Supervised estimation of Granger-based causality between time series

Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data. Secondly, we introduce a new perspective, that looks at the problem in a way that is typical of the machine learning literature. Then, we formulate the problem of causality detection as a supervised learning task, by proposing a classification-based approach. A classifier is trained to identify causal interactions between time series for the chosen model and by means of a proposed feature space. In this paper, we are interested in comparing this classification-based approach with the standard Geweke measure of causality in the time domain, through simulation study. Thus, we customized our approach to the case of a MAR model and designed a feature space which contains causality measures based on the idea of precedence and predictability in time. Two variations of the supervised method are proposed and compared to a standard Granger causal analysis method. The results of the simulations show that the supervised method outperforms the standard approach, in particular it is more robust to noise. As evidence of the efficacy of the proposed method, we report the details of our submission to the causality detection competition of Biomag2014, where the proposed method reached the 2nd place. Moreover, as empirical application, we applied the supervised approach on a dataset of neural recordings of rats obtaining an important reduction in the false positive rate

La determinazione delle CSC nella bonifica dei siti produttivi

1. Introduzione. - 2. Il caso di specie e i termini del problema. - 3. La determinazione delle CSC tra colonna A e colonna B della tabella 1: introduzione. - 4. (segue): il valore del \uabdato urbanistico\ubb. - 5. (segue): la particolare filosofia del Codice dell\u2019ambiente. - 6. (segue): Il processo di identificazione dei limiti e le motivazioni della scelta. - 7. La difficile condivisione delle posizioni assunte dal T.A.R. Venezia. - 8. Conclusioni

Classification-based prediction of effective connectivity between timeseries with a realistic cortical network model

Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data

Working within the shadow: What do we do with 'not-yet' data?

Purpose The purpose of this paper is to explore the possibilities opened up by those messy, unclear and indeterminate data in research situations that may be described as being in the shadow and may as such remain in a state of vagueness and indeterminacy. Design/methodology/approach The paper draws on the extant literature on shadow organizing and post-qualitative methodologies. It focuses attention on not-yet (or shadow data) in order to ponder over what researchers do to data when they are not (yet) black-boxed as such. At the same time, it investigates what it is that not-yet data do to researchers. Findings Four types of ‘not-yet’ data – illegible, wondrous and disorienting, hesitant, and worn out – are presented and discussed. Illegible data is when a researcher is in the position of not knowing how to interpret what is in front of her/him. A second illustration is constructed around wonder, and poses the question of the feelings of surprise and disorientation that arise when facing uncanny realities. In a third situation, not-yet data is narrated as hesitation, when a participant feels conflicting desires and the researchers hesitates in interpreting. The fourth illustration depicts not-yet data as data that have been corrupted, that vanish after time or are worn out. Practical implications Not-yet data belong to researchers practice but can also be found in other professional practices which are concerned with the indeterminacy of shadowy situations. It is argued that situations like these constitute opportunities for learning and for the moral and professional development, so long as indeterminacy is kept open and a process of ‘slowing down’ both action and interpretation is nurtured. Originality/value This paper is of value for taking the metaphor of shadow organizing further. Moreover, it represents a rare attempt to bring the vast debate on post-qualitative research/methodologies into management studies, which with very few exceptions seems to have been ignored by organization studies

Superadditivity of anticanonical Iitaka dimension for contractions with F-split fibres

In this paper, we study a version of the Iitaka conjectures for anticanonical divisors over perfect fields of positive characteristic. That is, we prove the inequality $\kappa(X,-K_X) \leq \kappa(X_y,-K_{X_y})+ \kappa(Y,-K_Y)$, for a contraction $f: X \rightarrow Y$ with general fibre $X_y$ having good arithmetic properties.Comment: Comments welcome

Riflessi di concezioni totemiche in alcuni nomi dialettali del ghiaccio

In the framework of previous research on totemic names related to the perception of landscape, the author points out the presence of a few dialectal names referred to ‘ice’ which can be inter-preted in their continuity with a prehistoric system of beliefs