近年来，脑功能网络的组织促进了人类大脑的理解。为进一步探究人类大脑功能网络的变化，本研究主要使用有效连接方法对比任务前、后静息态fMRI数据变化。区别于以往大脑整体网络的改变，研究使用基于格兰杰因果分析(GCA)的有效连接方法针对ACT-R脑网络内部开展。结果表明：短暂认知任务前后，ACT-R网络模型内部有效连接发生较大改变；在后静息态，程序性模块(Cad)作为一个主要信息接收点接收来自其他模块的因果影响。 Recently, the organization of functional network promotes the understanding of the human brain. To further explore the functional reorganization affected by a short-time cognitive performance in human brain, we used the method of Granger causality analysis (GCA) to compare two resting fMRI data before and after a problem solving task. Distinguished from the view of the brain network as a whole in previous studies, GCA focused on the internal organization within a brain network. The re-sults showed that taking the ACT-R network as an example, the effective connectivity within the ACT-R network significantly changed after the brief cognitive task. In the post-resting state, proce-dural module (Cad) acted as a main information receiver received influence from other modules.

“Economic Growth and Energy Consumption in OECD Countries: A Causality Analysis” by Daria Kostyannikova examines the relationship between economic growth and different measures of energy consumption such as coal, oil, natural gas and total energy consumption in a panel of 21 OECD countries from 1965 to 2011 by using modern timeseries techniques. Toda and Yamamoto procedure is used to determine the direction of causality, and the Bounds test is employed in series integrated of different orders where a Johansen Cointegration test cannot be performed. As a robustness check the same analyses are made but excluding the last seven years of the oil price surge and global financial crisis. Although causality results are not uniform across different countries, the same patterns are found in the US where unidirectional causality runs from economic growth to energy consumption, and no causality is found in UK, Sweden, Denmark and Finland. This paper is a starting point in a further investigation of causal relationship to completely understand it and suggest more specific policies for each country

‘Why be quantitative?’Harold D. Lasswell asked that question several decades ago. His answer was: to take advantage of the rigor and precision that comes with quantification. Since then, quantification has spread across social science disciplines, putting qualitative approaches on the defensive. This paper examines the practices of quantitative sociologists in their study of historical processes. Much ritualism is found in those practices; much rhetoric in quantification. Alas, Lasswell's good intentions seem to have gone lost in a ritual called ‘hypothesis testing.’The author reflects critically upon his own practices and on the forms of quantification and the strategies of explanations that he has adopted.

‘Covert consciousness’ is a state in which consciousness is present without the capacity for behavioural response, and it can occur in patients with intraoperative awareness or unresponsive wakefulness syndrome. To detect and prevent this undesirable state, it is critical to develop a reliable neurobiological assessment of an individual's level of consciousness that is independent of behaviour. One such approach that shows potential is measuring surrogates of cortical communication in the brain using electroencephalography (EEG). EEG is practicable in clinical application, but involves many fundamental signal processing problems, including signal-to-noise ratio and high dimensional complexity. Symbolic analysis of EEG can mitigate these problems, improving the measurement of brain connectivity and the ability to successfully assess levels of consciousness. In this article, we review the problem of covert consciousness, basic neurobiological principles of consciousness, current methods of measuring brain connectivity and the advantages of symbolic processing, with a focus on symbolic transfer entropy (STE). Finally, we discuss recent advances and clinical applications of STE and other symbolic analyses to assess levels of consciousness.

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.

– Queries as a pioneering measure of public attention on various social issues have elicited considerable scholarly attention. The purpose of this paper is to address two fundamental questions, as follows: first, how do we identify niche queries that internet users search for on specific social issues?; and second, what are the measurement properties of queries data in gauging public attention on social issues? , – The present study uses public attention on environmental issues in the USA as the empirical setting of research. An iterative framework is developed to identify niche queries to measure public attention on environmental issues. The measurement properties of queries data are assessed by comparing the dynamics of public attention on environmental issues captured by queries data with that measured by the “most important problem” (MIP) question in Gallup opinion polls. , – A list of 39 niche queries that internet users search for on environmental issues is identified. The dynamics of public attention on environmental issues determined by the search trends of these 39 queries is found to positively correlate with that measured by Gallup MIP polls, whereas both dynamics can forecast each other well in a 12-month time frame. , – The findings of the study possess methodological and practical implications. The study shows that queries data are complementary to, rather than substitutes of, public opinion polls in measuring public attention on environmental issues. The iterative framework developed in the study can be applied in future studies to help researchers identify valid queries to measure public attention on other social issues, as it can minimize researchers’ subjective biases in selecting search queries. Policymakers and environmentalists can utilize our approach to monitor the status of public attention on environmental issues and implement campaigns to mobilize favorable public opinion when the decline of public attention is predicted by the trends of web search queries.

