Probabilistic Rules Research Articles

Theoretical framework for learning through structural plasticity

A growing body of research indicates that structural plasticity mechanisms are crucial for learning and memory consolidation. Starting from a simple phenomenological model, we exploit a mean-field approach to develop a theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules, and noisy stimuli. More importantly, it describes the effects of stabilization, pruning, and reorganization of synaptic connections. This framework is used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in training and testing procedures as the number of training patterns and other model parameters vary. The results are then compared with those obtained through simulations with firing-rate-based neuronal network models. Published by the American Physical Society 2024

Free-rider or contributor: A dilemma in spatial threshold public goods games

Late movers refer to individuals who adjust their strategies after observing the choices of others in a public goods game, can significantly impact the likelihood of public goods provision. This creates a social dilemma: late movers act as free-riders to rely on others to contribute, yet they also risk the public good not being provided if contributions fall short. To address this dilemma, we utilize a spatial threshold public goods game model to examine how the behaviors of late movers influence overall cooperative levels within a population. We propose a probabilistic rule for late movers that captures the dilemma between cooperating to enhance the collective good and free-riding to maximize individual benefits. Our findings indicate that strategic late movers can significantly enhance cooperation by adapting to local cooperation levels. However, an excessively high proportion of late movers may reduce the overall level of cooperation. Additionally, the formation of clusters by late movers results in higher cooperative levels compared to random distribution. Our study provides novel insights into the dynamics of cooperation within the framework of spatial threshold public goods games. By incorporating the role of late movers, we highlight how adaptive strategies can mitigate the free-rider problem and promote cooperative behaviors.

Some further results on random OBIC rules

We study the structure of probabilistic voting rules that are ordinal Bayesian incentive compatible (OBIC) with respect to independently distributed prior beliefs that can be considered generic (Majumdar and Sen (2004)). We first identify a class of priors, such that for each prior in that class there exists a probabilistic voting rule that puts a positive probability weight on “compromise” candidates. The class of priors include generic priors. Next, we consider a class of randomized voting rules that have a “finite range”. For this class of rules, we identify an appropriate generic condition on priors such that, any rule in this class is OBIC with respect to a prior satisfying the generic condition if and only if the rule is a random dictatorship.

Opacity enforcement in discrete event systems using differential privacy

Opacity is an important information security property that characterizes whether the secret of a system can be inferred by an intruder who can partially observe the behavior of the system. In this paper, we touch upon the enforcement of the current-state opacity and initial-state opacity of discrete event systems modeled by automata. Specifically, given a partially observed discrete event system that is not current-state opaque (resp., initial-state opaque), by observing the system's behavior, the intruder stands a chance to infer that the current state (resp., initial state) is within the secret of the system. To prevent the exposure of secret information, we introduce a differential privacy mechanism to enforce a non-opaque system to be opaque. Particularly, the designed mechanism is settled to receive the observations of the system. Suppose that there are two observations such that one of them can lead to the exposure of the secret and the other is randomly observed. When the two observations are received by the designed mechanism that provides differential privacy, a randomly modified output with an approximate probability is exported, exposing to the intruder. In this way, since the observation from which the secret can be inferred by the intruder, is befuddled with one randomly generated by the system, he/she is not able to deduce which one is truly generated and received by the mechanism, even if the probabilistic generation rule of the mechanism is public.

Interpretation of quantum theory in a cosmological perspective

We reconsider the problem of the interpretation of the quantum theory (QT) in the perspective of the entire universe and of the Bohr's idea that the classical language is the language of our experience and QT acquires a meaning only with a reference to it. We distinguish a classical or macroscopic state, and a quantum or microscopic one that is perceived only through the modifications that it induces in the first. The macroscopic state is specified by a set of variables, a classical energy–momentum tensor and conserved currents, which are supposed to have always a well-defined value. The microscopic state and dynamics are expressed by the usual QT formalism. For the macroscopic variables, a basic distribution of probability is postulated in terms of quantum operators and a statistical operator, what replace the usual probability rule of QT. For the universe, a variance of the ΛCDM model with Ω=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega = 1$$\\end{document} is assumed, one inflaton, a Goldstone type potential, initial time at t=-∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t = - \\infty$$\\end{document}, expectation values of all fundamental fields vanishing for t→-∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t \ o - \\infty$$\\end{document}. The scalar fluctuations in the cosmic microwave background is correctly explained, but the absence of the tensor fluctuations remains not understood. This seems to suggest that gravity is a pure classical phenomenon what perhaps could be accommodated in our framework.

