Stress relaxation in network materials: the contribution of the network

Network heterogeneity causes relaxation slow down. A stretched relaxation function describes the variation of stress in time. The stretch exponent has a minimum at the affine-non-affine network transition.

Escaping the Survival Trap: Network Transition among Early-Career Freelance Songwriters

People in the early stages of their careers often face a trade-off between cultivating a closed network that helps them secure the resources they need to survive or developing an open network that can help them succeed. Actors who overcome this trade-off transition from a closed network to an open network; those who fail to do so can be caught in a survival trap that jeopardizes their chances of having a successful career. We identify the factors that enable and constrain network transitions and test our theory on a sample of Korean pop (K-pop) freelance songwriters before they have attained their first commercial hit. These songwriters initially rely on a closed network of collaborators and transition toward an open network by working with fellow songwriters who are not connected to those collaborators. This network transition occurs faster among songwriters who eventually attain their first hit than among those who remain unsuccessful. Songwriters are more likely to collaborate with new distant colleagues when they have a reference group of commercially successful peers and when they have created stylistically similar songs in the past that have failed to become hits. However, most of their new distant colleagues also lack a hit, revealing a status barrier that constrains the network transition of early-career songwriters.

A Novel Multi-Scale Attention PFE-UNet for Forest Image Segmentation

The precise segmentation of forest areas is essential for monitoring tasks related to forest exploration, extraction, and statistics. However, the effective and accurate segmentation of forest images will be affected by factors such as blurring and discontinuity of forest boundaries. Therefore, a Pyramid Feature Extraction-UNet network (PFE-UNet) based on traditional UNet is proposed to be applied to end-to-end forest image segmentation. Among them, the Pyramid Feature Extraction module (PFE) is introduced in the network transition layer, which obtains multi-scale forest image information through different receptive fields. The spatial attention module (SA) and the channel-wise attention module (CA) are applied to low-level feature maps and PFE feature maps, respectively, to highlight specific segmentation task features while fusing context information and suppressing irrelevant regions. The standard convolution block is replaced by a novel depthwise separable convolutional unit (DSC Unit), which not only reduces the computational cost but also prevents overfitting. This paper presents an extensive evaluation with the DeepGlobe dataset and a comparative analysis with several state-of-the-art networks. The experimental results show that the PFE-UNet network obtains an accuracy of 94.23% in handling the real-time forest image segmentation, which is significantly higher than other advanced networks. This means that the proposed PFE-UNet also provides a valuable reference for the precise segmentation of forest images.

Node and Network Entropy—A Novel Mathematical Model for Pattern Analysis of Team Sports Behavior

Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game.

The Eigenvectors of the Transition Matrix as Predictors of the Dynamics of a Synchronous Boolean Network

The synchronous Boolean network model is a simple and powerful tool in describing, analyzing and simulating cellular biological networks. This paper seeks a complete understanding of the dynamics of such a model by utilizing conventional matrix methods, rather than scalar methods, or matrix methods employing the non-conventional semi-tensor products (STP) of matrices. The paper starts by relating the network transition matrix to its function matrix via a self-inverse (involutary) state matrix, which has a simple recursive expression, provided a recursive ordering is employed for the underlying basis vector. Once the network transition matrix is obtained, it can be used to generate a wealth of information including its powers, characteristic equation, minimal equation, 1-eigenvectors, and 0-eigenvectors. These might be used to correctly predict both the transient behavior and (more importantly) the cyclic behavior of the network. In a short-cut partial variant of the proposed approach, the step of computing the transition matrix might be by-passed. The reason for this is that the transition matrix and the function matrix are similar matrices that share the same characteristic equation and hence the function matrix might suffice when only the partial information supplied by the characteristic equation is all that is needed. We demonstrate the conceptual simplicity and practical utility of our approach via two illustrative examples. The first example illustrates the computation of 1-eigenvectors (that can be used to identify loops or attractors), while the second example deals with the evaluation of 0-eigenvectors (that can be used to explore transient chains). Since attractors are the main concern in the underlying model, then analysis of the Boolean network might be confined to the determination of 1-eigenvectors only.