Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.

The elimination of the consequences of transport emergencies

The problem of multicriteria optimization in relation to the decisions made about organizing the material and technical support for equipment and personnel of the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM of Russia) in the context of emergency response on transport has been explored in this article. The existing approaches have been indicated, and another approach to building a single generalized criterion by the given partial criteria for the multicriteria optimization problem has been proposed. The verbal statement of the considered problem of multicriteria optimization has been provided. The goal of the study is to develop a method for solving this multicriteria optimization problem using fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm. A substantial example has been provided, illustrating the application of the stated theoretical provisions for solving the problem of choosing the best option for the equipment and personnel of the EMERCOM of Russia to liquidate the consequences of emergency situations on transport. In terms of novelty, it must be noted that the indicator (output variable) and parameters (input variables) of the problem have been defined ambiguously, fuzzily, which allows to use the efficient mathematical tools of the theory of fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm to solve this problem.

Scientific Variables

Despite their centrality to the scientific enterprise, both the nature of scientific variables and their relation to inductive inference remain obscure. I suggest that scientific variables should be viewed as equivalence classes of sets of physical states mapped to representations (often real numbers) in a structure preserving fashion, and argue that most scientific variables introduced to expand the degrees of freedom in terms of which we describe the world can be seen as products of an algorithmic inductive inference first identified by William W. Rozeboom. This inference algorithm depends upon a notion of natural kind previously left unexplicated. By appealing to dynamical kinds—equivalence classes of causal system characterized by the interventions which commute with their time evolution—to fill this gap, we attain a complete algorithm. I demonstrate the efficacy of this algorithm in a series of experiments involving the percolation of water through granular soils that result in the induction of three novel variables. Finally, I argue that variables obtained through this sort of inductive inference are guaranteed to satisfy a variety of norms that in turn suit them for use in further scientific inferences.

Statically bounded-memory delayed sampling for probabilistic streams

Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program. In this paper, we the present conditions on a probabilistic program’s execution under which delayed sampling will execute in bounded memory. The two conditions are dataflow properties of the core operations of delayed sampling: the m -consumed property and the unseparated paths property . A program executes in bounded memory under delayed sampling if, and only if, it satisfies the m -consumed and unseparated paths properties. We propose a static analysis that abstracts over these properties to soundly ensure that any program that passes the analysis satisfies these properties, and thus executes in bounded memory under delayed sampling.

System of fuzzy automatic control of coal massif cutting by a shearer drum

Up to now, automatic control of the shearer speed has been performed to keep the actual speed at an operator-specified level or to keep the actual power at a stable level without overheating or overturning. However, the problem of control of coal seam cutting by the upper drum of a shearer in the case of a variable angle of drum – coal seam contact has yet to be studied. The aim of this work is to develop a method for synthesizing a system of fuzzy automatic control of coal massif cutting by a shearer drum based on an information criterion for the power efficiency of coal cutting with cutters. In this work, based on an information criterion for the power efficiency of coal cutting with cutters, a fuzzy inference algorithm is constructed for a system of automatic control of coal massif cutting by a shearer drum. In doing so, the parameters of the output linguistic variable term membership functions of the system and fuzzy operations are determined according to the recommendations of the classical Mamdani fuzzy inference algorithm using substantiated fuzzy production rules. The fuzzy inference algorithm constructed in this work is tested for efficiency based on the fraction of effective control actions generated by the fuzzy automatic control system. Using simulation, the efficiency of drum rotation speed control with the use of the proposed fuzzy inference algorithm is compared with that with the use of an uncontrolled shearer cutting drive. The study of the generation of control actions involving the upper shearer drum rotation speed showed that effective control actions were generated in the overwhelming majority of cases (about 93%). The proposed method forms a theoretical basis for the solution of the important scientific and practical problem of upper shearer drum rotation speed control automation with the aim to reduce specific power consumption and the amount of chips.

What we talk about when we talk about colors

Names for colors vary widely across languages, but color categories are remarkably consistent. Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies. But the mappings from colors to words are not identical across languages, which may reflect communicative needs—how often speakers must refer to objects of different color. Here we quantify the communicative needs of colors in 130 different languages by developing an inference algorithm for this problem. We find that communicative needs are not uniform: Some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Our analysis also reveals a hidden diversity in the communicative needs of colors across different languages, which is partly explained by differences in geographic location and the local biogeography of linguistic communities. Accounting for language-specific, nonuniform communicative needs improves predictions for how a language maps colors to words, and how these mappings vary across languages. Our account closes an important gap in the compression theory of color naming, while opening directions to study cross-cultural variation in the need to communicate different colors and its impact on the cultural evolution of color categories.

An existential crisis resolved: type inference for first-class existential types

Despite the great success of inferring and programming with universal types, their dual—existential types—are much harder to work with. Existential types are useful in building abstract types, working with indexed types, and providing first-class support for refinement types. This paper, set in the context of Haskell, presents a bidirectional type-inference algorithm that infers where to introduce and eliminate existentials without any annotations in terms, along with an explicitly typed, type-safe core language usable as a compilation target. This approach is backward compatible. The key ingredient is to use strong existentials, which support (lazily) projecting out the encapsulated data, not weak existentials accessible only by pattern-matching.