Objections

This chapter addresses theoretical and empirical objections that critics have presented against the epistemic argument for democracy presented in the previous chapter (the argument from collective wisdom). The objections this chapter addresses include those based on the average voter’s alleged incompetence and systematic biases, as well as those that challenge the relevance of deductive arguments for democracy. The metrics by which political scientists and economists claim to measure voters’ incompetence are elitist and the argument “garbage in, garbage out” on which people like Brennan rely to criticize democracy fail to take into account the fact that collective intelligence is not a linear function of individual competence but an emergent property that crucially depends on group properties, including cognitive diversity, and thus not captured by Brennan’s purely individualistic framework. Inferring from individual input to collective outcomes is thus neither empirical nor demonstrative. Systematic biases would be, and often are, a problem for democracy but not more than for oligarchies of knowers. In a free and diverse public sphere the public and its democratic representatives have more opportunities to debias themselves, at least over time, than small groups of homogenously thinking elites.

An Investigation of Team Inputs, Processes, And Emergent States on Performance in a Spaceship Bridge Simulation

This investigation serves to provide evidence on the effects of various input variables on intact teams through repeated team performance sessions in a team spaceship bridge simulation (i.e. Artemis). The Input Mediator/Moderator Output Input (IMOI) model provides a systems engineering an approach to understand various team and individual input variables contribution to the development of team processes and emergent states, ultimately leading to a team’s ability to perform together. While various prior research initiatives have served to contribute to the pool of evidence of which input variables are most highly predictive of a team’s overall performance, the need for further and recursive input to output investigations is needed. Our results indicate perceived team effectiveness and cohesion are significant predictors in team performance and that skill and knowledge of a simulated environment may overshadow team-specific effectiveness indicators as the team gains experience.

The Building Blocks of Child Bilingual Code-Mixing: A Cross-Corpus Traceback Approach

This paper offers an inductive, exploratory study on the role of input and individual differences in the early code-mixing of bilingual children. Drawing on data from two German-English bilingual children, aged 2–4, we use the traceback method to check whether their code-mixed utterances can be accounted for with the help of constructional patterns that can be found in their monolingual data and/or in their caregivers' input. In addition, we apply the traceback method to check whether the patterns used by one child can also be found in the input of the other child. Results show that patterns found in the code-mixed utterances could be traced back to the input the children receive, suggesting that children extract lexical knowledge from their environment. Additionally, tracing back patterns within each child was more successful than tracing back to the other child's corpus, indicating that each child has their own set of patterns which depends very much on their individual input. As such, these findings can shed new light on the interplay of the two developing grammars in bilingual children and their individual differences.

Assessment of risk from irradiation originating from mortars with mineral waste addition

With the ever-increasing trend of incorporation of various wastes into building materials, monitoring the potential rise in the radioactive load due to used waste is needed. This paper presents the results of our investigation into the radioactivity of cement mortars of various percentage of granulated blast furnace slag (GBFS). The calculated gamma indexes, Iγ, reached 0.19–0.22 and thus did not exceed, in case of any sample, the required level (Iγ = 1) for bulk materials. Indoor gamma absorbed dose, D, ranged from 41.08 to 47.80 nGy/h being lower by 47% than the world average ones are. The excess life cancer risk, ELCR70, obtained for the cement mortars ranged from 0.71 × 10−3 to 0.82 × 10−3 with the average value of 0.75 × 10−3. Linear correlation between the GBSF content and ionizing radiation was found for 226Ra radionuclide, while polynomic correlations have been found for the mass activities of 232Th and 40K radionuclides and the GBSF amount in the cement sample. Findings revealed that the overall 226Ra mass activity of the composite could be predicted based on the measurement of the mass activities of the individual input components.

