Joint Estimation of Linear and Non-linear Signal-to-Noise Ratio based on Neural Networks

A novel technique estimating ASE and non-linear SNR is presented. Our method is evaluated by simulations obtaining a std error of 0.23 dB for both ASE and non-linear SNR

Effects of NK-4 in a Transgenic Mouse Model of Alzheimer's Disease

Beta-amyloid (Aβ) peptides are considered to play a major role in the pathogenesis of Alzheimer's disease (AD) and molecules that can prevent pathways of Aβ toxicity may be potential therapeutic agents for treatment of AD. We have previously reported that NK-4, a cyanine photosensitizing dye, displays neurotrophic and antioxidant activities. In this study, we report the effects of NK-4 on the toxicity of Aβ and on cognitive function and Aβ concentration in a transgenic mouse model of AD (Tg2576). In vitro, NK-4 effectively protected neuronal cells from toxicity induced by Aβ. In addition, it displayed profound inhibitory activities on Aβ fibril formation. In vivo, Tg2576 mice received an intraperitoneal injection at 100 or 500 µg/kg of NK-4 once a day, five times a week for 9 months. Administration of NK-4 to the mice attenuated impairment of recognition memory, associative memory, and learning ability, as assessed by a novel object recognition test, a passive avoidance test, and a water maze test, respectively. NK-4 decreased the brain Aβ concentration while increasing the plasma amyloid level in a dose-dependent manner. NK-4 also improved memory impairments of ICR mice induced by direct intracerebroventricular administration of Aβ. These lines of evidence suggest that NK-4 may affect multiple pathways of amyloid pathogenesis and could be useful for treatment of AD

Research data supporting "Experimental Investigation of Physics- and ML-based QoT Estimation for WDM Systems"

The Excel file contains four sheets and each of them associated with the figures 2 to 5 in the paper. The processed data was obtained by analising the captured 400 data each contains seven channel power launch into the fibre and corresponding SNR from the experimental setup described in the paper. Sheet for Fig.2 contains number of training data and corresponding root mean square error (RMSE) for channel 4 which is calculated from 50 test data for three methods based on physics, neural network (NN) and Gaussian process regression (GPR). Sheet for Fig.3 includes the data for RMSE [dB] of all seven channels for the three methods where number of training data for physics based method is 50 and ML-based method is 250. Maximum estimation error data for all seven channel for three methods is in the sheet for Fig.4. Finally, the sheet for Fig.5 contains back-to-back measured SNRs of seven channel and those estimated from physics-based method after 1000 km transmission.supported by the UK EPSRC for funding via the Programme Grant TRANSNET (EP/R035342/1). This research was performed under the auspices of a Ciena University collaborative research gran

Experimental Investigation of Physics- and ML-based QoT Estimation for WDM Systems

With a seven-channel WDM transmission over 1000 km, we experimentally study the data-driven physics- and machine learning (ML)-based SNR estimation techniques. While the ML-based approach provides good estimation accuracy, the physics-based method performs close to it with more explainability and less training data requirements

Research data supporting "Physics-based Modeling for Hybrid Data Driven Models to Estimate SNR in WDM Systems"

The Excel file contains 7 sheets in total; they are associated to figures 2 to 7, and 9, respectively. The processed data was obtained by analysing 700 datapoints of 15 launch powers each, in addition to 700 datapoints of the corresponding resulting 15 SNR values after propagation in the experimental setup described in the paper. Sheet for Fig 2 contains the number of training datapoints and their corresponding overall root mean square error (RMSE) for each of the methods described in the paper. Sheet for Fig. 3. includes the SNR estimation error and their frequency of occurrence for each of the methods. Sheet for Fig 4 contains the average XPM and SPM values based on channel distance for each of the methods, and the values used for the plotting of the fit based on method 2. Sheet for Fig 5 contains the value of the per channel RMSE per method. Sheet for Fig 6 includes the per channel back to back SNR values computed from dividing the data into groups of 100 and applying each of the methods as described in the paper. Sheet for fig 7 contains the number of training datapoints and the corresponding RMSE for each of physical model 2, neural network (NN), and Gaussian process regression (GPR). Sheet for Fig 9 contains the number of training datapoints and the corresponding RMSE values for the GPR, and for the hybrid model when using 100 or 10 for the physical model part.This research was performed under the auspices of a Ciena University collaborative research grant

Physics-based Modeling for Hybrid Data-Driven Models to Estimate SNR in WDM Systems

Recently several machine learning methods have been proposed to estimate the SNR, based on launch data and other system factors. These data-driven methods typically require a large number of datasets for training and generally are not interpretable. In this paper, we propose an alternative approach that requires less data and is interpretable, specifically a hybrid algorithm combining a physical model with Gaussian process regression. We develop a measurement-informed physical model, systematically reducing the number of independent parameters based on the underpinning physics and improve the overall performance of the physical model marginally. The model is validated using measurements performed on a 15-channel wavelength-division multiplexed system propagating over 1,000 km of standard single-mode fiber. The proposed hybrid model is not only interpretable but also obtains better agreement with measurements than a Gaussian process regression model and a simple neural network model for a given number of training datapoints.Ciena and UK EPSRC TRANSNET project (EP/R035342/1

Physics-Based Modeling for Hybrid Data-Driven Models to Estimate SNR in WDM Systems

Recently several machine learning methods have been proposed to estimate the SNR, based on launch data and other system factors. These data-driven methods typically require a large number of datasets for training and generally are not interpretable. In this paper, we propose an alternative approach that requires less data and is interpretable, specifically a hybrid algorithm combining a physical model with Gaussian process regression. We develop a measurement-informed physical model, systematically reducing the number of independent parameters based on the underpinning physics and improve the overall performance of the physical model marginally. The model is validated using measurements performed on a 15-channel wavelength-division multiplexed system propagating over 1,000 km of standard single-mode fiber. The proposed hybrid model is not only interpretable but also obtains better agreement with measurements than a Gaussian process regression model and a simple neural network model for a given number of training datapoints

“Come Then Ye Classic Thieves of Each Degree”: The Social Context of the Persepolis Diaspora in the Early Nineteenth Century

The diaspora of fragments from the stone structures on the Persepolis terrace in Iran began in earnest in the early nineteenth century. Members of the embassy of Sir Gore Ouseley made the best-known collections in 1811. This paper sets these removals in the context of a broader series of British physical interventions and transactions between 1800 and 1828. Fragments moved within a gift economy operating between detachments of East India Company officers who were deployed in Qajar-ruled Persia in order to control the Persian Gulf and the overland route to Europe. Archival research has enabled the reconstruction of object biographies for three fragments in London and Edinburgh, and for several other fragments whose present location is not known to me. The case study contributes to our knowledge of the overall rate of the dispersal of carved relief from the site. Acquisitions of architectural fragments from the site accelerated significantly in the twentieth century; the patterns of removal in the nineteenth century reflect the difficult and variable prevailing conditions.</p