Statistical Aggregate Research Articles

Quantum and Component Analysis of P3a and P3b from Auditory Single Trial ERPs Differentiates Borderline Personality Disorder from Schizophrenia

Traditional approaches to EEG modelling use the methods of classical physics to reconstruct scalp potentials in terms of explicit physical models of cortical neuron ensembles. The principal difficulty with such approaches is that the multiplicity of cellular processes, with an intricate array of deterministic and random influencing factors, prevents the creation of consistent biophysical parameter sets. An original, empirically testable solution has been achieved in our previous studies by a radical departure from the deterministic equations of classical physics to the probabilistic reasoning of quantum mechanics. This crucial step relocates the models of elementary bioelectric sources of EEG signals from the cellular to the molecular level where ions are considered as elementary sources of electricity. The rationale is that, despite dramatic differences in cellular machineries, statistical factors governed by the rules of the central limit theorem produce the EEG waveform as a statistical aggregate of the synchronized activity of multiple microscale sources. Based on these innovations, we introduce a method of comprehensive computerized analysis of event related potentials directly from single trial recordings. This method provides a universal model of single trial ERP components in both frequency and time domains. For the first time, this tool provides effective quantification of all significant cognitive components in single trial ERPs and represents a viable alternative to the traditional method of averaging. We demonstrate the clinical significance of the additional information provided by the new method, using ERP data from patients with borderline personality disorder and schizophrenia. Referring to the P300 as an important objective marker of psychiatric disorders, we show that the new method reliably identifies P3a and P3b as the major components of the P3. The diagnostic significance of differentiating the P3a and P3b components of P3 is that it provides an objective electrophysiological measure that distinguishes borderline personality disorder from schizophrenia.

Advancing caching and automation with FDO

Advancing caching and automation with FDO

Adam Grener. Improbability, Chance, and the Nineteenth-Century Realist Novel.

Adam Grener. <i>Improbability, Chance, and the Nineteenth-Century Realist Novel</i>.

Bayesian dynamic extreme value modeling for conflict-based real-time safety analysis

Real-time safety analysis and optimization using surrogate safety measures such as traffic conflicts and techniques such extreme value theory (EVT) models is an emerging research topic in the context of proactive traffic safety management. However, the predictive performance and temporal transferability of the existing real-time safety analysis EVT models are subject to the assumption of invariant model parameters, which do not account for the temporal variability and is not suitable for real-time traffic data analysis. This study proposes a Bayesian dynamic extreme value modeling approach for conflict-based real-time safety analysis which integrates a Bayesian dynamic linear model with the extreme value distribution. The proposed approach has several unique advantages as it: 1) allows the model parameters to be time-varying; 2) integrates the newer data with prior information to recursively update the model parameters and account for state-space changes and react to sudden trend changes; 3) accounts for temporal variability and non-stationarity in conflict extremes; and 4) quantitatively evaluates the real-time safety levels of a road facility. The proposed approach is applied for cycle-by-cycle safety analysis at four signalized intersections in the city of Surrey, British Columbia. Traffic conflicts are characterized by the modified time to collision indicator. Three traffic parameters (traffic volume, shock wave area, and platoon ratio) at the signal cycle level are considered as covariates to account for non-stationarity. Several Bayesian dynamic and static extreme value models are developed and two safety indices, namely risk of crash (RC) and return level (RL), are generated to quantitatively represent the cycle-level safety. The RC directly reflects whether a cycle is risky while the RL can evaluate the safety levels of individual cycles. The results show that the dynamic model can identify more crash-risk cycles with either a positive RC or a positive RL than the static model and is more capable of differentiating the safety levels for individual cycles in terms of RL. Overall, the dynamic model outperforms the static model in terms of the statistical fit and aggregate crash estimation accuracy.