© 2020 Royal Meteorological Society Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in-depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.

whereK is the (fixed) number of mixture component, π is a vector ofmixing weights, andp(x|k) are the densities for each component. We consider some examples below. 2 Gaussian mixture models Consider the dataset of height and weight in Figure 1. It is cl ear that there are two subpopulations in this data set, and in this case they are easy to interpret: one represents ma le and the other females. Within each class or cluster, the data is fairly well represented by a 2D Gaussian (as can be seen from the fitted ellipses), but to model the data as a whole, we need to use a mixture of Gaussians (MoG)or aGaussian mixture model (GMM). This is defined as follows: p(x|θ) = K

this study investigates the relationship between acoustic patterns in contemporary electroacoustic compositions, and listeners9 real-time perceptions of their structure and affective content. Thirty-two participants varying in musical expertise (nonmusicians, classical musicians, expert computer musicians) continuously rated the affect (arousal and valence) and structure (change in sound) they perceived in four compositions of approximately three minutes duration. Time series analyses tested the hypotheses that sound intensity influences listener perceptions of structure and arousal, and spectral flatness influences perceptions of structure and valence. Results suggest that intensity strongly influences perceived change in sound, and to a lesser extent listener perceptions of arousal. Spectral flatness measures were only weakly related to listener perceptions, and valence was not strongly shaped by either acoustic measure. Differences in response by composition and musical expertise suggest that, particularly with respect to the perception of valence, individual experience (familiarity and liking), and meaningful sound associations mediate perception.

spectral analysis is usedto model seasonally unadjusted quarterly unemployment rate data for Australia, 1978(2) to 2002(3). Data are tested for three categories: persons, males andfemales. Dynamicout-oj-sampleforecasts arc madefor8 quarters usingspectral Ql1alysis models evaluated against ARIMA model counterparts.It is found ala! tile spectral analysis models achievehigherlevels of forecasting accuracytl1an ARIMAcounterparts, including turning pointforecast accuracy. These resuIts emerge in spite afweakerin-sample explanatory power of thespectral models agains! theARIMA models. It is cOllcluded theresults suggest that thespectral model is ultimately betterattuned tothevarious cyclical forces of the past unfolditig. into thefuture.

preview 1 The roles of customers in M&A integration Christina Oberg Linkoping University Department of Management and Engineering SE-581 83 Linkoping Sweden christina.oberg@liu.se Abstract The purpose of this paper is to describe what roles customers play in merger and acquisition (M&A) integration. Based on studies of eight M&As, it is concluded that customers may (i) limit integration intentions, (ii) be reasons for pre-integration reconsiderations, (iii) be used as an argument against integration, (iv) not act according to integration intentions, and (v) actively work against integration. Customers’ actual activities, but also how the M&A parties believe that customers will act, impacts the degree of integration. The paper contributes to research on M&A integration through pointing at how customers impact integration realisation. The paper further contributes to research on M&As according to the network approach through highlighting how an M&A is not merely a response or trigger to change, but an embedded event where actions, beliefs and mutuality impact integration realisation.The purpose of this paper is to describe what roles customers play in merger and acquisition (M&A) integration. Based on studies of eight M&As, it is concluded that customers may (i) limit integration intentions, (ii) be reasons for pre-integration reconsiderations, (iii) be used as an argument against integration, (iv) not act according to integration intentions, and (v) actively work against integration. Customers’ actual activities, but also how the M&A parties believe that customers will act, impacts the degree of integration. The paper contributes to research on M&A integration through pointing at how customers impact integration realisation. The paper further contributes to research on M&As according to the network approach through highlighting how an M&A is not merely a response or trigger to change, but an embedded event where actions, beliefs and mutuality impact integration realisation.

