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The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provide a detailed account on the 2020 instantiation of the SAT Competition, including the new competition tracks and benchmark selection procedures, overview of solving strategies implemented in top-performing solvers, and a detailed analysis of the empirical data obtained from running the competition. (C) 2021 The Authors. Published by Elsevier B.V.

In this paper, we explain the connection between information processing by complex systems and recurrent activity sequences in their dynamics. We argue that an understanding of information processing pathways in terms of these dynamical motifs is important for designing effective interventions. We then describe a recently completed study, where we video recorded 37 shared book reading (SBR) sessions, and thereafter annotated each of these sessions for 26 activities (reading the book, comments and questions, management talk by the teacher, and responses from the children). For all SBR sessions, the annotations consisted of sequences of one activity followed by another (transitions). We tested the empirical data against a null model where activities occur randomly, to identify 34 transitions that occur more frequently than by chance, and visualize these transitions that are statistically significant at the confidence level of p < 10−3 in the form of a static network. We then chose six significant transitions, and tested their extensions against the same null model to identify statistically significant length-3 sequences. This extension procedure was repeated to obtain length-4, length-5, and longer sequences until no further statistically significant extensions can be found. Finally, we organized the longest significant sequences into five families of dynamical motifs, and discuss their implications on the effectiveness of SBR.

Background:\ud False start analysis is the examination of incomplete saw marks created on bone in an effort to establish information on the saw that created them. The present study aims to use quantitative data from micro-CT cross-sections to predict the thickness of the saw blade used to create the mark. Random forest statistical models are utilised for prediction to present a methodology that is useful to both forensic researchers and practitioners.\ud \ud Method:\ud 340 false starts were created on 32 fleshed cadaveric leg bones by 38 saws of various classes. False starts were micro-CT scanned and seven measurements taken digitally. A regression random forest model was produced from the measurement data of all saws to predict the saw blade thickness from false starts with an unknown class. A further model was created, consisting of three random forests, to predict the saw blade thickness when the class of the saw is known. The predictive capability of the models was tested using a second sample of data, consisting of measurements taken from a further 17 false starts created randomly selected saws from the 38 in the experiment.\ud \ud Results:\ud Random forest models were able to accurately predict up to 100% of saw blade thicknesses for both samples of false starts.\ud \ud Conclusion:\ud This study demonstrates the applicability of random forest statistical regression models for reliable prediction of saw blade thicknesses from false start data. The methodology proposed enables prediction of saw blade thickness from empirical data and offers a significant step towards reduced subjectivity and database formation in false start analysis. Application of this methodology to false start analysis, with a more complete database, will allow complementary results to current analysis techniques to provide more information on the saw used in dismemberment casework.

In the design of Battery Management Systems (BMS) for a lithium-ion cell, it is crucial to accurately simulate the device in real time using mathematical models. Often, Equivalent Circuit Models (ECM) are used to this end, due to their simplicity and efficiency. However, they are purely phenomenological (their parameters are fitted to emulate empirical data) and their internal variables lack physical meaning. On the other hand, the most popular physics-based electrochemical model in the literature, the pseudo-two-dimensional (P2D) model, presents a high computational cost. In this paper, we review the single particle model (SPM), a physics-based model of reduced complexity that is suitable for real-time applications.

To support the process of identifying and scoping data-driven innovation, we are introducing the data-driven innovation (DDI) framework, which provides guidance in the continuous analysis of factors influencing the demand and supply sides of a data-driven innovation. The DDI framework describes all relevant aspects of any generic data-driven innovation and is backed by empirical data and scientific research encompassing a state-of-the-art analysis, an ontology describing the central dimensions of data-driven innovation, as well as a quantitative and representative research study covering more than 90 data-driven innovations. This chapter builds upon a short analysis of the nature of data-driven innovation and provides insights into how to best screen it. It details the four phases of the empirical DDI research study and discusses central findings related to trends, frequencies and distributions along the main dimensions of the DDI framework that could be derived by percentage-frequency analysis.

Behavioural synchrony among individuals is essential for group-living organisms. The functioning of synchronization in a multilevel society, which is a nested assemblage of multiple social levels between many individuals, remains largely unknown. The aim of the present study was to build a model that explained the synchronization of activity in a multilevel society of feral horses. Multi-agent-based models were used based on four hypotheses: A) horses do not synchronize, B) horses synchronize with any individual in any unit, C) horses synchronize only within units, and D) horses synchronize across and within units, but internal synchronization is stronger. The empirical data obtained from drone observations best supported hypothesis D. This result suggests that animals in a multilevel society coordinate with other conspecifics not only within a unit but also at an inter-unit level. In this case, inter-individual distances are much longer than those in most previous models which only considered local interaction within a few body lengths., 重層社会における群れを超えた休息行動の同期 --ドローンを用いた野生ウマ集団の行動分析--. 京都大学プレスリリース. 2021-10-27.

