Wind-tunnel free-flight investigation of a model of a forward-swept-wing fighter configuration

A wind-tunnel free-flight investigation was conducted to study the dynamic stability characteristics of a model of a forward-swept-wing fighter-airplane configuration at high angles of attack. Various other wind-tunnel techniques employed in the study included static- and dynamic- (forced-oscillation) force tests, free-to-roll tests, and flow-visualization tests. A unique facet of the study was the extreme level of static pitch instability (in excess of negative 32-percent static margin) inherent in the airframe design which precluded free-flight testing without stability augmentation in pitch. Results are presented which emphasize the high-angle-of-attack aerodynamics and the vehicle-component contributions to these characteristics. The effects of these aerodynamic characteristics on the high-angle-of-attack flying qualities of the configuration are discussed in terms of results of the wind-tunnel free-flight tests

Robust Detection of Dynamic Community Structure in Networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (`optimization variance') and over randomizations of network structure (`randomization variance'). Because the modularity quality function typically has a large number of nearly-degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.Comment: 18 pages, 11 figure

Spectral mapping of brain functional connectivity from diffusion imaging.

Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems