), a hybrid method based on cellular automata and simulated annealing is presented for solving bi-objective operations of multi-reservoir systems problems. The problem considers the bi-objective operation of a multi-reservoir system with the two conflicting objectives of water supply and hydropower generation. The MPCA-SA method uses two single-objective cellular automata acting in parallel to explore the problem search space and find the optimal solutions based on the probabilistic interaction with each other. Bi-objective operation of the Dez-Gotvand-Masjed Soleyman three-reservoir system, as a real-world system in southwestern Iran for a period of 60 months, is considered in order to evaluate the ability of the proposed method. In addition, a Non-dominated Sorting Genetic Algorithm (NSGAII) is also used to solve the problems and the results are compared with those of MPCA-SA, indicating the capabilities of the proposed MPCA-SA method. The results show that the MPCA-SA method is able to produce solutions comparable to those of NSGAII with a much-reduced computational cost equal to 1.2% of that required by the NSGAII, emphasizing the efficiency and practicality of the proposed method.