Many heads are more utilitarian than one

Moral judgments have a very prominent social nature, and in everyday life, they are continually shaped by discussions with others. Psychological investigations of these judgments, however, have rarely addressed the impact of social interactions. To examine the role of social interaction on moral judgments within small groups, we had groups of 4 to 5 participants judge moral dilemmas first individually and privately, then collectively and interactively, and finally individually a second time. We employed both real-life and sacrificial moral dilemmas in which the character's action or inaction violated a moral principle to benefit the greatest number of people. Participants decided if these utilitarian decisions were morally acceptable or not. In Experiment 1, we found that collective judgments in face-to-face interactions were more utilitarian than the statistical aggregate of their members compared to both first and second individual judgments. This observation supported the hypothesis that deliberation and consensus within a group transiently reduce the emotional burden of norm violation. In Experiment 2, we tested this hypothesis more directly: measuring participants' state anxiety in addition to their moral judgments before, during, and after online interactions, we found again that collectives were more utilitarian than those of individuals and that state anxiety level was reduced during and after social interaction. The utilitarian boost in collective moral judgments is probably due to the reduction of stress in the social setting.

Presentation of Financial Assets in Russian Macroeconomic Statistics

The purpose of the study. Currently, much attention is paid to the collection, presentation and analysis of data on the volume, structure and dynamics of economic assets. The value of economic assets, their composition, and effective asset management determine the trajectory of the development of the national economy and its individual sectors. Financial assets are an integral part of economic assets, their importance in the activities of economic entities is steadily increasing. The purpose of the work is to systematize theoretical and practical developments on the presentation of data on financial assets in modern macroeconomic statistics of Russia, to determine the directions for improving the methods of evaluating financial assets, the system of indicators characterizing their condition and movement.Materials and methods. The authors considered the classification of financial assets, applied structural and dynamic data analysis, as well as methods of theoretical research in the form of generalization, comparison and special analytical procedures.Results. The paper defines the main directions of studying financial assets based on existing international standards, taking into account national statistical practice. The current issues of the theory and practice of statistical observation and presentation of data on financial assets, the adaptation of concepts and classifications of international standards to the Russian system of statistical accounting are considered. The features of the presentation of data on financial assets at the macro level are formulated, which are reflected in the national accounts, the system of indicators of monetary statistics and statistics of foreign economic relations, statistical indicators of the Bank of Russia. The authors conducted a quantitative assessment of the structure and dynamics of indicators characterizing the state and movement of financial assets.Conclusion. Statistical analysis of economic transactions with financial assets at the level of institutional units and sectors of the economy allows us to identify not only the main trends in the development of the financial sector of the economy, but also makes it possible to comprehensively consider changes in the value and composition of financial assets at the macro level. The change in the value of financial assets is determined on a balance basis, that is, as the difference between the value of acquired and disposed financial assets.The authors examined in detail the methodological basis for forming a financial account in the context of financial instruments and sectors of the national economy. The financial account provides comprehensive information in the sectoral context on transactions with financial assets and liabilities for the reporting period, allows you to reflect the change in their value as a result of economic operations performed by institutional units, as well as to identify sectors of the economy that did not perform transactions with financial assets. The analysis of the financial account data makes it possible to analyze in detail the change in the value of financial assets at the macro level, to assess the share of the impact of each sector of the economy and the type of asset. A similar approach is applied in analytical procedures with respect to the net obligations assumed.Macroeconomic statistics reflect the movement of financial assets and liabilities by sectors of the national economy and characterize Russia’s relations with the “rest of the world” by determining the volume of net lending by Russia to other countries or determining its status as a net borrower.The issues of the theory and practice of presenting data on financial assets and their use are relevant and promising areas for improving national statistical accounting. Work in this direction continues, although the process of active implementation of international standards in statistical practice has been going on for quite a long time. The quality of primary accounting data on financial sector operations has improved significantly recently, but there are still problems with obtaining data in terms of the full coverage of the statistical aggregate under study, as well as the timing of obtaining reporting data. In addition, the appearance of a large number of new financial instruments and transactions, including modified ones, in the business turnover of institutional units makes it difficult to identify and classify them.The authors of the article have repeatedly addressed the issues of macroeconomic calculations in their publications, which allowed them to consider in more detail topical issues related to the processes of reflecting financial assets.