Smart mobility relates to the ability of integrating technology into transportation. When considering the transition to a greener mobility paradigm, several key elements of smart and sustainable dimensions have to be addressed. To allow outlining more efficient transportation solutions, current exploratory research presents and discusses a dedicated prospective conceptual framework. In this research, the discussion is supported by empirical data collected via a dedicated exploratory survey. The prospective framework structure and dimensions were approached in order to derive the focus from mere technical features, but also considering the final users’ perspectives and needs, so that its acceptability can be more effectively promoted. The main findings of this study are related to the greening effect of smart mobility, which allowed foreseeing increased benefits from integrating smart and sustainable solutions to improve mobility.

Small-to-medium scale enterprises (SMEs) in India involve many manufacturing operations which involves emission of air pollutants, gases with foul odor, etc. inside the workplace which results in an unhealthy environment. This in turn affects the workers’ health on the long term. There are certain industry grade air purifiers out there in the market. But, they are not economical and not smart enough to meet day-to-day operational requirements. So this article aims to present the concept, design and analysis of the prototype of a smart centralized air purifier system. The simulations are carried out in Ansys Fluent software to substantiate our model and empirical data. The proposed centralized air purifier is able to eliminate air pollutants of 10 microns and above from the polluted air in the industrial environments and it operates on the concepts of connected devices. Thus an economical, smart and healthy air purifier is developed to suit industrial applications.

The objective of this research was to study the components and indicators of problem-solving skills in robot programming activities for high school students. This is done by analyzing the second order of confirmatory factor analysis (CFA) based on data from the behavioral assessment with regard to the robot programming activities of 320 students from specialized science schools. The results of the research revealed that the problem-solving skills in robot programming activities had five components and 15 indicators. All the components were tested for consistency using CFA statistics with the support of R-Studio program. The model analysis results were found to be consistent with empirical data with Chi-Square = 98.273, df = 80.000, p-value = 0.081, GFI = 0.961, NFI (TLI) = 0.924, CFI = 0.985, RMSEA = 0.027, RMR = 0.007. This indicates that all the identified components and indicators are involved in problem-solving skills in the robot programming activities of high school students.

Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, being aggregate signals that lack cellular resolution, these signals are not easy to interpret directly in terms of neural functions. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important both for neuroscience and clinical applications. Over the years we have developed tools based on the combination of neural network modelling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. The purpose of this paper, which accompanies an Invited Plenary Lecture in this conference, is to review the main tools that we have developed to estimate neural parameters from mass signals, and to outline future challenges and directions for developing computational tools to invert aggregate neural signals in terms of neural circuit parameters.

Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram (MFD) provides an efficient description of network-wide traffic performance. In this paper, we show how temporal patterns of vehicle traffic define the performance of urban road networks. We present two high-resolution traffic datasets covering a year each. We introduce a methodology to quantify the similarity of macroscopic traffic patterns. We do so by using the concepts of the MFD and a dynamic time warping (DTW) based algorithm for time series. This allows us to derive a few representative MFD clusters that capture the essential macroscopic traffic patterns. We then provide an in-depth analysis of traffic heterogeneity in the network which is indicative of the previously found clusters. Thereupon, we define a parsimonious classification approach to predict the expected MFD clusters early in the morning with high accuracy.

Periodic-Frequent pattern mining is an empirical data mining technique that is used to investigate the customer purchase behavior pattern in the retail market. It provides the most extreme advantages to a retailer for product placements, product segmentation, store layout, selling strategies and different merchandising decisions to improve customer satisfaction as well as increase the sales. Traditionally, the single minimum support based frequent pattern mining approaches missed many lower supported items for high support threshold value, and on the other sites, the number of frequent patterns explode for low support threshold value. The retail market is highly seasonal, a number of items those are not periodic-frequent at all but those are periodic and frequent at a specific season, as results a huge number of seasonal periodic-frequent itemsets are remains out of reach. To resolve this problem, we propose an approach for extracting seasonally periodic-frequent patterns with user specified minimum support and maximum periodicity thresholds on multilevel retail supermarket dataset in this paper. The dataset has been created by collecting transaction slips in a Supermarket of Bangladesh. The results of our proposed approach extract many hidden seasonal patterns in transactional database for easy merchandising decisions and it shows the superiority over the traditional systems.

An important purpose of science is building and advancing general theories from empirical data. This process is complicated by the immense volume of empirical data and scientific theories in some fields. Particularly, the systematic linking of empirical data with theoretical constructs is currently lacking. Within this article, we propose a prototypical solution (i.e., a metamodel and graph database) for consolidating scientific knowledge by linking theoretical constructs with empirical data. We conducted a case study within the field of health behavior change where the system is used to record three scientific theories and three empirical studies as well as their mutual links. Finally, we demonstrate how the system can be queried to accumulate knowledge.

The central theme for this chapter is to highlight the importance of learning design for blended learning. This means anchoring blended learning on established learning approaches substantiated by theories, principles, and empirical data. Also important is the blending of various components related to learning, and how to achieve effective blending. Thus, we propose a blended learning design involving three major components: (1) design considerations from a learning perspective, (2) considerations of different dimensions of blended learning and (3) integrating different components for effective learning. A case example of blended knowledge building strategy was provided as an illustration of this design approach. We further suggest that this approach is transferable to other learning approaches, such as flipped classroom approach.

