Zeno of Citium was a Greek thinker from Citium , Cyprus. He was possibly of Phoenician descent,. Zeno was the founder of the Stoic school of philosophy, which he taught in Athens from about 300 BC. Based on the moral ideas of the Cynics, Stoicism laid great emphasis on goodness and peace of mind gained from living a life of Virtue in accordance with Nature. It proved very successful, and flourished as the dominant philosophy from the Hellenistic period through to the Roman era. Wikipedia.
Micusik B.,AIT Austrian Institute of Technology |
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2015
We present a novel view on the indoor visual localization problem, where we avoid the use of interest points and associated descriptors, which are the basic building blocks of most standard methods. Instead, localization is cast as an alignment problem of the edges of the query image to a 3D model consisting of line segments. The proposed strategy is effective in low-textured indoor environments and in very wide baseline setups as it overcomes the dependency of image descriptors on textures, as well as their limited invariance to view point changes. The basic features of our method, which are prevalent indoors, are line segments. As we will show, they allow for defining an efficient Chamfer distance-based aligning cost, computed through integral contour images, incorporated into a first-best-search strategy. Experiments confirm the effectiveness of the method in terms of both, accuracy and computational complexity. © 2015 IEEE. Source
Lelli D.,Istituto Zooprofilattico Sperimentale della Lombardia e dellEmilia Romagna |
Papetti A.,Istituto Zooprofilattico Sperimentale della Lombardia e dellEmilia Romagna |
Sabelli C.,ZENO |
Rosti E.,Centro Fauna Selvatica Il Pettirosso |
And 2 more authors.
Viruses | Year: 2013
Bats are natural reservoirs for many mammalian coronaviruses, which have received renewed interest after the discovery of the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS) CoV in humans. This study describes the identification and molecular characterization of alphacoronaviruses and betacoronaviruses in bats in Italy, from 2010 to 2012. Sixty-nine faecal samples and 126 carcasses were tested using pan-coronavirus RT-PCR. Coronavirus RNAs were detected in seven faecal samples and nine carcasses. A phylogenetic analysis of RNA-dependent RNA polymerase sequence fragments aided in identifying two alphacoronaviruses from Kuhl's pipistrelle (Pipistrellus kuhlii), three clade 2b betacoronaviruses from lesser horseshoe bats (Rhinolophus hipposideros), and 10 clade 2c betacoronaviruses from Kuhl's pipistrelle, common noctule (Nyctalus noctula), and Savi's pipistrelle (Hypsugo savii). This study fills a substantive gap in the knowledge on bat-CoV ecology in Italy, and extends the current knowledge on clade 2c betacoronaviruses with new sequences obtained from bats that have not been previously described as hosts of these viruses. © 2013 by the authors; licensee MDPI, Basel, Switzerland. Source
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 93.00K | Year: 2009
This Small Business Innovation Research Phase I project seeks to demonstrate the high-density feasibility of a novel memory, which has both volatile and non-volatile functionality. Such memory combines the non-volatile memory's ability to retain information in the absence of power (such as Flash memory) and the fast access speed and reliability of a volatile memory (such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM)). This memory is fabricated using silicon-based fabrication process, eliminating the need of new materials or new process technology developments. One of the many applications of the proposed memory is to enable power-efficient computing applications and mobile devices. A power-efficient memory such as the one proposed in this proposal can reduce the overall data center power consumption by up to 75%. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Polymers—a large class of materials that includes plastics—play a vast number of roles in daily life, but they lack many properties that would make them even more useful. As in cooking, a way around these limitations is to mix in other ingredients that have the right properties. Polymers conduct electricity poorly, for example, but adding carbon nanotubes (CNTs) or graphene sheets forms a strong, lightweight "nanocomposite" whose electrical conductivity can be more than a million times higher. But the variety of options can confound designers. If they can find the right combination of polymer and particles, manufacturers can mix up a nanocomposite that has just the right properties for a job—be it strength, flexibility, conductivity, or a host of others. But with so many polymers and nanoparticles to choose from, devising the best recipe is often a matter of trial and error. That's largely because there has been no way to predict the resulting mix's capabilities based on what each ingredient can do. Why not? In a word, math. The effect the added particles have on the polymer is profoundly influenced by their shape. But it's hard to account for the complex shapes of the particles mathematically; in fact, it's a famously difficult math problem. So it's tough to create models that account for this essential design variable. Materials designers have been forced to model their mixtures using the assumption that all particles were shaped like spheres—an unrealistic picture, to say the least. "It's been called the 'spherical cow' approach," says NIST materials scientist Jack Douglas. "It isn't too helpful when your particle is shaped like a bush or a dust-bunny or crumpled paper, which are what nanoparticles can look like in a mixture. CNTs, for example, aren't the idealized tubes you often see in magazines; their complicated shape depends sensitively on the exact conditions under which the particles are made." The team dealt with this issue by exploiting a kernel idea from a seven-decade-old math paper by Shizuo Kakutani, who suggested a way of more realistically modeling particle shapes in material property calculations. Using his ideas for practical materials science would have required far more number-crunching power than was available in Kakutani's day, but modern computers make this class of problems easier to handle. The team first created virtual nanoparticles that have the same physical shape as the real-world particles they want to analyze, and they then calculated the relevant properties using a publicly available software package (ZENO) developed partly at NIST. "We generate thousands of examples of the shapes we want, enough to represent variation in the real world," says Douglas. "That gives us enough information to make general statements about their behavior in the mix." Since polymer nanocomposites are central to many developing technologies relating to the energy, auto and airline industries, Douglas says, this theoretical effort promises to have an appreciable impact. The team's paper focuses on mixing CNTs or graphene with polymers, but the math has wider application. "We can use it in any problem in which objects of complex shape arise," he says. "For example, we are currently applying it to classify the shapes of stem cells as well as to biometric data." Explore further: Improvement in polymers for aviation More information: Fernando Vargas–Lara et al. Intrinsic conductivity of carbon nanotubes and graphene sheets having a realistic geometry, The Journal of Chemical Physics (2015). DOI: 10.1063/1.4935970
Zeno | Date: 2016-03-30
Data processing apparatus; Computer hardware; Blank integrated circuit cards; notebook computers; Blank USB flash drives; laptop computers; tablet computers; video telephones; smartphones; Blank video cassettes; Video cassette recorders; Flashlights for use in photography; cinematographic cameras; projection screens; Electric locks; electric door bells.