The influence of n-6 and n-3 fatty acids on kidney phospholipid composition and on eicosanoid production in aging rats

Changes in eicosanoid production may contribute to some of the complications of the aging process such as atherosclerosis and glomerular sclerosis. Polyunsaturated fatty acids of the n-6 and n-3 series are precursors of eicosanoids. We fed diets containing safflower oil as a source of n-6 fatty acids, fish oil as a source of n-3 fatty acids or beef tallow as a source of saturated fats to three groups of normal rats from 2–18 months of age. We demonstrated incorporation of the n-3 fatty acids, 20:5n-3 and 22:6n-3 into kidney phospholipids. Feeding of the diet containing n-3 fatty acids was associated with a markedly decreased giomerular production of PGE, 6-keto-PGF_1α and TXB₂. It also decreased the aortic production of 6-keto-PGF_1α and platelet production of TXB₂. No significant effect of n-6 fatty acids on dienoic eicosanoid production was observed. There were no adverse effects on kidney function as measured by urinary protein excretion and serum creatinine levels or on renal morphology by any diet. A diet enriched in n-3 fatty acids for 18 months remains effective in decreasing dienoic eicosanoids in the aging rat.     

Quadruped robot trotting over irregular terrain assisted by stereo-vision

Legged robots have the potential to navigate in challenging terrain, and thus to exceed the mobility of wheeled vehicles. However, their control is more difficult as legged robots need to deal with foothold computation, leg trajectories and posture control in order to achieve successful navigation. In this paper, we present a new framework for the hydraulic quadruped robot HyQ, which performs goal-oriented navigation on unknown rough terrain using inertial measurement data and stereo-vision. This work uses our previously presented reactive controller framework with balancing control and extends it with visual feedback to enable closed-loop gait adjustment. On one hand, the camera images are used to keep the robot walking towards a visual target by correcting its heading angle if the robot deviates from it. On the other hand, the stereo camera is used to estimate the size of the obstacles on the ground plane and thus the terrain roughness. The locomotion controller then adjusts the step height and the velocity according to the size of the obstacles. This results in a robust and autonomous goal-oriented navigation over difficult terrain while subject to disturbances from the ground irregularities or external forces. Indoor and outdoor experiments with our quadruped robot show the effectiveness of this framework.     

Seriation in the Presence of Errors: A Factor 16 Approximation Algorithm for <Emphasis Type="Italic">l</Emphasis><Subscript>∞</Subscript>-Fitting Robinson Structures to Distances

The classical seriation problem consists in finding a permutation of the rows and the columns of the distance (or, more generally, dissimilarity) matrix d on a finite set X so that small values should be concentrated around the main diagonal as close as possible, whereas large values should fall as far from it as possible. This goal is best achieved by considering the Robinson property: a distance d _R on X is Robinsonian if its matrix can be symmetrically permuted so that its elements do not decrease when moving away from the main diagonal along any row or column. If the distance d fails to satisfy the Robinson property, then we are lead to the problem of finding a reordering of d which is as close as possible to a Robinsonian distance.     

Word co-occurrence features for text classification

In this article we propose a data treatment strategy to generate new discriminative features, called compound-features (or c-features), for the sake of text classification. These c-features are composed by terms that co-occur in documents without any restrictions on order or distance between terms within a document. This strategy precedes the classification task, in order to enhance documents with discriminative c-features. The idea is that, when c-features are used in conjunction with single-features, the ambiguity and noise inherent to their bag-of-words representation are reduced. We use c-features composed of two terms in order to make their usage computationally feasible while improving the classifier effectiveness. We test this approach with several classification algorithms and single-label multi-class text collections. Experimental results demonstrated gains in almost all evaluated scenarios, from the simplest algorithms such as kNN (13% gain in micro-average F₁ in the 20 Newsgroups collection) to the most complex one, the state-of-the-art SVM (10% gain in macro-average F₁ in the collection OHSUMED).     

Nearly optimal solution of rational linear systems of equations with symbolic lifting and numerical initialization

Hensel’s symbolic lifting is a highly effective method for the solution of a general or structured (e.g. Toeplitz or Hankel) linear system of equations with integer or rational coefficients of bounded length. It can handle ill conditioned inputs, for which numerical methods become costly. Lifting amounts to recursive multiplications by vectors of the input coefficient matrices and its precomputed inverse modulo a fixed integer s. Such multiplications only involve small numbers of data movements and arithmetic operations with bounded precision. The known methods for precomputation of the inverse are more costly, however; in particular they involve more data movements. As our remedy for this bottleneck stage we create an auxiliary matrix sharing its inverse modulo s with the input matrix, and we readily compute this inverse by applying numerical iterative refinement, which is a numerical counterpart of lifting. In the case of general unstructured as well as Toeplitz, Hankel, and other popular structured inputs our hybrid algorithms involve a small number of data movements and optimal number of Boolean (that is bitwise) operations (up to a logarithmic factor). We extend the algorithms to nearly optimal computation of polynomial greatest common divisors (gcds), least common multiples (lcms) and Padé approximations, as well as the Berlekamp-Massey reconstruction of linear recurrences. We also cover Newton’s lifting for matrix inversion, specialize it to the case of structured input, and combine it with Hensel’s to enhance the overall efficiency. Our initialization techniques work for Newton’s lifting as well. Furthermore we extend all our lifting algorithms to allow their initialization modulo powers of two, thus implementing them in the binary base.     

Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground

There are several works that approach the modeling of navigation environments for the automatic planning of paths for Unmanned Aerial Vehicles (UAVs). However these works do not consider the evaluation of risks to the population in case of accidents with UAVs. The present work proposes a grid-based method to construct visibility graphs based on the evaluation of effects of impacts of UAVs on the ground and on a set of geographic information. The metric used for the evaluation is the minimum acceptable period between accidents of this type. The geographic information to be used consists of georeferenced images and digital elevation models of the surfaces of the navigation environments; urban and rural population density information of the cities in these environments; and the polygonal representation of the cities. The visibility graphs constructed by the proposed method allow the UAVs to plan the shortest paths with a decrease of the probability of fatalities due to accidents with impact of UAVs on the ground. Analyses of the results are presented in this work.     

Multi-layer atom chips for atom tunneling experiments near the chip surface

This paper describes the design and fabrication of an atom chip to be used in ultra-high-vacuum cells for cold-atom tunneling experiments. A fabrication process was developed to pattern micrometer- and nanometer-scale copper wires onto a single chip. The wires, with fabricated widths down to 200 nm, can sustain current densities of more than 7.5 × 10⁷ A/cm². Partially suspended wires, developed in order to reduce the Casimir–Polder force between atoms and surface, were also fabricated and tested. Extensive measurements for variable wire width show that the sustainable currents are sufficiently large to allow chip-based atom tunneling experiments. Such chips may allow the realization of an atom transistor.     

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.     

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter may be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features.