Dynamic clustering of time series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Flavonoid-membrane Interactions: A Protective Role of Flavonoids at the Membrane Surface?

Flavonoids can exert beneficial health effects through multiple mechanisms. In this paper, we address the important, although not fully understood, capacity of flavonoids to interact with cell membranes. The interactions of polyphenols with bilayers include: (a) the partition of the more non-polar compounds in the hydrophobic interior of the membrane, and (b) the formation of hydrogen bonds between the polar head groups of lipids and the more hydrophilic flavonoids at the membrane interface. The consequences of these interactions are discussed. The induction of changes in membrane physical properties can affect the rates of membrane lipid and protein oxidation. The partition of certain flavonoids in the hydrophobic core can result in a chain breaking antioxidant activity. We suggest that interactions of polyphenols at the surface of bilayers through hydrogen bonding, can act to reduce the access of deleterious molecules (i.e. oxidants), thus protecting the structure and function of membranes

Direct ETTIN-auxin interaction controls chromatin states in gynoecium development

Hormonal signalling in animals often involves direct transcription factor-hormone interactions that modulate gene expression. In contrast, plant hormone signalling is most commonly based on de-repression via the degradation of transcriptional repressors. Recently, we uncovered a non-canonical signalling mechanism for the plant hormone auxin whereby auxin directly affects the activity of the atypical auxin response factor (ARF), ETTIN towards target genes without the requirement for protein degradation. Here we show that ETTIN directly binds auxin, leading to dissociation from co-repressor proteins of the TOPLESS/TOPLESS-RELATED family followed by histone acetylation and induction of gene expression. This mechanism is reminiscent of animal hormone signalling as it affects the activity towards regulation of target genes and provides the first example of a DNA-bound hormone receptor in plants. Whilst auxin affects canonical ARFs indirectly by facilitating degradation of Aux/IAA repressors, direct ETTIN-auxin interactions allow switching between repressive and de-repressive chromatin states in an instantly-reversible manner

Increase in soil erosion after agricultural intensification: evidence from a lowland basin in France

International audienceChanges in agricultural practices impact sediment transfer in catchments and rivers. Long term archives of sediment deposits in agricultural plains of northwestern Europe are rarely available, however, for reconstructing and quantifying erosion and sedimentation rates for the second half of the 20th century. In this context, a multi-parameter analysis was conducted on sedimentary deposits accumulated in a pond created in the 11th century and draining a 24 km2 cultivated catchment in western France. This catchment is representative of cultivated and drained lowland environments where agriculture has intensified during the last 60 years.High resolution seismic profiles and surface sediment samples (n = 74) were used to guide the collection of cores (n = 3) representative of the sequence of sediment accumulated in the pond. The cores were analysed to quantify and characterize the evolution of sediment dynamics in the pond.The first land consolidation period (1954-1960) was characterized by a dominance of allochtonous material input to the pond. This input represents an erosion of 1900 to 2300 t.km−2.yr−1 originating from the catchment. Then, between 1970-1990, the terrigenous input decreased progressively and tended to stabilize. Eutrophication and associated primary production increased in the pond. These processes generated the majority of material accumulated in the pond during this period. Further land consolidation programs conducted in 1992 generated a new increase in soil erosion and sediment input to the reservoir. For the last 10 years, terrigenous input to the pond corresponds to a catchment-wide erosion rate between 90 and 102 t.km−2.yr−1. While a strong decrease is observed, it still represents a 60-fold increase of the sediment flux compared to the pre-intensification period. These large temporal variations of sedimentation rates over a few decades underline the dynamics of sediment transfer and raise questions about the sustainability of soil resources in lowland temperate environments

Information processing using a single dynamical node as complex system

Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing

A Broken Trust: Lessons from the Vaccine–Autism Wars: Researchers long ago rejected the theory that vaccines cause autism, yet many parents don't believe them. Can scientists bridge the gap between evidence and doubt?

Researchers long ago rejected the theory that vaccines cause autism, yet many parents don't believe them. Can scientists bridge the gap between evidence and doubt

Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201

Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.Comment: Preprint of the paper accepted at IWANN 201

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines