InfoMax Bayesian learning of the Furuta pendulum

We have studied the InfoMax (D-optimality) learning for the two-link Furuta pendulum. We compared InfoMax and random learning methods. The InfoMax learning method won by a large margin, it visited a larger domain and provided better approximation during the same time interval. The advantages and the limitations of the InfoMax solution are treated

Towards Independent Subspace Analysis in Controlled Dynamical Systems

In this paper we extend Independent Component Analysis (ICA) task to controlled dynamical systems. To our best knowledge this is the first work that considers the control task in this field, which may open the door for extended ICA applications. We treat Independent Subspace Analysis (ISA) task, the multidimensional generalization of ICA. In particular, we consider the identification problem of ARX models, i.e., hidden AutoRegressive dynamical systems subject to eXogenous control inputs. In our case, these ARX models are driven by independent multidimensional noise processes. The goal is the estimation of the hidden variables, that is, the parameters of the system and the driving noise. We aim efficient estimation by choosing suitable control values. For the optimal choice of the control we adapt the D-optimality principle, also known as 'InfoMax method'. To this end, we decouple the problem into a fully observable one and an ISA task. We solve the two problems and join the results to estimate the hidden variables. Numerical examples illustrate the efficiency of our method

Neurally Plausible, Non-combinatorial Iterative Independent Process Analysis

It has been shown recently that the identification of mixed hidden independent auto-regressive processes (independent process analysis, IPA), under certain conditions, can be free from combinatorial explosion. The key is that IPA can be reduced (i) to independent subspace analysis and then, via a novel decomposition technique called Separation Theorem, (ii) to independent component analysis. Here, we introduce an iterative scheme and its neural network representation that takes advantage of the reduction method and can accomplish the IPA task. Computer simulation illustrates the working of the algorithm

Multilayer Kerceptron

Multilayer Perceptrons (MLP) are formulated within Support Vector Machine (SVM) framework by constructing multilayer networks of SVMs. The coupled approximation scheme can take advantage of generalization capabilities of the SVM and the combinatory feature of the hidden layer of MLP. The network, the Multilayer Kerceptron (MLK) assumes its own backpropagation procedure that we shall derive here. Tuning rule will be provided for quadratic cost function, with regularization capability as well. A further appealing property of our approach is that by the aid of the so called kernel trick the MLK computations can be performed in the dual space

Separation Theorem for Independent Subspace Analysis

Here, a separation theorem about Independent Subspace Analysis (ISA), a generalization of Independent Component Analysis (ICA) is proven. According to the theorem, ISA estimation can be executed in two steps under certain conditions. In the first step, 1-dimensional ICA estimation is executed. In the second step, optimal permutation of the ICA elements is searched for. We shall show that elliptically symmetric sources, among others, satisfy the conditions of the theorem

Effect of storage on physical and functional properties of extracellular vesicles derived from neutrophilic granulocytes.

AIM: To carry out a systematic study on the effect of different storage conditions on the number as well as the physical and functional properties of antibacterial extracellular vesicles (EVs) derived from human neutrophilic granulocytes. METHODS: Production of EVs with antibacterial properties was initiated by opsonized Zymosan A particles. The number of released fluorescent EVs was determined by flow cytometry following careful calibration. Physical properties and size of EVs were investigated by flow cytometry, dynamic light scattering and electron microscopy. Functional properties of EVs were tested by bacterial survival assay. RESULTS: Storage at +20 degrees C or +4 degrees C resulted in a significant decrease of EV number and antibacterial effect after 1 day. Storage at -20 degrees C did not influence the EV number up to 28 days, but induced a shift in EV size and almost complete loss of antibacterial function by 28 days. Storage at -80 degrees C had no significant effect either on EV number or size and allowed partial preservation of the antibacterial function up to 28 days. Snap-freezing did not improve the results, whereas the widely used cryoprotectants induced EV lysis. CONCLUSION: Storage significantly alters both the physical and functional properties of EVs even if the number of EVs stays constant. If storage is needed, EVs should be kept at -80 degrees C, preferably not longer than 7 days. For functional tests, freshly prepared EVs are recommended

Post Nonlinear Independent Subspace Analysis

In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm

Relation between self-organized criticality and grain aspect ratio in granular piles

We investigate experimentally whether self-organized criticality (SOC) occurs in granular piles composed of different grains, namely, rice, lentils, quinoa, and mung beans. These four grains were selected to have different aspect ratios, from oblong to oblate. As a function of aspect ratio, we determined the growth (β) and roughness (α) exponents, the avalanche fractal dimension (D), the avalanche size distribution exponent (τ), the critical angle (γ), and its fluctuation. At superficial inspection, three types of grains seem to have power-law-distributed avalanches with a well-defined τ. However, only rice is truly SOC if we take three criteria into account: a power-law-shaped avalanche size distribution, finite size scaling, and a universal scaling relation relating characteristic exponents. We study SOC as a spatiotemporal fractal; in particular, we study the spatial structure of criticality from local observation of the slope angle. From the fluctuation of the slope angle we conclude that greater fluctuation (and thus bigger avalanches) happen in piles consisting of grains with larger aspect ratio. © 2012 American Physical Society