Quantum Theory of EEG with Application to the Single-Trial ERP Analysis

The probabilistic formalism of quantum mechanics is used to quantitatively link the electroencephalogram (EEG) with the underlying microscale activity of cortical neurons. Previous approaches applied methods of classic physics to reconstruct the EEG in terms of explicit physical models of cortical neurons and the volume conductor. However, the multiplicity of cellular processes with extremely intricate mixtures of deterministic and random factors prevented the creation of consistent biophysical parameter sets. To avoid the uncertainty surrounding the physical attributes of the neuronal ensembles, we undertake here a radical departure from deterministic equations of classical physics to the probabilistic reasoning of quantum mechanics. The crucial step is the relocation of the elementary bioelectric sources from cellular to molecular level. Using a novel method of time-frequency analysis with adaptive segmentation for digital processing of empirical EEG and single trial event related potentials (ERPs), we found universal “building blocks” of these cortical processes both in the frequency and time domains. This result is qualified as a phenomenon known in statistical physics and quantum mechanics as universality. Therationale is that despite dramatic differences in the cellular machineries, the statistical factors governed by the central limit theorem produce the EEG waveform as a statistical aggregate of the synchronized activities of large ensembles of closely located cortical neurons. Using these theoretical and empirical findings the probabilistic laws that control the microscale machinery generating the EEG are deduced.

Research on the question of determination of the total number of fraud victims committed via Internet

The object of this research is the statistical aggregate of people who by objective (external) reasons are prone to become the victims of fraud within the Russian segment of the global computer network. The subject of this research is the quantitative aspect of the aforementioned phenomenon. Special attention is given to the search of effective methods for determination of quantitative aspects of victimization. The goal consists in most accurate assessment of the total number of the potential victims of fraud committed via Internet. The main results of this work contain the most accurate among previously existed in criminology numbers of: potential victims of fraud in the Internet (the author specifies the number of users of the Russian segment of Internet aged from 6 to 80), persons out of 24-hour Internet audience of criminogenic age; and a number of statistical indicators of victimization. All of the listed above can assist proper organization of research carried out by scholars dealing with the problems of cybercrimes.

Как посчитать цифровую экономику: между реальностью и конструкцией

&lt;span&gt;Несмотря на многолетнее активное использование понятия «цифровая экономика» в научном и общественном дискурсе, четкого устоявшегося значения у него до сих пор нет, как не существует и единой методики количественной оценки этого явления как статистического агрегата, что ставит вопрос о его реальных масштабах. На наш взгляд, цифровая экономика&lt;/span&gt;&lt;span class="myHorizontalScaleForEmDashSpaceBefore"&gt; &lt;/span&gt;&lt;span&gt;–&lt;/span&gt;&lt;span class="myHorizontalScaleForEmDashSpaceAfter"&gt; &lt;/span&gt;&lt;span&gt;это пока лишь бизнес-модель и некий амбициозный глобальный проект социально-технической модернизации общества, который существует на стыке политического дискурса и реальности как конструируемый объект знания, статистических и прогнозных манипуляций и пока не воплотился в экономике. В статье анализируется эволюция различных концепций определения цифровой экономики и методик ее статистической количественной оценки. Авторы показывают, что соответствующие методы значительно видоизменялись в зависимости от экономической конъюнктуры, однако их основная цель&lt;/span&gt;&lt;span class="myHorizontalScaleForEmDashSpaceBefore"&gt; &lt;/span&gt;&lt;span&gt;–&lt;/span&gt;&lt;span class="myHorizontalScaleForEmDashSpaceAfter"&gt; &lt;/span&gt;&lt;span&gt;любыми средствами (зачастую в ущерб объективности) выработать определение цифровой экономики и оценить ее масштаб так, чтобы постоянно демонстрировать высокую динамику и опережающие темпы роста.&lt;/span&gt;

