An Autoencoder-Based Response Time Model and Its Application in Anomaly Detection

Most psychometric models of response times are primarily theory-driven, meaning they are based on various sets of assumptions about how the data should behave. Although useful in certain contexts, such models are often inadequate for the complexities of realistic testing situations and display a poor fit on empirical data. Therefore, as a functional alternative, the present study proposes a data-driven approach, an autoencoder-based response time model, to modeling response times of correctly answered responses. Also, this study introduces the application of the proposed model in anomaly detection (including aberrant examinee and item detection). The result shows this model has an acceptable performance in both response time modeling and anomaly detection.

Factors Associated With Childhood Asthma and Wheeze in Chinese Preschool-Aged Children

This study was prepared to identify and characterize potential factors associated with childhood asthma and wheeze in Chinese preschool-aged children. A comprehensive questionnaire was designed for children aged 3–6 years and their parents or guardians in Beijing and Tangshan from September to December 2020. The least absolute shrinkage and selection operator (LASSO) model was used to identify factors in a significant association with childhood asthma and wheeze, respectively. The LASSO model was internally validated using bootstrap resampling with 100 replications. A total of 9,529 questionnaires were certified as eligible for inclusion after stringent quality control. The prevalence of doctor-diagnosed childhood asthma and parent-reported wheeze was 2.8 and 6.2%, respectively. Factors simultaneously associated with childhood asthma and wheeze were children with a history of allergic rhinitis, hay fever, eczema, initial age of using antibiotics, body mass index category, and family history of asthma. Specifically, children's vitamin D supplement duration was significantly associated with childhood asthma, whereas the association with childhood wheeze was significant for intake frequency of night meals for children and their screen time. Modeling of significant factors in nomograms had decent prediction accuracies, with C-index reaching 0.728 and 0.707 for asthma and wheeze, respectively. In addition, internal validation was good, with bootstrap C-statistic of being 0.736 for asthma and 0.708 for wheeze. Taken together, our findings indicated that the development of asthma and wheeze among preschool-aged children was probably determined by the joint contribution of multiple factors including inherited, nutritional, unhealthy lifestyles, and history of allergic disease. Further validation in other groups is necessary.

Stochastic analysis of dynamic processes in the solar activity

Natural processes existing in complex objects of inanimate and living matter are of a stochastic and non-equilibrium nature. The main problem in the study of such systems is to determine the value of entropy as a quantitative measure of the uncertainty and systematicity of states of dynamical systems in different phase spaces. This paper presents a new method for analyzing active processes of solar dynamics using the theory of non-Markov random discrete processes (NMRDP). The NMRDP theory is based on the Zwanzig-Mori kinetic equations in a finite-difference discrete interpretation. This is consistent with the concept of non-equilibrium statistical condensed matter physics. Qualitative information about the set of behavioral patterns, relaxation processes, dynamic characteristics and internal properties of solar activity can be obtained using NMRDP modeling by the author’s methodological approach developed in this work. This approach is focused on the analysis of spectral frequency memory functions, dynamic orthogonal parameters, phase transformations, relaxation and kinetic processes and self-organization in complex physical systems. In this work, for modeling NMRDP, the author’s software package APSASA (automated program for solar activity stochastic analysis) was used, which also allows predicting the trend of solar activity for a limited period of time. Modeling NMRDP associated with active processes occurring on the Sun made it possible to build a mathematical model with whose help it is possible to study the regularities and randomness of stochastic processes, as well as to reveal the patterns arising from the recurrence and periodicity of solar activity.

Computation of gravitational particle production using adiabatic invariants

Analytic and numerical techniques are presented for computing gravitational production of scalar particles in the limit that the inflaton mass is much larger than the Hubble expansion rate at the end of inflation. These techniques rely upon adiabatic invariants and time modeling of a typical inflaton field which has slow and fast time variation components. A faster computation time for numerical integration is achieved via subtraction of slowly varying components that are ultimately exponentially suppressed. The fast oscillatory remnant results in production of scalar particles with a mass larger than the inflationary Hubble expansion rate through a mechanism analogous to perturbative particle scattering. An improved effective Boltzmann collision equation description of this particle production mechanism is developed. This model allows computation of the spectrum using only adiabatic invariants, avoiding the need to explicitly solve the inflaton equations of motion.

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Real-time modeling of regional troposphere has attracted considerable research attention in the current GNSS field, and its modeling products play an important role in global navigation satellite system (GNSS) real-time precise positioning and real-time inversion of atmospheric water vapor. Multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM) are used for real-time and final zenith tropospheric delay (ZTD) modeling of Hong Kong CORS network in this study. Real-time ZTD modeling experiment results for five consecutive days showed that the average deviation (bias) and root mean square (RMS) of FP, BP, SVM, and SUM reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. The final ZTD modeling experiment results showed that the bias and RMS of FP, BP, SVM, and SUM reduced by 3.80%, 49.78%, 25.71%, and 49.35% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. Accuracy of the five methods generally reaches millimeter level in most of the time periods. MS demonstrates higher precision and stability in the modeling of stations with an elevation at the average level of the survey area and higher elevation than that of other models. MS, SVM, and SUM exhibit higher precision and stability in the modeling of the station with an elevation at the average level of the survey area than FP. Meanwhile, real-time modeling error distribution of the five methods is significantly better than the final modeling. Standard deviation and average real-time modeling improved by 43.19% and 24.04%, respectively.