Stochastic Component Research Articles

Cognitive Hexagon-Controlled Intelligent Speech Interaction System

Several intelligent speech interaction (ISI) systems have emerged over the past four decades that have served the human community. The research papers show that these systems are very well connected to the cognitive hexagon and the six hybrid approaches. Where this hexagon reveals six distinct cognitive areas, one of the six hybrid perspectives gives rise to the dimensions of speech quality. This survey has been undertaken to reveal the dimensions of speech quality and to discuss the role of cognitive hexagonal regions on these dimensions with hybrid approaches. Here, ISI systems support this discussion and follow them as cognitive machines. An overview of the state of the art related to ISI systems is also described here. Techniques, such as processing [natural language (NL)], speech synthesis [speech-to-text (STT) or text-to-speech (TTS)], computing (voice/mobile), and audio mining are presented in this overview. These are contributing well with technologies, such as the Internet of Things (IoT), Voice over Internet Protocol (VoIP), and cloud-based systems (CBSs). In addition, stochastic components, such as reliability, availability, and failure rate were discussed to analyze whether the Quality of Service (QoS) of these ISI systems is described. Additionally, after the discussion, some aspects of the applications are also discussed along with the essential advantages and significant drawbacks.

Gravity models and the Law of Large Numbers

This paper examines the implications of uncertainty in gravity models of trade due to the violation of the Law of Large Numbers (LLN) that we document in the data. When the number of available technologies is finite and the LLN does not hold, the variance of the stochastic component in gravity models is large, which leads to the poor goodness of fit of gravity models and high uncertainty in comparative statics results. We offer a procedure that specifies counterfactual predictions in terms of distributions rather than point estimates and helps to account for such uncertainty. • We examine the implications of the Law of Large Numbers for trade gravity models. • Violations of the Law of Large Numbers lead to structural errors in gravity models. • The error terms are quantitatively important in the data and lead to uncertainty in the estimates of the gains from trade. • We develop a procedure that specifies counterfactual welfare predictions as distributions.

МЕТОДИЧНІ ПІДХОДИ ДО ОЦІНЮВАННЯ ТА ПРОГНОЗУВАННЯ ТРАНСАКЦІЙНИХ ВИТРАТ БІРЖОВОЇ ТОРГІВЛІ НА ФОНДОВОМУ РИНКУ

The article is devoted to the problem of forecasting transaction costs of operations in developed capital markets. The classification of transaction costs of exchange trading is given, the main types of transaction costs are determined, and their characteristics are given in terms of their measurability and forecasting capabilities. The formula for calculating transaction costs is derived, which includes the most significant components that can be quantified using model tools with an emphasis on stochastic components of transaction costs: expected spread costs and market impact.

Crimp: An efficient tool for summarizing multiple clusterings in population structure analysis and beyond.

When a data set is repeatedly clustered using unsupervised techniques, the resulting clusterings, even if highly similar, may list their clusters in different orders. This so-called 'label-switching' phenomenon obscures meaningful differences between clusterings, complicating their comparison and summary. The problem often arises in the context of population structure analysis based on multilocus genotype data. In this field, a variety of popular tools apply model-based clustering, assigning individuals to a prespecified number of ancestral populations. Since such methods often involve stochastic components, it is a common practice to perform multiple replicate analyses based on the same input data and parameter settings. Available postprocessing tools allow to mitigate label switching, but leave room for improvements, in particular, regarding large input data sets. In this work, I present Crimp, a lightweight command-line tool, which offers a relatively fast and scalable heuristic to align clusters across replicate clusterings consisting of the same number of clusters. For small problem sizes, an exact algorithm can be used as an alternative. Additional features include row-specific weights, input and output files similar to those of CLUMPP (Jakobsson & Rosenberg, 2007) and the evaluation of a given solution in terms of CLUMPP as well as its own objective functions. Benchmark analyses show that Crimp, especially when applied to larger data sets, tends to outperform alternative tools considering runtime requirements and various quality measures. While primarily targeting population structure analysis, Crimp can be used as a generic tool to correct multiple clusterings for label switching. This facilitates their comparison and allows to generate an averaged clustering. Crimp's computational efficiency makes it even applicable to relatively large data sets while offering competitive solution quality.

