Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201

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

<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 IC₅₀values 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

Deep Randomized Neural Networks

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

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

Selected isotope ratio measurements of light metallic elements (Li, Mg, Ca, and Cu) by multiple collector ICP-MS

The unique capabilities of multiple collector inductively coupled mass spectrometry (MC-ICP-MS) for high precision isotope ratio measurements in light elements as Li, Mg, Ca, and Cu are reviewed in this paper. These elements have been intensively studied at the Geological Survey of Israel (GSI) and other laboratories over the past few years, and the methods used to obtain high precision isotope analyses are discussed in detail. The scientific study of isotopic fractionation of these elements is significant for achieving a better understanding of geochemical and biochemical processes in nature and the environment

Autologous blood coagulum containing rhBMP6 induces new bone formation to promote anterior lumbar interbody fusion (ALIF) and posterolateral lumbar fusion (PLF) of spine in sheep

resent study, we evaluated an autologous bone graft substitute (ABGS) composed of recombinant human BMP6 (rhBMP6) dispersed within autologous blood coagulum (ABC) used as a physiological carrier for new bone formation in spine fusion sheep models. The application of ABGS included cervical cage for use in the anterior lumbar interbody fusion (ALIF), while for the posterolateral lumbar fusion (PLF) sheep model allograft devitalized bone particles (ALLO) were applied with and without use of instrumentation. In the ALIF model, ABGS (rhBMP6/ABC/cage) implants fused significantly when placed in between the denuded L4- L5 vertebrae as compared to control (ABC/cage) which appears to have a fibrocartilaginous gap, as examined by histology and micro CT analysis at 16 weeks following surgery. In the PLF model, ABGS implants with or without ALLO showed a complete fusion when placed ectopically in the gutter bilaterally between two decorticated L4-L5 transverse processes at a success rate of 88% without instrumentation and at 80% with instrumentation ; however the bone volume was 50% lower in the instrumentation group than without, as examined by histology, radiographs, micro CT analyses and biomechanical testing at 27 weeks following surgery. The newly formed bone was uniform within ABGS implants resulting in a biomechanically competent and histologically qualified fusion with an optimum dose in the range of 100 g rhBMP6 per mL ABC, while in the implants that contained ALLO, the mineralized bone particles were substituted by the newly formed remodeling bone via creeping substitution. These findings demonstrate for the first time that ABGS (rhBMP6/ABC) without and with ALLO particles induced a robust bone formation with a successful fusion in sheep models of ALIF and PLF, and that autologous blood coagulum (ABC) serves as a preferred physiological native carrier to induce new bone at low doses of rhBMP6 and to achieve a successful spinal fusion

Quantitative measurement of olivine composition in three dimensions using helical-scan X-ray micro-tomography

Olivine is a key constituent in the silicate Earth; its composition and texture informs petrogenetic understanding of numerous rock types. Here we develop a quantitative and reproducible method to measure olivine composition in three dimensions without destructive analysis, meaning full textural context is maintained. The olivine solid solution between forsterite and fayalite was measured using a combination of three-dimensional (3D) X-ray imaging techniques, 2D backscattered electron imaging, and spot-analyses using wavelength-dispersive electron probe microanalysis. The linear attenuation coefficient of natural crystals across a range of forsterite content from ∼73–91 mol% were confirmed to scale linearly with composition using 53, 60, and 70 kV monochromatic beams at I12-JEEP beamline, Diamond Light Source utilizing the helical fly-scan acquisition. A polychromatic X-ray source was used to scan the same crystals, which yielded image contrast equivalent to measuring the mol% of forsterite with an accuracy of 3 mm domains within a large crystal of San Carlos forsterite that varies by ∼2 Fo mol%. This offers a solution to an outstanding question of inter-laboratory standardization, and also demonstrates the utility of 3D, non-destructive, chemical measurement. To our knowledge, this study is the first to describe the application of XMT to quantitative chemical measurement across a mineral solid solution. Our approach may be expanded to calculate the chemistry of other mineral systems in 3D, depending upon the number, chemistry, and density of end-members