Multinational companies deal with production processes in various countries by operating global production networks. These production processes are allocated to production plants with different levels of autonomy regarding strategic and operative decisions. Typically, each plant and the whole network are managed by one or more network managers who have to deal with a decision overload in their daily business. 50% of their decisions are made in less than 9 minutes and only a small amount of decision tasks are dealt with for more than one hour. To reduce this dilemma, it was found that the distribution of decision autonomy can be enhanced. It depends on the company’s strategy and complexity dimensions in global production networks. However, so far there is little evidence on how to better distribute decision autonomy in global production networks in detail. Furthermore, it is not transparent at what level of cetralism a global production network should be managed without cutting the capabilities of production plants. This paper presents a methodology, which examines relevant strategy dimensions and derives guidance on how to distribute decisions in global production networks. First, the network and production strategies of global production networks are classified. Second, relevant complexity dimensions and decisions are introduced. Third, the influence of the distribution of decision autonomy on strategy dimensions is quantified by an impact model. Furthermore, the effect of complexity on the distribution of decision autonomy is quantified by an impact model. Here, the integration of empirical data was used to validate the different influences. Finally, the ideal distribution of decision autonomy for specific production plants in the global production network is derived. The methodology is applied in an industrial use case to prove its practical impact.

This research purposes to develop a structural equation model of enhancing online customer. The samples of this study consisted of 451 respondents who have purchased online room reservation via the website by convenient sampling method. The research instrument is a questionnaire. The data are analyzed by descriptive statistics and confirmatory factor analysis with AMOS. The result is the structural model of the confirmatory factor analysis of enhancing online customer engagement in online room reservation with empirical data Chi-Square/Df = .954, CFI = 1.000, RMSEA = .000, RMR = .006, and TLI = 1.

Crowdfunding has become an interesting and widely accepted alternative to finance start-ups and projects. This paper presents an introductory motivation by making transparent recent developments in crowdfunding; furthermore, it provides insights into the decision-making of the involved crowdfunding actors in their triadic relationship. Based on empirical data collected from five in-depth expert interviews we identified several factors, which influence the behavioral decision process of an entrepreneur when making a choice on a crowdfunding platform for their crowdfunding campaign. On the example of two of those identified factors we formalize the entrepreneur’s decision process using Shannon Entropy and give a numerical example. Results are interpreted and directions for further research are given.

In the experiments of comparative visual search reported here, each half of a display contains simple geometrical objects of three different colors and forms. The two hemifields are identical except for one mismatch either in color or form. The subject’s task is to find this difference. Eye-movement recording yields insight into the interaction of mental processes involved in the completion of this demanding task. We present a hierarchical model of comparative visual search and its implementation as a computer simulation. The evaluation of simulation data shows that this Three-Level Model is able to explain about 98% of the empirical data collected in six different experiments.

Researchers have proposed that contagion processes govern how information and behavior itself spreads through social networks. Empirical evidence for such contagion often makes unjustified, but implicit assumptions about the mechanisms underlying contagion. Here, we present an approximate Bayesian method that uses empirical data to draw inferences about the underlying mechanisms. We provide initial validation of our approach in three simulation experiments, each investigating how a real-world factor (e.g., noise) impacts inferential accuracy.

In order to improve the level of College Students' employment management, this paper puts forward an evaluation model of College Students' Employment Quality Based on AHP. First of all, this paper analyzes the evaluation index system of College Students' employment quality, and uses AHP to evaluate the employment quality of college students. The empirical data analysis results show that the accurate evaluation of the employment quality of college students can be achieved by using the analytic hierarchy process to evaluate the employment quality of College Students.

Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here.Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data.Conclusions:AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.

Information-theoretic quantities have found wide applications in understanding interactions in complex systems primarily due to their non-parametric nature and ability to capture non-linear relationships. Increasingly popular among these tools is conditional transfer entropy, also known as causation entropy. In the present work, we leverage this tool to study the interaction among car drivers for the first time. Specifically, we investigate whether a driver responds to its immediate front and its immediate rear car to the same extent and whether we can separately quantify these responses. Using empirical data, we learn about the important features related to human driving behavior. Results demonstrate the evidence that drivers respond to both front and rear cars, and the response to their immediate front car increases in the presence of jammed traffic. Our approach provides a data-driven perspective to study interactions and is expected to aid in analyzing traffic dynamics.

The modeling of walking behavior and design of walk-friendly urban pathways have been of interest to many researchers over the past decades. One of the major issues in pedestrian modeling is path planning decision-making in a dynamic walking environment with different pedestrian flows. While previous studies have agreed that pedestrian flow influences path planning, only a few studies have dealt with the empirical data to show the relationship between pedestrian flow and path planning behavior. This study introduces a new methodology for analyzing pedestrian trajectory data to find the dynamic walking conditions that influence the path planning decision. The comparison of the pedestrians&rsquo, path shows that the higher proportion of opposite flows are, the greater they influence the path selection decision. In this study, we investigate the relationship between the opposite flow changes and path planning behavior and find the spatial and temporal ranges of the opposite flow that affects the path planning behavior. Lastly, we find the ratio of pedestrians that update their paths with respect to the opposite flow rate.

Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002–2011), for which conventional analyses severely limit the quality of inference.

A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream (high efficacy), a procedure currently achieved by labor-intensive trial-and-error. Targeted perturbations will be greatly facilitated by understanding causal interactions within neural ensembles and predicting the efficacy of perturbation sites before intervention. Here, we address this issue by developing a computational framework to predict how single-site micorstimulation alters the ensemble spiking activity in an alert monkey’s prefrontal cortex. Our framework uses delay embedding techniques to infer the ensemble’s functional causal flow (FCF) based on the functional interactions inferred at rest. We validate FCF using ground truth data from models of cortical circuits, showing that FCF is robust to noise and can be inferred from brief recordings of even a small fraction of neurons in the circuit. A detailed comparison of FCF with several alternative methods, including Granger causality and transfer entropy, highlighted the advantages of FCF in predicting perturbation effects on empirical data. Our results provide the foundation for using targeted circuit manipulations to develop targeted interventions suitable for brain-machine interfaces and ameliorating cognitive dysfunctions in the human brain.

The new standard IEEE 802.11ah is ideal targeting large-scale and dense deployments of IoT and M2M solutions due to three specific characteristics: the low operating frequency allowing coverage up to 1km, the ability to simultaneously connect a large number of devices per access point, and low power consumption. Several studies on the characteristics of radio propagation in the bands used by the IEEE standard are being carried out worldwide. In this paper, we evaluate two urban path loss models and four specific path loss models for vegetated environments, based on a measurement campaign performed in an urban environment with a strong presence of vegetation, typical of living areas, squares and urban parks where IoT technologies should be commonly implemented in the coming years. The models proposed by TGah were also evaluated. Based on empirical data, the most accurate models are determined. The characterization of large-scale fading and small-scale fading are presented.

Computer-driven vehicles will behave differently from human-driven vehicles due to changes in perception abilities, precision control, and reaction times. These changes are expected to have profound impacts on capacity, yet few models of automated driving are based on empirical measurements of computer-driven vehicles in real traffic. To this end, this paper investigates characteristics of an early form of longitudinal control automation, a commercially available adaptive cruise control (ACC) system driven in real traffic. Two car-following models were calibrated to a vehicle with ACC. First, the Intelligent Driver Model was reformulated to comply with ACC design standards then calibrated to match speed and range data from the test vehicle. The vehicle with ACC was found to decelerate less severely than predicted by the model when tested in severe braking and unimpeded acceleration scenarios. Second, the Wiedemann 99 model was calibrated because it is the default car-following model in the traffic microsimulation software program Vissim and can therefore be implemented cheaply and quickly in sophisticated models of roadways worldwide. Four parameters of the Wiedemann 99 model were measured directly from field observations of the test vehicle: standstill distance, start-up time, unimpeded acceleration profile, and maximum desired deceleration. Simulation results in Vissim were found to match the adaptive cruise control in unimpeded acceleration tests. These findings will benefit researchers and modelers seeking more accurate models of car-following behavior with adaptive cruise control and automated longitudinal control.

Many real-world social networks constantly change their global properties over time, such as the number of edges, size and density. While temporal and local properties of social networks have been extensively studied, the origin of their dynamical nature is not yet well understood. Networks may grow or shrink if a) the total population of nodes changes and/or b) the chance of two nodes being connected varies over time. Here, we develop a method that allows us to classify the source of time-varying nature of temporal networks. In doing so, we first show empirical evidence that real-world dynamical systems could be categorized into two classes, the difference of which is characterized by the way the number of edges grows with the number of active nodes, i.e., densification scaling. We develop a dynamic hidden-variable model to formally characterize the two dynamical classes. The model is fitted to the empirical data to identify whether the origin of scaling comes from a changing population in the system or shifts in the connecting probabilities., Comment: 12 pages, 6 figures (plus 7 figures in SI)

Benefits assessment of investments in research projects is increasingly becoming important to funding agencies and stakeholders in order to demonstrate the effectiveness and benefits of funded projects and to provide guidance for future project appraisal and selection. This paper describes the development of a multi-criteria benefits assessment framework that is based on systems approach and developed using empirical data from rural road improvement projects funded by the UKAid. The framework is a scoring system where the outputs are presented as a report card or scorecard with scores and ratings assigned to the various subsystems. The scores represent the levels of benefits derived from conducting the research and implementation of products generated thereof. The framework is flexible enough to estimate the benefits for each subsystem independent of the other subsystems. The system can also be used as a decision support tool, providing quantitative information to validate funding and management decisions.

It is expected that the prototypes of unmanned merchant ships will be deployed in the next few years. However, there is no specific research on whether the introduction of unmanned ships will reduce the risk of ship collision accidents in which communication between vessels is critical. This work constitutes an attempt to bridge the gap identified above by applying the Hybrid Causal Logic (HCL) methodology to model general-level collision scenarios of unmanned ships. The HCL methodology has been selected for its proven applicability to risk assessments, even when empirical data may be insufficient. Collision scenarios involving unmanned ships have been created in which manned ships of the conventional collision scenario HCL model are replaced with unmanned ships. Then, collision scenarios capturing the interactions between a manned ship and an unmanned ship were modeled. By comparing the qualitative and quantitative results of the different scenarios, we can see that the introduction of unmanned ships may effectively reduce the occurrence of ship collision accidents.

