Synchronization in semiconductor laser rings

We examine the dynamics of semiconductor lasers coupled in a ring configuration. The lasers, which have stable output intensity when isolated, behave chaotically when coupled unidirectionally in a closed chain. In this way, we show that neither feedback nor bidirectional coupling is necessary to induce chaotic dynamics at the laser output. We study the synchronization phenomena arising in this particular coupling architecture, and discuss its possible application to chaos-based communications. Next, we extend the study to bidirectional coupling and propose an appropriate technique to optical chaos encryption/decryption in closed chains of mutually coupled semiconductor lasers.Comment: 15 pages, 7 figure

Community structures and role detection in music networks

We analyze the existence of community structures in two different social networks obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.Comment: 14 pages 7 figure

Senkronize bilateral üst üriner sistem ürotelyal karsinomu olgu sunumu]

Synchronous bilateral upper urinary tract urothelial cancer (UTUC) is a very rare form of urothelial cancer. In patients with high-risk unilateral UTUC, radical nephroureterectomy (RNU) is the gold standard treatment. However, there is no consensus on the treatment for synchronous bilateral UTUC. Evaluation of the patient and the tumor is recommended. Bilateral nephron-sparing surgery (NSS) was performed on a 53-year-old patient who presented with high-risk synchronous bilateral UTUC, and the outcome was reported

Science Learning in Playful Learning Environments: A Study from US Early Childhood Classrooms

Science may be a particularly vital subject in early life, serving not just to provide the foundation for future scientific understanding, but to expanding understanding and recognition of the value of young children's thinking and learning. therefore, designing learning environment to support children’s playful science learning is getting important. For this purpose, the current study was conducted to instigate how playful learning environments support children’s science learning. The data of this study was collected from four different US early childhood learning environments. The analysis of the data showed that children’s playful discoveries promotes their scientific skills and science learning. In these learning environments, children are encouraged to play more and explore a variety of situations in these learning environments thanks to the materials chosen and the design of the learning centers that encourage interaction between children. The findings of the current study suggest that exemplary practices should be developed in order to move away from traditional learning environments and to support learning through play, and to raise awareness on this issue, starting with teacher candidates

How to Combine Variational Bayesian Networks in Federated Learning

Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of performing high prediction accuracy, their lack of calibration and capability to quantify uncertainty is problematic for safety-critical applications. Different from deterministic models, probabilistic models such as Bayesian neural networks are relatively well-calibrated and able to quantify uncertainty alongside their competitive prediction accuracy. Both of the approaches appear in the federated learning framework; however, the aggregation scheme of deterministic models cannot be directly applied to probabilistic models since weights correspond to distributions instead of point estimates. In this work, we study the effects of various aggregation schemes for variational Bayesian neural networks. With empirical results on three image classification datasets, we observe that the degree of spread for an aggregated distribution is a significant factor in the learning process. Hence, we present an investigation on the question of how to combine variational Bayesian networks in federated learning, while providing benchmarks for different aggregation settings