Distance rendering and perception of nearby virtual sound sources with a near-field filter model

Headphone rendering of nearby virtual sound sources represents to date an open issue in 3-D audio, due to a number of technical challenges and temporal requirements involved in the measurement of individual Head-Related Transfer Functions (HRTFs). In order to tackle this problem, we propose a filter model of near-field effects based on the Distance Variation Function (Kan et al., 2009). Thanks to its simple structure and low order, the model can be applied to any far-field virtual auditory display to yield a realistic and computationally efficient near-field compensation of spectral and binaural effects. The model is subjectively evaluated in two psychophysical experiments where the relative distance of pairs of virtually rendered sound sources is judged. Results show that even though sound intensity overshadows subtler near-field effects when it is available as a cue for distance, the model is capable of offering relative distance information of near lateral virtual sources when intensity cues are removed. Furthermore, performances of the model in relative distance rendering are compared to those of alternative near-field rendering methods available in the literature.     

Linear codes associated to skew-symmetric determinantal varieties

In this article we consider linear codes coming from skew-symmetric determinantal varieties, which are defined by the vanishing of minors of a certain fixed size in the space of skew-symmetric matrices. In odd characteristic, the minimum distances of these codes are determined and a recursive formula for the weight of a general codeword in these codes is given.     

A bound on the Wasserstein-2 distance between linear combinations of independent random variables

We provide a bound on a distance between finitely supported elements and general elements of the unit sphere of ${?}^2\left({\mathbb{N}}^1\right)$. We use this bound to estimate the Wasserstein-2 distance between random variables represented by linear combinations of independent random variables. Our results are expressed in terms of a discrepancy measure related to Nourdin–Peccati’s Malliavin–Stein method. The main application is towards the computation of quantitative rates of convergence to elements of the second Wiener chaos. In particular, we explicit these rates for non-central asymptotic of sequences of quadratic forms and the behavior of the generalized Rosenblatt process at extreme critical exponent.     

Dependency distance: A new perspective on syntactic patterns in natural languages

Dependency distance, measured by the linear distance between two syntactically related words in a sentence, is generally held as an important index of memory burden and an indicator of syntactic difficulty. Since this constraint of memory is common for all human beings, there may well be a universal preference for dependency distance minimization (DDM) for the sake of reducing memory burden. This human-driven language universal is supported by big data analyses of various corpora that consistently report shorter overall dependency distance in natural languages than in artificial random languages and long-tailed distributions featuring a majority of short dependencies and a minority of long ones. Human languages, as complex systems, seem to have evolved to come up with diverse syntactic patterns under the universal pressure for dependency distance minimization. However, there always exist a small number of long-distance dependencies in natural languages, which may reflect some other biological or functional constraints. Language system may adapt itself to these sporadic long-distance dependencies. It is these universal constraints that have shaped such a rich diversity of syntactic patterns in human languages.     

可纠错pooling设计的一个构作

一个非时序性群试(NGT)算法在DNA筛选等领域都有重要应用,而NGT算法的一个数学模型是d-disjunct矩阵.通过Bd(δ**(n,d,k))的Hamming距离构作一个d-disjunct矩阵,其中δ**(n,d,k)是在δ(n,d,k)的基础上加上δc(n,α,k)构成的,1≤α≤m+1且α∈Z;证明了所构作的矩阵是可纠正1个错误、检测2个错误的d-disjunct矩阵.     

基于植物形态标记的福建常见榕树聚类分析

对福建省30种常见榕属植物的22个性状进行形态聚类分析,结果表明:榕属植物各种之间的平均欧氏距离是5.336 8,欧氏距离最近的是日本匍茎榕和珍珠莲,为1.715 5;硬叶榕和大果榕的欧氏距离最远,为9.770 7.从形态标记聚类图可看出,在纵切线位置(欧氏距离为7.575 1)可将福建省30种常见榕属植物分为6个类群,第1类包括6种榕属植物;第2类包括16种榕属植物;第3类、第5类有3种榕属植物;第4类、第6类只有1种榕属植物.研究结果为福建省榕属植物的分类提供可借鉴的资料.     

平面图3-可着色的3个充分条件

平面图3-可着色是指可用3种颜色对该图的顶点进行着色,使得相邻的顶点着不同的颜色.研究了平面图在长度不大于6的圈或长度不大于7的圈之间满足一定条件下是3-可着色的.