This paper discusses the outstanding problem of replicability of empirical data in the context of recent work on meta-analysis, especially within the field of evidence-based medicine. Specifically, it deals with the methodological issue of how to determine the degrees of heterogeneity between different collected studies. After critically reviewing the standard measures used to quantify meta-analytical heterogeneity, we argue that they should be revised in such a way to take into account the statistical power of the individual studies. We thus propose some new measures of heterogeneity. Subsequently, we apply them to re-assess concrete case-studies from clinical research, thereby showing explicitly how the relevant values of heterogeneity diverge from those obtained with the original measures.

Software Product Lines (SPLs) are rarely developed from scratch. Commonly, they emerge from monolithic architectures when there is a need to create tailored variants, or from existing variants created in an ad-hoc way once their separated maintenance and evolution become challenging. Despite the vast literature about re-engineering systems into SPLs and related technical approaches, there is a lack of detailed analysis about the process itself and the effort that is involved. We provide and analyze empirical data of an existing SPL extraction process: the ArgoUML monolithic architecture transition to ArgoUML-SPL. The analysis relies on information mined from the version control history of the source-code repository and the discussion with developers that took part in the process. The contribution of this study is an in-depth characterization of the process compared to previous works that focused only on the structural results of the final SPL. We made publicly available the dataset and the analysis scripts to be used as baseline for extractive SPL adoption research and practice.

The design of while loops is among the hardest lessons for beginners in programming. Their design is difficult because students must do more than solve a problem. They must also, for example, correctly sequence mutations, define loop invariants, and avoid infinite loops. Another difficulty students face is making while loops iterative. This issue is commonly ignored in programming textbooks for beginners despite being relevant to future courses such as Computer Architecture, Programming Languages, and Algorithms. The difficulty with making while loops iterative revolves around the elimination of delayed operations. This requires introducing beginners to the concept of control. This article presents a novel transformation recipe for beginners to make while loops iterative. The transformation recipe guides students on how to accumulate control information on a stack to eliminate delayed operations in the body of loops. In addition, this article presents descriptive statistics obtained from students on the maiden voyage of the new design recipe. The empirical data suggests that students feel that writing iterative while loops is hard, but enlightening. In addition, the empirical data suggests that students have a very favorable opinion about designing while loops.

Structure-function relationships are fundamental to studies of neural systems, yet the mechanistic underpinnings of how structural networks shape hemispheric lateralization remain elusive. For instance, the asymmetric neuroanatomic embedding of primary auditory cortices was shown when connectivity with all other brain areas were considered. Concomitantly, functional lateralization e.g., left hemispheric dominance of speech processing and right hemispheric dominance of music perception, is widely acknowledged. The present article provides a parsimonious mechanistic explanation based on computational modelling and empirical recordings to demonstrate emergence of hemispheric lateralization of brain function. For validation of the model, empirical EEG recordings of auditory steady state responses (ASSR) were undertaken, and empirical findings suggest right hemispheric dominance at the level of cortical sources in binaural and monaural hearing conditions. Subsequently, we demonstrate the entrainment and phase of oscillations in connected brain regions based on a neurodynamic model constrained by empirically derived structural connectivity matrix from diffusion data. For relevance, we have taken into consideration time-delays in neural communication stemming from fibre distances and neuronal coupling arising from fibre densities. Based on relevant network parameters, we could demonstrate the emergence of right hemispheric dominance of ASSR in binaural and monaural hearing conditions when auditory cortical areas were chosen as triggers of entrained phase oscillators. Furthermore, we discovered emergence of left-lateralized functional response when bilateral Broca’s area replaced auditory areas as triggers. Hence, a single unified mechanism based on entrainment of phase oscillators in a large-scale brain network could explain both emergence of right and left hemispheric laterality.Significance statementThe origin of hemispheric specialization of sensory processing is a fundamental question in neuroscience. For instance, speech and language are predominantly processed in the left hemispheric regions, while the right hemisphere is specialized for processing rhythmic, tonal, and melodic stimuli. Identification of the network mechanisms that give rise to such functional lateralization from structural constraints remains elusive. In the present study, we simulate neural activity observed during human EEG recordings of auditory steady-state responses from a biophysically realistic large-scale model constrained by underlying structural connectivity. Subsequently, we demonstrate how hemispheric lateralization of brain responses to sensory stimuli emerge from the time-delayed interactions among whole-brain neuronal ensembles.

SummaryChromosome number is a central feature of eukaryote genomes. Deciphering patterns of chromosome-number change along a phylogeny is central to the inference of whole genome duplications and ancestral chromosome numbers. ChromEvol is a probabilistic inference tool that allows the evaluation of several models of chromosome-number evolution and their fit to the data. However, fitting a model does not necessarily mean that the model describes the empirical data adequately. This vulnerability may lead to incorrect conclusions when model assumptions are not met by real data.Here, we present a model adequacy test for likelihood models of chromosome-number evolution. The procedure allows to determine whether the model can generate data with similar characteristics as those found in the observed ones.We demonstrate that using inadequate models can lead to inflated errors in several inference tasks. Applying the developed method to 200 angiosperm genera, we find that in many of these, the best-fitted model provides poor fit to the data. The inadequacy rate increases in large clades or in those in which hybridizations are present.The developed model adequacy test can help researchers to identify phylogenies whose underlying evolutionary patterns deviate substantially from current modelling assumptions and should guide future methods developments.