Revealing the unique features of each individual's muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

PaaS Optimization of Apache Applications Using System Parameter Tuning of Big Data Platforms in Distributed Computing

Widely used data processing platforms use distributed systems to process huge data efficiently. The aim of this article is to optimize the platform services by tuning only the relevant, tunable, system parameters and to identify the relation between the software quality metrics. The system parameters of data platforms based on the service level agreements can be defined and customized. In the first stage, the most significant parameters are identified and shortlisted using various feature selection approaches. In the second stage, the iterative runs of applications are executed for tuning these shortlisted parameters to identify the optimal value and to understand the impact of individual input parameters on the system output parameter. The empirical results imply significant improvement in performance and with which it is possible to render the proposed work optimizing the services offered by these data platforms.

Revealing the unique features of each individual’s muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualising each individual’s muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88.

Utilization of Fluidized Bed Combustion Fly Ash in the Design of Reuse Clay Soil in the Form of Self-Compacting Grouts

This paper addresses the influence of fluidized bed combustion fly ash (FBCA) and further liquefying additives on the formation of structure and on the resulting properties of self-compacting grouts based on clay soil. In order to give the best account of the influence of individual input materials, tests were conducted on samples without the use of fluidized bed combustion fly ash. Clay soil (Cl) and cement were used as input materials, and fluidized bed combustion fly ash (10% and 30%) and a liquefying additive (sodium carbonate 0.1%) were used as an admixture. It has been experimentally determined that the use of 10% FBCA with clay soil is most suitable for achieving the optimal spillage parameter of self-compacting grout (class SF2 (660–750 mm) and class SF3 (760–850 mm)). It was also found that fluidized bed combustion fly ash and the liquefying additive have a significant influence on the formation of the structure of the self-compacting grout and, due to their presence, the compressive strength of the samples increased up to 0.5 MPa after seven days of hardening. The reaction between 0.1% of sodium carbonate and clay soil increased the electrokinetic potential, which reduced the viscosity of the self-compacting grout. Within the research work, the verification of the developed self-compacting grout in situ was also carried out.

Reliable quantification of 18F-GE-180 PET neuroinflammation studies using an individually scaled population-based input function or late tissue-to-blood ratio

Purpose Tracer kinetic modeling of tissue time activity curves and the individual input function based on arterial blood sampling and metabolite correction is the gold standard for quantitative characterization of microglia activation by PET with the translocator protein (TSPO) ligand 18F-GE-180. This study tested simplified methods for quantification of 18F-GE-180 PET. Methods Dynamic 18F-GE-180 PET with arterial blood sampling and metabolite correction was performed in five healthy volunteers and 20 liver-transplanted patients. Population-based input function templates were generated by averaging individual input functions normalized to the total area under the input function using a leave-one-out approach. Individual population-based input functions were obtained by scaling the input function template with the individual parent activity concentration of 18F-GE-180 in arterial plasma in a blood sample drawn at 27.5 min or by the individual administered tracer activity, respectively. The total 18F-GE-180 distribution volume (VT) was estimated in 12 regions-of-interest (ROIs) by the invasive Logan plot using the measured or the population-based input functions. Late ROI-to-whole-blood and ROI-to-cerebellum ratio were also computed. Results Correlation with the reference VT (with individually measured input function) was very high for VT with the population-based input function scaled with the blood sample and for the ROI-to-whole-blood ratio (Pearson correlation coefficient = 0.989 ± 0.006 and 0.970 ± 0.005). The correlation was only moderate for VT with the population-based input function scaled with tracer activity dose and for the ROI-to-cerebellum ratio (0.653 ± 0.074 and 0.384 ± 0.177). Reference VT, population-based VT with scaling by the blood sample, and ROI-to-whole-blood ratio were sensitive to the TSPO gene polymorphism. Population-based VT with scaling to the administered tracer activity and the ROI-to-cerebellum ratio failed to detect a polymorphism effect. Conclusion These results support the use of a population-based input function scaled with a single blood sample or the ROI-to-whole-blood ratio at a late time point for simplified quantitative analysis of 18F-GE-180 PET.