How do animals regulate self-movement despite large variation in the luminance contrast of the environment? Insects are capable of regulating flight speed based on the velocity of image motion, but the mechanisms for this are unclear. The Hassenstein-Reichardt correlator model and elaborations can accurately predict responses of motion detecting neurons under many conditions but fail to explain the apparent lack of spatial pattern and contrast dependence observed in freely flying bees and flies. To investigate this apparent discrepancy, we recorded intracellularly from horizontal-sensitive (HS) motion detecting neurons in the hoverfly while displaying moving images of natural environments. Contrary to results obtained with grating patterns, we show these neurons encode the velocity of natural images largely independently of the particular image used despite a threefold range of contrast. This invariance in response to natural images is observed in both strongly and minimally motion-adapted neurons but is sensitive to artificial manipulations in contrast. Current models of these cells account for some, but not all, of the observed insensitivity to image contrast. We conclude that fly visual processing may be matched to commonalities between natural scenes, enabling accurate estimates of velocity largely independent of the particular scene.

Newtonian Cosmology is commonly used in astrophysical problems, because of its obvious simplicity when compared with general relativity. However it has inherent difficulties, the most obvious of which is the non-existence of a well-posed initial value problem. In this paper we investigate how far these problems are met by using the post-Newtonian approximation in cosmology.Comment: 12 pages, Late

A simple method to extract the far-infrared dielectric parameters of a homogeneous material from terahertz signals is explored in this paper. Provided with a reference, sample-probing terahertz signal and a known sample thickness, the method can determine the underlying complex refractive index of the sample within a few iterations based on the technique of fixed-point iteration. The iterative process is guaranteed to converge and gives the correct parameters when the material thickness exceeds 200 µm at a frequency of 0.1 THz or 20 µm at a frequency of 1.0 THz.

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

A simple and precise method based on fixed-point iteration is used to estimate dielectric parameters using terahertz time-domain spectroscopy (THz-TDS). The method converges and gives correct parameters when the sample thickness is greater than 200 mm at a frequency of 0.1 THz or 20 mm at a frequency of 1.0 THz. The technique in validated using measured terahertz data, obtained by probing a sample of high-resistivity silicon.Introduction: Terahertz or T-ray radiation lies in the 0.1-10 THz frequency range, and has emerged to fill the gap between the upper limit of electronics and the lower limit of photonics. One of the most widely used terahertz applications is materials characterisation using terahertz time-domain spectroscopy (THz-TDS). With the coherent and ultra-wide bandwidth nature of a single-cycle terahertz pulse, we can extract amplitude and phase information from the signal at each individual frequency. This information leads to a specific material parameter, a complex refractive index, which becomes useful in describing and distinguishing many materials.Background: Many parameter extraction methods for homogeneous materials based on a measurement of reference and probing terahertz signals have been proposed. The method introduced by Duvillaret et al.[1] models the error from the difference between estimated and measured data at each frequency. The error is then approximated by a paraboloid, and the complex refractive index at the apex of the paraboloid is found by a complicated numerical solution. Duvillaret et al. [2] and Dorney et al.[3] suggest a similar process for estimating the sample thickness. The process determines simultaneously a set of refractive indices at various guessed thicknesses, and uses the criterion of peak-to-peak or deepest total variation of the indices to select the correct thickness. This is applicable when the thickness of the sample is uncertain.The method in this Letter is derived from a regular fixed-point iteration method. Provided with the reference and probing terahertz signals and the sample thickness, the method gives a simple, rapid, and precise solution to the problem. The solution is mathematically convergent at moderate or higher sample thicknesses. However, for a sample, which has a thickness comparable to or thinner than the wavelength, the sensitivity of terahertz time-domain spectroscopy is usually inadequate and it is then possible to use a more sensitive system exploiting terahertz differential time-domain spectroscopy instead [4].