62 research outputs found

    Dynamics and topographic organization of recursive self-organizing maps

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    Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographicmaps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizingmap (SOM) for processing sequential data, recursive SOM (RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data. © 2006 Massachusetts Institute of Technology

    Bioimmunological activities of Candida glabrata cellular mannan

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    Candida glabrata is a second most common human opportunistic pathogen which causes superficial but also life-threatening systemic candidiasis. According to the localization of mannans and mannoproteins in the outermost layer of the cell wall, mannan detection could be one of the first steps in the cell recognition of Candida cells by the host innate immune system. Mannans from the cell wall provide important immunomodulatory activities, compromising stimulation of cytokine production, induction of dendritic cells maturation and T-cell immunity. The model of DCs represents a promising tool to study immunomodulatory interventions throughout the vaccine development. Activated DCs induce, activate and polarize T-cell responses by expression of distinct maturation markers and cytokines regulating the adaptive immune responses. In addition, they are uniquely adept at decoding the fungus-associated information and translate it in qualitatively different T helper responses. We find out, that C. glabrata mannan is able to induce proliferation of splenocytes and to increase the production of TNF-α and IL-4. Next, increased the expression of co-stimulatory molecules CD80 and CD86 and the proportion of CD4+CD25+ and CD4+CD28+ T cells during in vitro stimulation of splenocytes

    Adsorbate-Induced Segregation of Cobalt from PtCo Nanoparticles: Modeling Au Doping and Core AuCo Alloying for the Improvement of Fuel Cell Cathode Catalysts

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    Platinum, when used as a cathode material for the oxygen reduction reaction, suffers from high overpotential and possible dissolution, in addition to the scarcity of the metal and resulting cost. Although the introduction of cobalt has been reported to improve reaction kinetics and decrease the precious metal loading, surface segregation or complete leakage of Co atoms causes degradation of the membrane electrode assembly, and either of these scenarios of structural rearrangement eventually decreases catalytic power. Ternary PtCo alloys with noble metals could possibly maintain activity with a higher dissolution potential. First-principles-based theoretical methods are utilized to identify the critical factors affecting segregation in Pt–Co binary and Pt–Co–Au ternary nanoparticles in the presence of oxidizing species. With a decreasing share of Pt, surface segregation of Co atoms was already found to become thermodynamically viable in the PtCo systems at low oxygen concentrations, which is assigned to high charge transfer between species. While the introduction of gold as a dopant caused structural changes that favor segregation of Co, creation of CoAu alloy core is calculated to significantly suppress Co leakage through modification of the electronic properties. The theoretical framework of geometrically different ternary systems provides a new route for the rational design of oxygen reduction catalysts

    Terahertz probing of anisotropic conductivity and morphology of CuMnAs epitaxial thin films

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    Antiferromagnetic CuMnAs thin films have attracted attention since the discovery of the manipulation of their magnetic structure via electrical, optical, and terahertz pulses of electric fields, enabling convenient approaches to the switching between magnetoresistive states of the film for the information storage. However, the magnetic structure and, thus, the efficiency of the manipulation can be affected by the film morphology and growth defects. In this study, we investigate the properties of CuMnAs thin films by probing the defect-related uniaxial anisotropy of electric conductivity by contact-free terahertz transmission spectroscopy. We show that the terahertz measurements conveniently detect the conductivity anisotropy, that are consistent with conventional DC Hall-bar measurements. Moreover, the terahertz technique allows for considerably finer determination of anisotropy axes and it is less sensitive to the local film degradation. Thanks to the averaging over a large detection area, the THz probing also allows for an analysis of strongly non-uniform thin films. Using scanning near-field terahertz and electron microscopies, we relate the observed anisotropic conductivity of CuMnAs to the elongation and orientation of growth defects, which influence the local microscopic conductivity. We also demonstrate control over the morphology of defects by using vicinal substrates.Comment: 33 pages, 16 figure

    Deep Randomized Neural Networks

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    Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

    Deep Randomized Neural Networks

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    Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

    Antioxidant, antimicrobial and anticancer activity of the lichens Cladonia furcata, Lecanora atra and Lecanora muralis

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    <p>Abstract</p> <p>Background</p> <p>The aim of this study is to investigate in vitro antioxidant, antimicrobial and anticancer activity of the acetone extracts of the lichens <it>Cladonia furcata, Lecanora atra </it>and <it>Lecanora muralis</it>.</p> <p>Methods</p> <p>Antioxidant activity was evaluated by five separate methods: free radical scavenging, superoxide anion radical scavenging, reducing power, determination of total phenolic compounds and determination of total flavonoid content. The antimicrobial activity was estimated by determination of the minimal inhibitory concentration by the broth microdilution method against six species of bacteria and ten species of fungi. Anticancer activity was tested against FemX (human melanoma) and LS174 (human colon carcinoma) cell lines using MTT method.</p> <p>Results</p> <p>Of the lichens tested, <it>Lecanora atra </it>had largest free radical scavenging activity (94.7% inhibition), which was greater than the standard antioxidants. Moreover, the tested extracts had effective reducing power and superoxide anion radical scavenging. The strong relationships between total phenolic and flavonoid contents and the antioxidant effect of tested extracts were observed. Extract of <it>Cladonia furcata </it>was the most active antimicrobial agent with minimum inhibitory concentration values ranging from 0.78 to 25 mg/mL. All extracts were found to be strong anticancer activity toward both cell lines with IC<sub>50 </sub>values ranging from 8.51 to 40.22 μg/mL.</p> <p>Conclusions</p> <p>The present study shows that tested lichen extracts demonstrated a strong antioxidant, antimicrobial and anticancer effects. That suggest that lichens may be used as as possible natural antioxidant, antimicrobial and anticancer agents to control various human, animal and plant diseases.</p

    Classification of selected Slovak variety wines.

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    During a three years period, the content of the selected volatile substances has been detected in the Slovak varietal wines, Welschriesling, Grüner Veltliner and Müller Thurgau. The acquired data were achieved by means of different multivariation methods for the purpose of finding a combination of volatile substances that would enable a classification of the tested variety wines. A proper classification and finding of authentic Slovak variety wines was possible due to the lineal and quadratic discriminant analysis.During a three years period, the content of the selected volatile substances has been detected in the Slovak varietal wines, Welschriesling, Grüner Veltliner and Müller Thurgau. The acquired data were achieved by means of different multivariation methods for the purpose of finding a combination of volatile substances that would enable a classification of the tested variety wines. A proper classification and finding of authentic Slovak variety wines was possible due to the lineal and quadratic discriminant analysis

    Incubation of air-pollution-control residues from secondary Pb smelter in deciduous and coniferous organic soil horizons: leachability of lead, cadmium and zinc

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    Abstract not availableVladislav Chrastný, Aleš Vaněk, Michael Komárek, Juraj Farkaš, Ondřej Drábek, Petra Vokurková, Jana Němcov
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