Quantum dynamics in high codimension tilings: from quasiperiodicity to disorder

We analyze the spreading of wavepackets in two-dimensional quasiperiodic and random tilings as a function of their codimension, i.e. of their topological complexity. In the quasiperiodic case, we show that the diffusion exponent that characterizes the propagation decreases when the codimension increases and goes to 1/2 in the high codimension limit. By constrast, the exponent for the random tilings is independent of their codimension and also equals 1/2. This shows that, in high codimension, the quasiperiodicity is irrelevant and that the topological disorder leads in every case, to a diffusive regime, at least in the time scale investigated here.Comment: 4 pages, 5 EPS figure

High-Tc bolometers with silicon-nitride spiderwebsuspension for far-infrared detection

High-Tc GdBa2Cu3O7-δ (GBCO) superconducting transition edge bolometers with operating temperatures near 90 K have been made with both closed silicon-nitride membranes and patterned silicon-nitride (SiN) spiderweb-like suspension structures. As a substrate silicon-on-nitride (SON) wafers are used which are made by fusion bonding of a silicon wafer to a silicon wafer with a silicon-nitride top layer. The resulting monocrystalline silicon top layer on the silicon-nitride membranes enables the epitaxial growth of GBCO. By patterning the silicon-nitride the thermal conductance G is reduced from about 20 to 3 μW/K. The noise of both types of bolometers is dominated by the intrinsic noise from phonon fluctuations in the thermal conductance G. The optical efficiency in the far infrared is about 75% due to a goldblack absorption layer. The noise equivalent power NEP for FIR detection is 1.8 pW/√Hz, and the detectivity D* is 5.4×1010 cm √Hz/W. Time constants are 0.1 and 0.6 s, for the closed membrane and the spiderweb like bolometers respectively. The effective time constant can be reduced with about a factor 3 by using voltage bias. Further reduction necessarily results in an increase of the NEP due to the 1/f noise of the superconductor

Is Your Error My Concern? An Event-Related Potential Study on Own and Observed Error Detection in Cooperation and Competition

Electroencephalogram studies have identified an error-related event-related potential (ERP) component known as the error-related negativity or ERN, thought to result from the detection of a loss of reward during performance monitoring. However, as own errors are always associated with a loss of reward, disentangling whether the ERN is error- or reward-dependent has proven to be a difficult endeavor. Recently, an ERN has also been demonstrated following the observation of other’s errors. Importantly, other people’s errors can be associated with loss or gain depending on the cooperative or competitive context in which they are made. The aim of the current ERP study was to disentangle the error- or reward-dependency of performance monitoring. Twelve pairs (N = 24) of participants performed and observed a speeded-choice-reaction task in two contexts. Own errors were always associated with a loss of reward. Observed errors in the cooperative context also yielded a loss of reward, but observed errors in the competitive context resulted in a gain. The results showed that the ERN was present following all types of errors independent of who made the error and the outcome of the action. Consequently, the current study demonstrates that performance monitoring as reflected by the ERN is error-specific and not directly dependent on reward

Learning spatial correlations for Bayesian fusion in pipe thickness mapping

© 2014 IEEE. Pipe thickness maps are used to assess the condition in pipelines. Thickness maps are a 2.5D representation similar to elevation maps in robotics. Probabilistic frameworks, however, have barely been used in this context. This paper presents a general approach for generating probabilistic maps from heterogeneous sensor data. The key idea is to learn the spatial correlation of a sensor through Gaussian Process models and use it as priors for Bayesian fusion. This approach is applied to the novel application of pipe thickness mapping. Data from a 3D laser scanner on the outer surface of the pipe and thickness measurements from a contact ultrasonic sensor are fused into a single thickness map with associated uncertainty. Moreover, a dedicated algorithm to model the ultrasonic sensor using kernel density estimation is also proposed. The overall approach is evaluated using the full 3D profile (outer and inner surfaces) of the pipe section as ground truth

3D point cloud upsampling for accurate reconstruction of dense 2.5D thickness maps

This paper presents a novel robust processing methodology for computing 2.5D thickness maps from dense 3D collocated surfaces. The proposed pipeline is suitable to faithfully adjust data representation detailing as required, from preserving fine surface features to coarse interpretations. The foundations of the proposed technique exploit spatial point-based filtering, ray tracing techniques and the Robust Implicit Moving Least Squares (RIMLS) algorithm applied to dense 3D datasets, such as those acquired from laser scanners. The effectiveness of the proposed technique in overcoming traditional angular aliasing and corruption artifacts is validated with 3D ranging data acquired from internal and external surfaces of exhumed water pipes. It is shown that the resulting 2.5D maps can be more accurately and completely computed to higher resolutions, while significantly reducing the number of raytracing errors when compared with 2.5D thickness maps derived from our current approach