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Assessing the reliability and consistency of InSAR and GNSS data for retrieving 3D-displacement rapid changes, the example of the 2015 Piton de la Fournaise eruptions
No full textInternational audienc
Explicit Control of Dataflow Graphs with MARTE/CCSL
No full textInternational audienceProcess Networks are a means to describe streaming embedded applications. They rely on explicit representation of task concurrency, pipeline and data-flow. Originally, Data-Flow Process Network (DFPN) representations are independent from any execution platform support model. Such independence is actually what allows looking next for adequate mappings. Mapping deals with scheduling and distribution of computation tasks onto processing resources, but also distribution of communications to interconnects and memory resources. This design approach requires a level of description of execution platforms that is both accurate and simple. Recent platforms are composed of repeated elements with global interconnection (GPU, MPPA). A parametric description could help achieving both requirements. Then, we argue that a model-driven engineering approach may allow to unfold and expand an original DFPN model, in our case a so-called Synchronous DataFlow graph (SDF) into a model such that: a) the original description is a quotient refolding of the expanded one, and b) the mapping to a platform model is a grouping of tasks according to their resource allocation. Then, given such unfolding, we consider how to express the allocation and the real-time constraints. We do this by capturing the entire system in CCSL (Clock Constraint Specification Language). CCSL allows to capture linear but also synchronous constraints. Lastly, the system can be checked for the existence of a schedule satisfying all the constraints using a state space exploration technique. The approach is validated on a typical embedded system application allocated on a multi-core platform
A RADAR for information reconciliation in Question Answering systems over Linked Data1
No full textInternational audienc
Noninvasive Characterisation of Short-and Long-Term Recurrence of Atrial Signals During Persistent Atrial Fibrillation
No full textInternational audiencePropagation of electrical atrial activity (AA) during atrial fibrillation (AF) is a process characterized by different short-and long-term recurrence behaviours. Two anti-thetical (not mutually exclusive) hypotheses are proposed to noninvasively describe this nonstationary behaviour. The first hypothesis (H1) assumes a process with stationary spatial properties of AA propagation, but time-varying frequency properties, and vice versa for the second (H2). Based on H1 and H2, two phenomenological models were proposed, both able to replicate observations on AF patients, and a novel measure was introduced to assess the spatial variability of AA propagation (SVAAP) over short and long AA segments. Validity of the models was tested by looking at the relation between SVAAP-long and SVAAP-short on real observations from AF patients (high-density body surface potential maps recorded in 75 patients affected by persistent AF). H1 is confirmed if SVAAP-short is approximately equal to SVAAP-long. H2 if SVAAP-short is less than SVAAP-long. Results confirmed H2, showing that AA propagation during AF has strong nonstationary spatial properties. This could suggest new parameters to characterise AF sub-strate and predict therapy outcome
Following stage II retinal waves during development with a biophysical model : A biophysical model for retinal waves
No full textInternational audienceRetinal waves are bursts of activity occurring spontaneously in the developing retina of vertebrate species, contributing to the shaping of the visual system organization and disappear short after birth. Waves during development are a transient process which evolves dynamically exhibiting different features. Based on our previous modelling work [1,2], we now propose a classification of stage II retinal waves patterns as a function of acetylcholine coupling strength and a possible mechanism for waves generation. Our model predicts that spatiotemporal patterns evolve upon maturation or pharmacological manipulation and that waves emerge from a complete homogeneous state without the need of the variability of a neural population. Context & Motivation SACs dynamically change their synaptic coupling upon maturation Coupled cholinergic SACs [3] Cholinergic current evolution upon maturation [4]-70 mV 60mV 0 0.8 88 nM 300 nM 0.2 0.4 0.4 0.2 Bursting sAHP Increase of Calcium load during bursting Calcium controls the sAHP phase. Voltage is low and Calcium starts offloading Bursting starts again when the Calcium load is low enough Intracellular Calcium Concentration Our biophysical model reproduces experimental observations for individual and coupled SACs dynamics [3,4], where no previous computational model [5] has been able to do so before. Weak Moderate Strong Simulated Calcium Waves Weak Moderate Strong • Simulated Calcium waves of 4096 neurons on a square lattice. Black and white colours correspond to low and high activity respectively. Weak coupling: localised bumps of activity. Strong coupling: complete synchrony standing waves. Moderate coupling: propagating patterns. Following Retinal Waves Evolution Average Population Bursting Rate g (nS) A <FR > G 0.01 0.02 0.03 0.3 0 0.1 0.2 0.4 0.5 A B C E D • The average population bursting rate exhibits a sharp transition upon increasing the cholinergic coupling • Network simulations show patterns where waves do emerge (red) and where they do not (blue). A heat map of the average bursting period T , in seconds, of the network illustrates this observation. • Domaines are formed where the propagation of waves is restrained, as observed experimentally in [7]. In contrast to what is shown in [7], our model describes these domains without the need to add two neural population species. • Different types of spatiotemporal patterns are observed corresponding to each value of cholinergic coupling strength Evolution of the spatiotemporal patterns Evolution of the spatiotemporal patterns when increasing the cholinergic conduc-tance (from left to right) starting from a completely homogeneous state where all cells are identical. Conclusions and Perspectives • Spatio-temporal patterns emerge out of a complete homogeneous state showing that variability in neurons is not the underlying drive of pattern formation. • The role of variability in neural populations can be addressed from a computational aspect more easily. • Biophysical parameters (e.g. conductances) could vary upon maturation or pharmacological manipulations, affecting the characterstics of emerging waves. • A computational model able to follow the transitions of neural networks properties could help us gain a lot of insight in understanding the underlying mechanisms that drive them