In scientific research, evidence is often based on empirical data. Scholars tend to rely on students as participants in experiments in order to validate their thesis. They are an obvious choice when it comes to scientific research: They are usually willing to participate and are often themselves pursuing an education in the experiment&rsquo, s domain. The software engineering domain is no exception. However, readers, authors, and reviewers do sometimes question the validity of experimental data that is gathered in controlled experiments from students. This is why we will address this difficult-to-answer question: Are students a proper substitute for experienced professional engineers while performing experiments in a typical software engineering experiment. As we demonstrate in this paper, it is not a &ldquo, yes or no&rdquo, answer. In some aspects, students were not outperformed by professionals, but in others, students would not only give different answers compared to professionals, but their answers would also diverge. In this paper we will show and analyze the results of a controlled experiment in the source code quality domain in terms of comparing student and professional responses. We will show that authors have to be careful when employing students in experiments, especially when complex and advanced domains are addressed. However, they may be a proper substitution in cases, where non-advanced aspects are required.

The operational process of high-tech industry can be separated into a research and development stage (RDS) and a commercialization stage (CS). Within this, the research employees are shared by both stages, and part of the economic output of the CS becomes a feedback factor and continuously flows back to the RDS. Using this framework, this study establishes cooperative and non-cooperative two-stage data envelopment analysis (DEA) models to explore the efficiencies of regional high-tech industries in China. The proposed approach can calculate the overall efficiency and stage efficiencies simultaneously. Based on empirical data of high-tech industries in 29 regions of China from 2012 to 2016, it is concluded that (1) a harmony exists between the RDS and the CS in the cooperative case, while a disharmony happens between the RDS and CS in the non-cooperative case; (2) there exist distinct geographic characteristics regarding the stage inefficiencies of these regional high-tech industries.

In this paper, I argue that a number of elements in Danish have postpositional properties and could be regarded as postpositions. Since the particle construction and the postpositional construction have the same word order (NP < particle/postposition), it is necessary to distinguish between the two constructions. For this purpose, I introduce four syntactic tests. I examine the empirical data and argue that the postpositional elements come in two shapes: they either express (i) directionality (goal and source) or (ii) temporal and physical spaces.

Environmental conditions in buildings are linked to the physical and mental wellbeing of occupants. Thus, it follows that the internal environment affects human performance and user experience during sport and activity. There are several indices that are used to evaluate occupant thermal comfort, the Predicted Mean Vote (PMV) index being the metric most commonly used. PMV is designed to evaluate comfort for sedentary occupants with low metabolic rates; however, PMV has also been used to evaluate comfort for individuals engaged in high metabolic rate activities, such as those common in sport facilities. This paper investigates the implication of using PMV to evaluate thermal comfort in sport facilities using empirical data recorded over 24 months in a multi-purpose sports hall in the North of England. Data are used to develop and propose methodological modifications to improve the standard PMV model prediction to account for occupants having higher metabolic rates. The paper evaluates the use of metabolic rate data from different sources including the Compendium of Physical Activities and quantifies the impact that the metabolic weighting approach has on predicted comfort. Finally, a novel method is proposed to modify PMV for use where occupants have high metabolic rates. Despite the improvements made, the findings suggest that even a modified PMV may not be able to accurately evaluate the thermal comfort of people engaged in non-sedentary activity, recommending that use of the PMV index is restricted to activities with metabolic rates

Understanding the adoption and usage of any programming language feature is crucial for improving it. Existing studies indicate that Java annotations are widely used by developers. However, there is currently no empirical data on annotation usage in Android apps. Android apps are often smaller than general Java applications and typically use Android APIs or specific libraries catered to the mobile environment. Therefore, it is not clear if the results of existing Java studies hold for Android apps. In this paper, we investigate annotation practices in Android apps through an empirical study of 1,141 open-source apps. Using previously studied metrics, we first compare annotation usage in Android apps to existing results from general Java applications. Then, for the first time, we study why developers declare custom annotations. Our results show that the density of annotations and the values of various other annotation metrics are notably less in Android apps than in Java projects. Additionally, the types of annotations used in Android apps are different than those in Java, with many Android-specific annotations. These results imply that researchers may need to distinguish mobile apps while performing studies on programming language features. However, we also found examples of extreme usage of annotations with, for example, a large number of attributes, as well as a low adoption rate for most annotations. By looking at such results, annotation designers can assess adoption patterns and take various improvement measures, such as modularizing their offered annotations or cleaning up unused ones. Finally, we find that developers declare custom annotations in different apps but with the same purpose, which presents an opportunity for annotation designers to create new annotations.

