Quantum fermions from classical bits

A simple probabilistic cellular automaton is shown to be equivalent to a relativistic fermionic quantum field theory with interactions. Occupation numbers for fermions are classical bits or Ising spins. The automaton acts deterministically on bit configurations. The genuinely probabilistic character of quantum physics is realized by probabilistic initial conditions. In turn, the probabilistic automaton is equivalent to the classical statistical system of a generalized Ising model. For a description of the probabilistic information at any given time quantum concepts as wave functions and non-commuting operators for observables emerge naturally. Quantum mechanics can be understood as a particular case of classical statistics. This offers prospects to realize aspects of quantum computing in the form of probabilistic classical computing. This article is part of the theme issue ‘Quantum technologies in particle physics’.

The optimal emergency decision-making method with incomplete probabilistic information

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences.

Information structures and entropy measurement for a fuzzy probabilistic information system

An information system (IS), an important model in the field of artificial intelligence, takes information structure as the basic structure. A fuzzy probabilistic information system (FPIS) is the combination of some fuzzy relations in the same universe that satisfy probability distribution. A FPIS as an IS with fuzzy relations includes three types of uncertainties (i.e., roughness, fuzziness and probability). This paper studies information structures in a FPIS from the perspective of granular computing (GrC). Firstly, two types of information structures in a FPIS are defined by set vectors. Then, equality, dependence and independence between information structures in a FPIS are proposed, and they are depicted by means of the inclusion degree. Next, information distance between information structures in a FPIS is presented. Finally, entropy measurement for a FPIS is investigated based on the proposed information structures. These results may be helpful for understanding the nature of structures and uncertainty in a FPIS.

Heritage Language Variation and Change – How Complex Is It?

We focus on complexity from the comparative variationist perspective, a sociolinguistic approach that examines variable aspects of language (that is, different ways of saying the same thing). Arguably, variable elements are harder to acquire than categorical ones, as a Variability Matrix must be acquired along with every element. This matrix contains probabilistic information about when each form is (more) appropriate, according to an array of factors. These include inter-speaker (social) and intra-speaker (linguistic context) predictors. We ask how the Variability Matrix for predictors of a variable compares between heritage speakers (people living in a context where their language is a minority language) and homeland speakers (people living in a context where their language is a majority language), and how these can fairly be compared. In the variationist approach, multivariate regression analyses reveal the predictors (and levels within each predictor) of a response or dependent variable and their corresponding Variability Matrices. However, the variationist field lacks an established comparative methodology to determine how/if varieties differ. One shortcoming is that different-sized samples are often compared, implicating different levels of statistical significance even when the populations’ patterns are identical. Comparison of variable patterns in Heritage and Homeland Cantonese illustrates one solution. We revise analyses conducted previously of two morphosyntactic variables: prodrop and classifiers (Nagy, 2015; Nagy & Lo, 2019) in Cantonese, applying a bootstrap procedure to mitigate issues associated with unequal-sized datasets frequent in studies of minority and endangered varieties. From these analyses, we learn that heritage and homeland grammars’ degrees of complexity are similar: the matrices of (significant) frequencies are the same size. This approach allows us to consider not just which surface forms constitute the heritage vs. homeland varieties, but also the complexity of the decision-making process the speakers apply in selecting among the forms. As one might expect, heritage and homeland speakers are capable of equally complex processes.

Subjective probability is modulated by emotions

Information about risks and probabilities is ubiquitous in our environment, forming the basis for decisions in an uncertain world. Emotions are known to modulate subjective probability assessments when probabilistic information is emotionally valenced. Yet little is known about the role of emotions in subjective probability assessment of affectively neutral events. We investigated this in one correlational study (Study 1, N = 162) and one experimental study (Study 2, N = 119). As predicted, we found that emotional dominance modulated the degree of conservatism in respondents’ neutral probability estimates. Remarkably, this pattern also transferred to realistic risk assessments. Furthermore, respondents’ tendency to use the representativeness heuristic as a proxy for probability was increased in high dominance individuals. Our findings highlight the importance of considering emotions, particularly the little-understood emotion dimension dominance, in research on probabilistic cognition.

Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application

We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterization of the cover-basement interface. It consists of a cooperative inversion workflow relying on standalone inversion codes. Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining such structural constraints with petrophysical prior information. First, we perform the 1D probabilistic inversion of MT data for all sites and recover the respective probabilities of observing the cover-basement interface, which we interpolate to the rest of the study area. We then calculate the probabilities of observing the different rock units and partition the model into domains defined by combinations of rock units with non-zero probabilities. Third, we combine such domains with petrophysical information to apply spatially-varying, disjoint interval bound constraints to least-squares magnetic data inversion. We demonstrate the proof-of-concept using a realistic synthetic model reproducing features from the Mansfield area (Victoria, Australia) using a series of uncertainty indicators. We then apply the workflow to field data from the prospective mining region of Cloncurry (Queensland, Australia). Results indicate that our integration methodology efficiently leverages the complementarity between separate MT and magnetic data modelling approaches and can improve our capability to image the cover-basement interface. In the field application case, our findings also suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement.

SYSTEM-INFORMATION APPROACH TO UNCERTAINTY OF PROCESS AND SYSTEM PARAMETERS

The subject matter of research in the article is a system-information approach to the uncertainty of the parameters of processes and systems of the technosphere as one of the scientific directions of using information theory in metrology and other scientific areas. The system-information approach is based on the definition of the term "information" of the properties of the system, its content and meaning. The solution of the basic problem in metrology, obtaining "information" of the quantitative characteristics of the true value of the properties of objects and phenomena that reveal the regularities of the environment, is a complex scientific problem. The instrument for obtaining information about the properties of the system is the measurement process. One of the directions in the development of measurement theory is the concept of uncertainty. The goal of the work is to research of non-traditional solutions to problems of technical-cybernetic systems based on the system-information approach to the uncertainty of the parameters of processes and systems. The article solves the following tasks: to analyze the assessment of the parameters of technological processes and systems based on the system-information approach; to develop system-information methods and algorithms for the effective use of discrete-probabilistic information in technical-cybernetic systems; to develop principles and approaches for using the system-information assessment of the uncertainty of the Planck units, use of system-information modeling in various scientific directions. The following methods are used: system-information approach to processes and systems, methodology of system-information modeling of the measured value; system information methodology for the assessment of the measured quantity and uncertainty. The following results were obtained: developed a system-information methodology for assessing the nominal parameter has been developed, which provides indirect control over the independent parameters associated with it; systemic and information methods for the effective use of discrete-probabilistic information in technical and cybernetic systems have been developed; a system-information methodology for calculating the energy equivalent of product performance indicators has been developed; the principle of calculating the efficiency of manufacturing a product based on the energy equivalent of Planck units is formulated. Conclusions: The solution of the set tasks on the basis of the system-information approach to the uncertainty of the parameters of processes and systems makes it possible, from the system-information point of view, to study the regularities of the stages of the life cycle of technical-cybernetic systems and conservation laws.

Discounting Under Severe Weather Threat

The human and economic costs of severe weather damage can be mitigated by appropriate preparation. Despite the benefits, researchers have only begun to examine if known decision-making frameworks apply to severe-weather-related decisions. Using experiments, we found that a hyperbolic discounting function accurately described participant decisions to prepare for, and respond to, severe weather, although only delays of 1 month or longer significantly changed decisions to evacuate, suggesting that severe weather that is not imminent does not affect evacuation decisions. In contrast, the probability that a storm would impact the participant influenced evacuation and resource allocation decisions. To influence people’s evacuation decisions, weather forecasters and community planers should focus on disseminating probabilistic information when focusing on short-term weather threats (e.g., hurricanes); delay information appears to affect people’s evacuation decision only for longer-term threats, which may hold promise for climate-change warnings.

Predicting Cross-Species Infection of Swine Influenza Virus with Representation Learning of Amino Acid Features

Swine influenza viruses (SIVs) can unforeseeably cross the species barriers and directly infect humans, which pose huge challenges for public health and trigger pandemic risk at irregular intervals. Computational tools are needed to predict infection phenotype and early pandemic risk of SIVs. For this purpose, we propose a feature representation algorithm to predict cross-species infection of SIVs. We built a high-quality dataset of 1902 viruses. A feature representation learning scheme was applied to learn feature representations from 64 well-trained random forest models with multiple feature descriptors of mutant amino acid in the viral proteins, including compositional information, position-specific information, and physicochemical properties. Class and probabilistic information were integrated into the feature representations, and redundant features were removed by feature space optimization. High performance was achieved using 20 informative features and 22 probabilistic information. The proposed method will facilitate SIV characterization of transmission phenotype.