Determination of the main factors of the gross regional product level by econometric modeling methods for the totality of regions of the Russian Federation

The purpose of the research is to identify factors that determine the level of gross regional product, using the construction of econometric models. In terms of the economic theory the volume of gross added value of the region depends on existence of resources, efficiency of use of resources, level of effective demand for the consumption of gross added value, level of export, structure of the regional economy. In the article, by constructing econometric models with a different set of factors, the most essential and statistically significant factors determining the level of gross regional product were identified.Materials and methods. Official data of Federal State Statistics Service for 2016 formed empirical base of the research. The aggregate of the constituent entities of the Russian Federation was considered as a statistical aggregate as an object of study, and both the complete aggregate of subjects and the aggregate of entities with a gross regional product level of less than and more than a trillion rubles were studied. Research method is econometric modeling of gross regional product based on spatial data on constituent entities of the Russian Federation. The parameters of the multiple linear regression models in absolute values and in logarithms were estimated using the least squares method. To test the null hypotheses, a five percent significance level was adopted. Interval estimation of model parameters was carried out taking into account standard errors consistent under heteroskedasticity. The quality of models was defined by coefficient of determination, mean relative error of approximation, Ramsey’s test checking a null hypothesis about lack of the passed variables in model. Implementation of the research was carried out in the RStudio software product.Results. Eight qualitative models of multiple linear regression are constructed. Two models are constructed on the general set of constituent entities of the Russian Federation: 1) model in absolute values with three quantitative signs and one attributive sign characterizing group of entities on the level of a gross regional product; 2) three-factor model in logarithms. Two models are constructed on set of entities with a gross regional product less than one trillion rubles: 1) model in absolute values with seven quantitative signs, five of which were statistically significant; 2) four-factor model in logarithms. Four two-factor models are constructed on set of entities with a gross regional product more than one trillion rubles.Conclusion. In all constructed models the most significant factors of the level of gross regional product are: a) factor of fixed capital (average annual residual cost of fixed assets); b) factor of effective demand (expressed by one of the following indicators: social expenses of the budget, total income of the population, wage fund); c) factor of level of financial resources (expressed by the volume of average annual balances on ruble accounts of legal persons and balance of incomes and expenses of the enterprises and organizations). At creation of econometric models statistically insignificant were factors of resource efficiency (capital productivity, labor productivity, profitability), factors characterizing the volume of foreign economic activity and a share of the extractive industries. The economy of the regions in 2016 can be described as an economy of an extensive type, focusing on the use of domestic capital and financial resources.

Psychopathy by U.S. state: A translation of regional measures of the Big Five personality traits to regional measures of psychopathy

Rentfrow et al. (2013) constructs a cross-section of the “Big Five” personality traits and demonstrates their relationship with outcomes variables for the continental United States and the District of Columbia. Hyatt et al. (Forthcoming) creates a means of describing psychopathy in terms of the Big Five personality traits. When these two findings are combined, a state-level estimate of psychopathy is produced. The estimate is conjectural, and if correct, it only describes the levels of psychopathy of states in relation of one state to one another, and is contingent on one particular conceptualization of psychopathy. Among the typical predictions made regarding psychopathy, the variable with the closest bivariate relationship with this new statistical aggregate is the percentage of the population in the state living in an urban area. There is no clear bivariate relationship of regional psychopathy with homicide, violent crime, or property crime rates.

Psychopathy by U.S. State

Rentfrow et al. (2013) constructs a cross-section of the “Big Five” personality traits and demonstrates their relationship with outcomes variables for the continental United States and the District of Columbia. Hyatt et al. (Forthcoming) creates a means of describing psychopathy in terms of the Big Five personality traits. When these two findings are combined, a state-level estimate of psychopathy is produced. Among the typical predictions made regarding psychopathy, the variable with the closest univariate relationship with this new statistical aggregate is the percentage of the population in the state living in an urban area. There is not a clear univariate relationship with homicide rates.