The stop-signal paradigm is a popular procedure to investigate response inhibition—the ability to stop ongoing responses. It consists of a choice response time (RT) task that is occasionally interrupted by a stop stimulus signaling participants to withhold their response. Performance in the stop-signal paradigm is often formalized as race between a set of go runners triggered by the choice stimulus and a stop runner triggered by the stop signal. We investigated whether evidence-accumulation processes, which have been widely used in choice RT analysis, can serve as the runners in the stop-signal race model and support the estimation of psychologically meaningful parameters. We examined two types of the evidence-accumulation architectures: the racing Wald model (Logan et al. 2014) and a novel proposal based on the lognormal race (Heathcote and Love 2012). Using a series of simulation studies and fits to empirical data, we found that these models are not measurement models in the sense that the data-generating parameters cannot be recovered in realistic experimental designs.

Statistical AI is cutting-edge technology in the present landscape of AI research whilst Symbolic AI is generally regarded as good old-fashioned AI. Even so, we contend that induction, i.e., learning from empirical data, cannot constitute a full-fledged form of intelligence on its own, and it is necessary to combine it with deduction, i.e., reasoning on theoretical grounds, in order to achieve the ultimate goal of Strong AI or Artificial General Intelligence. We therefore think of the possibility of integrating Symbolic and Statistical AI, and discuss Quantum Linguistics by Bob Coecke et al., which, arguably, may be seen as the categorical integration of Symbolic and Statistical AI, and as a paradigmatic case of Integrated AI in Natural Language Processing. And we apply it to cognitive bias problems in the Kahneman-Tversky tradition, giving a novel account of them from the standpoints of Symbolic/Statistical/Integrated AI, and thus elucidating the nature of machine biases in them.

The brain shows a complex multiscale organization that prevents a direct understanding of how structure, function and dynamics are correlated. To date, advances in neural modeling offer a unique opportunity for simulating global brain dynamics by embedding empirical data on different scales in a mathematical framework. The Virtual Brain (TVB) is an advanced data-driven model allowing to simulate brain dynamics starting from individual subjects' structural and functional connectivity obtained, for example, from magnetic resonance imaging (MRI). The use of TVB has been limited so far to cerebral connectivity but here, for the first time, we have introduced cerebellar nodes and interconnecting tracts to demonstrate the impact of cerebro-cerebellar loops on brain dynamics. Indeed, the matching between the empirical and simulated functional connectome was significantly improved when including the cerebro-cerebellar loops. This positive result should be considered as a first step, since issues remain open about the best strategy to reconstruct effective structural connectivity and the nature of the neural mass or mean-field models generating local activity in the nodes. For example, signal processing is known to differ remarkably between cortical and cerebellar microcircuits. Tackling these challenges is expected to further improve the predictive power of functional brain activity simulations, using TVB or other similar tools, in explaining not just global brain dynamics but also the role of cerebellum in determining brain states in physiological conditions and in the numerous pathologies affecting the cerebro-cerebellar loops.

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. For example, it is well known that the social structure of animal populations can shape spreading dynamics, but it remains underexplored to what extent such dynamics are determined by the underlying habitat configuration. To address this gap, we propose a framework and model inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animals’ habitats, and highlight the need for a bottom-up generative model that can depict realistic variations in habitat structural connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we use our framework to demonstrate how transmission properties, such as the spread of a pathogen, can be impacted by both local connectivity and landscape-level characteristics of the habitat. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

This paper emphasizes on the possibility of merging Artificial Intelligence and Blockchain technologies to solve academic qualifications forgery issues in the educational sectors. Empirical data is collected through interviews with many specialist and technical people who is interested in the emerging technologies of the Fourth Industrial Revolution and focused group discussion in the field, as well as from reports in the reviewed literary articles. Scientific journals have also been accessed to analyze the paper goals and objectives. The findings are based on the conclusion suggest that emerging technologies can be integrated to become more efficient and effective in detecting fraud and forgery before it occurs. Considerable attention should be given to reducing and combating these issues because they have significant negative impacts on the economy and education. Accordingly, the study makes recommendations based on the results and areas of future research, considering the establishment of a unified and integrated system. Initially will be applied as a pilot in Sultanate of Oman, then gradually will be extended to the Gulf Cooperation Council States (GCC) and internationally particularly the affiliated and the recognized educational institutions to avoid the phenomena that affect the reputation and quality of education institutions and academic qualifications. In the conclusion considering the impacts of the proposed system in the education and economy as well in general. This research aims to investigate academic forgery cases in the world and then focuses in Oman. Furthermore, it explores the possibilities of merging technologies (Artificial Intelligence and Blockchain) to contribute in eliminating fraud before it occurs. In addition, to propose a framework that will help to find solutions based on the suggested integrated technologies. Finally, it assess the probabilities of the proposed solutions performance and its impacts in the academic sector.