A 3D finite element stochastic framework for the failure of tow-based discontinuous composites

Tow-Based Discontinuous Composites (TBDCs) combine high manufacturability with good mechanical properties. This work presents a novel 3D approach for the numerical modelling of TBDCs. The framework generates 3D orientation tensors by adding a stochastic component to the orientation tensors deterministically predicted by a process simulation. The actual TBDCs are thus idealised as Equivalent Laminates (ELs), resulting from these stochastic tensors. A physically based 3D failure criterion is presented for the prediction of failure initiation of the ELs. The consequent stiffness reduction is captured by a ply-discount method. The approach is validated for two TBDCs materials, with two different moulding conditions and different amounts of in-mould flow. The proposed approach accurately predicts the characteristic variability of these materials. As a consequence, the predicted strength was in good agreement with the experimental results of both materials tested.

The Yoccoz–Birkeland livestock population model coupled with random price dynamics

We study a random version of the population-market model proposed by Arlot, Marmi, and Papini in Arlot et al. (2019). The latter model is based on the Yoccoz–Birkeland integral equation and describes a time evolution of livestock commodities prices which exhibits endogenous deterministic, stochastic behavior. We introduce a stochastic component inspired by the Black–Scholes market model into the price equation, and we prove the existence of a random attractor and a random invariant measure. We compute numerically the fractal dimension and the entropy of the random attractor, and show its convergence to the deterministic one as the volatility in the market equation tends to zero. We also investigate in detail the dependence of the attractor on the choice of the time-discretization parameter. We implement several statistical distances to quantify the similarity between the discretized systems’ attractors and the original ones. In particular, following a work by Cuturi (2013), we use the Sinkhorn distance. This distance is a discrete and penalized version of the Optimal Transport Distance between two measures, given a transport cost matrix.

The opportunity for selection: A slippery concept in ecology and evolution.

Natural selection can only occur if individuals differ in fitness. For this reason, the variance in relative fitness has been equated with the 'opportunity for selection' ( ), which has a long, albeit somewhat controversial, history. In this paper we discuss the use/misuse of and related metrics in evolutionary ecology. The opportunity is only realised if some fraction of is caused by trait variation. Thus, does not imply that selection is occurring, as sometimes erroneously assumed, because all fitness variation could be independent of phenotype. The selection intensity on any given trait cannot exceed , but this upper limit will never be reached because (a) stochastic factors always affect fitness, and (b) there might be multiple traits under selection. The expected magnitude of the stochastic component of is negatively correlated with mean fitness. Uncertainty in realised is also larger when mean fitness or population/sample size are low. Variation in across time or space thus can be dominated (or solely driven) by variation in the strength of demographic stochasticity. We illustrate these points using simulations and empirical data from a population study on great tits Parus major. Our analysis shows that the scope for fecundity selection in the great tits is substantially higher when using annual number of recruits as the fitness measure, rather than fledglings or eggs, even after adjusting for the dependence of on mean fitness. This suggests nonrandom survival of juveniles across families between life stages. Indeed, previous work on this population has shown that offspring recruitment is often nonrandom with respect to clutch size and laying date of parents, for example. We conclude that one cannot make direct inferences about selection based on fitness data alone. However, examining variation in (the opportunity for fecundity selection adjusted for mean fitness) across life stages, years or environments can offer clues as to when/where fecundity selection might be strongest, which can be useful for research planning and experimental design.

Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend

Decentralized online learning (online learning in decentralized networks) has been attracting more and more attention, since it is believed that decentralized online learning can help data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers. Typically, the cooperation is achieved by letting the data providers exchange their models between neighbors, e.g., recommendation model. However, the best regret bound for a decentralized online learning algorithm is 𝒪( n √ T ), where n is the number of nodes (or users) and T is the number of iterations. This is clearly insignificant, since this bound can be achieved without any communication in the networks. This reminds us to ask a fundamental question: Can people really get benefit from the decentralized online learning by exchanging information? In this article, we studied when and why the communication can help the decentralized online learning to reduce the regret. Specifically, each loss function is characterized by two components: the adversarial component and the stochastic component. Under this characterization, we show that decentralized online gradient enjoys a regret bound \( {\mathcal {O}(\sqrt {n^2TG^2 + n T \sigma ^2})} \) , where G measures the magnitude of the adversarial component in the private data (or equivalently the local loss function) and σ measures the randomness within the private data. This regret suggests that people can get benefits from the randomness in the private data by exchanging private information. Another important contribution of this article is to consider the dynamic regret—a more practical regret to track users’ interest dynamics. Empirical studies are also conducted to validate our analysis.

