A control and automation system for wave basins

This paper presents the new active absorption wave basin, named Hydrodynamic Calibrator (HC), constructed at the University of São Paulo (USP), in the Laboratory facilities of the Numerical Offshore Tank (TPN). The square (14 m 14 m) tank is able to generate and absorb waves from 0.5 Hz to 2.0 Hz, by means of 148 active hinged flap wave makers. An independent mechanical system drives each flap by means of a 1HP servo-motor and a ball-screw based transmission system. A customized ultrasonic wave probe is installed in each flap, and is responsible for measuring wave elevation in the flap. A complex automation architecture was implemented, with three Programmable Logic Computers (PLCs), and a low-level software is responsible for all the interlocks and maintenance functions of the tank. Furthermore, all the control algorithms for the generation and absorption are implemented using higher level software (MATLAB /Simulink block diagrams). These algorithms calculate the motions of the wave makers both to generate and absorb the required wave field by taking into account the layout of the flaps and the limits of wave generation. The experimental transfer function that relates the flap amplitude to the wave elevation amplitude is used for the calculation of the motion of each flap. This paper describes the main features of the tank, followed by a detailed presentation of the whole automation system. It includes the measuring devices, signal conditioning, PLC and network architecture, real-time and synchronizing software and motor control loop. Finally, a validation of the whole automation system is presented, by means of the experimental analysis of the transfer function of the waves generated and the calculation of all the delays introduced by the automation system.

Mistletoes Play Different Roles in a Modular Host-Parasite Network

Antagonistic interactions between host plants and mistletoes often form complex networks of interacting species. Adequate characterization of network organization requires a combination of qualitative and quantitative data. Therefore, we assessed the distribution of interactions between mistletoes and hosts in the Brazilian Pantanal and characterized the network structure in relation to nestedness and modularity. Interactions were highly asymmetric, with mistletoes presenting low host specificity (i.e., weak dependence) and with hosts being highly susceptible to mistletoe-specific infections. We found a non-nested and modular pattern of interactions, wherein each mistletoe species interacted with a particular set of host species. Psittacanthus spp. infected more species and individuals and also caused a high number of infections per individual, whereas the other mistletoes showed a more specialized pattern of infection. For this reason, Psittacanthus spp. were regarded as module hubs while the other mistletoe species showed a peripheral role. We hypothesize that this pattern is primarily the result of different seed dispersal systems. Although all mistletoe species in our study are bird dispersed, the frugivorous assemblage of Psittacanthus spp. is composed of a larger suite of birds, whereas Phoradendron are mainly dispersed by Euphonia species. The larger assemblage of bird species dispersing Psittacanthus seeds may also increase the number of hosts colonized and, consequently, its dominance in the study area. Nevertheless, other restrictions on the interactions among species, such as the differential capacity of mistletoe infections, defense strategies of hosts and habitat types, can also generate or enhance the observed pattern.

Symmetry insights for design of supercomputer network topologies: roots and weights lattices

We present a family of networks whose local interconnection topologies are generated by the root vectors of a semi-simple complex Lie algebra. Cartan classification theorem of those algebras ensures those families of interconnection topologies to be exhaustive. The global arrangement of the network is defined in terms of integer or half-integer weight lattices. The mesh or torus topologies that network millions of processing cores, such as those in the IBM BlueGene series, are the simplest member of that category. The symmetries of the root systems of an algebra, manifested by their Weyl group, lends great convenience for the design and analysis of hardware architecture, algorithms and programs.

Enhancing weak signal transmission through a feedforward network

The ability to transmit and amplify weak signals is fundamental to signal processing of artificial devices in engineering. Using a multilayer feedforward network of coupled double-well oscillators as well as Fitzhugh-Nagumo oscillators, we here investigate the conditions under which a weak signal received by the first layer can be transmitted through the network with or without amplitude attenuation. We find that the coupling strength and the nodes' states of the first layer act as two-state switches, which determine whether the transmission is significantly enhanced or exponentially decreased. We hope this finding is useful for designing artificial signal amplifiers.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.