Analyzing X-ray variability by State Space Models

In recent years, autoregressive models have had a profound impact on the description of astronomical time series as the observation of a stochastic process. These methods have advantages compared with common Fourier techniques concerning their inherent stationarity and physical background. If autoregressive models are used, however, it has to be taken into account that real data always contain observational noise often obscuring the intrinsic time series of the object. We apply the technique of a Linear State Space Model which explicitly models the noise of astronomical data and allows to estimate the hidden autoregressive process. As an example, we have analysed a sample of Active Galactic Nuclei (AGN) observed with EXOSAT and found evidence for a relationship between the relaxation timescale and the spectral hardness.Comment: 4 pages, Latex, uses Kluwer Style file crckapb.cls To appear in Proc. of Astronomical Time Series, Tel Aviv, 199

Performance-based sub-selection of CMIP6 models for impact assessments in Europe

This is the final version. Available on open access from Copernicus Publications via the DOI in this recordCode and data availability: The code used to apply the ClimWIP method is publicly available via the ESMValTool (https://docs.esmvaltool.org/en/latest/recipes/recipe_climwip.html, ESMValTool, 2022). The data used in this study are available through the ESGF data portal at https://esgf-node.llnl.gov/projects/cmip6/ (CMIP, 2022). Further assessment plots for the models used in this paper are available on GitHub at https://github.com/tepmo42/cmip6_european_assessment (https://doi.org/10.5281/zenodo.782884, Palmer et al., 2023), as is a spreadsheet of all available assessments (for Europe) carried out for CMIP6 models to date. The RAPID-MOC monitoring project is funded by the Natural Environment Research Council and data (Frajka-Williams et al., 2021). E-OBS data (v.14.0, Cornes et al., 2018) can be found at https://www.ecad.eu/download/ensembles/download.php, in Cornes et al. (2018). The HadISST dataset (Rayner et al., 2003) is publicly available for download at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The ERA5 data (Hersbach et al., 2020) are available for download through the Copernicus Climate Change Service (2017) at https://cds.climate.copernicus.eu/cdsapp#!/home.We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to a multi-model ensemble of CMIP6 models to (a) filter the ensemble for performance against these climatological and processed-based criteria and (b) create a smaller subset of models based on performance that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least-realistic models leads to higher-sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted towards greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast, this shifts the distribution towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.European Union Horizon 2020Deutsche Forschungsgemeinschaft (DFG)European Research Council (ERC

Computerized general practice based networks yield comparable performance with sentinel data in monitoring epidemiological time-course of influenza-like illness and acute respiratory illness

<p>Abstract</p> <p>Background</p> <p>Computerized morbidity registration networks might serve as early warning systems in a time where natural epidemics such as the H₁N₁flu can easily spread from one region to another.</p> <p>Methods</p> <p>In this contribution we examine whether general practice based broad-spectrum computerized morbidity registration networks have the potential to act as a valid surveillance instrument of frequently occurring diseases. We compare general practice based computerized data assessing the frequency of influenza-like illness (ILI) and acute respiratory infections (ARI) with data from a well established case-specific sentinel network, the European Influenza Surveillance Scheme (EISS). The overall frequency and trends of weekly ILI and ARI data are compared using both networks.</p> <p>Results</p> <p>Detection of influenza-like illness and acute respiratory illness occurs equally fast in EISS and the computerized network. The overall frequency data for ARI are the same for both networks, the overall trends are similar, but the increases and decreases in frequency do not occur in exactly the same weeks. For ILI, the overall rate was slightly higher for the computerized network population, especially before the increase of ILI, the overall trend was almost identical and the increases and decreases occur in the same weeks for both networks.</p> <p>Conclusions</p> <p>Computerized morbidity registration networks are a valid tool for monitoring frequent occurring respiratory diseases and the detection of sudden outbreaks.</p

Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram.

It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data

On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity

In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activity. These patterns are thought to reflect relevant properties of the network, including anatomical features like its modularity. It is also assumed that the synaptic connections of the network constrain the repertoire of emergent, spontaneous patterns. Although the link between network architecture and network activity has been extensively investigated in the last few years from different perspectives, our understanding of the relationship between the network connectivity and the structure of its spontaneous activity is still incomplete. Using a general mathematical model of neural dynamics we have studied the link between spontaneous activity and the underlying network architecture. In particular, here we show mathematically how the synaptic connections between neurons determine the repertoire of spatial patterns displayed in the spontaneous activity. To test our theoretical result, we have also used the model to simulate spontaneous activity of a neural network, whose architecture is inspired by the patchy organization of horizontal connections between cortical columns in the neocortex of primates and other mammals. The dominant spatial patterns of the spontaneous activity, calculated as its principal components, coincide remarkably well with those patterns predicted from the network connectivity using our theory. The equivalence between the concept of dominant pattern and the concept of attractor of the network dynamics is also demonstrated. This in turn suggests new ways of investigating encoding and storage capabilities of neural networks

Brain Rhythms Reveal a Hierarchical Network Organization

Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or “virtual brains”, whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower complexity than virtual brains modeling normal neural function. We finally discuss the implications of our results for the neurobiology of health and disease

Non-stationarity and internal correlations of the occurrence process of mining-induced seismic events

A point process, e.g., the seismic process, is potentially predictable when it is non-stationary, internally correlated or both. In this paper, an analysis of the occurrence process of mining-induced seismic events from Rudna copper mine in Poland is presented. Stationarity and internal correlation are investigated in complete seismic time series and segmentally in subseries demonstrating relatively stable seismicity rates. It is shown that the complete seismic series are non-stationary; however, most of their shorter subseries become stationary. In the stationary subseries, the distribution of interevent time is closer to the exponential distribution, which is characteristic for the Poisson process. However, in most of these subseries, the differences between the interevent time and Poisson distributions are still significant, revealing correlations among seismic events