MODIFICATION OF A QUANTUM-INSPIRED GENETIC ALGORITHM FOR NUMERICAL OPTIMIZATION USING QUDIT UNDER CONDITIONS OF SIMULATING QUANTUM DECOHERENCE

The genetic algorithm for numerical optimization (GA) of the metaheuristic class is a method for finding optimal solutions based on the biological principles of natural selection and variability. GA is characterized by high operating speed, resistance to noise in the data, low probability of hitting the local extremum of the multimodal objective function, as well as the simultaneous application of probabilistic and deterministic rules for generating search space points. An alternative to the classical GA is the quantum-inspired genetic algorithm for numerical optimization (QIGA), which has advantages that are unattainable for GA by using the concepts and principles of quantum computing. The article proposes a new approach to the implementation of a quantum-inspired genetic numerical optimization algorithm for searching for the global maximum of the objective function, based on modeling the functioning of the GA by simulating the execution of quantum calculations based on qudit in the conditions of the existence of quantum decoherence in the era of noisy medium-scale quantum algorithms. For this purpose, to carry out quantum operations of rotating the states of multilevel quantum systems, the paper presents a density matrix based on Heisenberg-Weyl operators as an analogue of the Bloch sphere for qudits. The simulation of quantum decoherence is interpreted from the point of view of the influence of extraneous noise emanating from the environment on the qudit and is presented as the use of a normal random variable with zero mathematical expectation and unit variance in quantum gates. At the same time, the work presents detailed pseudocodes of the functioning of both the most modified quantum-inspired genetic algorithm for numerical optimization and its individual operations. Testing is carried out by conducting computational experiments with the implementation of a modified algorithm on two-dimensional and multidimensional functions of test optimization problems, as well as when solving an applied optimization problem of planning hybrid flow production in the manufacturing industry based on financial costs and solving the problem of increasing forecasting accuracy based on compact extreme learning machines. The experimental results demonstrate the superiority of the new algorithm over QIGA and classical optimization algorithms in the accuracy of the solution, the speed of convergence with the target value of the global maximum and the execution time of the algorithm.

Pairwise summation as a method for the additive combination of probabilities in qualitative risk assessments.

Qualitative frameworks are widely employed to tackle urgent animal or public health issues when data are scarce and/or urgent decisions need to be made. In qualitative models, the degree of belief regarding the probabilities of the events occurring along the risk pathway(s) and the outcomes is described in nonnumerical terms, typically using words such as Low, Medium, or High. The main methodological challenge, intrinsic in qualitative models, relates to performing mathematical operations and adherence to the rule of probabilities when probabilities are nonnumerical. Although methods to obtain the qualitative probability from the conditional realization of n events are well-established and consistent with the multiplication rule of probabilities, there is a lack of accepted methods for addressing situations where the probability of an event occurring can increase, and the rule of probability P(AUB)=P(A)+P(B)-P(A∩B) should apply. In this work, we propose a method based on the pairwise summation to fill this methodological gap. Our method was tested on two qualitative models and compared by means of scenario analysis to other approaches found in literature. The qualitative nature of the models prevented formal validation; however, when using the pairwise summation, results consistently appeared more coherent with probability rules. Even if the final qualitative estimate can only represent an approximation of the actual probability of the event occurring, qualitative models have proven to be effective in providing scientific-based evidence to support decision-making. The method proposed in this study contributes to reducing the subjectivity that characterizes qualitative models, improving transparency and reproducibility.

Logika, Penalaran, Dan Argumentasi Hukum

Nowdays, the understanding and knowledge in logic, reasoning, and legal argumentation become moreabsolute necessity not only for civitas academica in philosophy and law but most important for legal practitioners as polices, judges, prosecutors, attorneys, lawyers, and even all members of society whosedealing with legal problems everyday. As apart of generally reasoning, legal reasoning, although have some different characteristics, is attached to valid principles of reasoning such as rules of reasoning, rules of sillogism, rules of inductive probability, and informal fallacies. Thus legal reasoning is not a distinct and separate kind of reasoning from logic as science of how to think rightly, appropriately, and valid (as a branch of philosophy) but rather how to applied rules of reasoning from logic principles to legal problems. This article explores the rules of thinking in induction and sillogism. IRAC (Issue, Rule, Argument, dan Conclusion) model as an application of inductive and sillogistic reasoning in law will to end this article.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Ecological risk of metals in Andean water resources: A framework for early environmental assessment of mining projects in Peru