Investigating temporal and spatial patterns in the stochastic component of ZTD time series over Europe

The EUREF Permanent GNSS Network (EPN) provides a unique atmospheric dataset over Europe in the form of Zenith Total Delay (ZTD) time series. These ZTD time series are estimated independently by different analysis centers, but a combined solution is also provided. Previous studies showed that changes in the processing strategy do not affect trends and seasonal amplitudes. However, its effect on the temporal and spatial variations of the stochastic component of ZTD time series has not yet been investigated. This study analyses the temporal and spatial correlations of the ZTD residuals obtained from four different datasets: one solution provided by ASI (Agenzia Spaziale Italiana Centro di Geodesia Spaziale, Italy), two solutions provided by GOP (Geodetic Observatory Pecny, Czech Republic), and one combined solution resulting from the EPN’s second reprocessing campaign. We find that the ZTD residuals obtained from the three individual solutions can be modeled using a first-order autoregressive stochastic process, which is less significant and must be completed by an additional white noise process in the combined solution. Although the combination procedure changes the temporal correlation in the ZTD residuals, it neither affects its spatial correlation structure nor its time-variability, for which an annual modulation is observed for stations up to 1,000 km apart. The main spatial patterns in the ZTD residuals also remain identical. Finally, we compare two GOP solutions, one of which only differs in the modeling of non-tidal atmospheric loading at the observation level, and conclude that its modeling has a negligible effect on ZTD values.

Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes. The datasets for the throughput prediction model include penetration rates from blast hole drilling (measurement while drilling), geological domains, material types, rock density, and throughput rates of the operating mill, offering an accessible and cost-effective method compared to other geometallurgical programs. First, the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates. A regression model was constructed to predict throughput rates as a function of blended rock properties, which are informed by a material tracking approach in the mining complex. Finally, the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling. This way, common shortfalls of existing geometallurgical throughput prediction models, that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling, are overcome. A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8. By integrating the prediction model and new stochastic components into optimization, the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill. Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7% per period can be mitigated this way.

Deterministic and Stochastic Components of Cortical Down States: Dynamics and Modulation.

Slow oscillations are an emergent activity of the cerebral cortex network consisting of alternating periods of activity (Up states) and silence (Down states). Up states are periods of persistent cortical activity that share properties with that of underlying wakefulness. However, the occurrence of Down states is almost invariably associated with unconsciousness, both in animal models and clinical studies. Down states have been attributed relevant functions, such as being a resetting mechanism or breaking causal interactions between cortical areas. But what do Down states consist of? Here, we explored in detail the network dynamics (e.g., synchronization and phase) during these silent periods in vivo (male mice), in vitro (ferrets, either sex), and in silico, investigating various experimental conditions that modulate them: anesthesia levels, excitability (electric fields), and excitation/inhibition balance. We identified metastability as two complementary phases composing such quiescence states: a highly synchronized "deterministic" period followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamical properties of the resulting rhythm, as well as the responsiveness to incoming inputs or refractoriness. We propose detailed Up and Down state cycle dynamics that bridge cortical properties emerging at the mesoscale with their underlying mechanisms at the microscale, providing a key to understanding unconscious states.SIGNIFICANCE STATEMENT The cerebral cortex expresses slow oscillations consisting of Up (active) and Down (silent) states. Such activity emerges not only in slow wave sleep, but also under anesthesia and in brain lesions. Down states functionally disconnect the network, and are associated with unconsciousness. Based on a large collection of data, novel data analysis approaches and computational modeling, we thoroughly investigate the nature of Down states. We identify two phases: a highly synchronized "deterministic" period, followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamic properties of the resulting rhythm and responsiveness to incoming inputs. This finding reconciles different theories of slow rhythm generation and provides clues about how the brain switches from conscious to unconscious brain states.

Forecasting crude oil prices volatility by reconstructing EEMD components using ARIMA and FFNN models

The energy sector which includes gas and oil is concerned to explore and develop refined oil and it’s a multitrillion business. As crude oil is a very important source of energy, and it has a very valuable impact on a country’s economic growth, national security, and social stability. Therefore, accurately predicting the crude oil price volatility is a very important topic of research and still, it is a challenge for researchers to accurately forecast crude oil prices. Therefore, this study is conducted to address the said problem significantly. This research presents a novel hybrid method for reconstructing EEMD IMFs that involves two steps. Visual analysis of Average Mutual Information (AMI) graphs were used to rebuild IMFs. EEMD IMFs were split into two components called stochastic and deterministic. In the proposed method, reconstruction of IMFs of EEMD was done at two stages to see if the stochastic components have more variation. Later, ARIMA and FFNN models were used to test the suggested method’s performance. For this purpose, Brent crude oil prices data was used, and the hybrid model EEMD-S2D1D2-ARIMA/FFNN outperformed the other existing hybrid model with minimum MAE = 0.2323, RMSE = 0.3058 and MAPE = 0.5273. A simulation study was also conducted to check the robustness of the proposed method for N = 50, 500, 1,000, 2000, 5,000, and 7,500. The simulation results also confirm that the unpredictability present in the reconstructed IMFs of the hybrid models EEMD-ARIMA/FFNN and EEMD-SD-ARIMA/FFNN has been reduced by the proposed hybrid models.

