Using Recurrent Networks for Dimensionality Reduction

This report explores how recurrent neural networks can be exploited for learning high-dimensional mappings. Since recurrent networks are as powerful as Turing machines, an interesting question is how recurrent networks can be used to simplify the problem of learning from examples. The main problem with learning high-dimensional functions is the curse of dimensionality which roughly states that the number of examples needed to learn a function increases exponentially with input dimension. This thesis proposes a way of avoiding this problem by using a recurrent network to decompose a high-dimensional function into many lower dimensional functions connected in a feedback loop.

Exact Solution of the Nonlinear Dynamics of Recurrent Neural Mechanisms for Direction Selectivity

Different theoretical models have tried to investigate the feasibility of recurrent neural mechanisms for achieving direction selectivity in the visual cortex. The mathematical analysis of such models has been restricted so far to the case of purely linear networks. We present an exact analytical solution of the nonlinear dynamics of a class of direction selective recurrent neural models with threshold nonlinearity. Our mathematical analysis shows that such networks have form-stable stimulus-locked traveling pulse solutions that are appropriate for modeling the responses of direction selective cortical neurons. Our analysis shows also that the stability of such solutions can break down giving raise to a different class of solutions ("lurching activity waves") that are characterized by a specific spatio-temporal periodicity. These solutions cannot arise in models for direction selectivity with purely linear spatio-temporal filtering.

Using the PfEMP1 Head Structure Binding Motif to Deal a Blow at Severe Malaria

Plasmodium falciparum (Pf) malaria causes 200 million cases worldwide, 8 million being severe and complicated leading to similar to 1 million deaths and similar to 100,000 abortions annually. Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) has been implicated in cytoadherence and infected erythrocyte rosette formation, associated with cerebral malaria; chondroitin sulphate-A attachment and infected erythrocyte sequestration related to pregnancy-associated malaria and other severe forms of disease. An endothelial cell high activity binding peptide is described in several of this similar to 300 kDa hypervariable protein's domains displaying a conserved motif (GACxPxRRxxLC); it established H-bonds with other binding peptides to mediate red blood cell group A and chondroitin sulphate attachment. This motif (when properly modified) induced PfEMP1-specific strain-transcending, fully-protective immunity for the first time in experimental challenge in Aotus monkeys, opening the way forward for a long sought-after vaccine against severe malaria.

Frequency of Low-level Mosaicism in X-Cromosome in Couples with Antecedent of Recurrent Miscarriages

Recurrent miscarriage occurs in around 1 to 7 percent of couples. The etiology involves genetic, immunologic, anatomic, hormonal, metabolic, thrombophilic and infectious factors. With the aim of establishing the frequency of low-level mosaicism in the X-chromosome, in a population of couples with prior recurrent miscarriages, a prospective case-control cytogenetic study took place on 20 couples, at the biogenetic laboratory in CECOLFES (Colombian Center of Fertility and Sterility). Clinical pathologic evaluation, anatomic, hormonal, infectious, andrologic and genetic studies were performed. As a conventional method in cytogenetic techniques, banding GTG was used for the study of structural and numeric chromosomal abnormalities whereas the molecular method of Fluorescence In Situ Hybridization (FISH) was used to confirm the mosaicism in sexual chromosomes. According to paraclinic results from the participating couples, diagnosis showed immunologic (75%), anatomic (30%), hormonal (25%), male (25%), infectious (25%), genetic (15%) and idiophatic factors (10%). Results from the cytogenetic analysis, were 10% of low-level mosaicism in the X-chromosome in two women whose final diagnosis included genetic and infectious factors for one and genetic and immunologic factors for the other. Only 10 % of the total miscarriages from the couples were evaluated. Conclusions include aspects such as multifactorial evidence of pathogenesis in recurrent miscarriage, the sub-diagnosis of genetic factors and the need to focus future investigations on cytogenetic interpretation and the clinicalpathological association between low-level mosaicism in the X-cromosome and recurrent miscarriage.

Gerunda i els ausetans: una qüestió recurrent

Article que tracta del tema recurrent de la pertinença o no de Gerunda a la tribu ibèrica dels ausetans

A new motif in the formation of peptide nanotubes: the crystallographic signature

Terminally protected acyclic tripeptides Boc-Tyr(1)-Val(2)-Tyr(3)-OMe 1 and Boc-Tyr(1)-lle(2)-Tyr(3)-OMe 2 self-assemble into nanotubes in crystals through various noncovalent interactions with an average internal diameter of 5 Angstrom (0.5 nm), and the tubular ensemble is developed through the hydrogen-bonded side chains of tyrosine residues. The inside of the hollow nanotubular structures is hydrophilic; however, no solvent molecules have been crystallographically detected.

Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

L’amant tenu par la bride : Itinéraires d'un motif courtois chez Gaucelm Faidit et sur un coffret limousin

A comparison between selected lyrics and iconography of the late twelfth-century troubadour Gaucelm Faidit and a 'courtly love' image on a secular casket of Limoges enamel produced in the same period.