Metallic contaminants in Andean water resources influenced by mining activities poses risks to aquatic ecosystems and a challenge to regulatory agencies responsible for environmental compliance. In this study, the Ecological Risk Assessment (ERA) framework was adapted to assess dissolved heavy metal concentrations at 283 surface water monitoring stations near to six mining projects during the dry and wet seasons. Reports from OEFA-Peru on Early Environmental Assessment (EEA) were used to apply various criteria and non-parametric statistical tests. They included ecological, ecotoxicological, chemical, and regulatory factors. The main goal of this research was to identify, analyze, characterize, and compare the risks present at different trophic levels. These levels were categorized as T1 (Microalgae), T2 (Zooplankton and Benthic invertebrates), and T3 (Fish). Individual risk (IR) was estimated using the quotient model, while total risk (TR) was assessed using the additive probability rule. Rainbow trout (Oncorhynchus mykiss), representing trophic level T3, showed the highest sensitivity to Fe and Cu. Statistical tests ranked the IR as Fe > Cu > Zn > Mn > Pb (p < 0.01). The TR was more prevalent during the wet season compared to the dry season (p < 0.01). Notably, around 50 % of the monitoring stations (n = 142) were classified as high risk, and 9 % (n = 13) showed extremely high-risk values for Cu and Fe. The adapted ERA framework demonstrated great effectiveness in identifying critical points of metal contamination in high Andean aquatic ecosystems under mining influence. However, specialized studies are suggested that allow the sources of pollution to be associated with specific regulatory actions.

Robustness assessment of weakly coupled cyber-physical power systems under multi-stage attacks

Evaluating the robustness of cyber-physical system (CPS) under multi-stage attacks is fundamental to enhancing CPS resilience. Currently, many models inadequately consider information flow, which is not aligned with reality. The initial focus of this paper is on introducing a weakly coupled interdependent model for CPS. This model considers probabilistic line outage rules, cyber flow transmission characteristics, and the probabilities of inter-system coupling failures. Subsequently, the multi-stage attack strategies are presented and employed by the proposed model during disaster scenarios, along with the proposed robustness assessment indicators. The numerical results illustrate the robustness of the weakly-coupled CPS model under multi-stage attacks. This lays a modeling and simulation foundation for repair crew to enhance the CPS resilience.

Enhancing information freshness in multi-class mobile edge computing systems using a hybrid discipline

Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

Zero-Trust Architecture (ZTA): Designing an AI-Powered Cloud Security Framework for LLMs’ Black Box Problems

Businesses are becoming more interested in developing and testing Large Language Models (LLMs) in their own settings to support decision-making and growth as a result of the rapid emergence of AI and cloud computing. Here’s the dilemma, though: to what extent do you believe these models and the data they were trained on? We don’t know the feature list of an LLM, which presents the first obstacle when discussing trust and the reasons why there should be zero trust. Although it may seem a bit extreme, this is accurate for two reasons. When it comes to GenAI models nowadays, the more multimodal and more capabilities they have, the better. This way of thinking is great for exploring and confirming if GenAI can address a business problem, but it’s a surefire way to run into trouble when attempting to put things into production in an organizational setting. An enterprise cybersecurity architecture known as a zero-trust architecture (ZTA) is built on the ideas of zero trust and is intended to stop data breaches, enhance privacy, and restrict internal lateral movement. This article discusses ZTA, its logical aspects, probable deployment scenarios, AI rules, threats and limitations in order to provide a detailed understanding of why enterprises must adapt a ZTA framework in a cloud-based environment for AI model deployment.

Mechanical control of cell proliferation patterns in growing epithelial monolayers

Cell proliferation plays a crucial role in regulating tissue homeostasis and development. However, our understanding of how cell proliferation is controlled in densely packed tissues is limited. Here we develop a computational framework to predict the patterns of cell proliferation in growing epithelial tissues, connecting single-cell behaviors and cell-cell interactions to tissue-level growth. Our model incorporates probabilistic rules governing cell growth, division, and elimination, also taking into account their feedback with tissue mechanics. In particular, cell growth is suppressed and apoptosis is enhanced in regions of high cell density. With these rules and model parameters calibrated using experimental data for epithelial monolayers, we predict how tissue confinement influences cell size and proliferation dynamics and how single-cell physical properties influence the spatiotemporal patterns of tissue growth. In this model, mechanical feedback between tissue confinement and cell growth leads to enhanced cell proliferation at tissue boundaries, whereas cell growth in the bulk is arrested, recapitulating experimental observations in epithelial tissues. By tuning cellular elasticity and contact inhibition of proliferation we can regulate the emergent patterns of cell proliferation, ranging from uniform growth at low contact inhibition to localized growth at higher contact inhibition. We show that the cell size threshold at G1/S transition governs the homeostatic cell density and tissue turnover rate, whereas the mechanical state of the tissue governs the dynamics of tissue growth. In particular, we find that the cellular parameters affecting tissue pressure play a significant role in determining the overall growth rate. Our computational study thus underscores the impact of cell mechanical properties on the spatiotemporal patterns of cell proliferation in growing epithelial tissues.

Deployment of a human-centred clinical decision support system for pulmonary embolism: evaluation of impact on quality of diagnostic decisions

Pulmonary embolism (PE) is a serious condition that presents a diagnostic challenge for which diagnostic errors often happen. The literature suggests that a gap remains between PE diagnostic guidelines and...