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science CHARACTERIZATION OF ARSENIC RESISTANT BACTERIAL COMMUNITIES IN THE RHIZOSPHERE OF AN ARSENIC HYPERACCUMULATOR Pteris vittata L. By Anhui Huang May 2009 Chair: Lena Q. Ma Cochair: Max Teplitski Major: Soil and Water Science The arsenic (As) hyperaccumulator fern Pteris vittata L. produces large amounts of root exudates, which are hypothesized to solubilize arsenic and maintain a unique rhizosphere microbial community. A group of rhizosphere arsenic resistant bacteria were isolated and identified from two arsenic-contaminated sites where P. vittata growed. Twelve aerobic or facultative anaerobic bacterial isolates (Naxibacter sp. AH4, Mesorhizobium sp. AH5, Methylobacterium sp. AH6, Enterobacter sp. AH10, Pseudomonas sp. AH21, AH34, AH43, AH45, Bacillus sp. AH22, Acinetobacter sp. AH23 and Caryophanon sp. AH28) were resistant to 400 mM arsenic, the highest level of arsenic resistance reported to date. Two levels of arsenic detoxifications were proposed and studied. General resistance mechanisms were investigated by studying microbial growth characteristics under osmotic /oxidative stresses induced by sodium arsenate, sodium chloride, polyethylene glycol 6000 (PEG6000), or hydrogen peroxide. Arsenic specific resistant mechanisms were determined by identifying the two functional arsC or arrA genes based on PCR and Southern Hybridization method. Similar hydrogen peroxide inhibitions with broad-host pathogen Salmonella typhimurium were observed, and bacteria grew better

fMRI studies have suggested that there are two different attention networks: the dorsal attention network (DAN) and the ventral attention network (VAN). The emotion network has also been discovered by some researches. The dorsal attention network controls goal-oriented top-down deployment of attention; the ventral attention network controls stimulus-driven bottom-up deployment of attention; the emotion network will feed back the stimulus especially fearful expressions from the environment. The interaction within these networks has been noticed but few studies have been carried out. The purpose of this study is to explore the interaction within these networks. The regions of interest were acquired by using the GLM analysis after which the granger causality among these ROIs was calculated. Connections among ROIs were considered as causal when their respective granger causality value is greater than the mean value of all granger causalities. According to the results, there is interaction within the three networks, which suggested that the ventral attention network may distract the dorsal attention network and that the emotion network may influence both attention networks.

epilepsy is a neurological disease with the feature of repeated seizures. The probability to predict upcoming seizures is an issue that attracts both researchers and clinicians. Meanwhile, the directed transfer function (DTF) analysis as mechanism for feature extraction in the intracranial electroencephalographic (iEEG) recordings applied to epileptic prediction could reflect the dynamics changes of brain activity before and after seizure onsets. In the present paper, a cortical network calculated by the method of DTF was investigated with the iEEG recordings from a patient undergoing presurgical evaluation. Particularly, the closeness centrality metric was evaluated to identify critical network nodes in the cortical network during both interictal and preictal states of an epileptic seizure. Obtained values of the closeness centrality obviously increased at 20s and 40s preictal in onset 1 and onset 2, respectively. As a conclusion, a significant change of the closeness centrality during preictal states would be a useful indication to early predict an upcoming seizure in patients with epilepsy.

choice. This work generally argues that anxiety affects vote choice indirectly by causing citizens to more thoroughly search for and process political information. This indirect effect of anxiety leads citizens to rely less on heuristics, such as party, and more on substantive information, such as policy positions. The most prominent example of this scholarship is the Affective Intelligence (AI) theory of emotions. In this paper; we use cross-sectional and panel survey data to test AI against two simpler alternatives: (1) that emotions directly influence candidate evaluations and (2) that candidate evaluations directly influence emo tions. Wefirst show that these simpler alternatives can produce the complex, cross-sectional interactions that provide the principal support for AL Then, using panel data to better assess causal direction, we find little support for AI, some evidence that emotions directly influence candidate evaluations, and strong evidence that candidate evaluations directly influence emotions. Scholars, we conclude, should be hesitant to abandon these simpler explanations in favor of AL.

as schools take actions that are linked to improved performance. ew contemporary public policies are as controversial as school choice. School privatization advocates argue that market-based school systems can educate students better than public schools. Chubb and Moe (1988, 1990), for example, contend that the institutional features of the public school system-particularly its bureaucratic structure and democratic governance-make it an ineffective organization that depresses student performance. Their remedy is to convert schools into more market-based institutions with less bureaucracy and less democratic control (Chubb and Moe 1988, 1990). Opponents of school choice contend the proponents' arguments lack empirical support, fail to consider the multiple goals of public education, and support policies that generate more costs than benefits (Henig 1994; Paris 1995; Witte 1992; Smith and Meier 1995). Lost in the public debate is the realization that some points of dispute are important theoretical questions in their own right. Both Chubb and Moe (1990) and their harshest and somewhat intemperate critics (Smith and Meier 1994) use neo-institutional arguments derived from organization theory. Some issues at dispute, as a result, are relevant not only to public policy debates but are also testable propositions that promise theoretical insight into how institutions respond to and influence their environments (Thompson 1967; March and Olsen 1989). This research examines one small, but crucial, empirical question in the school choice debate-what is the relationship between levels of bureaucracy and organizational performance? Chubb and Moe (1990) contend that bureaucracy leads to poor performance on the part of public schools. Smith and Meier (1994), in contrast, argue that bureaucracy is an adaptation to poor performance, that when organizations recognize their

