Synchronisation of the EDML and EDC ice cores for the last 52 kyr by volcanic signature matching

A common time scale for the EPICA ice cores from Dome C (EDC) and Dronning Maud Land (EDML) has been established. Since the EDML core was not drilled on a dome, the development of the EDML1 time scale for the EPICA ice core drilled in Dronning Maud Land was based on the creation of a detailed stratigraphic link between EDML and EDC, which was dated by a simpler 1D ice-flow model. The synchronisation between the two EPICA ice cores was done through the identification of several common volcanic signatures. This paper describes the rigorous method, using the signature of volcanic sulfate, which was employed for the last 52 kyr of the record. We estimated the discrepancies between the modelled EDC and EDML glaciological age scales during the studied period, by evaluating the ratio R of the apparent duration of temporal intervals between pairs of isochrones. On average R ranges between 0.8 and 1.2 corresponding to an uncertainty of up to 20% in the estimate of the time duration in at least one of the two ice cores. Significant deviations of R up to 1.4–1.5 are observed between 18 and 28 kyr before present (BP), where present is defined as 1950. At this stage our approach does not allow us unequivocally to find out which of the models is affected by errors, but assuming that the thinning function at both sites and accumulation history at Dome C (which was drilled on a dome) are correct, this anomaly can be ascribed to a complex spatial accumulation variability (which may be different in the past compared to the present day) upstream of the EDML core.

Nonverbale Synchronisation in Dyaden: ein Vergleich von kooperativen und kompetitiven Interaktionen

Nonverbales Verhalten spielt in zwischenmenschlichen Interaktionen eine bedeutende Rolle. Es beeinflusst mutmaßlich den Verlauf eines Gesprächs, die Beziehung zwischen den Interaktionspartnern und die reziproke Sympathie. Trotz dieser immensen Bedeutung wird nonverbales Verhalten von den Beteiligten meist nicht bewusst wahrgenommen. Die vorliegende Arbeit widmet sich diesem wirkungsvollen Phänomen und vergleicht nonverbales Verhalten in kooperativen und kompetitiven Interaktionsbedingungen. Im Fokus der Experimentalstudie steht die nonverbale Synchronisation. Diese bezieht sich auf den dynamischen, quantitativen Aspekt der Koordination nonverbalen Verhaltens zwischen den Interaktionspartnern, konkret auf die Angleichung der Bewegungsenergie. Primäres Ziel ist es zu evaluieren, ob und inwiefern das nonverbale Verhalten, insbesondere das Ausmaß an Synchronisation, zwischen kooperativen und kompetitiven Bedingungen variiert. Hierzu wurden in einer Stichprobe von N = 168 gesunden Teilnehmern fünf standardisierte Interaktionen à fünf Minuten mit gleichgeschlechtlichen Dyaden realisiert. Die Probanden kannten sich vorher nicht. Die Interaktanten sahen sich aufgefordert, sowohl aktive Kooperationen zu etablieren, als auch in bestimmten Aufgaben zu konkurrieren. Kompromiss, Konsens, Konflikt, ungleicher Konflikt und Spiel waren die verschiedenen Bedingungen. Die resultierenden Synchronisationswerte basieren auf digitalen Videoaufnahmen von dyadischen Interaktionen, die durch das automatisierte, objektive Verfahren der Motion Energy Analysis (MEA) quantifiziert wurden. Neben den Synchronisationsprozessen wurden auch globale Bewegungscharakteristika wie Geschwindigkeit oder Maxima durch MEA erfasst. Des Weiteren wurden mittels Fragebögen individuelle Charakteristika (u.a. Empathie, Stimmung) sowie Einschätzungen (z.B. Sympathie) der Probanden erhoben und mit Synchronie in Zusammenhang gesetzt. Die Ergebnisse zeigen, dass sich nonverbale Synchronisation auf signifikant höherem Level manifestiert, als dies per Zufall erwartet werden könnte. Dabei war die Synchronisation in der spielerischen Kooperation am höchsten ausgeprägt - gefolgt von kompetitiven und (seriös-)kooperativen Interaktionen. Außerdem korrespondierten höhere Synchronisationswerte mit verstärkt positiven Affekten und verringerten negativen Emotionen. Darüber hinaus waren globale Bewegungsparameter wie Dauer und Komplexität oder der Prozentsatz von Bewegungen über dem Schwellenwert positiv mit Synchronisationsprozessen assoziiert. Das MEA-Verfahren sowie ein erstmals erprobtes Interaktionsparadigma konnten validiert werden.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Design methodology for the servo control of high speed multi-axis machinery (BL): decoupling and synchronisation of a generic machine by blockwise decentralised MIMO control