SOME CHAOTIC MATHEMATICAL MODELS WITH STOCHASTIC RESETTING

A system of three nonlinear equations that depict chaotic behavior was considered in this work. We looked at four cases: one with a power-law property; one with a fading memory effect; one with a transition from stretched exponential to power-law; and one with a stochastic component. We have presented a detailed analysis of stability and we have suggested a number similar to the reproductive number in the case of epidemiology. At this stage, we did not provide a physical meaning for such a number. However, such a number may be useful. A numerical scheme based on the Lagrange polynomial interpolation was used to solve the respective models numerically. Numerical simulations were performed for different values of fractional orders and densities of randomness and piecewise ideas.

Stochastic Continuous Modeling for Pillar Stress Estimation and Comparison with 2D Numerical, and Analytical Solutions in an Underground Stone Mine

Pillar collapses are events that due to their severe consequences can be classified as high risk. The design of pillars in underground room-and-pillar operations should migrate to risk-based design approaches. The authors of this work proposed a risk-based pillar design methodology that integrates stochastic discrete element modeling for pillar strength estimation, and stochastic finite volume modeling (FVM) for stress estimation. This paper focuses on the stochastic FVM component for stress estimation. The mining and geomechanical aspects of a case study mine (CSM) are described and pillar stresses are estimated by using three approaches: (1) analytical solutions, (2) 2D finite element modeling, and (3) 3D finite volume modeling. This operation extracts a 30° dipping deposit, which makes current underground stone mine design guidelines inapplicable for this CSM. This work compares results from each stress estimation approach and discusses uses the point estimate method as a simplified stochastic approach to evaluate the effect of rock mass elastic properties variability on pillar stress distribution. Results from this work show that the three estimation approaches lead to different estimations, possibly, due to the wide range of assumptions each estimation approach considers. It was also determined that the horizontal to vertical stress ratio has a significant impact on pillar stress magnitude. Therefore, it is recommended to perform in situ stress measurements, or assume worst-case-scenario values to account and reduce uncertainty due to this parameter. The stochastic stress estimation approach used in this paper provides results that can integrate a risk-based pillar design framework.

Deterministic Brownian-like Motion: Electronic Approach

Brownian motion is a dynamic behavior with random changes over time (stochastic) that occurs in many vital functions related to fluid environments, stock behavior, or even renewable energy generation. In this paper, we present a circuit implementation that reproduces Brownian motion based on a fully deterministic set of differential equations. The dynamics of the electronic circuit are characterized using four well-known metrics of Brownian motion, namely: (i) Detrended Fluctuation Analysis (DFA), (ii) power law in the power spectrum, (iii) normal probability distribution, and (iv) Mean Square Displacement (MSD); where traditional Brownian motion exhibits linear time growth of the MSD, a Gaussian distribution, a −2 power law of the frequency spectrum, and DFA values close to 1.5. The obtained results show that for a certain combination of values in the deterministic model, the dynamics in the electronic circuit are consistent with the expectations for a stochastic Brownian behavior. The presented electronic circuit improves the study of Brownian behavior by eliminating the stochastic component, allowing reproducibility of the results through fully deterministic equations, and enabling the generation of physical signals (analog electronic signals) with Brownian-like properties with potential applications in fields such as medicine, economics, genetics, and communications, to name a few.

Stochastic modeling of meteorological elements for solving environmental monitoring problems

The purpose of the research presented in the publication is developing the stochastic model for simulating the series of meteorological elements used in predicting for transferring of biogenic elements into water bodies from the territory of agricultural land. The object of study is artificial series of meteorological elements with given statistical characteristics. As initial data, the meteorological data of the State Institution “Republican Hydrometeorological Center” for the Minsk object obtained for the period 2000–2020 were used. For development of the stochastic model, the following indicators were used: daily amount of total solar radiation, maximum and minimum air temperature, relative air humidity, amount of atmospheric precipitation, number of days with precipitation, maximum half-hour share of precipitation, wind speed at a height of 10 m above the earth’s surface. Artificial meteorological series are obtained on the basis of a sample set of randomly generated daily values with using the Monte Carlo method. The stochastic component of precipitation generation is a Markov chain gamma model; total precipitation – inverse method of two-parameter gamma distribution; relative air humidity – Simpson distribution; wind speed – inverse Weibull distribution method; the minimum and maximum air temperatures and solar radiation are determined using the Matalas first-order recurrent filter. For each of the elements, a differentiated correspondence of the statistical parameters determined from the measured and calculated series of meteorological elements were proved. The results of investigation can be used for modeling and forecasting of weather in areas with limited meteorological observation data for solving applied problems in biological and ecological systems.

Biological neural network model based on a non-linear stochastic system

As a contribution to recent activities in the field of modelling electro-biological phenomena, we would like to present an investigation on the electrical properties of a network of neuron and propose a simplified model for brain activity, based on a biological neural network represented by a non-linear system with stochastic components. The network’s fundamental element is represented by Izhikevich model, and including several types of responses, grouped into excitatory and inhibitory response. The network’s electrical activity is compared to a biological signal obtained from an electroencephalogram recording, and it is noted that the correlation between the biological and the simulated signal increases with the number of neurons in the network.

Quantifying the Selective, Stochastic, and Complementary Drivers of Institutional Evolution in Online Communities.

Institutions and cultures usually evolve in response to environmental incentives. However, sometimes institutional change occurs due to stochastic drivers beyond current fitness, including drift, path dependency, blind imitation, and complementary cooperation in fluctuating environments. Disentangling the selective and stochastic components of social system change enables us to identify the key features of long-term organizational development. Evolutionary approaches provide organizational science with abundant theories to demonstrate organizational evolution by tracking beneficial or harmful features. In this study, focusing on 20,000 Minecraft communities, we measure these drivers empirically using two of the most widely applied evolutionary models: the Price equation and the bet-hedging model. As a result, we find strong selection pressure on administrative and information rules, suggesting that their positive correlation with community fitness is the main reason for their frequency change. We also find that stochastic drivers decrease the average frequency of administrative rules. The result makes sense when viewed in the context of evolutionary bet-hedging. We show through the bet-hedging result that institutional diversity contributes to the growth and stability of rules related to information, communication, and economic behaviors.

Non-stationary response determination of nonlinear systems subjected to combined deterministic and evolutionary stochastic excitations

A semi-analytical method is proposed for determining the response of a lightly damped single-degree-of-freedom nonlinear system subjected to combined deterministic and non-stationary stochastic excitations. This is attained by combining the stochastic averaging and statistical linearization methodologies. Specifically, first, the system response is decomposed into two components, namely the deterministic and the stochastic parts. This leads to a set of coupled differential sub-equations governing, respectively, the deterministic and the stochastic component of the response. Next, aiming at solving the set of differential sub-equations, an additional expression is derived by applying the statistical linearization methodology, followed by the application of a stochastic averaging step to the stochastic sub-equations. Therefore, an equivalent time-varying linear system is defined for the original nonlinear system. The stochastic averaging method is then applied to the linearized system for reducing its order, and thus, its complexity from a solution perspective. In this regard, an additional equation is derived, which connects the deterministic and stochastic components of the response. The latter and the deterministic differential sub-equations are solved simultaneously for determining the system response. A single-degree-of-freedom nonlinear system exhibiting quadratic and cubic nonlinear stiffness is considered for assessing the reliability of the proposed technique. The obtained results are compared with pertinent Monte-Carlo simulation estimates.

Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations

The purpose of this research is to investigate the multi-area economic emission dispatch problem (MEEDP) in the presence of renewable energy resources (RES) to improve the energy sustainability and climatic benefits. MEEDP is a multi-objective problem in smart grids, with the purpose of minimizing the operating costs and emissions of thermal units. RES have made a substantial contribution to greenhouse gases emission control and environmental sustainability. The integration of RES into conventional grids, which is becoming increasingly prevalent, spread the research scope of MEEDP and needs to be re-examined. This work considers two renewable sources (wind and solar) along with thermal plants subjected to significant number of previously uncombined system level limitations such as power capacity limit, prohibited zones, transmission network losses, dynamic ramp limits, tie-line limits and multiple fueling options. The operating cost is computed as summation of predictive and stochastic components. The predictive part is calculated by utilization of cumulative distribution function for each wind and solar system. A swarm intelligence-based crow search optimization algorithm (CSOA) is modeled to handle the complex constrained MEEDP with adjusted predictive part of RES. Six benchmark test systems with multi-dimensional constraints have been chosen to validate the adaptability and efficacy of the presented approach. Regardless of the complexity of the problem, the proposed approach provides the best feasible solution with a finer convergence rate. Finally, the simulation results depict that the integration of the corresponding system constraints gives legitimacy to the system and delivers reliable output.