[fre] Cet article compare les performances des filtres de Hodrick-Prescott et de Baxter-King avec une methode de filtrage fondee sur les proprietes multi-resolution des ondelettes. Globalement, les trois methodes restent comparables si la composante cyclique theorique est definie dans la bande de frequences usuelle comprise entre six et trente-deux trimestres. Cependant, l’approche fondee sur les ondelettes fournit des informations supplementaires sur le cycle des affaires (stabilite dans le temps…). Nos resultats s’appuient sur des experiences de Monte-Carlo. Notre methode appliquee au PIB americain montre aussi que l’estimation de la composante du cycle economique fondee sur les taux de croissance est meilleure que celle deduite du PIB en niveau et apporte des elements d’information sur la periode de « grande moderation » de l’apres 1980. [eng] We compare the performance of Hodrick-Prescott and Baxter-King filters with a filtering method based on the multi-resolution properties of wavelets. We show that the three methods remain broadly comparable if the theoretical cyclical component is defined in the usual waveband, ranging between six and thirty-two quarters. However the wavelet-based approach provides additional information on the business cycle, such as its time stability. We apply our method – which is based on Monte Carlo simulation experiments – to U.S. GDP. We find that the estimate of the business-cycle component using growth-rate data is superior to the estimate using the GDP level ; it also provides fuller information on the “ Great Moderation ” period that began in the mid-1980s.

[1] We use statistical techniques to quantify the effect of interannual variations in vegetation within land covers on surface temperature in North America and Eurasia from satellite measures of surface greenness and ground based meteorological observations. During the winter, reductions in the extent of snow cover cause (in a statistical sense) temperature to rise. During the summer, increases in terrestrial vegetation within land covers cause (in a statistical sense) temperature to fall. Temperature-induced increases in vegetation have slowed increases in surface temperature, but this feedback may be limited by the range over which temperature has a positive effect on vegetation.

[1] There is growing evidence that major climate modes are involved in determining decadal variability in global mean temperature. These modes represent major oceanic and atmospheric signals and on decadal scales their collective interplay leads to climate shifts manifesting themselves as regime changes in global temperature trend. Here we investigate whether the collective role of these modes is extended within a regime, i.e. to shorter time scales. We apply nonlinear prediction in order to assess directional influences in the climate system. We show evidence that input from four major climate modes from the Atlantic and Pacific improves the prediction of global temperature and thus these modes Granger cause global temperature. Moreover, we find that this causality is not a result of a particular mode dominating but a result of the nonlinear collective behavior in the network of the four modes.

[1] Rainfall variability occurs over a wide range of time scales owing to processes initiated by cloud microphysics and sustained by atmospheric circulation. A central topic in rainfall research is to determine whether rainfall variability at a given scale is caused by dynamics acting at some other scales. Random multiplicative cascades (RMCs) are standard approaches for describing rainfall variability across such a wide range of time scales. Their popularity stems from their ability to reproduce rainfall self-similarity and long-range correlations as well as intermittency buildup at finer scales. However, standard RMCs only predict instantaneous flow of variance (energy or activity) from large to fine scales and cannot account for scale-wise causal relationships. Such relationships reveal themselves through noninstantaneous cascade mechanisms, namely, large-scale events influencing finer-scale events at later times (i.e., forward causal cascade) or conversely (inverse causal cascade). The presence of causal cascade signatures within the rainfall process is explored here using both continuous wavelet decomposition (CWT) and scale-by-scale causality measures such as cross-scale correlation and linearized transfer entropy. The causality hypothesis is further tested against results from toy models, surrogate data, and a scalar turbulence time series (water vapor) to ensure that rainfall causality is not an artifact of the estimation method or resulting from the redundancy in CWT. The analysis demonstrates the presence of causal cascades (mainly forward) in rainfall series when sampled at fine temporal resolutions (seconds). These causal relationships tend to vanish when rainfall is aggregated at coarser time scales (hours and longer).