MotivationPhylogeny inference via maximum likelihood is NP-hard. Current methods make simplifying assumptions such as stationarity, homogeneity, and time-reversibility for computational ease. The stationarity assumption is violated by empirical observations of GC content evolution, and might systematically bias phylogeny inference. The general Markov model (GM) is a suitable alternative to stationary models because it allows for the evolution of GC content. Related work on the GM model has predominantly focused on inferring unrooted trees using either the log-det distance or phylogenetic invariants.MethodsWe adapted the structural EM framework to perform tree search under the GM model (SEM-GM). Additionally, we implemented a minimum spanning tree framework called MST-backbone to improve the scalability of SEM-GM by constraining search through tree space. MST-backbone(SEM-GM) was used to infer unrooted trees, which are subsequently rooted under the GM model; the latter procedure is called rSEM-GM. We compared our method with RAxML-NG, IQ-TREE, and FastTree on simulated data. We validated our methods on six empirical datasets.ResultsEstimated experimental phylogenies are rooted with high accuracy under the GM model (recall ranging from 80% to 94%). However, virus phylogenies are not realistically rooted, suggesting that the GM model may be overtrained on some empirical datasets. The comparative analysis of simulated data suggests that MST-backbone(SEM-GM) and FastTree scale linearly whereas rSEM-GM, RAxML-NG, and IQ-TREE scale quadratically. The results on empirical data suggest that it is not necessary to use the general time-reversible model for computational ease.Availabilityhttps://github.com/prabhavk/mst-backbone-sem-gmContactprabhav.kalaghatgi@molgen.mpg.deSupplementary informationSupplementary data are available online

With the growing number of GPU-based supercomputing platforms and GPU-enabled applications, the ability to accurately model the performance of such applications is becoming increasingly important. Most current performance models for GPU-enabled applications are limited to single node performance. In this work, we propose a methodology for end-to-end performance modeling of distributed GPU applications. Our work strives to create performance models that are both accurate and easily applicable to any distributed GPU application. We combine trace-driven simulation of MPI communication using the TraceR-CODES framework with a profiling-based roofline model for GPU kernels. We make substantial modifications to these models to capture the complex effects of both on-node and off-node networks in today's multi-GPU supercomputers. We validate our model against empirical data from GPU platforms and also vary tunable parameters of our model to observe how they might affect application performance.

Flood risk modelling aims to quantify the probability of flooding and the resulting consequences for exposed elements. The assessment of flood damage is a core task that requires the description of complex flood damage processes including the influences of flooding intensity and vulnerability characteristics. Multi-variable modelling approaches are better suited for this purpose than simple stage-damage functions. However, multi-variable flood vulnerability models also often have problems to predict damage for regions other than those for which they have been developed. A transfer of vulnerability models usually results in a drop of model predictive performance. Here we investigate the question of whether data from the open data source OpenStreetMap is suitable to model flood vulnerability of residential buildings and whether the underlying standardized data model is helpful to transfer models across regions. We develop a new data set by calculating numerical spatial measures for residential building footprint geometries and combine these variables with an empirical data set of observed flood damage. From this data set random forest regression models are learned using regional sub-sets and are tested for predicting flood damage in other regions. This regional split-sample validation approach reveals that the predictive performance of models based on OpenStreetMap data is comparable to alternative multi-variable models, which use comprehensive and detailed information about preparedness, socio-economic status and other aspects of residential building vulnerability. However, our results show that using numerical spatial measures derived from OpenStreetMap building geometries does not resolve all problems of model transfer. Still, we conclude that these variables are useful proxies for flood vulnerability modelling, because these data are consistent, openly accessible, and thus make it easier and more cost-effective to transfer vulnerability models to other regions.

Population genetic analyses often use summary statistics to describe patterns of genetic variation and provide insight into evolutionary processes. Among the most fundamental of these summary statistics areπanddXY, which are used to describe genetic diversity within and between populations, respectively. Here, we address a widespread issue inπanddXYcalculation: systematic bias generated by missing data of various types. Many popular methods for calculatingπanddXYoperate on data encoded in the Variant Call Format (VCF), which condenses genetic data by omitting invariant sites. When calculatingπanddXYusing a VCF, it is often implicitly assumed that missing genotypes (including those at sites not represented in the VCF) are homozygous for the reference allele. Here, we show how this assumption can result in substantial downward bias in estimates ofπanddXYthat is directly proportional to the amount of missing data. We discuss the pervasive nature and importance of this problem in population genetics, and introduce a user-friendly UNIX command line utility,pixy, that solves this problem via an algorithm that generates unbiased estimates ofπanddXYin the face of missing data. We comparepixyto existing methods using both simulated and empirical data, and show thatpixyalone produces unbiased estimates ofπanddXYregardless of the form or amount of missing data. In sum, our software solves a long-standing problem in applied population genetics and highlights the importance of properly accounting for missing data in population genetic analyses.

Problem: developers are increasingly adopting security practices in software projects in response to cyber threats. Despite the additional effort required to perform those practices, current cost models either do not consider security as an input or were not properly validated with empirical data. Hypothesis: increasing degrees of application of security practices and security features, motivated by security risks, lead to growing levels of added software development effort. Such an effort increase can be quantified through a parametric model that takes as input the usage degrees of security practices and requirements and outputs the additional software development effort. Contributions: the accurate prediction of secure software development effort will support the provision of a proper amount of resources to projects. We also expect that the quantification of the security effort will contribute to advance research on the cost-effectiveness of software security.