Industrial policy as a driver of economic growth: experience of European Union member states for Ukraine

The article analyzes the experience of reforming of industrial policy of the EU member states on the way to sustainable economic development. Based on the conducted analysis the priority areas of high added value in Ukraine are outlined: transport, aerospace, energy engineering, production of equipment for alternative energy, including new materials and energy storage devices; production of weapons, military and special equipment; information and communication technologies. The development of the diversified and high-tech industries, especially the processing industry, is the key to ensuring the economic growth in Ukraine. The de-industrialization in Ukraine in recent years has been slowing down the movement towards the stable and sustained growth. To estimate trends in the structure of Ukraine’s GDP and their impact on economic growth, the taxonomic indicator of development level has been calculated. The units of the statistical aggregate (data from Germany, Poland and Ukraine) were mapped. The relative position of the state was determined as the distance from the statistical aggregate unit to the selected reference base (reference point). The smaller the value of the taxonomic indicator of development is, the closer the country is to the standard. The indicators of GDP, GDP growth rate and the share of industrial production belong to stimulants; the level of inflation and the share of agriculture in GDP - to disincentives. The corresponding calculations permit to draw the following conclusions: - Poland was the closest to the standard in 2000 (index 0.650), although Ukraine demonstrated at that time the highest growth rates and the biggest share of industry in GDP; - in 2017 the gap was increasing - Ukraine was inferior to both countries (with indicators 0.325 and 0.336), due to lower rates of economic growth and a decrease in the share of industrial production against the backdrop of inflationary phenomena. Ukraine should take into account that no country focused on agricultural development has succeeded in economic development. Progressive foreign experience can highlight the criterion of positive changes - an increase in the proportion of technologically complex productions with high added value in the structure of reproduction.

Assessment of advanced Taylor models, the Taylor factor and yield-surface exponent for FCC metals

High-resolution crystal plasticity-finite element method (CPFEM) simulations are performed to provide new reference values of the Taylor factor M and the isotropic yield surface exponent a for high stacking fault energy face-centred-cubic (FCC) polycrystalline metals with random orientations. The visco-plastic Taylor factor with strain rate sensitivity M˜ is introduced and linearly extrapolated to its zero strain rate sensitivity limit to give the new reference value of M. The linear extrapolation technique is also employed to define the new reference value of a. The obtained new reference values are 2.7 and 6.9, for M and a, respectively, which are much smaller than the reference values currently used for FCC materials based on full constraint (FC) Taylor model calculations, i.e. 3.07 for M and 8 for a. Other state-of-the-art Taylor-type models, e.g. ALamel, ALamel with the type III relaxation (ALamel-T3) and the visco-plastic self-consistent (VPSC) models, can also give values for M and a much smaller than the FC-Taylor calculations. The performance of the CPFEM and these state-of-art Taylor-type models in terms of resolving deformation and stress fields within the aggregate can only be assessed in a statistical manner since all are statistical aggregate models. Selected statistical distributions are analysed for all models, by means of local deviations of the velocity gradient tensor, of the plastic deformation-rate tensor and of the stress tensor etc., for uniaxial tensile deformation. The ALamel models are found to provide similar statistics as CPFEM, whereas the VPSC model results are qualitatively different. The intra-grain analysis for CPFEM demonstrates that the intra-grain interactions are as much important as the local interactions at the grain boundaries.

Partitioning-based mechanisms under personalized differential privacy.

Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms.

Identification Method of the Russian Federation Arctic Zone Regions Statistical Aggregate as the Object of Strategy Development and a Source of Sustainable Growth

The lack of accurate identification of the territories belonging to the Russian Arctic zone is a specific feature. As a result, the problem of statistical observations of circumpolar regions arises. Russian Arctic zone is considered in an article in the format of the threshold spatial socio-economic entity. The identification of the regions included in the Russian Arctic zone is based on the concept of unstable aggregates. The probit regression tools are used to determine the thresholds and to form a threshold aggregates in the study. Equation parameter estimation is performed by a quasi-Newton method. The indicators that reflect a variety of criteria for classifying territories (municipalities) as the Arctic zone (based on not only geographical, but also climatic and biological criteria) were used as the initial data. These calculations allowed forming the list of territories identified as the Russian Federation Arctic zone.