Traditional machinery for manufacturing processes are characterised by actuators powered and co-ordinated by mechanical linkages driven from a central drive. Increasingly, these linkages are replaced by independent electrical drives, each performs a different task and follows a different motion profile, co-ordinated by computers. A design methodology for the servo control of high speed multi-axis machinery is proposed, based on the concept of a highly adaptable generic machine model. In addition to the dynamics of the drives and the loads, the model includes the inherent interactions between the motion axes and thus provides a Multi-Input Multi-Output (MIMO) description. In general, inherent interactions such as structural couplings between groups of motion axes are undesirable and needed to be compensated. On the other hand, imposed interactions such as the synchronisation of different groups of axes are often required. It is recognised that a suitable MIMO controller can simultaneously achieve these objectives and reconciles their potential conflicts. Both analytical and numerical methods for the design of MIMO controllers are investigated. At present, it is not possible to implement high order MIMO controllers for practical reasons. Based on simulations of the generic machine model under full MIMO control, however, it is possible to determine a suitable topology for a blockwise decentralised control scheme. The Block Relative Gain array (BRG) is used to compare the relative strength of closed loop interactions between sub-systems. A number of approaches to the design of the smaller decentralised MIMO controllers for these sub-systems has been investigated. For the purpose of illustration, a benchmark problem based on a 3 axes test rig has been carried through the design cycle to demonstrate the working of the design methodology.

Study of synchronisation in chains of coupled non-linear oscillators and resonators

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Probabilistic synchronisation of pinning control

This paper is concerned with synchronization of complex stochastic dynamical networks in the presence of noise and functional uncertainty. A probabilistic control method for adaptive synchronization is presented. All required probabilistic models of the network are assumed to be unknown therefore estimated to be dependent on the connectivity strength, the state and control values. Robustness of the probabilistic controller is proved via the Liapunov method. Furthermore, based on the residual error of the network states we introduce the definition of stochastic pinning controllability. A coupled map lattice with spatiotemporal chaos is taken as an example to illustrate all theoretical developments. The theoretical derivation is complemented by its validation on two representative examples.

Suppression of neuronal phase synchronisation in cat cerebral cortex

We wish to acknowledge the support of the Brazilian agencies: CNPq, CAPES, and FAPESP (2015/07311-7 and 2011/19296-1).

Tweaking Synchronisation by Link Addition

8 pages, 7 figures ACKNOWLEDGEMENT The authors wish to thank the Nesin foundation for an amazing working group activity in Nesin Math Village and wish to thank Tiago Pereira for fruitful discussions. PS and JK acknowledge gratefully the support of BMBF, CoNDyNet, FK. 03SF0472A. TP acknowledges FAPESP (No. 2012/22160-7 and No. 2015/02486-3) and IRTG 1740. DE acknowledge support by the Leibniz Association (WGL) under Grant No. SAW-2013-IZW-2.

Suppression of phase synchronisation in network based on cat’s brain

We wish to acknowledge the support of the Brazilian agencies: CNPq, CAPES, and FAPESP (2015/07311-7 and 2011/19296-1).

Abstracting and correlating heterogeneous events to detect complex scenarios

The research presented in this thesis addresses inherent problems in signaturebased intrusion detection systems (IDSs) operating in heterogeneous environments. The research proposes a solution to address the difficulties associated with multistep attack scenario specification and detection for such environments. The research has focused on two distinct problems: the representation of events derived from heterogeneous sources and multi-step attack specification and detection. The first part of the research investigates the application of an event abstraction model to event logs collected from a heterogeneous environment. The event abstraction model comprises a hierarchy of events derived from different log sources such as system audit data, application logs, captured network traffic, and intrusion detection system alerts. Unlike existing event abstraction models where low-level information may be discarded during the abstraction process, the event abstraction model presented in this work preserves all low-level information as well as providing high-level information in the form of abstract events. The event abstraction model presented in this work was designed independently of any particular IDS and thus may be used by any IDS, intrusion forensic tools, or monitoring tools. The second part of the research investigates the use of unification for multi-step attack scenario specification and detection. Multi-step attack scenarios are hard to specify and detect as they often involve the correlation of events from multiple sources which may be affected by time uncertainty. The unification algorithm provides a simple and straightforward scenario matching mechanism by using variable instantiation where variables represent events as defined in the event abstraction model. The third part of the research looks into the solution to address time uncertainty. Clock synchronisation is crucial for detecting multi-step attack scenarios which involve logs from multiple hosts. Issues involving time uncertainty have been largely neglected by intrusion detection research. The system presented in this research introduces two techniques for addressing time uncertainty issues: clock skew compensation and clock drift modelling using linear regression. An off-line IDS prototype for detecting multi-step attacks has been implemented. The prototype comprises two modules: implementation of the abstract event system architecture (AESA) and of the scenario detection module. The scenario detection module implements our signature language developed based on the Python programming language syntax and the unification-based scenario detection engine. The prototype has been evaluated using a publicly available dataset of real attack traffic and event logs and a synthetic dataset. The distinct features of the public dataset are the fact that it contains multi-step attacks which involve multiple hosts with clock skew and clock drift. These features allow us to demonstrate the application and the advantages of the contributions of this research. All instances of multi-step attacks in the dataset have been correctly identified even though there exists a significant clock skew and drift in the dataset. Future work identified by this research would be to develop a refined unification algorithm suitable for processing streams of events to enable an on-line detection. In terms of time uncertainty, identified future work would be to develop mechanisms which allows automatic clock skew and clock drift identification and correction. The immediate application of the research presented in this thesis is the framework of an off-line IDS which processes events from heterogeneous sources using abstraction and which can detect multi-step attack scenarios which may involve time uncertainty.