We inferred the classification of the Paleotropical climbing fern genus Arthropteris and its close relative Psammiosorus, a monotypic genus endemic to Madagascar. The classification of these ferns has until now been poorly understood. To address this, we sampled more than half of the species diversity covering the whole range of the genus including the outlying occurrence at the Juan Fernández Islands. To reconstruct phylogenetic relationships, we obtained DNA sequences from up to six plastid genome regions, including coding and non–coding regions, for these two genera and representatives of all families of the eupolypod I clade, with an emphasis on the Tectariaceae. These data were analyzed using maximum parsimony, maximum likelihood, and Bayesian inference. We also obtained divergence time estimates. Three questions were addressed. (1) We established that Arthropteris and Psammiosorus form a well–supported clade representing a separate taxon based on their morphological distinctiveness, phylogenetic relationships, and separation since the Eocene from other accepted families of eupolypod ferns. (2) Psammiosorus was found to be nested within Arthropteris. (3) Our analyses supported recognition of a previously doubted species endemic to the karst regions of southern China and northern Vietnam. As a consequence of our results, we describe the new family Arthropteridaceae and introduce the new combination Arthropteris paucivenia for the Madagascan endemic previously treated under Psammiosorus.

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

The decrease in the amplitude and resolution of seismic waves with depth, the so-called earth Q effects, can be conveniently defined in terms of frequency-dependent amplitude attenuation and velocity dispersion. In this paper, we modify Kolsky's (1953 Stress Waves in Solids (Oxford: Clarendon)) attenuation-dispersion model so that it has an accurate representation of the velocity dispersion within the seismic band. Such a modification may lead to at least two advantages: (1) an accurate phase correction in inverse Q filtering that follows, and (2) a good match to other earth Q models. The latter suggests that, when applying them to design an inverse Q filter, filtered seismic sections should in principle be comparable with each other.

[1] Taking account the earth Q effect (i.e., frequencydependent amplitude decay and velocity dispersion) during seismic migration simultaneously, the resultant seismic image is expected to be high resolution with true amplitude and correct timing. We present here a stabilized algorithm for such a ''migration incorporating inverse Q filtering'' scheme, so that the imaging technique is applicable to seismographs with long recording time. The earth model is assumed to be 1-D with continuous variations vertically in velocity and attenuation, and the imaging algorithm is implemented easily and efficiently in the frequency-wavenumber domain. Although such a 1-D model suits mostly to the cases in lithospheric-scale regional geophysics, it is sometimes also suitable to the exploration seismic studies. The method is demonstrated using a real data example from exploration seismics with an attempt to recover a target reflection underneath a group of strong coal-seam reflections.