Characterizing and Visualizing Predictive Uncertainty in Numerical Ensembles Through Bayesian Model Averaging

Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g., the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. This paper presents a new visual strategy to help quantify and characterize a numerical ensemble's predictive uncertainty: i.e., the ability for ensemble constituents to accurately and consistently predict an event of interest based on ground truth observations. Our strategy employs a Bayesian framework to first construct a statistical aggregate from the ensemble. We extend the information obtained from the aggregate with a visualization strategy that characterizes predictive uncertainty at two levels: at a global level, which assesses the ensemble as a whole, as well as a local level, which examines each of the ensemble's constituents. Through this approach, modelers are able to better assess the predictive strengths and weaknesses of the ensemble as a whole, as well as individual models. We apply our method to two datasets to demonstrate its broad applicability.

Role of Formation of Statistical Aggregates in Chlorophyll Fluorescence Concentration Quenching

Using extensive Monte Carlo simulations, a comprehensive investigation has been carried out on the phenomenon of chlorophyll fluorescence concentration quenching. Our results reveal that statistical aggregations of chlorophylls act mainly as trapping sites for excitation energy and lead to fluorescence quenching. Due to transition dipolar-dipolar interactions between the chlorophylls within a statistical aggregate, the associated oscillator strength changes in comparison to a monomer, and excited energy states show splitting. Further, as the lower energy states are more likely associated with lower oscillator strengths, the fluorescence intensity is observed to decrease. Due to the rapid energy transfer between chlorophyll molecules after photoexcitation, the excitonic energy can easily reach a statistical aggregate, where trapping of the exciton and its subsequent decay occur. With an increase in the chlorophyll concentration, the probability of statistical aggregation increases, thereby accentuating the fluorescence quenching effect.

A standardized well-to-wheel model for the assessment of bioethanol and hydrogen from cellulosic biomass

The well-to-wheel (WtW) results of biomass-based chains are found to be significantly sensitive to changes in the elements of the chain model such as the land use change. Our new standardization model is based on the conviction that the synthesis of a statistical aggregate of the possibilities that are defined by the major models in the field including GREET and GEMIS would build reliability into the result by buffering against the changes in the elements of the chain model. In this paper we assess a chosen set of biomass-based chains in terms of energy and GHG emissions using the innovative concept of the standardization transport model (STM). Hydrogen was found to be very attractive with the use of waste wood. On the other hand, sugar ethanol was found to be a promising fuel for the reduction of GHG emissions. Unfavorable land use changes and high fertilizers use should be avoided to maximize confidence in significant reductions from sugar ethanol.

Sticking to the facts: a systematic review of fibrin glue for pilonidal disease

Pilonidal disease occurs when hair invading the natal cleft causes inflammation and abscess formation. Opinions vary on best practice, and most procedures have considerable morbidity and high recurrence rates of 6-40%. This study systematically reviews the use of fibrin glue in the treatment of pilonidal disease. Outcomes measured were healing time and recurrence rate. ScienceDirect and PubMed databases were searched for relevant literature, yielding seven papers including five small trials. The total number of patients receiving fibrin glue treatments across all trials was 85. There were no exclusion criteria in this review. Fibrin glue treatments had equivalent or better reported healing times than conventional therapies at an average of 2-6 weeks, and low recurrence rates between 0 and 17% at follow-up periods between 4 and 28 months. Considerable heterogeneity in study methodologies and surgical techniques prevented statistical significance or aggregate figures from being determined. There appears to be early promise for the use of fibrin glue in the treatment of pilonidal disease and an impetus for definitive research.