Functional source separation applied to induced visual gamma activity

Objective of this work was to explore the performance of a recently introduced source extraction method, FSS (Functional Source Separation), in recovering induced oscillatory change responses from extra-cephalic magnetoencephalographic (MEG) signals. Unlike algorithms used to solve the inverse problem, FSS does not make any assumption about the underlying biophysical source model; instead, it makes use of task-related features (functional constraints) to estimate source/s of interest. FSS was compared with blind source separation (BSS) approaches such as Principal and Independent Component Analysis, PCA and ICA, which are not subject to any explicit forward solution or functional constraint, but require source uncorrelatedness (PCA), or independence (ICA). A visual MEG experiment with signals recorded from six subjects viewing a set of static horizontal black/white square-wave grating patterns at different spatial frequencies was analyzed. The beamforming technique Synthetic Aperture Magnetometry (SAM) was applied to localize task-related sources; obtained spatial filters were used to automatically select BSS and FSS components in the spatial area of interest. Source spectral properties were investigated by using Morlet-wavelet time-frequency representations and significant task-induced changes were evaluated by means of a resampling technique; the resulting spectral behaviours in the gamma frequency band of interest (20-70 Hz), as well as the spatial frequency-dependent gamma reactivity, were quantified and compared among methods. Among the tested approaches, only FSS was able to estimate the expected sustained gamma activity enhancement in primary visual cortex, throughout the whole duration of the stimulus presentation for all subjects, and to obtain sources comparable to invasively recorded data.

Carbon nanotube-polycarbonate composites for ultrafast lasers

Carbon nanotube polycarbonate composites with controlled nanotube-bundle size are prepared by dispersion with conjugated polymers followed by blending with polycarbonate. The composite has uniform sub-micrometer nanotube bundles in high concentration, shows strong nonlinear optical absorption, and generates 193 fs pulses when used as passive mode-locker in a fiber laser.

Wideband-tuneable, nanotube mode-locked, fibre laser

Ultrashort-pulse lasers with spectral tuning capability have widespread applications in fields such as spectroscopy, biomedical research and telecommunications1–3. Mode-locked fibre lasers are convenient and powerful sources of ultrashort pulses4, and the inclusion of a broadband saturable absorber as a passive optical switch inside the laser cavity may offer tuneability over a range of wavelengths5. Semiconductor saturable absorber mirrors are widely used in fibre lasers4–6, but their operating range is typically limited to a few tens of nanometres7,8, and their fabrication can be challenging in the 1.3–1.5 mm wavelength region used for optical communications9,10. Single-walled carbon nanotubes are excellent saturable absorbers because of their subpicosecond recovery time, low saturation intensity, polarization insensitivity, and mechanical and environmental robustness11–16. Here, we engineer a nanotube–polycarbonate film with a wide bandwidth (>300 nm) around 1.55 mm, and then use it to demonstrate a 2.4 ps Er31-doped fibre laser that is tuneable from 1,518 to 1,558 nm. In principle, different diameters and chiralities of nanotubes could be combined to enable compact, mode-locked fibre lasers that are tuneable over a much broader range of wavelengths than other systems.

An edge-based matching kernel through discrete-time quantum walks

In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the line graph structure are twofold. First, for a graph, its directed line graph is a dual representation and each vertex of the line graph represents a corresponding edge in the original graph. Second, we show that the discrete-time quantum walk can be seen as a walk on the line graph and the state space of the walk is the vertex set of the line graph, i.e., the state space of the walk is the edges of the original graph. As a result, the directed line graph provides an elegant way of developing new edge-based matching kernel based on discrete-time quantum walks. For a pair of graphs, we compute the h-layer depth-based representation for each vertex of their directed line graphs by computing entropic signatures (computed from discrete-time quantum walks on the line graphs) on the family of K-layer expansion subgraphs rooted at the vertex, i.e., we compute the depth-based representations for edges of the original graphs through their directed line graphs. Based on the new representations, we define an edge-based matching method for the pair of graphs by aligning the h-layer depth-based representations computed through the directed line graphs. The new edge-based matching kernel is thus computed by counting the number of matched vertices identified by the matching method on the directed line graphs. Experiments on standard graph datasets demonstrate the effectiveness of our new kernel.