bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simple model of saturable localised surface plasmon
Authors: Oka Hisaki , Ohdaira Yasuo 
Published: February 2018

AbstractLocalised surface plasmons (LSPs) are now applied to various fields, such as bio-sensing, solar cell, molecular fluorescence enhancement and quantum-controlled devices at nanometre scale. Recent experiments show that LSPs are optically saturated by high-intensity light. Absorption saturation arises as a result of strong optical nonlinearity and cannot be explained by the conventional boson model of LSPs. Here, we propose a simple model of saturable LSPs using an effective dipole approximation. The strategy is to directly compare the classical linear optical response of an LSP with that obtained from a saturable quantum two-level system in the limit of weak excitation. The second quantization can then be performed by replacing a classical polarizability with a quantum dipole operator. Taking an ellipsoidal nanometal as an example, we analyse in detail the optical response of a single ellipsoidal nanometal to validate our model. Our numerical results show that the plasmon resonance frequency and spectral linewidth decrease as the aspect ratio of the ellipsoid increases, which is similar to the size dependence observed in early experiments.
Micro-computed tomography for the quantification of blocked fibers in hemodialyzers
Authors: Vanommeslaeghe Floris , Van Biesen Wim , Dierick Manuel , Boone Matthieu , Dhondt Annemieke , Eloot Sunny 
Published: February 2018

AbstractA novel technique based on micro-CT scanning is developed to quantify coagulation in fibers of hemodialyzers. This objectivation is needed to allow accurate assessment of thrombogenicity of dialyzers used during hemodialysis, for example when comparing different strategies to avoid coagulation and/or fiber blocking. The protocol allowed imaging at a resolution of 25 µm, making it possible to count the open, non-coagulated fibers in a non-invasive way. In 3 fresh, non-used FX600 hemodialyzers, patent fiber counts were extremely consistent (10748 ± 2). To illustrate the potential of this technique, different dialysis parameters currently used as surrogates for fiber blocking were evaluated during 20 hemodialysis sessions. After dialysis, the FX600 dialyzers were visually scored for clotting, dried and subsequently weighed and scanned. The number of patent fibers (10003 [8763,10330], range 534–10692) did not correlate with any of the recorded surrogate parameters. Micro-CT scanning is a feasible, objective, non-invasive, accurate and reproducible tool for quantification of the degree of fiber blocking in a hemodialyzer after use, making it a potential gold standard for use in studies on fiber blocking during renal replacement therapies.
People Can Create Iconic Vocalizations to Communicate Various Meanings to Naïve Listeners
Authors: Perlman Marcus , Lupyan Gary 
Published: February 2018

AbstractThe innovation of iconic gestures is essential to establishing the vocabularies of signed languages, but might iconicity also play a role in the origin of spoken words? Can people create novel vocalizations that are comprehensible to naïve listeners without prior convention? We launched a contest in which participants submitted non-linguistic vocalizations for 30 meanings spanning actions, humans, animals, inanimate objects, properties, quantifiers and demonstratives. The winner was determined by the ability of naïve listeners to infer the meanings of the vocalizations. We report a series of experiments and analyses that evaluated the vocalizations for: (1) comprehensibility to naïve listeners; (2) the degree to which they were iconic; (3) agreement between producers and listeners in iconicity; and (4) whether iconicity helps listeners learn the vocalizations as category labels. The results show contestants were able to create successful iconic vocalizations for most of the meanings, which were largely comprehensible to naïve listeners, and easier to learn as category labels. These findings demonstrate how iconic vocalizations can enable interlocutors to establish understanding in the absence of conventions. They suggest that, prior to the advent of full-blown spoken languages, people could have used iconic vocalizations to ground a spoken vocabulary with considerable semantic breadth.
Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals
Authors: Yeh Chien-Hung , Shi Wenbin 
Published: February 2018

AbstractJudiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.
Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network
Authors: Kim Sungho , Lim Meehyun , Kim Yeamin , Kim Hee-Dong , Choi Sung-Jin 
Published: February 2018

AbstractNeuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). Previously investigated synaptic devices can emulate the functionality of biological synapses successfully by utilizing various nano-electronic phenomena; however, the impact of intrinsic synaptic device variability on the system performance has not yet been studied. Here, we perform a device-to-system level simulation of different synaptic device variation parameters in a designed neuromorphic system that has the potential for unsupervised learning and pattern recognition. The effects of variations in parameters such as the weight modulation nonlinearity (NL), the minimum-maximum weight (G
min
and G
max
), and the weight update margin (ΔG) on the pattern recognition accuracy are analyzed quantitatively. These simulation results can provide guidelines for the continued design and optimization of a synaptic device for realizing a functional large-scale neuromorphic computing system.