Dynamics of clustered opinions in complex networks

A simple model for simulating tug of war game as varying the player number in a team is discussed to identify the slow pace of fast change. This model shows that a large number of information sources leads slow change for the system. Also, we introduce an opinion diffusion model including the effect of a high degree of clustering. This model shows that the de facto standard and lock-in effect, well-known phenomena in economics and business management, can be explained by the network clusters.Comment: 11 pages, 2 figure

Recognition of partially occluded threat objects using the annealed Hopefield network

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features

Determination of HART I Blade Structural Properties by Laboratory Testing

The structural properties of higher harmonic Aeroacoustic Rotor Test (HART I) blades were measured using the original set of blades tested in the German-dutch wind tunnel (DNW) in 1994. the measurements include bending and torsion stiffness, geometric offsets, and mass and inertia properties of the blade. the measured properties were compared to the estimated values obtained initially from the blade manufacturer. The previously estimated blade properties showed consistently higher stiffness, up to 30 percent for the flap bending in the blade inboard root section

Development and application of a self-referencing glucose microsensor for the measurement of glucose consumption by pancreatic ?-cells

Glucose gradients generated by an artificial source and ?-cells were measured using an enzyme-based glucose microsensor, 8-?m tip diameter, as a self-referencing electrode. The technique is based on a difference measurement between two locations in a gradient and thus allows us to obtain real-time flux values with minimal impact of sensor drift or noise. Flux values were derived by incorporation of the measured differential current into Fick's first equation. In an artificial glucose gradient, a flux detection limit of 8.2 ± 0.4 pmol·cm-2·s-1 (mean ± SEM, n = 7) with a sensor sensitivity of 7.0 ± 0.4 pA/mM (mean ± SEM, n = 16) was demonstrated. Under biological conditions, the glucose sensor showed no oxygen dependence with 5 mM glucose in the bulk medium. The addition of catalase to the bulk medium was shown to ameliorate surface-dependent flux distortion close to specimens, suggesting an underlying local accumulation of hydrogen peroxide. Glucose flux from ?-cell clusters, measured in the presence of 5 mM glucose, was 61.7 ± 9.5 fmol·nL-1·s-1 (mean ± SEM, n = 9) and could be pharmacologically modulated. Glucose consumption in response to FCCP (1 ?M) transiently increased, subsequently decreasing to below basal by 93 ± 16 and 56 ± 6%, respectively (mean ± SEM, n = 5). Consumption was decreased after the application of 10 ?M rotenone by 74 ± 5% (mean ± SEM, n = 4). These results demonstrate that an enzyme-based amperometric microsensor can be applied in the self-referencing mode. Further, in obtaining glucose flux measurements from small clusters of cells, these are the first recordings of the real-time dynamic of glucose movements in a biological microenvironment. <br/

Quantitative and empirical demonstration of the Matthew effect in a study of career longevity

The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: "For to all those who have, more will be given." Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew "rich get richer" effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his/her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect, and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals, and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.Comment: 13 pages, 7 figures, 4 Tables; Revisions in response to critique and suggestions of referee

Transfer learning for predicting source terms of principal component transport in chemically reactive flow

The objective of this study is to evaluate whether the number of requisite training samples can be reduced with the use of various transfer learning models for predicting, for example, the chemical source terms of the data-driven reduced-order model that represents the homogeneous ignition process of a hydrogen/air mixture. Principal component analysis is applied to reduce the dimensionality of the hydrogen/air mixture in composition space. Artificial neural networks (ANNs) are used to tabulate the reaction rates of principal components, and subsequently, a system of ordinary differential equations is solved. As the number of training samples decreases at the target task (i.e.,for T0 > 1000 K and various phi), the reduced-order model fails to predict the ignition evolution of a hydrogen/air mixture. Three transfer learning strategies are then applied to the training of the ANN model with a sparse dataset. The performance of the reduced-order model with a sparse dataset is found to be remarkably enhanced if the training of the ANN model is restricted by a regularization term that controls the degree of knowledge transfer from source to target tasks. To this end, a novel transfer learning method is introduced, parameter control via partial initialization and regularization (PaPIR), whereby the amount of knowledge transferred is systemically adjusted for the initialization and regularization of the ANN model in the target task. It is found that an additional performance gain can be achieved by changing the initialization scheme of the ANN model in the target task when the task similarity between source and target tasks is relatively low.Comment: 41 pages, 14 figure