Waste Management Policy in Bali Province, Indonesia

Purpose: Indonesia is a country that still experiences problems in waste management, even ranking second in the world in terms of plastic waste in the sea. The waste problem also occurs in Bali, which is a province in Indonesia and is famous as a world tourist destination. Even though the tourism industry places cleanliness and beauty as the main elements. Realizing this, the government has issued various regulations and programs to tackle the waste problem in Bali. This research aims to analyze the public's response to waste management regulations based on probability rules. Theoretical framework: As settlements develop, waste management becomes more complex. So when the government and regional governments issue regulations related to waste and waste management, then if what is stated in the regulation is followed by waste management that is good for the environment, economically sustainable for a particular area and socially acceptable then it will certainly be followed and implemented by public. Methods: Where the analysis is carried out with the SMIC-Prob tool. Furthermore, a policy path (with MULTIPOL analysis) was also prepared to map each policy and action so that it fits into the three scenarios previously identified, namely source-based waste management, communal-based waste management, and government waste management. Findings: The results of the probability analysis (SMIC-Prob) show that the community's response to waste management regulations is to manage waste through TPS3R, and it can be said that the community still relies on waste management outside the household. Meanwhile, the results of the MULTIPOL analysis show that for source-based waste management and by the government, policies are needed in the construction of waste management facilities. Meanwhile, in communal-based waste management, the priority policies are institutional strengthening and community empowerment. Implications: The policy path has provided an overview of policy mapping and actions if waste management is source-based, communal-based, and implemented by the government. Where for source-based waste management and by the government, policies are needed in the construction of waste management facilities. Meanwhile, in communal-based waste management, the priority policies are institutional strengthening and community empowerment.

Simulating staff activities in healthcare environments: An empirical multi-agent modeling approach

The layout of the healthcare environment profoundly impacts healthcare efficiency, safety, employee satisfaction, and the sustainability of operations, mainly based on occupant behavior analysis. However, due to the increase in professionalism and interprofessional collaboration among healthcare practitioners and the complexity of the physical environment, existing research tools face challenges in describing and analyzing diverse occupant behaviors. This study aims to offer an empirically grounded multi-agent modeling approach as a research tool to simulate and reproduce occupant behavior within healthcare settings. Employing a design science research method, the proposed approach encompasses four stages: empirical data collection, development of behavioral rules, agent-based modeling, and validation. A continuous 24-h observational study involving doctors, nurses, and auxiliary staff within a trauma intensive care unit is conducted to demonstrate the development and application process. Results show that the proposed modeling approach achieves a simulation deviation of 8.31 % in individual walking distances and a 16.26 % deviation in overall staff spatial visitation frequency compared to real-world observations. We believe that using simple if-then rules to represent events and probability rules to describe other basic activities can simulate complex staff activities in a healthcare environment. Interdisciplinary expert evaluation acknowledged the utility of this approach. This study offers a comprehensive dataset of behaviors and contributes methodologically to evaluating the efficiency of built environment layouts. The innovation in this study lies in its scalability, extending from events and individuals to encompass the all staff in levels of modeling and validation, ensuring a consistent alignment with empirical data at each stage.

Novel linkage approach to join community-acquired and national data

BackgroundCommunity optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.MethodsAs the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.ResultsA sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.ConclusionsThe potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Research on Bayesian Statistics Teaching with R

Bayesian statistics uses the mathematical rules of probability to combine data with prior information to yield inferences which are always more precise than would be obtained by either source of information alone. The current Bayesian education has not yet attracted enough attention from the education community in China. The rise of MCMC and probabilistic programming languages has profoundly reshaped Bayesian statistics. Compared to precise mathematical analysis, the simulation-based teaching can help students to avoid tedious statistical calculations and cultivate Bayesian thinking.

Management strategy evaluation of harvest control rules for Pacific Herring in Prince William Sound, Alaska

Abstract Management strategy evaluation (MSE) provides a mechanism to test the relative performance of alternative management strategies on a fishery. For Pacific herring in Prince William Sound, Alaska, no directed fisheries have occurred in over 30 years, providing an opportunity to evaluate potential management strategies before a fishery is opened. Here we evaluate and compare 10 harvest control rules (HCRs) ranging from simple threshold rules to rules accounting for population age structure, biomass trends, and weight distribution using an MSE integrated with a fully Bayesian stock assessment estimation model. We developed a utility function that shows simple threshold HCRs outperform the more complex rules, especially for catch stability. According to this utility function, the best rule had a lower limit threshold than the current default rule, while the worst rule had a higher limit threshold. Our simulations demonstrate that sufficient computing power exists for MSEs based on Bayesian estimation methods, thus opening a pathway for MSEs to simulation test probabilistic control rules, which provide a buffer against scientific uncertainty and should reduce the risk of overfishing.